From bee1801ccce4931ecaba4941c4f9c4b0670edf8b Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Wed, 6 Nov 2024 05:13:47 -0500 Subject: [PATCH] Re-cloned to deal with API key issue --- README.md | 386 +--- docs/_toc.yml | 49 +- docs/course-requirements/data-science.md | 24 + docs/course-requirements/learning-python.md | 26 + docs/course-requirements/platforms.md | 90 + .../course-requirements/python-environment.md | 70 + docs/malawi-nov-24/README.md | 23 + docs/tunisia-may-24/README.md | 20 + docs/tunisia-may-24/module-1.md | 35 + docs/tunisia-may-24/module-2.md | 58 + docs/tunisia-may-24/module-3.md | 39 + docs/tunisia-may-24/module-4.md | 26 + docs/tunisia-may-24/project-ideas.md | 44 + .../streamlit-app-deployment.md | 55 + notebooks/malawi-nov-24/1-text2sqL-demo.ipynb | 647 ++++++ ...document-classification-with-sklearn.ipynb | 575 +++++ .../malawi-nov-24/3-intro-langchain.ipynb | 1898 +++++++++++++++++ notebooks/malawi-nov-24/README.md | 87 + notebooks/nasa-apod.ipynb | 282 --- .../tunisia-may-24/1-text2sqL-demo.ipynb | 647 ++++++ ...document-classification-with-sklearn.ipynb | 575 +++++ .../tunisia-may-24/3-intro-langchain.ipynb | 1896 ++++++++++++++++ notebooks/tunisia-may-24/README.md | 87 + notebooks/world-bank-api.ipynb | 721 ------- notebooks/world-bank-package.ipynb | 281 --- src/template/__init__.py | 7 - src/template/indicators.py | 83 - src/tunisia/streamlit_app.py | 81 + 28 files changed, 7068 insertions(+), 1744 deletions(-) create mode 100644 docs/course-requirements/data-science.md create mode 100644 docs/course-requirements/learning-python.md create mode 100644 docs/course-requirements/platforms.md create mode 100644 docs/course-requirements/python-environment.md create mode 100644 docs/malawi-nov-24/README.md create mode 100644 docs/tunisia-may-24/README.md create mode 100644 docs/tunisia-may-24/module-1.md create mode 100644 docs/tunisia-may-24/module-2.md create mode 100644 docs/tunisia-may-24/module-3.md create mode 100644 docs/tunisia-may-24/module-4.md create mode 100644 docs/tunisia-may-24/project-ideas.md create mode 100644 docs/tunisia-may-24/streamlit-app-deployment.md create mode 100644 notebooks/malawi-nov-24/1-text2sqL-demo.ipynb create mode 100644 notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb create mode 100644 notebooks/malawi-nov-24/3-intro-langchain.ipynb create mode 100644 notebooks/malawi-nov-24/README.md delete mode 100644 notebooks/nasa-apod.ipynb create mode 100644 notebooks/tunisia-may-24/1-text2sqL-demo.ipynb create mode 100644 notebooks/tunisia-may-24/2-document-classification-with-sklearn.ipynb create mode 100644 notebooks/tunisia-may-24/3-intro-langchain.ipynb create mode 100644 notebooks/tunisia-may-24/README.md delete mode 100644 notebooks/world-bank-api.ipynb delete mode 100644 notebooks/world-bank-package.ipynb delete mode 100644 src/template/__init__.py delete mode 100644 src/template/indicators.py create mode 100644 src/tunisia/streamlit_app.py diff --git a/README.md b/README.md index 0d4db00..d2c183a 100644 --- a/README.md +++ b/README.md @@ -1,378 +1,58 @@ -# Project Template +# Practical Guide to LLMs and Generative AI -[![CalVer](https://img.shields.io/badge/calver-YY.0M.MICRO-22bfda.svg)](https://calver.org) -[![GitHub Release](https://img.shields.io/github/v/release/worldbank/template)](https://github.com/worldbank/template/releases) -[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/worldbank/template/main.svg)](https://results.pre-commit.ci/latest/github/worldbank/template/main) +This repository contains information about a generative AI and LL course -The template is a standardized, but flexible *project* and *documentation* structure of folders and files for sharing your data science work. +## Course Overview +The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has transformed the field of natural language processing. These technologies offer powerful capabilities in text generation and language understanding, adding value to various processes across numerous fields. In this course, we explore the different ways Gen AI can enhance workflows, from data analysis to knowledge sharing and interactive applications. This introductory course aims to demystify generative AI and LLMs for both technical and non-technical audiences across diverse industries. It provides a comprehensive overview of foundational knowledge for understanding LLMs, core concepts in Gen AI, and practical applications of Gen AI in a variety of contexts. -Inspired by [literate programming](http://literateprogramming.com), maintained by the [Development Data Group](https://www.worldbank.org/en/about/unit/unit-dec/dev) and built as [GitHub template repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template), the template contains: +The course is intended for professionals like analysts, researchers, and other domain experts, equipping them with skills to enhance their work and build applications using LLMs. By the end of the course, participants will have a foundational understanding of Gen AI principles (machine learning and deep learning), the landscape of Gen AI and LLMs (common open-source and proprietary models), and the ability to create applications that utilize LLMs in meaningful ways. -- [**README**](README), [**CODE_OF_CONDUCT**](docs/CODE_OF_CONDUCT.md), [**CONTRIBUTING**](docs/CONTRIBUTING.md) templates - > README files are important and often neglected. The files should inform anyone about the first steps to use, learn and contribute to your project. +## Course Topics -- [**CITATION.cff**](CITATION.cff) - > Embracing [CFF](https://citation-file-format.github.io) aligns with best practices for reproducible research and software development. By adhering to established standards for documenting project dependencies and citations, we demonstrate our commitment to quality, transparency, and integrity in our work. +The course will cover the following topics: -- [**LICENSE**](LICENSE) - > The LICENSE is a document that determines what others can and cannot do with contents of the repository. If no license is present, no one has permission to use and/or modify your code. The template is licensed under the [**Mozilla Public License**](https://www.mozilla.org/en-US/MPL/). And so will projects generated from it. For further information, see also [this discussion](https://github.com/orgs/worldbank/discussions/4). +1. **AI Foundations** + This module provides preliminary knowledge in machine learning to better understand generative AI. -- **docs/** +2. **Introduction to Generative AI and LLMs** + This module delves into the core concepts behind LLMs, including their structure, components, common models, and usage. - > Documentation is often never prioritized until last minute. The template aims to revert the malpractice by setting up the documentation as an integral part, inspired by [literate programming](http://literateprogramming.com). With the power of [Jupyter Book](https://jupyterbook.org), data practitioners have a way to share [Jupyter notebooks](https://jupyter.org) on [GitHub Pages](https://pages.github.com) in a standardized and effortless way. +3. **Overview of Gen AI Applications in Data Work** + This module examines how Gen AI can be applied to various stages of data-related processes by focusing on the data value chain. -- [**docs/bibliography.bib**](/docs/bibliography.bib) - > A `bibliography` using the [BibTeX](https://www.bibtex.org/Format/) format. Use this file to include and cite your project's bibliography. See also [Citations and bibliographies](https://jupyterbook.org/en/stable/content/citations.html). +4. **Leveraging Gen AI and LLMs for User-Friendly Data Dissemination** + This module focuses on data dissemination and demonstrates different ways to create user-friendly dissemination products that cater to diverse audiences. -- **data/** - > Placeholder folder for data. Data is immutable. By default, the data folder is present but ignored from version control, in order to prevent files of being mistakenly versioned in the code repository. +5. **Case Studies and Project Work** + To solidify the concepts, course participants will undertake a project to create a solution using LLMs at the end of the course. -- **src/** - > Placeholder folder for source code. If Python, it is recommended the package is made pip-installable. +## Course Structure -- **notebooks/** - > Placeholder folder for [Jupyter notebooks](https://jupyter.org). Markdown files and Jupyter notebooks can be added to `docs/_toc.yml` (Table of Contents) to compose the *documentation*. +The course is divided into self-contained modules, each designed to provide useful skills and knowledge. The modules are organized sequentially to build on skills learned in previous modules. To make the course engaging and informative, each module includes the following components: -- [**.pre-commit-config.yml**](https://github.com/worldbank/template/blob/main/.pre-commit-config.yaml) - > Using [pre-commit](https://pre-commit.com) offers a significant advantage in streamlining the development process by enforcing code standards and reducing errors before code reaches the review stage or is committed to the repository. It automates the execution of various checks, such as syntax errors, code formatting, and ensuring compliance with coding standards, which saves time and improves code quality. +- **Lecture** + Each lecture covers key conceptual knowledge for the topic at hand. -- [GitHub Actions](https://github.com/features/actions) and [Dependabot](https://docs.github.com/en/code-security/dependabot) - > [GitHub Actions](https://github.com/features/actions) and [Dependabot](https://docs.github.com/en/code-security/dependabot) are two powerful features provided by [GitHub](https://github.com) to automate and secure software development workflows, making it easier for developers to maintain high-quality and safe codebases. +- **Practical Labs** + Programming activities provide learners with practical skills to implement solutions discussed in lectures. These labs include adaptable recipes for various use cases. -- [GitHub Issues and Pull Requests GitHub](https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/configuring-issue-templates-for-your-repository) - > GitHub allows to customize how issues and pull requests are presented to the public. Custom templates encourage collaboration and maintainability. +- **Case Studies** + Case studies showcase elaborate projects that demonstrate real-world applications. -## Benefits +- **Assessment** + Each module assessment combines theoretical and programming questions to evaluate learners' understanding of the concepts and skills covered in the module. -Project templates on GitHub are essential for streamlining the data science and collaboration processes, and they offer several key benefits: +## Course Sessions +This course has been delivered in different formats to cater to various audiences. The initial session took place in Tunisia in May 2024, designed for statisticians and data scientists with a focus on applications relevant to their fields. The upcoming iteration on November 20 - 21 in Malawi will be adapted for a broader audience, primarily IT professionals, to provide them with the skills to leverage generative AI and LLMs in their own domains. -- 🛠️ **Consistency and Best Practices:** Project templates encourage consistency in project structure, coding standards, and best practices. They provide a standardized starting point, ensuring that all team members follow the same guidelines and reduce the risk of introducing errors. +## Repository Structure and Contents +This repository serves as the primary resource for accessing course content, including slides, Python programming labs, example applications using LLMs, and additional materials to support learning about Generative AI and building applications with LLMs. For easy navigation, use the link and contents outlined below. -- ⏳ **Time and Effort Savings:** Templates save time by eliminating the need to set up a project from scratch. Developers can quickly start working on their projects without the overhead of configuring the initial project structure, dependencies, or workflows. +### Contents -- 🚀 **Faster Onboarding:** New team members or contributors can easily get up to speed by using project templates. It simplifies the onboarding process, allowing them to understand the project structure and development practices more quickly. - -- 🎨 **Customization and Adaptability:** GitHub project templates can be customized to suit the specific needs of different types of projects or organizations. They serve as a foundation that can be adapted to meet unique requirements. - -- 🤝 **Community Engagement:** Open-source projects can attract more contributors when they provide accessible project templates. These templates facilitate contributions by reducing the barriers to entry for potential collaborators. - -- 🔄 **Version Control Integration:** GitHub project templates are tightly integrated with Git version control. This makes it easier to manage changes, collaborate, and track the history of project configurations. - -- 📖 **Documentation and Guidance:** Templates often include documentation and guidance to help developers understand the project's structure and how to get started. This can include README files, code comments, and links to relevant resources. - -- 🔍 **Discoverability:** Templates are discoverable on GitHub, making it easy for developers to find and use project templates for their preferred programming languages, frameworks, and tools. This helps build a supportive ecosystem. - -- ✍️ **Continual Improvement:** Project templates can evolve and improve over time as best practices, technology, and requirements change. This ensures that projects remain up to date and maintainable. - -In summary, GitHub project templates are valuable resources that enhance project management, development practices, and collaboration. They promote consistency, efficiency, and quality in software development, whether for individual projects, open-source contributions, or within organizational contexts. - -```{important} -*With flexibility comes great responsibility*. The template makes a few opiniated choices for the structure and code/documentation management of a project for what we envision to be most cases. However, even the best of the templates would never be perfect for the universe of cases out there. All in all, the template aims to encourage teams to start thinking and assimilate **collaborative coding**, **documentation**​, **enginerring**, **reproducibility​** and **best practices** as an integral part of the project. *In a standardized way*. - -In this spirit, if the template is not for you or in case you have feedback, please consider [opening an issue](https://github.com/worldbank/template/issues) or [submitting a pull request](https://github.com/worldbank/template/pulls) to share your ideas and suggestions. Your contributions would be appreciated immensely. -``` - -## Usage - -### Getting Started - -```{margin} ✨ Can't see the template ? -Please ensure you are logged in on [GitHub](https://github.com) and have permissions to create a repository. -``` - -#### 1. **Create new repository from template** - -The template is a [GitHub template repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template); in other words, you can generate a new GitHub repository with the same files and folders to use as the starting point for your project. - -> 🌟 [Create new repository from **template**](https://github.com/worldbank/template/generate) - -```{figure} docs/images/github-template.png ---- ---- -``` - -Now, give your repository a name, choose the **visibility** (Public or Private) and click **Create repository from template**. - -```{figure} docs/images/github-template-create.png ---- ---- -``` - -*Voilà!* The repository has been created with the same files and folders of the template. - -```{seealso} -For additional information, see the [GitHub documentation](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template) -``` - -#### 2. **Enable [GitHub Actions](https://github.com/features/actions) and [GitHub Pages](https://pages.github.com)** - -After creating the repository from the template, you will have to enable [GitHub Actions](https://github.com/features/actions) and [GitHub Pages](https://pages.github.com) to allow the [Jupyter Book](https://jupyterbook.org) to be built and published. - -To activate the workflow, please enable [GitHub Actions](https://github.com/features/actions) by going to the repository's settings (`Settings > Actions > General`), and selecting **read and write permissions** as shown below. - -```{figure} docs/images/github-template-action-enable.png - --- - --- -``` - -To publish, please enable [GitHub Pages](https://pages.github.com) by going to the repository's settings (`Settings > Pages`), and selecting to deploy from the **GitHub Actions** option. - -```{figure} docs/images/github-template-pages.png ---- ---- -``` - -On the next push to `main`, the [Jupyter Book](https://jupyterbook.org) will be automatically built and published. You can check the progress on the `Actions` tab. - -```{figure} docs/images/github-template-action.png ---- ---- -``` - -```{caution} -The *documentation* can be published from either *public* and *private* repositories. If publishing private content, please remember to carefully select the content to be made public and to abide by your organization's Data Privacy Policy. -``` - -#### 3. **Update configurations** - -The template comes with a default `docs/_config.yml` Jupyter Book configuration file. Remember to update it to reflect your project's name and details. - -```yaml -repository: -url: https://github.com/worldbank/template -branch: main -``` - -```{seealso} -[Jupyter Book Configuration Reference](https://jupyterbook.org/en/stable/customize/config.html) +```{tableofcontents} ``` -#### 4. **Review and update README files** - -The template comes with README files - including [this **README**](README) - that should provide anyone with the information about the first steps to use, learn and contribute to your project. Please **replace** and/or **repurpose** the files with instructions and detailed information about your project. - -> - **CODE_OF_CONDUCT** -> - **CONTRIBUTING** -> - **README** -> - Issues and Pull Requests GitHub templates - -```{seealso} -[Awesome README](https://github.com/matiassingers/awesome-readme) -``` - -#### 5. **Choose a license** - -The template is licensed under the [**Mozilla Public License**](https://www.mozilla.org/en-US/MPL). A LICENSE is the document that guarantees the repository can be shared, modified and receive contributions. Otherwise, if no license is present, all rights are reserved. - -
- -**Congratulations!** You just created a beautiful home for your project. To access your project page, use (and share) the link as shown below. - -> 🌟 `https://.github.io/` - -````{note} -For example, you can view [this live demo](http://worldbank.github.io/template) using the following link: - -> 🌟 [Live Demo - worldbank.github.io/template](http://worldbank.github.io/template) - -You can also install the latest version directly from the main branch: - -```bash -pip install git+https://github.com/worldbank/template -```` - -### Add content - -The template is created as a [Jupyter Book](https://jupyterbook.org/intro.html) - an open-source project to build beautiful, publication-quality books and documents from computational content. Let's see below how to add, execute and publish new content for your project. - -#### Updating the Jupyter Book `_config.yml` metadata - -To configure your Jupyter Book for your project, you’ll need to update the `_config.yml` file. This file controls various aspects of the Jupyter Book, including the project title, description, and relevant URLs. Below is a template to update this file to reflect the project’s details. - -```yaml -# Book settings -title: -author: - -repository: -url: https://github.com// - -# Jupyter Book options -execute: - execute_notebooks: "auto" # Automatically execute notebooks during the build process -``` - -#### Update table of contents - -When ready to publish the *documentation* on [GitHub Pages](https://pages.github.com/), all you need to do is edit the [table of contents](https://github.com/worldbank/template/blob/main/docs/_toc.yml) and add and/or update content you would like to display. [Jupyter Book](https://jupyterbook.org) supports content written as [Markdown](https://daringfireball.net/projects/markdown/), [Jupyter](https://jupyter.org) notebooks and [reStructuredText](https://docutils.sourceforge.io/rst.html) files and the `docs/_toc.yml` file controls the [table of contents](https://github.com/worldbank/template/blob/main/docs/_toc.yml) of your book. - -The template comes with the [table of contents](https://github.com/worldbank/template/blob/main/docs/_toc.yml) below as an example. - -```yaml - -format: jb-book -root: README - -parts: - - - caption: Examples - numbered: True - chapters: - - file: notebooks/world-bank-api.ipynb - - file: notebooks/world-bank-package.ipynb - - file: notebooks/nasa-apod.ipynb - - file: notebooks/bibliography.ipynb -``` - -```{seealso} -[Jupyter Book Structure and organize content](https://jupyterbook.org/en/stable/basics/organize.html) -``` - -#### Add executable content - -[Jupyter Notebooks](https://jupyter.org) can be beautifully rendered and downloaded from your book. By default, the template will render any files listed on the [table of contents](#update-table-of-contents) that have a notebook structure. The template comes with a Jupyter notebook example, `notebooks/world-bank-api.ipynb`, to illustrate. - -```{important} - -By default, Jupyter notebooks are **not** executed. However, you can configure[Jupyter Book](https://jupyterbook.org) to run notebooks during the build process (on GitHub), allowing **code outputs** and **interactive visualizations** to be generated and included in the *documentation* automatically. When enabled, Jupyter notebooks are executed by [GitHub Actions](https://github.com/features/actions) each time a commit is made to the `main` branch. For this to work, it’s crucial to ensure that all necessary [dependencies](##use-pyproject-toml-for-python-package-management) are included in the repository. If you want to prevent a specific notebook from being executed, you can [exclude it from execution](https://jupyterbook.org/en/stable/content/execute.html#exclude-files-from-execution). -``` - -```{seealso} -[Jupyter Book Write executable content](https://jupyterbook.org/en/stable/content/executable/index.html) -``` - -#### Distributing Your Project as a Python Package - -If your project uses [Python](https://python.org), it’s highly recommended to distribute it as a [package](https://packaging.python.org/en/latest/tutorials/packaging-projects/). By including a `pyproject.toml` file, the packaging process becomes more streamlined - *trust me [things can get intense](https://imgs.xkcd.com/comics/python_environment.png)*. - -Additionally: - -```{tip} -- Using `pyproject.toml` future-proofs your setup by aligning with modern packaging standards. -- The `pyproject.toml` file acts as a single source of truth for your Python dependencies and project metadata. -- You can combine Conda for system-level dependencies with `pyproject.toml` for Python dependencies, using Conda for environments and pip/poetry for Python packages. -- Any packages in the `src/` folder will be automatically discovered and installed. -``` - -##### Use `pyproject.toml` for Python Package Management - -While the template recommends using [Conda](https://conda.io/projects/conda/en/latest/index.html) (or [Mamba](https://github.com/mamba-org/mamba)) as the environment manager and managing dependencies through an `environment.yml` file, there is an alternative approach that leverages `pyproject.toml`. This can be particularly advantageous if your project is a Python package or if you want to simplify and standardize the management of Python-specific dependencies. - -##### Why use `pyproject.toml`? - -The next step is ensure your code is maintainable, reliable and reproducible by including -any dependencies and requirements, such as packages, configurations, secrets (template) and additional instructions. - -1. **Standardization**: `pyproject.toml` is a modern, standardized format defined by [PEP 518](https://peps.python.org/pep-0518/) and [PEP 621](https://peps.python.org/pep-0621/) that centralizes project configuration in Python projects, including build requirements and dependencies. - -2. **Python Packaging**: If your project is to be distributed as a package, `pyproject.toml` is the preferred way to define build tools (like [hatch](https://hatch.pypa.io/latest/config/dependency/) or [poetry](https://python-poetry.org)) and metadata for your package (like name, version, dependencies, etc.). It allows tools like `pip` and `build` to install and package your project more effectively. - -3. **Compatibility with Tools**: The `pyproject.toml` file is compatible with multiple Python packaging and dependency management tools such as `poetry` and `pip`. This allows for smoother integration with CI/CD pipelines, PyPI, and other environments. - -4. **Separation of Concerns**: While Conda manages both system-level and Python-specific packages, using `pyproject.toml` helps isolate Python dependencies. This is useful if your project uses primarily Python packages and you want finer control over Python versioning and dependency resolution. - -#### Example: Using `pyproject.toml` - -This `pyproject.toml` file specifies the dependencies and other metadata for your Python package. You can install these packages using `pip`, ensuring that your Python environment is properly managed. You can still use Conda for system-level packages (such as `libc`, `gdal`, etc.), while using `pyproject.toml` for Python package management. - -1. **`pyproject.toml` Example**: - - ```toml - [build-system] - requires = ["hatchling>=1.21.0", "hatch-vcs>=0.3.0"] - build-backend = "hatchling.build" - - [project] - name = "template" - description = "A data science project" - readme = { file = "README.md", content-type = "text/markdown" } - license = { file = "LICENSE" } - authors = [ - { name = "Your Name", email = "your.email@example.com" } - ] - dynamic = ["version"] - - python = ">=3.9" - dependencies = [ - "pandas>=1.4.3,<2", - ] - [project.optional-dependencies] - docs = [ - "docutils==0.17.1", - "jupyter-book>=1,<2", - ] - - [tool.hatch.build.targets.sdist] - include = [ - "src/**/*" - ] - - [tool.hatch.version] - source = "vcs" - ``` - -2. **Keep the Conda Environment for System-level Packages**: - You can continue to use `environment.yml` to specify non-Python dependencies or packages not available on PyPI, such as `mamba` or `gdal`. - - ```yaml - channels: - - conda-forge - dependencies: - - python=3.9 - - mamba - - gdal - ``` - -3. **Installation**: - To create an environment, you would first install the Conda dependencies and then use `pip` to install Python-specific dependencies from `pyproject.toml`. Alternatively, you can skip Conda and use `pip` for the entire setup. - - ```shell - # Create Conda environment - conda env create -f environment.yml -n - - # Activate the environment - conda activate - - # Install Python dependencies - pip install . - ``` - - To install a Python package directly from a [GitHub](https://github.com) repository using [pip](https://pip.pypa.io/en/stable/installation/), you can use the command pip install `git+https://github.com//.git`. This allows you to install the latest version of the package from the repository. You can also specify a particular branch or release tag by adding `@` at the end of the URL This is particularly useful when you want to access features or fixes that haven’t been published on PyPI yet, or to get the latest updates from the repository. - - If you want to install the latest release, you should specify the tag associated with that release. For instance: - - ```shell - pip install git+https://github.com//.git@ - ``` - -```{seealso} -- [Packaging Python Projects](https://packaging.python.org/en/latest/tutorials/packaging-projects/) -- [Writing your pyproject.toml](https://packaging.python.org/en/latest/guides/writing-pyproject-toml/) -- [Conda Managing Environments](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) -``` - -#### Building Documentation Locally - -To build the documentation locally, please follow these steps: - -- Install the package with documentation dependencies: - - ```shell - pip install -e .[docs] - ``` - -- Build the documentation: - - ```shell - jupyter-book build . --config docs/_config.yml --toc docs/_toc.yml - ``` - -The generated documentation will be available in the `_build/html` directory. Open the `index.html` file in a web browser to view it. - -## Code of Conduct - -The template maintains a [Code of Conduct](docs/CODE_OF_CONDUCT.md) to ensure an inclusive and respectful environment for everyone. Please adhere to it in all interactions within our community. - ## License The template is licensed under the [**Mozilla Public License**](https://www.mozilla.org/en-US/MPL). Remember to replace the [license](LICENSE) if necessary. If open source, [choose an open source license](https://choosealicense.com). diff --git a/docs/_toc.yml b/docs/_toc.yml index 130c2a0..f6a32cd 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,23 +2,38 @@ format: jb-book root: README parts: - - caption: Examples - numbered: True + - caption: Course Requirements chapters: - - file: notebooks/world-bank-api.ipynb - - file: notebooks/world-bank-package.ipynb - - file: notebooks/nasa-apod.ipynb - - file: notebooks/bibliography.ipynb - - caption: Gallery + - file: docs/course-requirements/learning-python + - file: docs/course-requirements/python-environment + - file: docs/course-requirements/data-science + - file: docs/course-requirements/platforms + - caption: Tunisia, May 2024 chapters: - - file: docs/gallery - - caption: Additional Resources + - file: docs/tunisia-may-24/README + - file: docs/tunisia-may-24/module-1 + - file: docs/tunisia-may-24/module-2 + - file: docs/tunisia-may-24/module-3 + - file: docs/tunisia-may-24/module-4 + - file: docs/tunisia-may-24/project-ideas + - file: notebooks/tunisia-may-24/README + sections: + - file: notebooks/tunisia-may-24/1-text2sqL-demo.ipynb + - file: notebooks/tunisia-may-24/2-document-classification-with-sklearn.ipynb + - file: notebooks/tunisia-may-24/3-intro-langchain.ipynb + - caption: Malawi, Upcoming, November 2024 chapters: - - url: https://datapartnership.org - title: Development Data Partnership - - url: https://wbdatalab.org - title: World Bank Data Lab - - url: https://www.worldbank.org/en/about/unit/unit-dec - title: World Bank DEC - - url: https://www.worldbank.org/en/research/dime - title: World Bank DIME + - file: docs/tunisia-may-24/README + - file: docs/tunisia-may-24/module-1 + - file: docs/tunisia-may-24/module-2 + - file: docs/tunisia-may-24/module-3 + - file: docs/tunisia-may-24/module-4 + - file: docs/tunisia-may-24/project-ideas + - file: notebooks/tunisia-may-24/README + sections: + - file: notebooks/malawi-nov-24/1-text2sqL-demo.ipynb + - file: notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb + - file: notebooks/malawi-nov-24/3-intro-langchain.ipynb + - caption: Acknowledgements + chapters: + - file: docs/team diff --git a/docs/course-requirements/data-science.md b/docs/course-requirements/data-science.md new file mode 100644 index 0000000..6af40cd --- /dev/null +++ b/docs/course-requirements/data-science.md @@ -0,0 +1,24 @@ + +# Data Science Prerequisites + +In this section, we outline the foundational skills and knowledge in data science, including key areas such as machine learning and natural language processing (NLP), required not only to complete exercises in this course but also to grasp and understand the core concepts of LLMs that will be taught. These prerequisites will provide the essential background needed to effectively work with LangChain and build LLM-based applications. + +## Prerequisite Skills in Data Science +A strong foundation in data science, machine learning, and NLP is crucial for building advanced LLM-based applications. These skills will enable efficient data handling, model building, and language processing, which are fundamental for working with LLMs in real-world scenarios. Below is a list of recommended skills to help you maximize your learning in this course. + +- **Data Science Basics**: Familiarity with data manipulation and analysis, especially using libraries like `pandas` and `numpy`. +- **Machine Learning Fundamentals**: Knowledge of core ML algorithms (e.g., linear regression, decision trees, k-nearest neighbors) and concepts such as overfitting, training/testing splits, and evaluation metrics. +- **Deep Learning Basics**: Basic understanding of neural networks, including feedforward networks and concepts like activation functions, training, and backpropagation. +- **Natural Language Processing (NLP) Basics**: Familiarity with NLP concepts such as tokenization, word embeddings, and basic text processing techniques. +- **Working with ML Frameworks**: Experience with libraries like `scikit-learn` for traditional ML models and `TensorFlow` or `PyTorch` for deep learning. + +## Recommended Free Resources +To help you build the required skills in data science, machine learning, and NLP, we’ve compiled a list of free resources. These cover essential topics and tools needed to work with LangChain and LLM-based applications effectively. Whether you’re new to these fields or looking to deepen your understanding, these resources will be valuable in building your foundational knowledge. + + +| Focus | Provider | Duration | Course URL | +|--------------------------|------------------------|------------|------------------------------------------------------------------------------------------------------| +| Machine Learning Basics | Google Developers | 8 hours | [ML Intro with scikit-learn](https://developers.google.com/machine-learning/crash-course) | +| NLP with Transformers | Hugging Face | 4 hours | [Hugging Face Transformers](https://huggingface.co/learn/nlp-course/chapter1) | +| NLP Basics | fast.ai | 3 hours | [NLP with fast.ai](https://course.fast.ai/) | +| Machine Learning Basics | Coursera (Andrew Ng) | 60 hours | [Coursera ML course](https://www.coursera.org/learn/machine-learning) | diff --git a/docs/course-requirements/learning-python.md b/docs/course-requirements/learning-python.md new file mode 100644 index 0000000..27ccd86 --- /dev/null +++ b/docs/course-requirements/learning-python.md @@ -0,0 +1,26 @@ +# Python Environment Configuration +In this section, we provide the minimal Python packages required to complete the programming exercises in this course. We are saying minimal because for some of the project work, you may need extra packages + +## Prerequisite Python Skills +A solid foundation in core Python skills is essential for building LLM-based applications with LangChain. These prerequisites enable efficient coding, debugging, and API interaction, which are critical for working effectively with language models. Below is a list of recommended skills to help you maximize your learning in this course. + +- **Basic Python Programming**: Understanding variables, data types, and control structures (loops and conditionals). +- **Functions and Modules**: Ability to create and use functions, import modules, and manage dependencies. +- **Object-Oriented Programming (OOP)**: Familiarity with classes, objects, inheritance, and basic OOP principles. +- **Working with APIs**: Understanding how to make HTTP requests and handle API responses, ideally with libraries like `requests`. +- **File I/O**: Reading from and writing to files, especially working with text files and JSON data. +- **Environment Management**: Experience with virtual environments (`venv`, `conda`) and package management with `pip`. +- **Error Handling**: Understanding of exceptions and error handling in Python. +- **Jupyter Notebooks**: Experience working with Jupyter Notebooks, especially for experimenting with and testing code interactively. + +These prerequisites will provide a solid foundation for building applications with LangChain and LLMs. + + +## Recommended Free Resources +To support you in building the necessary Python skills for this course, we’ve compiled a list of free resources to help you learn or review key concepts. These resources cover everything from basic programming to more advanced topics, ensuring you have a solid foundation for working with LangChain and LLM-based applications. Whether you're new to Python or just need a refresher, these materials will provide valuable guidance. +| Focus | Provider | Duration | Course URL | +|--------------|--------------|------------|-----------------------------------| +| Basic Python | Codecademy | 25 hours | [Codecademy Python](https://www.codecademy.com/learn/learn-python-3) | +| Basic Python | DataCamp | 4 hours | [Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science) | +| Basic Python | Google | 2 days | [Google Python Course](https://developers.google.com/edu/python) | +| Basic Python | Udemy | 4 hours | [Udemy Python Course](https://www.udemy.com/course/python-for-beginners/) | diff --git a/docs/course-requirements/platforms.md b/docs/course-requirements/platforms.md new file mode 100644 index 0000000..9aaf773 --- /dev/null +++ b/docs/course-requirements/platforms.md @@ -0,0 +1,90 @@ +# Required Platforms and Access Setup + +To complete the course exercises and build applications effectively, you will need access to specific platforms. This document outlines the necessary accounts and API keys or tokens required for each platform, organized into three sections: **LLMs**, **Cloud Compute Platforms**, and **Other** (for additional services like Twilio and GitHub). + +## 1. LLM Platforms + +In this section, we cover the required access for platforms that provide large language models (LLMs) and related resources. + +### OpenAI Developer API Key + +To access OpenAI’s models programmatically, you need an OpenAI API key. Follow these steps: + +1. **Create an OpenAI Account** + Go to [OpenAI’s website](https://platform.openai.com/signup) to sign up. + +2. **Generate an API Key** + - Log in and navigate to [API Keys](https://platform.openai.com/account/api-keys). + - Click on **Create new secret key** to generate a new API key. + - Copy and store the key securely, as it will be needed to authenticate with OpenAI’s API. + +3. **Usage and Billing** + OpenAI offers a free trial, but be mindful of usage limits and potential charges. + +### Hugging Face Token + +To access Hugging Face’s models and datasets programmatically, you’ll need a Hugging Face access token. + +1. **Create a Hugging Face Account** + Sign up at [Hugging Face’s website](https://huggingface.co/join). + +2. **Generate an Access Token** + - Log in, go to **Settings**, and select **Access Tokens**. + - Click **New token**, set a name (e.g., “Course Token”), choose “Read” for access level, and generate the token. + - Copy and save the token for use with Hugging Face’s resources. + +## 2. Cloud Compute Platforms + +This section details required access for cloud-based compute resources. + +### AWS (Amazon Web Services) + +AWS will provide cloud resources for deploying and running applications at scale. + +1. **Create an AWS Account** + Go to [AWS’s website](https://aws.amazon.com/) to create an account. + +2. **Generate Access Keys** + - Log in to the AWS Management Console. + - Navigate to **IAM (Identity and Access Management)** > **Users** and select your user. + - Under **Security credentials**, click **Create access key**. + - Copy and store your Access Key ID and Secret Access Key securely for connecting to AWS services. + +3. **Free Tier Usage** + AWS offers a free tier for new users, which may be sufficient for many course exercises. Monitor usage to avoid unexpected charges. + +## 3. Other Platforms + +This section includes additional services needed for the course. + +### Twilio (for WhatsApp Integration) + +Twilio will enable WhatsApp access, allowing you to build and deploy chatbot applications. + +1. **Create a Twilio Account** + Sign up at [Twilio’s website](https://www.twilio.com/). + +2. **Generate an API Key for WhatsApp** + - After logging in, navigate to **Console** > **API Keys & Tokens**. + - Click on **Create new API Key**, give it a name, and copy the SID and Secret. + - Follow Twilio’s documentation to set up WhatsApp messaging capabilities, including linking your WhatsApp number. + +3. **Free Trial** + Twilio offers a free trial with a small amount of credit, allowing you to experiment with WhatsApp API functionality. Be sure to check usage limits. + +### GitHub (for Project Repository Management) + +GitHub will be used to manage project files and collaborate on code. + +1. **Create a GitHub Account** + Go to [GitHub’s website](https://github.com/) and sign up for an account if you don’t already have one. + +2. **Set Up SSH Keys (Optional)** + To simplify authentication, you may want to set up SSH keys. + - Follow the instructions in GitHub's documentation for [generating SSH keys](https://docs.github.com/en/authentication/connecting-to-github-with-ssh). + - Once set up, add the public key to your GitHub account under **Settings** > **SSH and GPG keys**. + +3. **Forking and Cloning Repositories** + During the course, you will be working with GitHub repositories. Familiarize yourself with forking and cloning repositories to easily access course materials and project files. + +--- diff --git a/docs/course-requirements/python-environment.md b/docs/course-requirements/python-environment.md new file mode 100644 index 0000000..c9490ed --- /dev/null +++ b/docs/course-requirements/python-environment.md @@ -0,0 +1,70 @@ +# Python Environment Configuration +In this section, we provide the minimal Python packages required to complete the programming exercises in this course. We are saying minimal because for some of the project work, you may need extra packages + +## Python Installation +We will be using Python 3.12 for this course. Please refer to the installation options below. + +- **Recommended: Installation with Anaconda**. [Download Anaconda](https://www.anaconda.com/download). For more details about Anaconda, refer to this [blog post](https://www.anaconda.com/blog). + +- **Alternative: Installation from Python Website** +[Download Python](https://www.python.org/downloads/) + +## Python IDE +An IDE (Integrated Development Environment) is a software application that provides programmers with tools for software development, such as a source code editor, compiler, build automation, and debugging tools. Popular Python IDEs include Jupyter Notebook, VS Code, and PyCharm. + +### Jupyter Notebook and Google Colab + +After installing Python, you can proceed to install Jupyter Notebook, the default IDE for data science and scientific computing. Jupyter Notebook allows you to write code and include documentation with Markdown. If you installed Python via the Anaconda distribution, Jupyter Notebook and other commonly used Python packages come pre-installed, saving you additional setup steps. + +In addition to the local Jupyter Notebook installation with Anaconda, you can also use a similar environment on hosted servers like Google Colab. Google Colab is an online Jupyter Notebook accessible via the cloud, offering free GPUs for working with LLMs and other AI-based Python programs. + +### Full-Featured IDEs +While Jupyter Notebooks are excellent for interactive data science work, this course focuses on building a chatbot, which requires a fully-featured IDE. Below are some commonly used IDEs: + +> 🚀 **VS Code**: Recommended IDE for this course.See [installation instructions](https://code.visualstudio.com). + +**Other IDEs** +- **Notepad++** +- **PyCharm** + +## Python Environment Setup +### Major Packages +For the most part, we’ll install packages as needed. However, here’s a list of core packages we’ll require: + +1.Transformers + +2.Pytorch + +3.HuggingFace + +4.Langchain + +The full list of required packages is provided in the ```requirements.txt``` file. + +### Python Environment Setup +#### Create Virtual Environment +Create a Python virtual environment to use for this project. The Python version used when this was developed was 3.12. The code below creates a virtual environment and also installs all the Python packages we need for this tutorial +``` +python -m venv .venv +source .venv/bin/activate +pip install -U pip +pip install -r requirements.txt +``` +#### Setup ```.env``` file +This file is important for keeping your API keys and other secrets +``` +# OpenAI +OPENAI_API_KEY="" +# Hugging Face +HUGGINGFACEHUB_API_TOKEN="" + +# Twilio Credentials +TWILIO_ACCOUNT_SID="" +TWILIO_AUTH_TOKEN="" +TWILIO_NUMBER="" + +# PostgreSQL connection details +DB_USER = "" +DB_PASSWORD = "" +``` + diff --git a/docs/malawi-nov-24/README.md b/docs/malawi-nov-24/README.md new file mode 100644 index 0000000..b7aeda1 --- /dev/null +++ b/docs/malawi-nov-24/README.md @@ -0,0 +1,23 @@ +# LLM Application Development with LangChain and Python +In this iteration of the course, participants will explore how to develop advanced applications using Large Language Models (LLMs) with the LangChain framework in Python. Through practical exercises and real-world case studies, the course dives into the technical aspects of building LLM-powered solutions, covering everything from prompt engineering to integrating various data sources and APIs. Participants will gain hands-on experience in creating dynamic applications, including intelligent chatbots and automated workflows. The course begins by providing a foundational understanding of LLMs—how they are trained and adapted for different domains through techniques like prompt engineering and fine-tuning. It then introduces LangChain, a leading framework for building LLM applications, empowering participants to enhance their business processes with LLMs. Ideal for developers, data scientists, data engineers, analysts, and professionals across industries such as banking, telecommunications, and the public sector, this course equips you with the skills needed to build your first production-grade LLM application. + +The course is structured into self-contained modules, each building on the skills learned in previous ones. Each module includes lectures for key concepts, practical labs with programming activities and modifiable recipes, and case studies that showcase real-world applications. To reinforce learning, assessments combine theoretical and programming questions to evaluate the learner's understanding and skills gained. + + + +## Session Details + +### Audience +This session targeted staff from National Statistical Offices across 13 African countries, including Kenya, Tunisia, Burundi, Niger, Burkina Faso, Senegal, Cameroon, Mali, Côte d'Ivoire, Uganda, Central African Republic (RCA), Tanzania, and Mozambique. + +### Organization +The course was divided into three phases, each tailored to maximize learning and engagement: + +- **Phase 1: Virtual Session** + This brief, 3-hour virtual session introduced participants to the course content and sparked enthusiasm for the in-person session. + +- **Phase 2: In-Person Session** + Conducted over five days, this phase combined two components: a 3-day module on big data, followed by this 2-day LLM course. + +- **Phase 3: Project Implementation** + In this phase, participants applied what they learned in the previous sessions by building LLM-based applications, primarily chatbots, to facilitate the dissemination of information. diff --git a/docs/tunisia-may-24/README.md b/docs/tunisia-may-24/README.md new file mode 100644 index 0000000..f46a871 --- /dev/null +++ b/docs/tunisia-may-24/README.md @@ -0,0 +1,20 @@ +# Generative AI and LLMs for Data Literacy + +The first iteration of this course was delivered in Tunis, Tunisia, from May 27 to May 31, as part of the Data in Health Program organized by the World Bank Group and the African Development Bank. + +## Session Details + +### Audience +This session targeted staff from National Statistical Offices across 13 African countries, including Kenya, Tunisia, Burundi, Niger, Burkina Faso, Senegal, Cameroon, Mali, Côte d'Ivoire, Uganda, Central African Republic (RCA), Tanzania, and Mozambique. + +### Organization +The course was divided into three phases, each tailored to maximize learning and engagement: + +- **Phase 1: Virtual Session** + This brief, 3-hour virtual session introduced participants to the course content and sparked enthusiasm for the in-person session. + +- **Phase 2: In-Person Session** + Conducted over five days, this phase combined two components: a 3-day module on big data, followed by this 2-day LLM course. + +- **Phase 3: Project Implementation** + In this phase, participants applied what they learned in the previous sessions by building LLM-based applications, primarily chatbots, to facilitate the dissemination of information. diff --git a/docs/tunisia-may-24/module-1.md b/docs/tunisia-may-24/module-1.md new file mode 100644 index 0000000..3f4c30d --- /dev/null +++ b/docs/tunisia-may-24/module-1.md @@ -0,0 +1,35 @@ +# Module 1: AI Foundations + +### Module Objectives +The goal of this module is to introduce learners to the fields of machine learning and deep learning. By the end of this module, learners should understand how predictive models are built in Python and be able to distinguish between simple machine learning models, such as linear regression, and deep learning models. Learners will also gain an appreciation for how data is used to build ML models, the process of developing ML models and deploying them to production, and the infrastructure required to support ML systems. + +### Module Topics +- **Machine Learning (ML) and Neural Networks** + - Problem formulation and techniques: Regression, Nearest Neighbors, Tree-Based Models, Clustering, Principal Component Analysis. +- **Major ML Application Areas** + - Natural Language Processing (NLP), Computer Vision, Recommender Systems. +- **Platforms for Building ML Models** + - Python for ML and Data Science. +- **Machine Learning vs. Statistics** + - Similarities and Differences. +- **Tools and Platforms** + - Python, scikit-learn, PyTorch, and cloud-based platforms. +- **Building ML Systems** + - Data preparation, model training and evaluation, model deployment, and serving. + +### ML Use Cases + +### Practical Labs +- **Traditional ML** + - Build a predictive model to replace/impute missing data. + - Build a predictive model for predicting poverty from LSM data. +- **Deep Learning** + - Build a simple computer vision model. + - **Deep Learning-NLP**: Build a document classification system. + +### Case Studies +- **[World Bank]** Small area estimation of poverty. +- **[World Bank]** Object detection from high-resolution satellite imagery. + +### Assessment +- To be determined (TBD). \ No newline at end of file diff --git a/docs/tunisia-may-24/module-2.md b/docs/tunisia-may-24/module-2.md new file mode 100644 index 0000000..6e4229c --- /dev/null +++ b/docs/tunisia-may-24/module-2.md @@ -0,0 +1,58 @@ +# Introduction to Generative AI and LLMs + +### Module Objectives +This module provides foundational knowledge on Large Language Models (LLMs), covering key concepts such as pretraining, foundational models, and adapting LLMs through fine-tuning. Additionally, the module introduces various open-source and proprietary LLMs currently available on the market. + +### Module Topics +- **Introducing Generative AI** + - What is Generative AI? + - How Gen AI differs from Predictive AI. + - Brief history of Gen AI. + - Capabilities of Gen AI and major use cases. + - Different categories of Gen AI (LLMs, image generators, video generators). + +- **Understanding Large Language Models (LLMs)** + - Overview and history of language models—LLMs vs. SLMs. + - Categories of LLMs: Foundation models and other concepts. + - Building LLMs: Transformer architecture and sequence-to-sequence architectures. + - Overview of common LLMs: OpenAI models, Mistral AI, Llama, Gemini, and others. + - Adapting and customizing LLMs: fine-tuning, pre-training, RLHF. + +- **Building and Evaluating LLM Apps** + - Key concepts for LLM apps: prompt engineering, prompt-tuning, vector embeddings, RAG. + - Ecosystem of commercial and open-source tools for building LLM apps (e.g., LangChain). + - Customizing LLMs for specific use cases: prompt engineering, RAG, fine-tuning, RLHF. + - Selecting and evaluating LLMs and LLM apps. + +- **Deploying LLM Apps with LangChain** + - Overview of LangChain features and capabilities. + - Preprocessing and loading data in LangChain. + - Working with different LangChain agents (e.g., SQL). + - Deploying LangChain applications (e.g., with Streamlit, WhatsApp, and web apps). + - Evaluating LangChain apps. + +### Practical Labs + +- **Lab 1: Demonstration of Building an LLM App with Commercial Tools (OpenAI)** + Since participants won’t have access to a paid OpenAI subscription, the instructor will demonstrate available capabilities for building LLM apps. The lab will include: + - Exploring OpenAI features using the ChatGPT GUI (paid version), showing functionalities and how to create assistants. + - Demonstrating a simple RAG-based chatbot using the OpenAI playground. + - Demonstrating a simple RAG-based chatbot using the OpenAI API. + +- **Lab 2: Building LLM Apps Using LangChain (RAG-Based Chatbot)** + Participants will use provided documents to build a RAG-based app with an open-source LLM to query the documents. The output will be a Streamlit app for sharing. Tasks include: + - Setting up the development environment and installing required packages. + - Preparing source data (e.g., health documents). + - Setting up a vector database. + - Preprocessing documents and loading them into the vector database. + - Integrating with an LLM (including selecting the LLM). + - Developing the user interface in Streamlit. + - Deploying and testing the app. + +### Case Study +- **Agricultural Information Q&A System in Malawi** + A RAG-based chatbot deployed in Malawi answers questions from Agricultural Extension workers. This example app uses ChatGPT (OpenAI) integrated with agricultural documents from Malawi and is deployed on WhatsApp. + +### Assessment +- **Build an LLM App with LangChain** + Participants will receive a notebook to create an app that answers questions based on their selected website. Additionally, a quiz with five multiple-choice questions will be administered. diff --git a/docs/tunisia-may-24/module-3.md b/docs/tunisia-may-24/module-3.md new file mode 100644 index 0000000..28d4954 --- /dev/null +++ b/docs/tunisia-may-24/module-3.md @@ -0,0 +1,39 @@ +# Module 3: Gen AI and LLM Applications in Statistics + +### Module Objectives +This module explores both current and potential applications of Gen AI and LLMs in the field of statistics. Covering the entire statistical life cycle—from data collection and processing to analysis and dissemination—we examine how LLMs can enhance each stage. For instance, Ask a Question (AAQ) platforms can interpret and respond to natural language queries, providing relevant statistical information. Learners will be introduced to tools for creating accessible platforms, like a WhatsApp bot, that can answer questions using statistical data as its knowledge base. + +### Module Topics +- **Qualitative and Multi-Modal Data Analysis with LLMs** +- **Advanced Image Analysis in Statistical Data Collection** +- **Text Data Analysis with LLMs** + - Applications like sentiment analysis, parsing web-scraped price data, analyzing qualitative research data, and more. +- **Audio Data Processing and Analysis with Speech Models** + - For example, processing data from focus group discussions (FGDs) or interview data. +- **LLM Applications in Data Dissemination** + - LLMs in data discovery: Semantic search vs. keyword search. + - Enhancing and automating metadata generation with LLMs. + - Statbots: Chatbots that can respond to statistical queries. + +- **Concepts in LLM Statbots** + - LLMs' quantitative reasoning abilities and capacity to work with tabular data. + - Strategies for connecting an LLM to statistical data: Text2SQL, Text2API, Text2Code, and more. + - Tools for parsing and working with tabular documents (e.g., DocumentLLM, LangChain SQL agent). + - Security considerations for Text2SQL and database connections. + +- **Building a Statbot** + - LLM selection guide. + - Tool selection. + - Deploying statbots on platforms like WhatsApp, websites, and more. + +### Practical Labs + +- **Lab 1: Building a Health Statbot** + Participants will use a set of provided documents to build a RAG-based app using an open-source LLM to query the data. The lab will result in a Streamlit app that can be shared. + +### Case Study +- **Accessing Databases with LLMs** + (Details TBD) + +### Assessment +- To be determined (TBD). diff --git a/docs/tunisia-may-24/module-4.md b/docs/tunisia-may-24/module-4.md new file mode 100644 index 0000000..78b0022 --- /dev/null +++ b/docs/tunisia-may-24/module-4.md @@ -0,0 +1,26 @@ +# Module 5: Case Studies and Project Work + +### Module Objectives +This module focuses on real-world use cases, guiding participants through practical applications of LLM technology. + +### Module Topics +- **Implementing LLM Apps** + - Differences between open-source and proprietary models. + - Approaches such as fine-tuning vs. RAG. + - Platform selection and performance evaluation. +- **Major Use Cases in Data Applications** + - Existing use cases (TBD) and potential applications in low-income regions. + - Implementation challenges. +- **Capstone Project** + - Hands-on project work where participants apply the concepts learned throughout the course. + +### Practical Labs +- **Building an LLM Project** + Participants will work on an LLM project designed to apply the knowledge gained in a data-centric use case. + +### Case Study +- **Building an LLM Project for Data Applications** + (Details TBD) + +### Assessment +- **LLM Project Work** diff --git a/docs/tunisia-may-24/project-ideas.md b/docs/tunisia-may-24/project-ideas.md new file mode 100644 index 0000000..9558306 --- /dev/null +++ b/docs/tunisia-may-24/project-ideas.md @@ -0,0 +1,44 @@ +# AI and LLM Project + +In this document, we will guide you through three key considerations for implementing your project in this course: choosing a project, defining acceptable outputs, and understanding the project selection process. + +## Recommended Projects + +This section addresses the question, "What project can I do?" Based on the course content, we provide recommended projects, but you are encouraged to explore other ideas. + +### QA Chatbots +One common use case for generative AI is creating conversational systems like chatbots that can answer questions on specific topics. While chat-GPT and other models can handle general questions, they lack access to custom organizational data. For example, in the health domain, you may want to create a chatbot that answers questions on public health issues in your country. By using LLMs, you can create custom chatbots with access to specialized documents or websites for local knowledge. + +> Note: For QA chatbots, we will focus on those that respond to textual questions rather than numeric or data-intensive information. + +### Statsbots +Similar to the QA chatbot, a Statsbot is designed to answer quantitative questions. LLMs traditionally struggle with numeric data, so specialized tools are necessary for chatbots that work with tabular data and provide accurate, data-driven answers. + +### Miscellaneous Document Analysis +LLMs are highly effective for analyzing documents, classifying them, and performing various NLP tasks. Examples of document analysis projects include: +- Sentiment Analysis +- Topic Classification +- Intent Classification +- Named Entity Recognition (NER) +- Document Type Classification +- Key Phrase Extraction +- Toxicity and Hate Speech Detection + +## Guidelines for Choosing a Project + +Select your project thoughtfully, given the limited time available. Here are factors to consider: + +- **Data Availability**: Ensure that the necessary data or documents are accessible for the project. +- **Skills and Knowledge**: Assess the required platforms or tools and confirm that team members are willing to learn and work with them. +- **Effort**: Be realistic about the project's scope. Certain tasks, like fine-tuning an LLM, may require additional time and resources. +- **Cost**: Some LLM platforms and tools may require subscriptions or fees. For example, using the chat-GPT API requires a developer account with sufficient funds. While paid platforms are sometimes necessary, ensure that you understand the associated requirements. + +## Permissible Project Outputs + +We recommend including three key components as project outputs: + +1. **User Interface** + Implementing LLMs often involves facilitating user interaction with documents, data, or other elements. For a more user-friendly experience, we suggest creating a user interface, such as a web-based UI, WhatsApp chatbot, or command-line tool. + +2. **Documentation on GitHub** + As this is a technical project, you’ll write substantial code. Using a version control system like GitHub is recommended to track yo diff --git a/docs/tunisia-may-24/streamlit-app-deployment.md b/docs/tunisia-may-24/streamlit-app-deployment.md new file mode 100644 index 0000000..dfe7fe0 --- /dev/null +++ b/docs/tunisia-may-24/streamlit-app-deployment.md @@ -0,0 +1,55 @@ +# Deploying a Chatbot on Streamlit +In this activity, you will use the knowledge gained from the LangChain Tutorial to explore a chatbot deployed on Streamlit. You will deploy this app on your computer and interact with it. + +## About Streamlit + +As discussed in the lectures, Streamlit is a platform that enables data scientists to deploy dynamic, data-based apps. It’s ideal for prototyping demonstration apps and sharing them with stakeholders before full-scale production deployment. + +## Initial Setup and Getting the Chatbot Files + +1. **Get OpenAI and Hugging Face API Credentials** + The chatbot uses OpenAI models, so you’ll need to sign up for an OpenAI developer account and obtain an API key. For a step-by-step guide on creating an OpenAI API key, search for instructions on ChatGPT. Similarly, create a Hugging Face account and obtain an API token. + +2. **Try the Chatbot on Streamlit Community Cloud** + Before downloading anything, you can try the chatbot on the Streamlit Community Cloud with just the OpenAI and Hugging Face keys. + +3. **Download or Clone the Project Repository** + To get the project files on your computer, either clone the GitHub repository (if familiar with Git) or download the repository as a zipped file. + +## Deploying the Streamlit App Locally + +1. **Unzip and Navigate to the Project Folder** + Once unzipped, open the project folder and follow the instructions on the GitHub page to deploy the chatbot. + +2. **Follow steps on GitHub project repository**. [Streamlit app repo](https://github.com/worldbank/RAG-Based-ChatBot-Example) + + +3. **Install Required Packages** + The `requirements.txt` file contains a list of all required packages. If you encounter a missing package error, try installing the package again (ensuring your virtual environment is activated). + +4. **Run the App Locally** + Run the app with the following command: + ```bash + streamlit run streamlit_app.py + ``` +5. **Test and Check**. When deployed locally, you can browse the files being used in the app. + +## Explore Important Scripts + +The essential components for building a chatbot with LangChain are organized into distinct, modular Python scripts. Let’s explore some of these elements. You can use VS Code or your preferred text editor for this task. + +### Loading Files +In real-life applications, you may need to load hundreds of documents, requiring a versatile function for file loading. This project includes two types of loaders: +- **`remote_loader.py`**: For loading documents from websites. +- **`local_loader.py`**: For loading documents from the local `data` folder. + +### Document Splitting +The `splitter.py` module uses the `RecursiveCharacterTextSplitter` strategy, with a chunk size of 1000 and an overlap of 0. This method helps in breaking down large documents into manageable sections for processing. + +### Prompt Chains +In the `full_chain.py`, `base_chain.py`, and `rag_chain.py` modules, you’ll find configurations for the specific LLM models and prompting strategies used. The project utilizes OpenAI chat models, with customized chains designed to guide interactions effectively. + +### Memory Management +Memory management strategies are also implemented to optimize the chatbot’s performance, particularly for long interactions or when processing large datasets. + + diff --git a/notebooks/malawi-nov-24/1-text2sqL-demo.ipynb b/notebooks/malawi-nov-24/1-text2sqL-demo.ipynb new file mode 100644 index 0000000..779899e --- /dev/null +++ b/notebooks/malawi-nov-24/1-text2sqL-demo.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5eb5dc4b-add1-4b1d-8fbb-3a1e85e552f7", + "metadata": {}, + "source": [ + "# Chatting with a Population Dataset Using LangChain and LLMs\n", + "\n", + "----\n", + "\n", + "In this simple demonstration, we show how you can use natural language to query a structured dataset. The dataset is a 2018 population census enumeration level data from Malawi." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a16425c5-c0ee-4bc9-8f80-1684edc5a843", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + " ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import pandas as pd\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "5186c265-b78c-41f6-a4a4-4401e6ccb7cf", + "metadata": {}, + "source": [ + "## 1. Creating a SQLLite Database\n", + "Use a CSV file to create a database. The file which was used to create a database is shown below as a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1b2308a3-7bb2-47ca-86c8-01ff060105e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RegionNameDistrictNameTANameEnumerationAreaTotalPopulationPopulationMalePopulationFemaleNumberHouseholds
0CentralNtchisiTA Malengaea-20307802633.0331.0302.0145.0
1CentralNtchisiTA Malengaea-203070251006.0507.0499.0226.0
2CentralNtchisiTA Malengaea-203070071503.0740.0763.0338.0
3CentralNtchisiTA Malengaea-203070051139.0553.0586.0251.0
4CentralNtchisiTA Malengaea-203070121400.0668.0732.0284.0
\n", + "
" + ], + "text/plain": [ + " RegionName DistrictName TAName EnumerationArea TotalPopulation \\\n", + "0 Central Ntchisi TA Malenga ea-20307802 633.0 \n", + "1 Central Ntchisi TA Malenga ea-20307025 1006.0 \n", + "2 Central Ntchisi TA Malenga ea-20307007 1503.0 \n", + "3 Central Ntchisi TA Malenga ea-20307005 1139.0 \n", + "4 Central Ntchisi TA Malenga ea-20307012 1400.0 \n", + "\n", + " PopulationMale PopulationFemale NumberHouseholds \n", + "0 331.0 302.0 145.0 \n", + "1 507.0 499.0 226.0 \n", + "2 740.0 763.0 338.0 \n", + "3 553.0 586.0 251.0 \n", + "4 668.0 732.0 284.0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pop = pd.read_csv(\"mw-ea-pop.csv\")\n", + "df_pop.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "1a28fd50-a6c4-4929-8212-b2f4c9889b80", + "metadata": {}, + "source": [ + "## 2. Setup LangChain for Connecting to Database\n", + "The tool we will use is called LangChain. Its a popular tool for creating apps ontop of LLMs. During the course, we will delve more into using LangChain." + ] + }, + { + "cell_type": "markdown", + "id": "cbe6719b-2c38-49d7-8c96-622fc6900207", + "metadata": {}, + "source": [ + "### 2.1 Import LangChain Packages and Setup Connection" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e1ed50a5-adc2-4ab2-a4ca-ad9e487fa464", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.utilities import SQLDatabase\n", + "from langchain.chains import create_sql_query_chain\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\n", + "\n", + "from operator import itemgetter\n", + "\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import PromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough" + ] + }, + { + "cell_type": "markdown", + "id": "ef46f940-9e27-4e49-b84a-41001ba9a79d", + "metadata": {}, + "source": [ + "### 2.2 Create the SQL Agent and a Chain" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c44bb470-9afd-4fcd-bf67-c3cc08ddfcb9", + "metadata": {}, + "outputs": [], + "source": [ + "# Test connection to the database\n", + "db = SQLDatabase.from_uri(\"sqlite:///mydatabase.db\")\n", + "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n", + "\n", + "execute_query = QuerySQLDataBaseTool(db=db)\n", + "write_query = create_sql_query_chain(llm, db)\n", + "chain = write_query | execute_query" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2da6cdd0-012b-4fb2-8ae3-bde91d67aa20", + "metadata": {}, + "outputs": [], + "source": [ + "answer_prompt = PromptTemplate.from_template(\n", + " \"\"\"Given the following user question, corresponding SQL query, and SQL result, answer the user question.\n", + "\n", + "Question: {question}\n", + "SQL Query: {query}\n", + "SQL Result: {result}\n", + "Answer: \"\"\"\n", + ")\n", + "\n", + "answer = answer_prompt | llm | StrOutputParser()\n", + "chain = (\n", + " RunnablePassthrough.assign(query=write_query).assign(\n", + " result=itemgetter(\"query\") | execute_query\n", + " )\n", + " | answer\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "6f39f651-c3e4-4fa7-b67a-b6b8c91de57f", + "metadata": {}, + "source": [ + "## 3. Chat with the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2ff768f4-7622-43db-afbe-4155bf5eeff2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'There are 33 districts in Malawi.'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"How many districts are there in Malawi\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b6d13719-e60e-4cff-afdb-c76253a65fc1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# VERIFY THIS INFORMATION USING PYTHON\n", + "df_pop.DistrictName.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a6305316-c437-4e86-bb3a-8f75450897e6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'There are 9,042,289 women in Malawi.'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"How many women are there in Malawi\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "23e0abae-b0af-4965-9c37-00cafc30db5d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9042289.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# VERIFY THIS INFORMATION USING PYTHON\n", + "df_pop.PopulationFemale.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e18bb38b-6313-4fd8-b2b8-b079c0e21e31", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'There are 246,415 women in Salima district.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"How many women are there in Salima district\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d1f8a6ca-6d53-4eca-80ae-09eb07ad886f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "246415.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can check that the answer above is correct using Python code\n", + "df_pop.query('DistrictName == \"Salima\"')['PopulationFemale'].sum()" + ] + }, + { + "cell_type": "markdown", + "id": "358519f6-98e3-4d45-ba6d-96560c67dcab", + "metadata": {}, + "source": [ + "### Complicated question" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "96f94c49-1d06-4765-8af1-12b2d217b8da", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'Approximately 51.48% of the population in Malawi is female.'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"What percent of the population is female in Malawi?\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2033ecd1-0714-4bb0-a582-7dde4c366364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "51.482681744085504" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fem = df_pop.PopulationFemale.sum()\n", + "tot = df_pop.TotalPopulation.sum()\n", + "\n", + "fem/tot*100" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c267ea6e-6028-4716-9731-30beabf8b3f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'Based on the SQL query and result provided, we are only retrieving the population of males in the specified region (Central, Ntchisi, TA Malenga) for the last four years. We are not directly comparing the number of men over the years to determine if they are increasing. To answer the user question accurately, we would need to retrieve the population data for men in Malawi over the last four years and compare the numbers to see if there is an increase.'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"are the number of men increasing in the four last years in malawi\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "986fd189-6019-4a8a-bf3a-4fdc4f6e708e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'The fertility rate of Malawi can be calculated by dividing the total female population by the total population. \\n\\nFor the first set of data:\\nFertility rate = total_female_population / total_population\\nFertility rate = 1303 / 2604\\nFertility rate = 0.5008\\n\\nFor the second set of data:\\nFertility rate = total_female_population / total_population\\nFertility rate = 9042289 / 17563749\\nFertility rate = 0.5143\\n\\nTherefore, the fertility rate of Malawi is approximately 0.5008 for the first set of data and 0.5143 for the second set of data.'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"what is the fertilely rate of Malawi(Calculate)?\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "markdown", + "id": "8e977b26-373b-4482-b613-bd1f70276b6f", + "metadata": {}, + "source": [ + "## 4. EXERCISE: What Question Do You Want Me to Try?\n", + "Share any question in the chat you would like me to try based on this dataset so that we see how much it can handle. \n", + "\n", + "- **Share your question on the chat**\n", + "- **I will run the question here and we will inspect the response together**" + ] + }, + { + "cell_type": "markdown", + "id": "c07e2bc8-c41c-49dd-ac6b-1a77d6c6e168", + "metadata": {}, + "source": [ + "## 5. What We will Do During the Course\n", + "During the course we will use LangChain to build our own **Ask-A-Question (AAQ)** type \n", + "of Chatbot to enable a user to chat with a dataset by asking natural language questions. \n", + "We will build an interactive app like [this](https://llm-examples.streamlit.app) using Streamlit and be able to share it with others." + ] + }, + { + "cell_type": "markdown", + "id": "3b84bb26-3dff-4d1b-af02-8edc18ac2f36", + "metadata": {}, + "source": [ + "# Deployment\n", + "1. Web app\n", + "2. WhatsApp \n", + "2. Chatbot on website of NSO or Health ministry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72accb41-83da-4781-bf06-2488db5f91d1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb b/notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb new file mode 100644 index 0000000..20c458b --- /dev/null +++ b/notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb @@ -0,0 +1,575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a Document Classification System\n", + "The NumPy (Numerical Python) library used for working iwith arrays, and the Scikit-learn library is a python library built on NumPy, SciPy and matplotlib for data analytics and machine learning. The NLTK (Natural Language Toolkit) provides access to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensuring that you have the necessary libraries\n", + "# !pip install nltk\n", + "# !pip install numpy\n", + "# !pip install scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import nltk\n", + "from nltk.corpus import reuters\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.naive_bayes import MultinomialNB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Load your data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Reuters-21578 dataset is one of the most widely used data collections for text categorization research. It is a collection of documents with news articles and the original corpus has 10,369 documents and a vocabulary of 29,930 word and has labeled categories such as \"earnings\", \"acquisitions\".. etc. You can read metadata about the dataset on [Hugging Face](https://huggingface.co/datasets/ucirvine/reuters21578)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package reuters to\n", + "[nltk_data] /Users/dunstanmatekenya/nltk_data...\n", + "[nltk_data] Package reuters is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# download the dataset\n", + "nltk.download('reuters')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the Reuters-21578 dataset\n", + "documents = reuters.fileids()\n", + "train_docs = list(filter(lambda doc: doc.startswith(\"train\"), documents))\n", + "test_docs = list(filter(lambda doc: doc.startswith(\"test\"), documents))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Prepare your data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare the data by extracting the raw text and category labels for both the training and testing documents. Assumption is that each document has only one category label, so we take only the first category label for each document." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare the data\n", + "train_data = [reuters.raw(doc_id) for doc_id in train_docs]\n", + "train_labels = [reuters.categories(doc_id)[0] for doc_id in train_docs]\n", + "test_data = [reuters.raw(doc_id) for doc_id in test_docs]\n", + "test_labels = [reuters.categories(doc_id)[0] for doc_id in test_docs]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question-How many different classes are in the training data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore some of the training examples" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Article content: COMPUTER TERMINAL SYSTEMS <CPML> COMPLETES SALE\n", + " Computer Terminal Systems Inc said\n", + " it has completed the sale of 200,000 shares of its common\n", + " stock, and warrants to acquire an additional one mln shares, to\n", + " <Sedio N.V.> of Lugano, Switzerland for 50,000 dlrs.\n", + " The company said the warrants are exercisable for five\n", + " years at a purchase price of .125 dlrs per share.\n", + " Computer Terminal said Sedio also has the right to buy\n", + " additional shares and increase its total holdings up to 40 pct\n", + " of the Computer Terminal's outstanding common stock under\n", + " certain circumstances involving change of control at the\n", + " company.\n", + " The company said if the conditions occur the warrants would\n", + " be exercisable at a price equal to 75 pct of its common stock's\n", + " market price at the time, not to exceed 1.50 dlrs per share.\n", + " Computer Terminal also said it sold the technolgy rights to\n", + " its Dot Matrix impact technology, including any future\n", + " improvements, to <Woodco Inc> of Houston, Tex. for 200,000\n", + " dlrs. But, it said it would continue to be the exclusive\n", + " worldwide licensee of the technology for Woodco.\n", + " The company said the moves were part of its reorganization\n", + " plan and would help pay current operation costs and ensure\n", + " product delivery.\n", + " Computer Terminal makes computer generated labels, forms,\n", + " tags and ticket printers and terminals.\n", + " \n", + "\n", + " n\\, Label: acq\n" + ] + } + ], + "source": [ + "print(\"Article content: {} n\\, Label: {}\".format(train_data[1], train_labels[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Vectorizing the text data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Vectorize the text data using the TfidVectorizer from scikit-learn. TF-IDF is an abbreviation for Term Frequency Inverse Document Frequency. This is very common algorithm to transform text into a meaningful representation of numbers which is used to fit machine algorithm for prediction. \n", + "- Its worth noting that nowadays, this vectorization approach is not commonly used. We will cover **word embeddings** tomorrow which is a better approach to represent words as numbers because **vector embeddings** can capture semantic meanings better.\n", + "\n", + "For the sklearn TF-IDF vectorizer, you can learn more about it [here](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Vectorize the text data\n", + "vectorizer = TfidfVectorizer(stop_words=\"english\", max_features=1000)\n", + "X_train = vectorizer.fit_transform(train_data)\n", + "X_test = vectorizer.transform(test_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question: What role are the ```stop words``` playing in the code above? You might have learned this from Prof. Mohamad Ali already." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Training a Linear Support Vector Machine (LinearSVC) classifier using the vectorized training data and corresponding label" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearSVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearSVC()" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the classifier\n", + "classifier = LinearSVC()\n", + "classifier.fit(X_train, train_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Evaluate the classifier used and calculate the accuracy score as well as some other metrics (Precision, Recall and F-1 score)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.876117919841007\n", + " precision recall f1-score support\n", + "\n", + " acq 0.95 0.96 0.96 719\n", + " alum 0.33 0.18 0.24 22\n", + " barley 1.00 0.71 0.83 14\n", + " bop 0.77 0.80 0.79 30\n", + " carcass 0.79 0.65 0.71 17\n", + " castor-oil 0.00 0.00 0.00 1\n", + " cocoa 0.94 1.00 0.97 17\n", + " coconut 0.00 0.00 0.00 2\n", + " coconut-oil 0.00 0.00 0.00 2\n", + " coffee 0.89 0.96 0.92 25\n", + " copper 0.93 0.93 0.93 15\n", + " corn 0.85 0.81 0.83 48\n", + " cotton 1.00 0.86 0.92 14\n", + " cpi 0.62 0.62 0.62 24\n", + " cpu 0.00 0.00 0.00 1\n", + " crude 0.79 0.93 0.86 182\n", + " dfl 0.00 0.00 0.00 1\n", + " dlr 0.70 0.72 0.71 43\n", + " dmk 0.00 0.00 0.00 1\n", + " earn 0.98 0.99 0.98 1083\n", + " fuel 1.00 0.22 0.36 9\n", + " gas 0.75 0.33 0.46 9\n", + " gnp 0.59 0.89 0.71 19\n", + " gold 0.96 0.96 0.96 26\n", + " grain 0.71 0.77 0.74 77\n", + " groundnut 0.00 0.00 0.00 3\n", + " heat 1.00 0.75 0.86 4\n", + " hog 1.00 0.50 0.67 4\n", + " housing 1.00 0.67 0.80 3\n", + " income 1.00 0.80 0.89 5\n", + " instal-debt 1.00 1.00 1.00 1\n", + " interest 0.78 0.76 0.77 124\n", + " ipi 1.00 1.00 1.00 11\n", + " iron-steel 0.69 0.64 0.67 14\n", + " jet 0.00 0.00 0.00 1\n", + " jobs 0.73 0.85 0.79 13\n", + " l-cattle 0.00 0.00 0.00 2\n", + " lead 0.83 0.42 0.56 12\n", + " lei 1.00 1.00 1.00 3\n", + " livestock 0.50 0.50 0.50 6\n", + " lumber 0.00 0.00 0.00 5\n", + " meal-feed 0.20 0.17 0.18 6\n", + " money-fx 0.65 0.65 0.65 96\n", + " money-supply 0.80 0.83 0.81 29\n", + " naphtha 0.00 0.00 0.00 1\n", + " nat-gas 0.64 0.54 0.58 13\n", + " nickel 0.00 0.00 0.00 1\n", + " oilseed 0.54 0.54 0.54 13\n", + " orange 0.75 0.33 0.46 9\n", + " palladium 0.00 0.00 0.00 1\n", + " palm-oil 0.67 1.00 0.80 4\n", + " pet-chem 1.00 0.50 0.67 6\n", + " platinum 0.00 0.00 0.00 3\n", + " potato 1.00 0.67 0.80 3\n", + " propane 0.00 0.00 0.00 2\n", + " rape-oil 0.00 0.00 0.00 1\n", + " reserves 1.00 0.64 0.78 14\n", + " retail 1.00 1.00 1.00 1\n", + " rice 0.00 0.00 0.00 1\n", + " rubber 0.69 1.00 0.82 9\n", + " ship 0.39 0.41 0.40 39\n", + " silver 0.00 0.00 0.00 0\n", + " soy-oil 0.00 0.00 0.00 2\n", + " soybean 0.00 0.00 0.00 2\n", + "strategic-metal 0.00 0.00 0.00 6\n", + " sugar 0.71 0.96 0.81 25\n", + " tea 0.00 0.00 0.00 3\n", + " tin 0.71 0.50 0.59 10\n", + " trade 0.70 0.93 0.80 76\n", + " veg-oil 0.54 0.64 0.58 11\n", + " wpi 0.62 0.56 0.59 9\n", + " yen 0.00 0.00 0.00 6\n", + " zinc 0.00 0.00 0.00 5\n", + "\n", + " accuracy 0.88 3019\n", + " macro avg 0.53 0.48 0.49 3019\n", + " weighted avg 0.86 0.88 0.87 3019\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "# Evaluate the classifier\n", + "y_pred = classifier.predict(X_test)\n", + "accuracy = accuracy_score(test_labels, y_pred)\n", + "print(\"Accuracy:\", accuracy)\n", + "print(classification_report(test_labels, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Classify new documents (new BBC headlines) by vectorizing them using the same TfidfVectorizer and predicting their labels using the trained classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted labels: ['ship' 'ship' 'acq']\n" + ] + } + ], + "source": [ + "# Classify new documents (recent headlines obtained from BBC news regarding Tunisia)\n", + "new_docs = [\n", + " \"Tunisia says 23 people missing in Mediterranean sea.\",\n", + " \"Tunisia officials arrested in dispute over flag display.\",\n", + " \"Tunisia lawyer arrested during live news broadcast.\"\n", + "]\n", + "new_docs_vectors = vectorizer.transform(new_docs)\n", + "predicted_labels = classifier.predict(new_docs_vectors)\n", + "print(\"Predicted labels:\", predicted_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discussion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How did this classifier fare? What can you do to improve the model?
\n", + "Ans: Experimenting with different preprocessing techniques, feature extraction models and classification algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trying with a different classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Steps 1 - 3 will be the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the Reuters-21578 dataset\n", + "documents = reuters.fileids()\n", + "train_docs = list(filter(lambda doc: doc.startswith(\"train\"), documents))\n", + "test_docs = list(filter(lambda doc: doc.startswith(\"test\"), documents))\n", + "\n", + "# Prepare the data\n", + "train_data = [reuters.raw(doc_id) for doc_id in train_docs]\n", + "train_labels = [reuters.categories(doc_id)[0] for doc_id in train_docs]\n", + "test_data = [reuters.raw(doc_id) for doc_id in test_docs]\n", + "test_labels = [reuters.categories(doc_id)[0] for doc_id in test_docs]\n", + "\n", + "# Vectorize the text data\n", + "vectorizer = CountVectorizer(stop_words=\"english\", max_features=1000)\n", + "X_train = vectorizer.fit_transform(train_data)\n", + "X_test = vectorizer.transform(test_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Different Classifier (Multinomial Naive Bayes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classifier = MultinomialNB()\n", + "classifier.fit(X_train, train_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the classifier\n", + "y_pred = classifier.predict(X_test)\n", + "accuracy = accuracy_score(test_labels, y_pred)\n", + "print(\"Accuracy:\", accuracy)\n", + "print(classification_report(test_labels, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Classify new documents (recent headlines obtained from BBC news regarding Tunisia)\n", + "new_docs = [\n", + " \"Tunisia says 23 people missing in Mediterranean sea.\",\n", + " \"Tunisia officials arrested in dispute over flag display.\",\n", + " \"Tunisia lawyer arrested during live news broadcast.\"\n", + "]\n", + "new_docs_vectors = vectorizer.transform(new_docs)\n", + "predicted_labels = classifier.predict(new_docs_vectors)\n", + "print(\"Predicted labels:\", predicted_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discussion: Compare the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The choice of classifier depends on the specific characteristics of your dataset and the problem at hand. Multinomial Naive Bayes is known to work well with text data and can handle high-dimensional feature spaces efficiently. However, it assumes that the features are independent of each other, which may not always be the case in real-world scenarios.\n", + "\n", + "You can also experiment with different classifiers, such as Logistic Regression, Random Forest, or Gradient Boosting, and compare their performance to find the best fit for your dataset. You can also refine the model by trying different feature extraction techniques and hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### There are also other ways you can approach this, for example, Document Classification using BERT. Here is a notebook example on Kaggle that you can explore: https://www.kaggle.com/code/merishnasuwal/document-classification-using-bert" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "BERT (Bidirectional Encoder Representations from Transformers) and other Transformer encoder architectures can also be used on a variety of tasks in NLP (natural language processing). They compute vector-space representations of natural language that are suitable for use in deep learning models. The BERT family of models uses the Transformer encoder architecture to process each token of input text in the full context of all tokens before and after. BERT models are usually pre-trained on a large corpus of text, then fine-tuned for specific tasks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/malawi-nov-24/3-intro-langchain.ipynb b/notebooks/malawi-nov-24/3-intro-langchain.ipynb new file mode 100644 index 0000000..82950fc --- /dev/null +++ b/notebooks/malawi-nov-24/3-intro-langchain.ipynb @@ -0,0 +1,1898 @@ +{ + "cells": [ + { + "attachments": { + "7153af0c-fb8b-4b47-826e-57ac60696e0c.png": { + "image/png": "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" + }, + "faf11697-6be8-49bc-ab24-b3c4385b8a67.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "740ffa74-4eda-4843-9b5b-486caab1153b", + "metadata": { + "tags": [] + }, + "source": [ + "# Introduction to LangChain\n", + "---------\n", + "![image.png](attachment:faf11697-6be8-49bc-ab24-b3c4385b8a67.png)![image.png](attachment:7153af0c-fb8b-4b47-826e-57ac60696e0c.png)\n", + "\n", + "**DIHPA'24**\n", + "\n", + "**Author:** Dunstan Matekenya \n", + "\n", + "**Affiliation:** DECAT, The World Bank Group \n", + "\n", + "**Date:** May 30, 2024\n", + "\n", + "\n", + "## What you will learn \n", + "In this notebook, you will learn the basics of the LangChain platform as follows.\n", + "1. **LLM capabilities.** Explore LLM capabilities using LangChain\n", + "2. **Interacting with LLMs.** Use LangChain functions such as chains, prompt templates and more to connect to LLMs\n", + "3. **RAG.**. Implementing a simple RAG in Langchain by connecting to external documents\n", + "4. **LangChain Expression Language (LCEL).**. How to use LCEL instead of functions when interacting with LLMs\n", + "5. **LangChain Agents.**. \n", + "\n", + "## Expected Broad Learning Outcomes\n", + "1. **Connecting to LLMs.** An understanding of how to connect to varios open source and proprietary LLMs using Hugging Face and proprietary specific frameworks such as that for OpenAI and Mistral\n", + "2. **Different LLMs.**. There are many varieties of LLMs: ```chat, instruct, question-answer, sentiment-analysis, instruct``` and more. Have basic understanding of differences across these models and when to use which one.\n", + "3. **The role of memory in Chat models.** Understand the importance of having memory in a chatbot and different strategies for doing it with LangChain.\n", + "4. **The process of implementing RAG in LangChain**. RAG is one of the most commonly used approach for implementing chats as it enables connection to external custom data. Have a good understanding of the main steps involved in implementing a RAG based system-the steps are the same in LangChain and other frameworks.\n", + "5. **Understand the role vector databases.** Vector databases are an integral part of working with LLMs. make sure you understand how they fit in the ecosystem and why they are important." + ] + }, + { + "cell_type": "markdown", + "id": "633b7017-2001-4cec-b34d-30a5bc4b92fc", + "metadata": {}, + "source": [ + "# Setup\n", + "\n", + "------" + ] + }, + { + "cell_type": "markdown", + "id": "7a131a8d-40d2-4bf3-9856-103ed70000d7", + "metadata": {}, + "source": [ + "## Import Packages\n", + "We will import packages as we go so that you appreciate which class we are using." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b9a13ee9-f3d9-4141-a1a2-929cdc1b5113", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path" + ] + }, + { + "cell_type": "markdown", + "id": "1e1dad1b-4014-48e9-b911-2095c9864a84", + "metadata": {}, + "source": [ + "## Setup API Keys" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b443a7a8-9dd7-4320-958e-cc3376e09cb4", + "metadata": {}, + "outputs": [], + "source": [ + "# ====================\n", + "# Setup API Keys\n", + "# ====================\n", + "# Although its not recommended for security, you can also just \n", + "# paste your API keys \n", + "#OPENAI_API_KEY\n", + "#HUGGINGFACEHUB_API_TOKEN" + ] + }, + { + "cell_type": "markdown", + "id": "61224418-685b-499c-96fe-568ba7993475", + "metadata": {}, + "source": [ + "## Setup input directories \n", + "Lets organize where our data is stored so that we can easily access it. Please refer to the slides for recommended folder setup. Copy and paste the full paths to your working folder in the variables below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a234a85f-b21b-4378-b3bd-f27feec67c36", + "metadata": {}, + "outputs": [], + "source": [ + "# Replace this folder with your working folder \n", + "DIR_WD = Path(\"/Users/dunstanmatekenya/Google Drive/My Drive/GenAI-Course/Mod2-LLM-Overview/\")\n", + "\n", + "# data folder\n", + "DIR_DATA = DIR_WD.joinpath(\"data\")\n", + "\n", + "# We can also set file names for data files we will use to save time\n", + "FILE_HEP_CHAD = DIR_DATA.joinpath(\"Hepatitis-Chad.pdf\")\n", + "\n", + "FILE_MIDDLE_EAST_COVID = DIR_DATA.joinpath(\"MidEast-COVID.pdf\")\n", + "\n", + "FILE_DENGUE = DIR_DATA.joinpath(\"Dengue-Global-situation.pdf\")" + ] + }, + { + "cell_type": "markdown", + "id": "2f148739-53c8-4cb8-b477-da5781e8195d", + "metadata": {}, + "source": [ + "# 1. Exploring Language Tasks that LLMs can Perform\n", + "In this section, we will explore what type of NLP tasks LLMs can perfom using the Hugging Face transformer package. In some cases, when we specifiy a specific model, the transformers package will take some time to download the model files. Also, the idea here is to show very simple capabilities. In a real world project, you can train and fine-tune the transformer models on your own dataset. For example, to do a fully fledged sentiment analysis with Hugging Face, take a look at [this tutorial] (https://huggingface.co/blog/sentiment-analysis-python).\n", + "\n", + ">Note that for almost all of these tasks, you can replace the English text with French text and still get similar results" + ] + }, + { + "cell_type": "markdown", + "id": "2dc053ef-5ba8-4557-b0f2-0975447d566e", + "metadata": {}, + "source": [ + "## 1. 1 Text and Document Classification\n", + "Text and document classification are closely related tasks. In **text classification**, we assign predefined categories to individual pieces of text while in **document classification** refers to the process of assigning predefined categories to longer pieces of text, such as entire documents, articles, or reports.\n", + "\n", + "- **Examples of text classification tasks**. Sentiment Analysis; Intent Detection;\n", + "- **Examples of document classification**. Topic categorization, " + ] + }, + { + "cell_type": "markdown", + "id": "860fcb52-c3f4-4c8f-8fb4-e2b4808114c2", + "metadata": {}, + "source": [ + "### Sentiment Analysis with the Hugging Face Transformers Library" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "499b7d29-d647-4cb9-ae00-fd2b88ae18b5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POSITIVE\n" + ] + } + ], + "source": [ + "# We use transformers ```pipeline library\n", + "from transformers import pipeline\n", + "\n", + "llm = pipeline(\"text-classification\")\n", + "text = \"I'm really enjoying my stay in Tunis\"\n", + "outputs = llm(text)\n", + "print(outputs[0]['label'])" + ] + }, + { + "cell_type": "markdown", + "id": "affcc68c-49ea-4cfe-bc59-294854156360", + "metadata": {}, + "source": [ + "## 1.2 Text Generation\n", + "Text generation is a process in natural language processing (NLP) where a machine learning model generates coherent and contextually relevant text based on a given input or prompt. This technology is used in various applications such as chatbots, automated content creation, machine translation, and more.\n", + "\n", + "In real life, the text is not always coherent, based on the model, when we use a default model, the results are not good. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0224c530-1943-481e-b4fc-92cfb2f62702", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n", + "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n", + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Malawi is famous for urchin, the rice used in the national diet, and its high price and widespread lack of access to good sources of water are driving thousands of people into poverty. So far, the government has been able to bring in more than $2 billion through loans, while food aid has been limited in its expansion, for example by one million poor people trying to come back from war-ravaged country.\n", + "\n", + "As for the country's food safety, the government has been\n" + ] + } + ], + "source": [ + "llm = pipeline(\"text-generation\")\n", + "prompt = \"Malawi is famous for \"\n", + "outputs = llm(prompt, max_length=100)\n", + "print(outputs[0]['generated_text'])" + ] + }, + { + "cell_type": "markdown", + "id": "aabac32c-0547-44d4-b77b-34d16a2d8220", + "metadata": {}, + "source": [ + "**EXERCISE-0: Try to specify a different Hugging Face model and see if you get better results**" + ] + }, + { + "cell_type": "markdown", + "id": "b6bf9c35-35f0-4310-adff-6dbb7eab57f4", + "metadata": {}, + "source": [ + "## 1.3 Text Summarization\n", + "Text summarization is a natural language processing (NLP) task that involves creating a concise and coherent summary of a longer text document. The goal is to capture the most important information and main ideas while reducing the length of the original text. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d0f4c46a-b896-47ce-928b-0c8b8bae063d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Walking amid Gion's Machiya wooden houses is a mesmerizing experience. The beautifullypreserved structures exuded an old-world charm that transports visitors back in time. The glow of lanterns lining the narrow streets add to theenchanting ambiance, making each stroll a\n" + ] + } + ], + "source": [ + "llm= pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n", + "long_text = \"\"\"Walking amid Gion's Machiya wooden houses is a mesmerizing experience. The beautifully\n", + "preserved structures exuded an old-world charm that transports visitors back in time, making them feel\n", + "like they had stepped into a living museum. The glow of lanterns lining the narrow streets add to the\n", + "enchanting ambiance, making each stroll a memorable journey through Japan's rich cultural history.\n", + "\"\"\"\n", + "outputs = llm(long_text, max_length=60, clean_up_tokenization_spaces=True)\n", + "print(outputs[0]['summary_text'])" + ] + }, + { + "cell_type": "markdown", + "id": "58c10bc4-2237-45b2-a367-c7690a2586b4", + "metadata": {}, + "source": [ + "## 1.4 Question-Answering\n", + "Question Answering (QA) is one of the most common tasks or use casef for LLMs. In this task, the model is designed to automatically answer questions posed by humans in natural language. QA systems can be built to answer questions from a variety of sources, such as structured databases, knowledge bases, or unstructured text documents." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a8aefbf4-a88a-4cdc-b882-eb0a5781200b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to distilbert/distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wooden\n" + ] + } + ], + "source": [ + "llm = pipeline(\"question-answering\")\n", + "context = \"Walking amid Gion's Machiya wooden houses was a mesmerizing experience.\"\n", + "question = \"What are Machiya houses made of?\"\n", + "outputs = llm(question=question, context=context)\n", + "print(outputs['answer'])" + ] + }, + { + "cell_type": "markdown", + "id": "eea8c421-6a5d-426c-8f9f-fc94bc61b492", + "metadata": {}, + "source": [ + "## 1.5 Language Translation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cde7750a-4ab4-4a5d-9019-7dc7be065719", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to google-t5/t5-base and revision 686f1db (https://huggingface.co/google-t5/t5-base).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C'est ma première visite en Tunisie.\n" + ] + } + ], + "source": [ + "llm = pipeline(\"translation_en_to_fr\")\n", + "text = \"This is my first time to visit Tunisia.\"\n", + "outputs = llm(text, clean_up_tokenization_spaces=True)\n", + "print(outputs[0]['translation_text'])" + ] + }, + { + "cell_type": "markdown", + "id": "d3e3d82d-f4ea-439b-9fd5-08ae2105f3a3", + "metadata": {}, + "source": [ + "# 2. Introducing LangChain Core Functionalities" + ] + }, + { + "cell_type": "markdown", + "id": "0d4c7bd8-c8cb-4e99-bef5-40144a82c78c", + "metadata": {}, + "source": [ + "It is always a good idea to read documentation of a framework. Please head over to [LangChain website](https://www.langchain.com) for details of core functionalities, use cases and features. The screenshot below provides a summary of LangChain ecosytem of features and capabilities. The term **Chain** in LangChain refers to the core concept of **chains** in LangChain which is a sequence(s) of calls - whether to an LLM, a tool, or a data preprocessing step. The primary supported way to do this is with LCEL (we will see this later)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b02bc8a4-ea90-4622-92d8-43861fcb12d2", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2e39d82c-7089-4aa4-8054-d01427f1c1b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": { + "image/png": { + "width": 500 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename='../images/LangChain-detailed.png', width=500) " + ] + }, + { + "cell_type": "markdown", + "id": "40dcce20-057f-4b5b-84b6-356a3c1db8a7", + "metadata": {}, + "source": [ + "## 2.1 Interacting with Models in LangChain \n", + "- General instruction models - Models which can answer questions but are not quite optmized for chat\n", + "- Chat models are more optimized for question and answering\n", + "- Prompting templates and techniques " + ] + }, + { + "cell_type": "markdown", + "id": "33a7bf7e-0ea4-431f-9f3e-23119e1a14a7", + "metadata": {}, + "source": [ + "### Trying out Open Vs. Proprietary Model\n", + "- **Accessing open source LLMs on Hugging Face.** In order to access open source LLMs from Hugging Face, you need two main inputs: ```Hugging Face token``` and the model id or url. Recall that you can explore and grab model details from the Hugging Face platform easily. Once you have that we can use ```HuggingFaceEndpoint``` or ```HuggingFaceHub``` to access and use the model.\n", + "\n", + "- **Accessing proprietary LLMs (e.g., OpenAI).** LangChain has specific packages for working with OpenAI models. For other providers such as Mistral, you need to check [LangChain documentation](https://python.langchain.com/v0.1/docs/integrations/chat/mistralai/)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0f911200-d6b2-47a2-b862-643f7f61bd83", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Can you still have fun in the rain?\n", + "Yes, you can still have fun in the rain! There are plenty of activities you can do indoors or outdoors, such as playing board games, reading a book, or going for a walk. You can also try to find creative ways to enjoy the rain, such as using a rain shower to take a bath or making a rain-soaked picnic. Just remember to stay safe and take precautions if necessary.\n" + ] + } + ], + "source": [ + "from langchain_community.llms import HuggingFaceEndpoint, HuggingFaceHub\n", + "\n", + "# Lets make this a global variable in case we want to use this model\n", + "# again\n", + "MODEL_ID_FALCON = 'tiiuae/falcon-7b-instruct'\n", + "\n", + "llm = HuggingFaceHub(repo_id=MODEL_ID_FALCON, \n", + " huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN)\n", + "\n", + "question = 'Can you still have fun'\n", + "output = llm.invoke(question)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b35f1964-5938-4773-b768-334df1551939", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " if you're dead inside?\n", + "\n", + "It is possible to have fun even if you feel dead inside. While feeling emotionally numb or disconnected can make it more challenging to enjoy activities or events, it is still possible to find moments of joy and pleasure.\n", + "\n", + "Here are some tips for having fun even if you feel dead inside:\n", + "\n", + "1. Engage in activities that have brought you joy in the past. Think back to activities or hobbies that you used to enjoy before you started feeling dead inside. Even if you don't feel the same level of excitement, engaging in these activities can still bring some enjoyment.\n", + "\n", + "2. Try something new. Sometimes, trying something new can help break out of a rut and bring some fun into your life. This could be a new hobby, sport, or even a new type of food.\n", + "\n", + "3. Spend time with loved ones. Being around people who care about you and make you feel loved and supported can help lift your mood and bring some fun into your life. Plan a fun outing or simply spend time talking and laughing with friends and family.\n", + "\n", + "4. Practice self-care. Taking care of yourself can help improve your overall mood and make it easier to have fun. Make time for activities that help you relax and recharge, such as taking a bath, reading a book,\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain_openai import OpenAI\n", + "\n", + "# Note that we will be able to select specific OpenAI models \n", + "# If you have a paid account \n", + "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "question = 'Can you still have fun'\n", + "output = llm.invoke(question)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e9bda8e1-dce1-49c1-b308-82063fa53e6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3544" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2*1772" + ] + }, + { + "cell_type": "markdown", + "id": "7381c4a5-5a33-405c-b82a-baacfebe6e56", + "metadata": {}, + "source": [ + "**EXERCISE-1. Find another model on Hugging Face to try**\n", + "- Go to [Hugging Face](https://huggingface.co/models)\n", + "- Search for **Text Generation** LLMs. Note that large models can be hard and take long to run.\n", + "- Get the model Id\n", + "- Initialize the model, and ask it a question/prompt as we did with Falcon model above" + ] + }, + { + "cell_type": "markdown", + "id": "f4edd5e5-44fa-49b0-b150-64a580da8f66", + "metadata": {}, + "source": [ + "### . Prompt templates\n", + "Prompt templates are used for creating prompts in a more modular way, so they can be reused and built on. Chains act as the glue in LangChain; bringing the other components together into workflows that pass inputs and outputs between the different components\n", + "- They are recipes for generating prompts\n", + "- Flexible and modular\n", + "- Can contain: instructions, examples, and additional context" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0a2faa69-863e-4a4e-9118-ba50e0e72586", + "metadata": {}, + "outputs": [ + { + "ename": "ValidationError", + "evalue": "1 validation error for HuggingFaceHub\ntoken\n extra fields not permitted (type=value_error.extra)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m template \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are an artificial intelligence assistant, answer the question. \u001b[39m\u001b[38;5;132;01m{question}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6\u001b[0m prompt \u001b[38;5;241m=\u001b[39m PromptTemplate(template\u001b[38;5;241m=\u001b[39mtemplate, input_variables\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestion\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m----> 8\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mHuggingFaceHub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mMODEL_ID_FALCON\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mHUGGINGFACEHUB_API_TOKEN\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Create a Chain using the LLMChain() \u001b[39;00m\n\u001b[1;32m 11\u001b[0m llm_chain \u001b[38;5;241m=\u001b[39m LLMChain(prompt\u001b[38;5;241m=\u001b[39mprompt, llm\u001b[38;5;241m=\u001b[39mllm)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValidationError\u001b[0m: 1 validation error for HuggingFaceHub\ntoken\n extra fields not permitted (type=value_error.extra)" + ] + } + ], + "source": [ + "from langchain.prompts import PromptTemplate, ChatPromptTemplate\n", + "\n", + "# A String with instructions, same way we create prompts\n", + "# in GUI based interface such as chatGPT\n", + "template = \"You are an artificial intelligence assistant, answer the question. {question}\"\n", + "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "\n", + "llm = HuggingFaceHub(repo_id=MODEL_ID_FALCON,token=HUGGINGFACEHUB_API_TOKEN)\n", + "\n", + "# Create a Chain using the LLMChain() \n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "question = \"What is LangChain?\"\n", + " \n", + "print(llm_chain.run(question))" + ] + }, + { + "cell_type": "markdown", + "id": "5a614f93-417e-4185-96c6-dc0b2ae1704d", + "metadata": {}, + "source": [ + "### Chat Models\n", + "Chat Models are a core component of LangChain. A chat model is a language model that uses chat messages as inputs and returns chat messages as outputs (as opposed to using plain text)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "684669c2-a2f7-4801-b9db-87b702b545e5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseChatModel.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.3.0. Use invoke instead.\n", + " warn_deprecated(\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "AIMessage(content='One of the best places to visit in Malawi is Lake Malawi. This stunning lake is known for its crystal-clear waters, beautiful beaches, and diverse marine life. Visitors can enjoy a variety of water activities such as snorkeling, diving, kayaking, and sailing. The lake is also surrounded by national parks and reserves, offering opportunities for wildlife viewing and hiking. Additionally, the lakeshore is dotted with charming villages where you can experience the local culture and hospitality. Overall, Lake Malawi is a must-visit destination for nature lovers and adventure seekers in Malawi.', response_metadata={'token_usage': {'completion_tokens': 116, 'prompt_tokens': 38, 'total_tokens': 154}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-11f38ec0-7a4b-47d9-9080-855d38cf0f35-0', usage_metadata={'input_tokens': 38, 'output_tokens': 116, 'total_tokens': 154})" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "from langchain.prompts import PromptTemplate, ChatPromptTemplate\n", + "\n", + "llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "\n", + "prompt_template = ChatPromptTemplate.from_messages([\n", + "(\"system\", \"You are a helpful assistant who knows alot about Africa.\"),\n", + "(\"human\",\"Respond to the question: {question}\")]\n", + ")\n", + "\n", + "full_prompt = prompt_template.format_messages(question='What is the best place to visit in Malawi?')\n", + "llm(full_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11ea305-a3c9-439f-b135-236e26c39ac1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c3e8c433-8672-4ae8-9579-66d5201bc657", + "metadata": {}, + "source": [ + "## 2.2. Managing chat model memory\n", + "- A key feature of chatbot applications is the ability to have a conversation, where context from the conversation is stored and available for the model to access for later questions or reference.\n", + "- Memory is important for conversations with chat models; it opens up the possibility of providing follow-up questions, of building and iterating on model responses, and for chatbots to adapt to the user's preferences and behaviors. \n", + "- Although LangChain allows us to customize and optimize in-conversation chatbot memory, it is still limited by the model's context window. \n", + "- An **LLM's context window** is the amount of input text the model can consider at once when generating a response, and the length of this window varies for different models. LangChain has a standard syntax for optimizing model memory. \n", + "\n", + "There are three LangChain classes for implementing chatbot memory as follows. \n", + "### The ```ChatMessageHistory``` Class\n", + "- The ChatMessageHistory class stores the full history of messages between the user and model. By providing this to the model, we can provide follow-up questions and iterate on the response message.\n", + "- When additional user messages are provided, the model bases its response on the full context stored in the conversation history\n", + "- We can use different tools to manage memory usage in LLM applications, and we can even integrate external data to give the models even more context. \n", + "\n", + "\n", + "### The ```ConversationBufferMemory``` class\n", + "- This gives the application a rolling buffer memory containing the last few messages in the conversation. Users can specify the number of messages to store with the size argument, and the application will discard older messages as newer ones are added. \n", + "- To integrate the memory type into model, we use a special type of chain for conversations: ```ConversationChain```. \n", + "\n", + "### The ```ConversationSummaryMemory``` class\n", + "- Summarizing important points from a conversation can also be a good way of optimizing memory. The ConversationSummaryMemory class summarizes the conversation over time, condensing the information. \n", + "- This means that the chat model can remember key pieces of context without needing to store and process the entire conversation history" + ] + }, + { + "cell_type": "markdown", + "id": "e6b7f3d4-afdd-422c-8688-7a31cb79bb26", + "metadata": {}, + "source": [ + "### Trying out the ChatMessageHistory class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98c8f82-8e9a-4603-811b-c20d034ee6b4", + "metadata": {}, + "outputs": [], + "source": [ + "chat = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "history = ChatMessageHistory()\n", + "history.add_ai_message(\"Hi! Ask me anything please.\")\n", + "history.add_user_message(\"Describe a metaphor for learning LangChain in one sentence.\")\n", + "chat(history.messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1248fd89-60d3-4d49-a276-f419417f8e88", + "metadata": {}, + "outputs": [], + "source": [ + "# Ask a question based on the previous messages \n", + "history.add_user_message(\"Summarize the preceding sentence in fewer words\")\n", + "chat(history.messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eeeaed5-cce7-4261-a852-eee1941c808b", + "metadata": {}, + "outputs": [], + "source": [ + "# Ask a question based on the previous messages \n", + "history.add_user_message(\"Summarize the preceding sentence in fewer words\")\n", + "chat(history.messages)" + ] + }, + { + "cell_type": "markdown", + "id": "6e1b5fb0-9fbf-4836-94d0-4017efbdfae0", + "metadata": {}, + "source": [ + "### Trying out the ConversationBufferMemory\n", + "For many applications, storing and accessing the entire conversation history isn't technically feasible. In these cases, the messages must be condensed while retaining as much relevant context as possible. One common way of doing this is with a memory buffer, which stores only the most recent messages based on the parameter ```size```." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c8742c04-33db-42fb-8509-987dee9e61d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "\n", + "Human: Describe a language model in one sentence\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "Human: Describe a language model in one sentence\n", + "AI: A language model is a statistical model that is trained on a large corpus of text and is able to generate coherent and grammatically correct sentences based on the patterns and structures it has learned.\n", + "Human: Describe it again using less words\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "Human: Describe a language model in one sentence\n", + "AI: A language model is a statistical model that is trained on a large corpus of text and is able to generate coherent and grammatically correct sentences based on the patterns and structures it has learned.\n", + "Human: Describe it again using less words\n", + "AI: A language model is a computer program that can generate sentences based on patterns it has learned from a large amount of text.\n", + "Human: Describe it again fewer words but at least one word\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "Human: Describe a language model in one sentence\n", + "AI: A language model is a statistical model that is trained on a large corpus of text and is able to generate coherent and grammatically correct sentences based on the patterns and structures it has learned.\n", + "Human: Describe it again using less words\n", + "AI: A language model is a computer program that can generate sentences based on patterns it has learned from a large amount of text.\n", + "Human: Describe it again fewer words but at least one word\n", + "AI: A language model is a program that generates sentences from text patterns.\n", + "Human: What did I first ask you? I forgot.\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "' You asked me to describe a language model in one sentence.'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.memory import ChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory\n", + "from langchain.chains import LLMChain, ConversationChain, RetrievalQA, RetrievalQAWithSourcesChain\n", + "# Create an Open AI Chat Model\n", + "chat = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Create the memory object with size set to 2\n", + "memory = ConversationBufferMemory(size=4)\n", + "buffer_chain = ConversationChain(llm=chat, memory=memory, verbose=True)\n", + "\n", + "# \n", + "buffer_chain.predict(input=\"Describe a language model in one sentence\")\n", + "buffer_chain.predict(input=\"Describe it again using less words\")\n", + "buffer_chain.predict(input=\"Describe it again fewer words but at least one word\")\n", + "buffer_chain.predict(input=\"What did I first ask you? I forgot.\")" + ] + }, + { + "cell_type": "markdown", + "id": "efd52d16-a392-4aad-83e7-2e044b6d0c43", + "metadata": {}, + "source": [ + "**EXERCISE-2. For the ```ConversationBufferMemory```, change the buffer size to 1 or 2 and see what happens**" + ] + }, + { + "cell_type": "markdown", + "id": "8e535976-cabe-4162-b8ca-e84532dc783c", + "metadata": {}, + "source": [ + "## ConversationSummaryMemory\n", + "For longer conversations, storing the entire memory, or even a long buffer memory, may not be technically feasible. In these cases, a summary memory implementation can be a good option. Summary memories summarize the conversation at each step to retain the key context for the model to use. This works by using another LLM for generating the summaries, alongside the LLM used for generating the responses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7325752-2d3d-499e-b7ae-31122b1f64d5", + "metadata": {}, + "outputs": [], + "source": [ + "# ==============================================\n", + "# PLEASE FOLLOW INSTRUCTIONS AND COMPLETE CODE\n", + "# ==============================================\n", + "\n", + "# Use openAI model from earlier as a summary model\n", + "summary_llm = YOUR CODE HERE\n", + "\n", + "# Complete code below by putting in summary model above\n", + "memory = ConversationSummaryMemory(llm=summary_llm)\n", + "\n", + "# Create a chat model to use in the Conversation chain below (refer\n", + "# previous cells where we created OpenAI chat model\n", + "chat_model = YOUR CODE HERE\n", + "\n", + "# Create a conversation chain as we did before \n", + "summary_chain = YOUR CODE HERE\n", + "\n", + "summary_chain.predict(input=\"Please tell me about Malawi.\")\n", + "summary_chain.predict(input=\"Does that affect Malawi's income?\")" + ] + }, + { + "cell_type": "markdown", + "id": "f4c250ad-6f8a-42a6-99d5-bdb7b75df1e1", + "metadata": {}, + "source": [ + "# 3. Adding External Documents to LLMs\n", + "As mentioned in the lectures, LLMs are trained on a specific dataset (often publicly available internet data) up to some point in time. Therefore, if you have some custom organization documents or data, the LLMs will not be able to provide answers based on that information. Furthermore, if there is any new information which came after the LLM was trained, the LLM will not have that information either. \n", + "\n", + "The main remedy to deal with this is to provide the LLM with external documents. Adding external documents further helps with **hallucinations** as the LLM has little opportunity to make up stuff (hallucinate) when it has access to this extra knowledge.\n", + "\n", + "In LangChain, there are three main steps to provide external documents to the LLM (essentially create a Retrieval Augmented Generation)-**RAG Chatbot**\n", + "1. Identify the data sources (documents, datasets, websites, databases etc).\n", + "\n", + "2. Load the documents into LangChain using document loaders. LangChain can work with different document sources, please see [the documentation](https://python.langchain.com/v0.1/docs/integrations/document_loaders/). \n", + "\n", + "3. Splitting the documents into chunks. \n", + "\n", + "4. Create vector embeddings and store into a vector database for retrievval" + ] + }, + { + "cell_type": "markdown", + "id": "8eb4a208-8c25-4060-bf4b-c4eb06e26557", + "metadata": {}, + "source": [ + "### 3.1 Document Loaders\n", + "LangChain has more than 160 document loaders. Some loaders are provided by 3rd parties who manage unique document formats. These include Amazon S3, Microsoft, Google Cloud, Jupyter notebooks, pandas DataFrames, unstructured HTML, YouTube audio transcripts, and more. " + ] + }, + { + "cell_type": "markdown", + "id": "9efebd7e-59c0-4aef-81a3-5d9a500d1319", + "metadata": {}, + "source": [ + "#### PDF Document Loader\n", + "- Requires installation of the ```pypdf``` package as a dependency.\n", + "- There are many different types of PDF loaders in LangChain, and there is documentation available online for each." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "fc21c844-25b1-4447-a7ea-4aff7ad450ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pypdf in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (3.8.1)\n" + ] + } + ], + "source": [ + "!pip install pypdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "417c534c-4a01-49a4-9c8f-16450dec011a", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import PyPDFLoader\n", + "loader = PyPDFLoader(str(FILE_DENGUE))\n", + "data = loader.load()\n", + "print(data[0])" + ] + }, + { + "cell_type": "markdown", + "id": "bdb6fbaf-34c0-4fc6-8570-76aa853e78a5", + "metadata": {}, + "source": [ + "**EXERCISE-3. Explore other LangChain Loaders**\n", + "\n", + "Check the LangChain [document loaders documentation](https://python.langchain.com/v0.1/docs/integrations/document_loaders/) \n", + "and also check [here](https://python.langchain.com/v0.1/docs/modules/data_connection/) for most commonly used loaders.\n", + "1. Identify 5 document loaders you find interesting. What are third party document loaders?\n", + "2. **HTML loaders**. Explore the html or webpage loaders. \n", + "3. Pick one of your favourite webpages and load it using the ```UnstructuredHTMLLoader``` loader module. Refer to the [documentation](UnstructuredHTMLLoader) on how to import the module.\n", + "4. How do you think this changes your approach to ```web-scraping```. Do you think web scraping will change or not with this new capabilities to just connect to a website and query it?" + ] + }, + { + "cell_type": "markdown", + "id": "07bde0cf-56cb-4726-a3f0-cfc839ba1d3e", + "metadata": {}, + "source": [ + "### 3.2 Preparing documents for vector database and retrieval\n", + "In this stage, there are two sub-steps:\n", + "- The document is split to enhance efficiency in storage, indexing and ultimately efficient retrieval. Furthermore, chunking also helps with ensuring the document (which act as context) can fit in the context window \n", + "- An embedding model is used to convert the documents into ```vector embeddings```\n", + "- The vectorized data is stored into a vector database." + ] + }, + { + "cell_type": "markdown", + "id": "ba116fde-3896-40d2-b921-18c2de13b56d", + "metadata": {}, + "source": [ + "#### Splitting/Chunking Documents\n", + "- Given a PDF document, one naive splitting option would be to separate the document into lines as they appear in the document. This would be simple to implement but could be problematic. Key context required for understanding one line is often found in a different line, and these lines would be processed separately, so we need another strategy which can maintain context across pieces of texts in the document-enter the **overlap concept**.\n", + "We will compare two document splitting methods from LangChain. \n", + ">- **CharacterTextSplitter** splits text based on a specified separator, looking at individual characters. This method splits based on the separator first, then evaluates chunk size and chunk overlap.\n", + ">- **RecursiveCharacterTextSplitter** attempts to split by several separators recursively until the chunks fall within the specified chunk size. There are many other methods that use natural language processing to infer meaning and split appropriately. Optimizing this is an active area of research.\n", + "\n", + "There isn't one strategy that works for all situations when it comes to splitting documents. \n", + "It's often the case of experimenting with multiple methods, and seeing which one strikes the right balance between retaining sufficient context and managing chunk size." + ] + }, + { + "cell_type": "markdown", + "id": "633a6334-4e51-4e28-b2a9-1967fd36d6b7", + "metadata": {}, + "source": [ + "##### CharacterTextSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9cabe5b1-557f-4480-83ef-9637682546c5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Created a chunk of size 52, which is longer than the specified 24\n" + ] + } + ], + "source": [ + "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n", + "quote = 'One machine can do the work of fifty ordinary humans.\\\n", + "No machine can do the work of one extraordinary human.'\n", + "\n", + "chunk_size = 24\n", + "chunk_overlap = 3\n", + "\n", + "ct_splitter = CharacterTextSplitter(separator=\".\", \n", + " chunk_overlap=chunk_overlap, chunk_size=chunk_size)\n", + "\n", + "docs = ct_splitter.split_text(quote)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2a6a8893-4f70-40db-a1a2-66055f532d3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['One machine can do the work of fifty ordinary humans',\n", + " 'No machine can do the work of one extraordinary human']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs" + ] + }, + { + "cell_type": "markdown", + "id": "08337229-f503-429b-8345-e5d987f0d774", + "metadata": {}, + "source": [ + "##### RecursiveCharacterTextSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "431f2615-19d8-45a7-b626-f78e45332534", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['One machine can do the', 'work of fifty ordinary', 'humans.No machine can', 'do the work of one', 'extraordinary human.']\n" + ] + } + ], + "source": [ + "# Using the same variables: chunk_size and chunk_overlap, instatiate RecursiveCharacterTextSplitter\n", + "rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap)\n", + "docs = rc_splitter.split_text(quote)\n", + "print(docs)" + ] + }, + { + "cell_type": "markdown", + "id": "8777067d-35d7-471d-983f-fec0a6aafbc0", + "metadata": {}, + "source": [ + "#### Load data into a vector database\n", + "At this stage, you will be faced with a decision to choose which vector database to use. \n", + "For our simple demonstration purpose, we will use [chromadb](https://www.trychroma.com), an open source vector database solution. The type of vector database solution you choose can depend on numerous factors such as:\n", + "- How large are the documents you will be processing\n", + "- How much money you have to spend on the project\n", + "- Efficiency/latency requirements for your use case, if you need to provide solution in real-time/fast, you may need a different solution\n", + "- Accuracy requirements. Sometimes there is a tradeoff between accuracy and latecy.\n", + "- Integration requirements with existing platforms. In somecases, people use ```PostgreSQL``` because they are already using it and it has enough add on extensions for vector database capabilities.\n", + "\n", + "Another decision choice is the **embedding model**- the LLM which converts the text/documents into vectors. There are many options on the market and the choice comes down to things such as:\n", + "- Available budget\n", + "- Compatibility with the LLM you are using in the generation phase. People do use a different embedding model from the generation model\n", + "> embedding_llm = Mistral, \n", + "> chat_model = ChatOpenAI\n", + "- Nature of documents, size and alot of other factors" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "79cbfc3e-d388-4410-8dff-56a46a47c53d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting sentence_transformers\n", + " Downloading sentence_transformers-3.0.0-py3-none-any.whl (224 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.7/224.7 kB\u001b[0m \u001b[31m827.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.15.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (0.23.2)\n", + "Requirement already satisfied: scipy in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (1.10.1)\n", + "Requirement already satisfied: scikit-learn in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (1.2.2)\n", + "Requirement already satisfied: torch>=1.11.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (2.1.2)\n", + "Requirement already satisfied: tqdm in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (4.65.0)\n", + "Requirement already satisfied: Pillow in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (9.4.0)\n", + "Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (4.41.2)\n", + "Requirement already satisfied: numpy in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (1.23.5)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2023.12.2)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (4.8.0)\n", + "Requirement already satisfied: packaging>=20.9 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (23.2)\n", + "Requirement already satisfied: pyyaml>=5.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (6.0.1)\n", + "Requirement already satisfied: filelock in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (3.9.0)\n", + "Requirement already satisfied: requests in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2.28.1)\n", + "Requirement already satisfied: networkx in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (2.8.4)\n", + "Requirement already satisfied: sympy in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (1.12)\n", + "Requirement already satisfied: jinja2 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (3.1.2)\n", + "Requirement already satisfied: regex!=2019.12.17 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (2022.7.9)\n", + "Requirement already satisfied: tokenizers<0.20,>=0.19 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (0.19.1)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (0.4.3)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (2.2.0)\n", + "Requirement already satisfied: joblib>=1.1.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (1.1.1)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from jinja2->torch>=1.11.0->sentence_transformers) (2.1.1)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2024.2.2)\n", + "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2.1.1)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (1.26.15)\n", + "Requirement already satisfied: idna<4,>=2.5 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (3.4)\n", + "Requirement already satisfied: mpmath>=0.19 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sympy->torch>=1.11.0->sentence_transformers) (1.3.0)\n", + "Installing collected packages: sentence_transformers\n", + "Successfully installed sentence_transformers-3.0.0\n" + ] + } + ], + "source": [ + "!pip install sentence_transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ad4c76d-7248-49d6-acf2-3c1193bd2dcb", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_community.vectorstores import Chroma\n", + "from langchain_community.embeddings import HuggingFaceEmbeddings\n", + "\n", + "\n", + "# Lets load the Cholera paper and then store it in a database\n", + "loader = PyPDFLoader(str(FILE_HEP_CHAD))\n", + "data = loader.load()\n", + "\n", + "chunk_size = 100\n", + "chunk_overlap = 10\n", + "\n", + "# Split with RecursiveCharacterTextSplitter\n", + "rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap)\n", + "docs = rc_splitter.split_documents(data)\n", + "\n", + "# Lets use openAI embedding model\n", + "#embedding_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_KEY)\n", + "embedding_model = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n", + "# Directory to store our database-set this to the data directory\n", + "vectordb = Chroma(persist_directory=str(DIR_DATA), embedding_function=embedding_model)\n", + "\n", + "# Store the databse\n", + "vectordb.persist()\n", + "\n", + "# Create the database\n", + "docstorage = Chroma.from_documents(docs, embedding_model)" + ] + }, + { + "cell_type": "markdown", + "id": "e30d865a-42fb-4325-b53c-84da656a0703", + "metadata": {}, + "source": [ + "**EXERCISE-4. Explore what functionality is available under the database object ```docstorage_cholera```**\n", + "- You can use ```dir(object)``` to check available attributes and functions\n", + "- Note that there many search related functions which enables you to control how user queries are searcherd when building Chatbots" + ] + }, + { + "cell_type": "markdown", + "id": "52ea226f-8de3-4c5c-a69e-9d8971b047cf", + "metadata": {}, + "source": [ + "### 3.3 Retrieval\n", + "Now that we have added our external file. Lets use the added document as context in our LLM chains and ask questions again." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5af5276c-4235-44c8-926f-652acf3d16dd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "LLM Output without using RAG-external document from WHO website\n", + "============================================================\n", + "\n", + "\n", + "As of September 2021, there are several ongoing disease outbreaks in Chad. These include:\n", + "\n", + "1. COVID-19: Chad has been experiencing a surge in COVID-19 cases since April 2021, with a peak in July. As of September 2021, there have been over 5,000 confirmed cases and over 170 deaths.\n", + "\n", + "2. Cholera: A cholera outbreak was declared in June 2021 in the Lake Chad region, affecting areas near the border with Nigeria. As of September 2021, there have been over 2,000 suspected cases and 50 deaths.\n", + "\n", + "3. Measles: Chad has been experiencing a measles outbreak since January 2020. As of September 2021, there have been over 20,000 suspected cases and over 300 deaths, mainly affecting children under the age of 5.\n", + "\n", + "4. Yellow fever: A yellow fever outbreak was declared in November 2020, affecting several regions in Chad. As of September 2021, there have been over 60 confirmed cases and 10 deaths.\n", + "\n", + "5. Meningitis: Chad is currently experiencing a meningitis outbreak, with over 2,000 suspected cases and 200 deaths reported since the beginning of 2021.\n", + "\n", + "\n", + "\n", + "============================================================\n", + "LLM Output with RAG-external document from WHO website\n", + "============================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Yes, there is currently a hepatitis E outbreak in Chad, specifically in the eastern Ouaddai province. This outbreak was last reported on May 8, 2024.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.chains import RetrievalQA\n", + "\n", + "# Create LLM as before \n", + "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Create retriever with \n", + "qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docstorage.as_retriever())\n", + "\n", + "# The question we will ask the LLM\n", + "# You can ask these questions in French and LLM will also answer in French\n", + "question = \"Are there any disease outbreaks in Chad?\"\n", + "\n", + "# Answer without RAG\n", + "output = llm.invoke(question)\n", + "print()\n", + "print(\"=\"*60)\n", + "print(\"LLM Output without using RAG-external document from WHO website\")\n", + "print(\"=\"*60)\n", + "print(output)\n", + "\n", + "# For RAG Chain, we put in the question as dictionary\n", + "print()\n", + "print(\"=\"*60)\n", + "print(\"LLM Output with RAG-external document from WHO website\")\n", + "print(\"=\"*60)\n", + "print(qa.run(question))" + ] + }, + { + "cell_type": "markdown", + "id": "3c735436-9e4a-410d-8f37-4f290cf51e1b", + "metadata": {}, + "source": [ + "**EXERCISE-5. Implement a simple RAG as we did above**\n", + "1. Use the ```FILE_MIDDLE_EAST_COVID``` file to create a new Chroma database\n", + "2. Implement a RAG chainas we did above.\n", + "3. Compare answers between a the LLM with RAG and no RAG\n", + "\n", + "**Hint.** Copy and paste the code from above and edit it." + ] + }, + { + "cell_type": "markdown", + "id": "6375cfcc-c6a9-406a-8e72-cd0a04a9b2ac", + "metadata": {}, + "source": [ + "### 3.4 Retrieval with sources reference\n", + "In reallife applications, you will have hundreds or thousands of documents. A user of your system may need to know the spurce of the answrs they are getting. Most RAG systems are able to provide details of where the information is coming from. For example, in the RAG-Malawi example, the RAG system can provide the page numbers. In this case, with LangChain, you can you can just provide information about the document where the answer came from.\n", + "\n", + "One method of mitigating the risk of LLM hallucinations from RAG is using RetrievalQAWithSourcesChain, which also returns the data source of the answer. Aside from the chain class, the code is exactly the same as RetrievalQA. However, this class returns a dictionary containing a 'sources' key and an 'answer' key. The 'sources' key refers to the file where the answer came from, which is helpful when there are many documents in the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "399ddc48-1e99-43a4-ae28-298d5427b367", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chains import RetrievalQAWithSourcesChain\n", + "\n", + "qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docstorage.as_retriever())\n", + "\n", + "results = qa({\"question\": \"Are there any disease outbreaks in Chad?\"},\n", + " return_only_outputs=True)\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "id": "d7b0f265-8583-4357-9040-8dd75429179c", + "metadata": {}, + "source": [ + "# 4. LangChain Expression Language (LCEL)\n", + "> In summary, LCEL is a different (recommended) syntax of achieving the same things we have done in LangChain\n", + "\n", + "LCEL is a key part of the LangChain toolkit. We can use it to connect prompts, models, and retrieval components using a **pipe (|)** operator rather than task-specific classes. It also lets us create complex workflows that work well in production environments. These chains have built-in support for batch processing, streaming, and asynchronous execution. This makes it easy to integrate with other LangChain tools and utilities like **LangSmith** and **LangServe**.\n", + "\n", + "A few notes about the chain with LCEL\n", + "- The ```| (pipe)``` in LCEL indicates that the output from one component will be used as the input to the next." + ] + }, + { + "cell_type": "markdown", + "id": "8229521e-790e-41b8-9fd8-159a54cae8c7", + "metadata": {}, + "source": [ + "## 4.1 A Simple Chain with LCEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88efb378-36b2-4499-a033-c3145101475e", + "metadata": {}, + "outputs": [], + "source": [ + "model = ChatOpenAI(openai_api_key=OPENAI_API_KEY)\n", + "prompt = ChatPromptTemplate.from_template(\"You are a helpful personal assistant. \\\n", + "Answer the following question: {question}\")\n", + "\n", + "# Create Chain in LCEL fashion\n", + "llm_chain = prompt | model\n", + "\n", + "# Recall how we created a chain before \n", + "#llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "\n", + "# Run using invoke\n", + "print(llm_chain.invoke(\"What is the capital of Tunisia?\"))" + ] + }, + { + "cell_type": "markdown", + "id": "1ac8949f-0ad0-4d22-8dbf-e8fd992f4065", + "metadata": {}, + "source": [ + "## 4.2 RAG with LCEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68b9b2eb-db18-49e2-b75e-2c27fb862f2e", + "metadata": {}, + "outputs": [], + "source": [ + "model = ChatOpenAI(openai_api_key = OPENAI_API_KEY)\n", + "\n", + "embedding_model = OpenAIEmbeddings(openai_api_key = OPENAI_API_KEY)\n", + "vectorstore = Chroma.from_texts([\"Dunstan stayed in Tunis, the capital of Tunisia from Sunday May 26 to Satarday May 31.\"],embedding=embedding_model)\n", + "retriever = vectorstore.as_retriever()\n", + "\n", + "template = \"\"\"Answer the question based on the context:{context}. Question: {question}\"\"\"\n", + "prompt = ChatPromptTemplate.from_template(template)\n", + "\n", + "chain = ({\"context\": retriever,\"question\": RunnablePassthrough()} | prompt | model | StrOutputParser())\n", + "chain.invoke(\"When did Dunstan visit Tunisia?\")" + ] + }, + { + "cell_type": "markdown", + "id": "c6ac7202-4bb2-446d-85bf-e55bed0f53b1", + "metadata": {}, + "source": [ + "## 4.3 More things you can do with LCEL\n", + "There are alot of things you can do with LCEL. For example,\n", + "- **Batch or Streaming**. LCEL chains can be run in ```batch``` mode or ```streaming``` mode\n", + "- **Sequential chains.**. Sequential chains utilize step-by-step processing of inputs, where the output from one step becomes the input for the next. This enables a clear and organized flow of information within the chain. They provide flexibility in constructing custom pipelines by combining different components, such as prompts, models, retrievers, and output parsers, to suit specific use cases and requirements.\n", + "- **Passing Data Across Chains.** There are many cases where your application will require the use of several chains that pass outputs between them" + ] + }, + { + "cell_type": "markdown", + "id": "826bc2fc-cb3b-4d23-abab-52451461e0c4", + "metadata": {}, + "source": [ + "### Using sequential chaining to create Python code and check it with LCEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bc0c20c-167c-454c-a87a-db01aa8f2855", + "metadata": {}, + "outputs": [], + "source": [ + "coding_prompt = PromptTemplate.from_template(\n", + " \"\"\"Write Python code to loop through the following list, printing each element: {list}\"\"\")\n", + "validate_prompt = PromptTemplate.from_template(\n", + " \"\"\"Consider the following Python code: {answer} If it doesn't use a list comprehension, update it to use one. If it does use a list comprehension, return the original code without explanation:\"\"\")\n", + "\n", + "llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Create the sequential chain\n", + "chain = ({\"answer\": coding_prompt | llm | StrOutputParser()}\n", + " | validate_prompt\n", + " | llm \n", + " | StrOutputParser() )\n", + "\n", + "# Invoke the chain with the user's question\n", + "print(chain.invoke({\"list\": \"[3, 1, 4, 1]\"}))" + ] + }, + { + "cell_type": "markdown", + "id": "2ae9fb57-5565-4bdf-a977-67f735260e51", + "metadata": {}, + "source": [ + "# 5. LangChain Agents\n", + "In LLMs and Gen AI, the idea behind agents is to use language models to determine which a sequence of actions to take to meet a pre-defined objective. Thus, the LLM is able solve complex problems or perform complex tasks by planning, determing what tools to use and what knowledge to get until the task is solved without explicit supervision.\n", + "\n", + "- Agents often use tools, which, in LangChain, are functions used by the agent to interact with the system. These tools can be high-level utilities to transform inputs, or they can be specific to a series of tasks. Agents can even use chains and other agents as tools!\n", + "- In LangChain, there different agent types. See [this documentation](https://python.langchain.com/v0.1/docs/modules/agents/agent_types/) for explanation of how the agents are categorized. \n", + "## Components of a LangChain Agent\n", + "There are four primary components to LangChain agents. \n", + "- The user input in the form of a prompt represents the initial input provided by the user. \n", + "- The definition for handling the intermediate steps explains how to handle and process actions during the agent's execution. \n", + "- The agent also needs to have a definition for the tools and model behavior to execute. \n", + "- The output parser formats the output generated by the model into the most appropriate format for the use case. Agents can be defined for specificity or high-level thought processes." + ] + }, + { + "cell_type": "markdown", + "id": "00fa06af-c231-4237-8d62-dc42ff0f59de", + "metadata": {}, + "source": [ + "## 5.1 Zero-Shot ReAct agent\n", + "ReAct stands for **Reasoning and Acting**. This simplifies the answer to infer as much context as possible. \n", + "We start by importing the initialize_agent function and AgentType for agent creation and configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1e3cf87-c98c-4dfa-9ab4-65bdee6f47df", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents import initialize_agent, AgentType, load_tools\n", + "\n", + "# Define LLM\n", + "llm = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Define what tools the agent will will use, it can be more than one tool\n", + "tools = load_tools([\"llm-math\"], llm=llm)\n", + "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n", + "agent.run(\"What is 10 multiplied by 50?\")" + ] + }, + { + "cell_type": "markdown", + "id": "aab0fb85-881a-4b00-b1a6-1cee7d7a3f75", + "metadata": {}, + "source": [ + "## 5.2 Other Agents \n", + "There are alot of other agents and tools in LangChain. For example, in order to interact with a database or structured dataset we will utilise an ```SQLAgent```" + ] + }, + { + "cell_type": "markdown", + "id": "971ae0ee-c01d-4fea-b992-110d6c7e0edf", + "metadata": {}, + "source": [ + "# 6. Evaluating LLM Outputs in LangChain\n", + "As mentioned in Lectures, its important to evaluate LLM model outputs as well as all ML based outputs fot that matter. \n", + "Although Gen AI may seem very smart, the models still make alot of mistakes. As such, evaluating AI applications is important for several reasons. \n", + "- First, it checks if the AI model can accurately interpret and respond to a variety of inputs. This is vital in applications where responses inform decision-making, and reliability is paramount. \n", + "- Evaluation also help identify the strengths and weaknesses of a model, which allows for targeted and continuous improvements, and builds trust among users and stakeholders. \n", + "- Evaluation allows us to re-align model output with human intent, getting to the ideal responses faster.\n", + "\n", + "## LangChain evaluation tools\n", + "LangChain has built-in evaluation tools for comparing model outputs based on common criteria, such as relevance and correctness. It also provides tools for defining custom criteria, which we can tailor to specific use cases. Finally, the ```QAEvalChain class``` is another tool that can be used to measure how well an AI's response answers a specific question using ground truth responses." + ] + }, + { + "cell_type": "markdown", + "id": "abf96d76-5901-4fe0-83b5-881e6e340b92", + "metadata": {}, + "source": [ + "## 6.1 LangChain Built-in Evaluation Metrics" + ] + }, + { + "cell_type": "markdown", + "id": "c7752f75-65d4-4a91-83dc-3b4740e544d9", + "metadata": {}, + "source": [ + "**EXERCISE-6: Explore Evalution Metrics in LangChain**\n", + "- run this import statement: ```from langchain.evaluation import Criteria```\n", + "- use ``list`` function pn Criteria to check the list of available functions" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ab44f6f3-2489-4670-88f4-a3361c6a7fc3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'Step 1: Identify the criterion - relevance.\\n\\nStep 2: Read the input and submission to determine if they are referring to a real quote from the text.\\n\\nStep 3: The input is asking a math question, not referring to a quote from the text.\\n\\nStep 4: The submission is referring to a different topic, the capital of New York state, and not a quote from the text.\\n\\nStep 5: Therefore, the submission does not meet the criterion of relevance.\\n\\nConclusion: The submission does not meet the criterion of relevance.', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.evaluation import load_evaluator\n", + "\n", + "\n", + "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "evaluator = load_evaluator(\"criteria\", criteria=\"relevance\",llm=llm)\n", + "eval_result = evaluator.evaluate_strings(prediction=\"The capital of New York state is Albany\",input=\"What is 26 + 43?\")\n", + "print(eval_result)" + ] + }, + { + "cell_type": "markdown", + "id": "28a2c168-9b70-427d-9e0b-231212ec7699", + "metadata": {}, + "source": [ + "**EXERCISE-7: Try doing the same evaluation above with a different LLM (e.g., Mistral)**" + ] + }, + { + "cell_type": "markdown", + "id": "ce53d768-a26c-4509-948e-c464ebd20310", + "metadata": {}, + "source": [ + "## 6.2 Defining Custom Metrics\n", + "To customize the criteria, we need to evaluate the specific use case and define a dictionary named custom_criteria. This example adds simplicity, bias, clarity, and truthfulness criteria. Custom criteria work by mapping criteria names to the questions that are used to evaluate the strings. To use these new criteria, create an evaluator object, but this time, using our custom_critera." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b248976-85a5-4ef3-9998-48cf1afa9311", + "metadata": {}, + "outputs": [], + "source": [ + "custom_criteria = {\"simplicity\": \"Does the language use brevity?\",\n", + " \"bias\": \"Does the language stay free of human bias?\",\n", + " \"clarity\": \"Is the writing easy to understand?\",\n", + " \"truthfulness\": \"Is the writing honest and factual?\"}\n", + "\n", + "evaluator = load_evaluator(\"criteria\", criteria=custom_criteria,\n", + " llm=llm)\n", + "eval_result = evaluator.evaluate_strings(input=\"What is the best Italian restaurant in New York City?\",\n", + "prediction=\"That is a subjective statement and I cannot answer that.\")\n", + "print(eval_result)" + ] + }, + { + "cell_type": "markdown", + "id": "ef61e633-de72-4019-940c-7021b0e7c2e1", + "metadata": {}, + "source": [ + "## 6.3 QAEvalChain\n", + "Question-Answering (QA) is one of the most popular applications LLMs. But it is often not always obvious to determine what parameters (e.g., chunk size) or components (e.g., model choice, VectorDB) yield the best QA performance in the system we are building. The QA eval chain is an LLM chain for evaluting performance of an LLM on QA task. Refer to this detailed [LangChain blog post](https://blog.langchain.dev/auto-eval-of-question-answering-tasks/) for details about QAEvalChain." + ] + }, + { + "cell_type": "markdown", + "id": "1585ea3a-1be2-40db-9c88-774c03e220a7", + "metadata": {}, + "source": [ + "### 6.3.1 Trying out QAEvalChain\n", + "As a metric, QAEvalChain focuses on the **accuracy** and **relevance** of the response. In this chain, RAG will be used to store the document and ground truth responses, and an evaluation model instance is used to compare the semantic meaning of a model's results with the ground truth. \n", + "\n", + "First, we load our data source, in this case, a PDF document, and split it into chunks. Next, we set up the embeddings model, vector database, and LLM, and combine them in a chain. The input_key is set to \"question\", as questions will be used to query the database" + ] + }, + { + "cell_type": "markdown", + "id": "3e59bac0-ed4c-4ffe-b64c-423f88f1aab3", + "metadata": {}, + "source": [ + "### Create a RAG Retriever " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2fc52a82-7b90-4b0f-953a-32cb90beee54", + "metadata": {}, + "outputs": [], + "source": [ + "# Lets load the Cholera paper and then store it in a database\n", + "loader = PyPDFLoader(str(FILE_DENGUE))\n", + "data = loader.load()\n", + "\n", + "chunk_size = 100\n", + "chunk_overlap = 50\n", + "\n", + "# Split with RecursiveCharacterTextSplitter\n", + "rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap)\n", + "docs = rc_splitter.split_documents(data)\n", + "\n", + "# Lets use openAI embedding model\n", + "embedding_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_KEY)\n", + "\n", + "# Directory to store our database-set this to the data directory\n", + "vectordb = Chroma(persist_directory=str(DIR_DATA), embedding_function=embedding_model)\n", + "\n", + "# Store the databse\n", + "vectordb.persist()\n", + "\n", + "# Create the database\n", + "docstorage = Chroma.from_documents(docs, embedding_model)\n", + "\n", + "# LLM\n", + "llm = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Define the retriever chain\n", + "qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docstorage.as_retriever(), input_key=\"question\")" + ] + }, + { + "cell_type": "markdown", + "id": "a9dddef3-e7ab-4c3e-9086-121be7b3b8a4", + "metadata": {}, + "source": [ + "## Define a Question Set as Key-Value Pairs in a Dict\n", + "This is a ground-truth dataset which a list of questions and their correct responses." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f4d95da4-f130-4a2f-b0e5-40d22146674e", + "metadata": {}, + "outputs": [], + "source": [ + "question_set = [{\"question\": \"Did dengue cases increase in 2023?\",\n", + " \"answer\": \"Yes, in 2023, there was an increase in cases globally.\"},\n", + " {\"question\": \"According to the document, which are the top four regions affected by arboviral diseases?\",\n", + " \"answer\": \"Africa is oe of the top four regions\"},\n", + " {\"question\": \"How is dengue virus transimitted to humans?\",\n", + " \"answer\": \"through the bite of infected mosquitoes\"}]" + ] + }, + { + "cell_type": "markdown", + "id": "7a509f17-4bec-4bdc-805e-321b37581a84", + "metadata": {}, + "source": [ + "## Run QAEVAL" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "5dff29e4-037b-464f-9135-3f311daf4727", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'results': ' CORRECT'}, {'results': ' INCORRECT'}, {'results': ' CORRECT'}]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.evaluation import QAEvalChain\n", + "predictions = qa.apply(question_set)\n", + "eval_chain = QAEvalChain.from_llm(llm)\n", + "\n", + "results = eval_chain.evaluate(question_set,predictions, question_key=\"question\",prediction_key=\"result\", answer_key='answer')\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "id": "db20cb10-2438-43b2-b9d0-df3ae73d6418", + "metadata": {}, + "source": [ + "**EXERCISE-7 (Do this in Your Groups): Run Evaluation on a Custom Eval Dataset for a RAG Chatbot QA Task**\n", + "1. Create a RAG LLM Chain as we have done before.\n", + "Please identify a PDF document to use which contains some new information that the LLMs do not have. \n", + "Note that it can be a French or English document.\n", + "2. Create 5 pairs of questions and correct answers to use to evaluate your RAG\n", + "3. Run QAEVAL on the eval dataset and report how many responses did the LLM get correct.\n", + "4. Do this again with a different LLM (e.g., Falcon or Mistral) and compare performance across models. *Note that your eval dataset remains the same.*" + ] + }, + { + "cell_type": "markdown", + "id": "20065378-5cd5-4b0f-ae9a-c2869075441a", + "metadata": {}, + "source": [ + "# 7. Summary\n", + "-----\n", + "In this notebook, we covered the basics of how to use LangChain to interact with both proprietary models from OpenAI and open source LLMs through Hugging Face library. We noted that there are two approaches to building Chains with LangChain: either using the functions or using the LCEL syntax. We covered key topics as follows: creating chains and interacting with LLMs; managing memeory of chat models; setup a RAG based chains which incorprates external documents and evaluating LLM outputs. \n", + "\n", + "What we have covered in this notebook is the tip of the ice-berg just to get you started on building LLM based applications with LangChain and other tools. There are alot of other things to learn and check.\n", + "- What are other frameworks whoch perform the same tasks as LangChain?\n", + "- LangChain Agents and LLM agents in general\n", + "- Vector databases and their role \n", + "- How to work with different document sources (e.g., websites)\n", + "- How to choose embedding models and the influence they have on generation\n", + "- Which model to use: instruct/chat/text generation\n", + "- and more " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab0ee5b1-8133-4405-98fc-e56057daece6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python3.12-audio", + "language": "python", + "name": "audio" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/malawi-nov-24/README.md b/notebooks/malawi-nov-24/README.md new file mode 100644 index 0000000..38f8eff --- /dev/null +++ b/notebooks/malawi-nov-24/README.md @@ -0,0 +1,87 @@ +# Programming Activities for the Course + +This document outlines the programming activities for the course, focusing on hands-on projects to apply the concepts learned. The document is organized into two main sections: an introduction to LLM capabilities and LangChain, followed by a practical exercise on deploying a chatbot with Streamlit. + +## LLM Foundations-understanding the ML Process + +## Introducing LLM Capabilities and LangChain + +In this section, we explore the foundational capabilities of Large Language Models (LLMs) and how they can be applied in real-world scenarios. By leveraging LLMs, you can build applications such as chatbots, document analyzers, and automated support systems. + +### Understanding LLM Capabilities +LLMs are capable of generating human-like text, answering questions, summarizing content, and even performing tasks like sentiment analysis and named entity recognition. These models can process and interpret vast amounts of textual data, making them ideal for a variety of applications across domains. + +### Introducing LangChain +LangChain is a powerful framework that simplifies the process of integrating LLMs into applications. It provides modular components and utility functions to create chains (pipelines) that combine different tasks, such as prompting, data processing, and memory management, all within a cohesive system. LangChain makes it easier to build applications that require complex interactions with language models, including: + +- **Prompt Engineering**: Designing effective prompts to achieve desired responses from the LLM. +- **Data Handling**: Loading, processing, and storing large document corpora. +- **Chain Management**: Creating workflows that link multiple steps, such as data loading, prompt generation, and response handling. + +### Building Applications with LangChain +LangChain supports various use cases, including: + +1. **QA Chatbots**: Answering user questions based on specific datasets. +2. **Document Analysis**: Extracting information, summarizing content, or classifying documents. +3. **Automated Support Systems**: Handling customer service or FAQ queries. + +In this course, we will apply these capabilities to build a QA chatbot using LangChain, deploy it on Streamlit, and explore its functionality through real-world examples. + +--- + + +# Deploying a Chatbot on Streamlit +In this activity, you will use the knowledge gained from the LangChain Tutorial to explore a chatbot deployed on Streamlit. You will deploy this app on your computer and interact with it. + +## About Streamlit + +As discussed in the lectures, Streamlit is a platform that enables data scientists to deploy dynamic, data-based apps. It’s ideal for prototyping demonstration apps and sharing them with stakeholders before full-scale production deployment. + +## Initial Setup and Getting the Chatbot Files + +1. **Get OpenAI and Hugging Face API Credentials** + The chatbot uses OpenAI models, so you’ll need to sign up for an OpenAI developer account and obtain an API key. For a step-by-step guide on creating an OpenAI API key, search for instructions on ChatGPT. Similarly, create a Hugging Face account and obtain an API token. + +2. **Try the Chatbot on Streamlit Community Cloud** + Before downloading anything, you can try the chatbot on the Streamlit Community Cloud with just the OpenAI and Hugging Face keys. + +3. **Download or Clone the Project Repository** + To get the project files on your computer, either clone the GitHub repository (if familiar with Git) or download the repository as a zipped file. + +## Deploying the Streamlit App Locally + +1. **Unzip and Navigate to the Project Folder** + Once unzipped, open the project folder and follow the instructions on the GitHub page to deploy the chatbot. + +2. **Follow steps on GitHub project repository**. [Streamlit app repo](https://github.com/worldbank/RAG-Based-ChatBot-Example) + + +3. **Install Required Packages** + The `requirements.txt` file contains a list of all required packages. If you encounter a missing package error, try installing the package again (ensuring your virtual environment is activated). + +4. **Run the App Locally** + Run the app with the following command: + ```bash + streamlit run streamlit_app.py + ``` +5. **Test and Check**. When deployed locally, you can browse the files being used in the app. + +## Explore Important Scripts + +The essential components for building a chatbot with LangChain are organized into distinct, modular Python scripts. Let’s explore some of these elements. You can use VS Code or your preferred text editor for this task. + +### Loading Files +In real-life applications, you may need to load hundreds of documents, requiring a versatile function for file loading. This project includes two types of loaders: +- **`remote_loader.py`**: For loading documents from websites. +- **`local_loader.py`**: For loading documents from the local `data` folder. + +### Document Splitting +The `splitter.py` module uses the `RecursiveCharacterTextSplitter` strategy, with a chunk size of 1000 and an overlap of 0. This method helps in breaking down large documents into manageable sections for processing. + +### Prompt Chains +In the `full_chain.py`, `base_chain.py`, and `rag_chain.py` modules, you’ll find configurations for the specific LLM models and prompting strategies used. The project utilizes OpenAI chat models, with customized chains designed to guide interactions effectively. + +### Memory Management +Memory management strategies are also implemented to optimize the chatbot’s performance, particularly for long interactions or when processing large datasets. + + diff --git a/notebooks/nasa-apod.ipynb b/notebooks/nasa-apod.ipynb deleted file mode 100644 index 18553cb..0000000 --- a/notebooks/nasa-apod.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", - "metadata": { - "tags": [] - }, - "source": [ - "# Securely Using API Keys\n", - "\n", - "> The following are (opinionated) best practices to store and use API keys in your source code. If you disagree, please consider [contributing](https://github.com/worldbank/template/issues/new/choose). " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ac0ed1c0", - "metadata": {}, - "source": [ - "## Environment Variables\n", - "\n", - "An [environment variable](https://en.wikipedia.org/wiki/Environment_variable) is a dynamic-named value that can be used to store information on a computer. For instance, an environment variable can be used to store settings and/or privileged information (e.g. API keys) on your local computer or server.\n", - "\n", - "To set a environment variable to a new value, in **Unix-like** systems, you must pass a `name` and a `value` pair as shown below in the terminal.\n", - "\n", - "```shell\n", - "export SECRET_API_KEY = \n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "080bd097-f128-4759-946d-793368230804", - "metadata": { - "tags": [] - }, - "source": [ - "The `value` is accessible by the `name` without being exposed throughout the system. In particular, in [Python](https://python.org), the value can be retrieve as follows." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8d023b4e-496b-440c-91a7-199bceb44d7d", - "metadata": { - "tags": [] - }, - "source": [ - "```python\n", - "secret_api_key = os.getenv(\"SECRET_API_KEY\")\n", - "```" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "54a99582-d509-4ab8-be42-ddb4921c0f45", - "metadata": { - "tags": [] - }, - "source": [ - "Alternatively, it is customary to use a `.env` file to organize and load environments variables as needed. Packages such as [dotenv](https://www.npmjs.com/package/dotenv) and [python-dotenv](https://pypi.org/project/python-dotenv/) will automatically load environments variables for you from the `.env` file.\n", - "\n", - "```shell\n", - "source .env\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "3c0cc26a-2a99-49b0-a406-d57f31fff8ee", - "metadata": {}, - "source": [ - "With [Python](https://python.org)," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "960398ce-eadb-45e3-b160-53e6c9250dd0", - "metadata": { - "tags": [ - "remove_output" - ] - }, - "outputs": [], - "source": [ - "from dotenv import load_dotenv\n", - "\n", - "load_dotenv()" - ] - }, - { - "cell_type": "markdown", - "id": "bda573d0-c877-42e6-8ee5-3000b780b4b7", - "metadata": { - "tags": [] - }, - "source": [ - "With [Jupyter](https://jupyter.org)," - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2e700464-b50d-4b06-b0aa-afaafa17e68e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%load_ext dotenv\n", - "%dotenv" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "660db869", - "metadata": {}, - "source": [ - "The template includes `.env.example` as an example; to use, simply rename it to `.env` and add your settings and secrets to it. Please note that `.env` **must** never be committed/versioned (for example, to GitHub) and **should** be ignored on `.gitignore`. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "74484f7e", - "metadata": {}, - "source": [ - "```{tip}\n", - "While environments variables are a convenient way to minimize the security risk, it is important to emphasize secrets are still stored in plaintext in your computer. It is strongly recommended to use instead a secret manager, such as [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/) or [1Password](https://developer.1password.com/docs/cli/secret-references).\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "14e89727", - "metadata": {}, - "source": [ - "## Astronomy Picture of the Day" - ] - }, - { - "cell_type": "markdown", - "id": "b4c0f3e8-7756-41bb-aa21-cc2eee5ff67f", - "metadata": {}, - "source": [ - "One of the most popular APIs is NASA's [Astronomy Picture of the Day](https://apod.nasa.gov/apod/astropix.html). Let's see in the following example how to use the NASA API with a secret API key." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d797ef77-6ca4-4f9d-a1f8-abbfd9884b07", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import httpx\n", - "from IPython.display import Image" - ] - }, - { - "cell_type": "markdown", - "id": "ece37244", - "metadata": {}, - "source": [ - "First, you will have to [generate your API key](https://api.nasa.gov) and set up the environment variable `NASA_API_KEY` with its value. Now you are ready to use it in your code. For instance, in this example, we assign it to `api_key`. Please note that the value is never exposed and the notebook can be securely shared with anyone. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7b914e66-7ae8-4d8b-9621-d6dc5ec49631", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "api_key = os.getenv(\"NASA_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "10b5b12a", - "metadata": {}, - "source": [ - "Now, we are ready to make the request to the NASA API. According to the [documentation](https://github.com/nasa/apod-api#docs), the `api_key` is passed a parameter to the GET request. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1990c3b9-f145-4c1f-bbb5-82f50801a011", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "async with httpx.AsyncClient() as client:\n", - " r = await client.get(\n", - " \"https://api.nasa.gov/planetary/apod\", params={\"api_key\": api_key}\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "e952e343", - "metadata": {}, - "source": [ - "Voilà!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bd1cb597-0144-43e8-bed8-12145a831a0c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(url=r.json()[\"hdurl\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c7cb67e-c7ba-4ed3-bee0-36d303c1517d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "ce6d896885f4e28373aa2ff7c44f136ed5a497e2abd203a79a632f5859ed7bb5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tunisia-may-24/1-text2sqL-demo.ipynb b/notebooks/tunisia-may-24/1-text2sqL-demo.ipynb new file mode 100644 index 0000000..779899e --- /dev/null +++ b/notebooks/tunisia-may-24/1-text2sqL-demo.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5eb5dc4b-add1-4b1d-8fbb-3a1e85e552f7", + "metadata": {}, + "source": [ + "# Chatting with a Population Dataset Using LangChain and LLMs\n", + "\n", + "----\n", + "\n", + "In this simple demonstration, we show how you can use natural language to query a structured dataset. The dataset is a 2018 population census enumeration level data from Malawi." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a16425c5-c0ee-4bc9-8f80-1684edc5a843", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + " ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import pandas as pd\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "5186c265-b78c-41f6-a4a4-4401e6ccb7cf", + "metadata": {}, + "source": [ + "## 1. Creating a SQLLite Database\n", + "Use a CSV file to create a database. The file which was used to create a database is shown below as a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1b2308a3-7bb2-47ca-86c8-01ff060105e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RegionNameDistrictNameTANameEnumerationAreaTotalPopulationPopulationMalePopulationFemaleNumberHouseholds
0CentralNtchisiTA Malengaea-20307802633.0331.0302.0145.0
1CentralNtchisiTA Malengaea-203070251006.0507.0499.0226.0
2CentralNtchisiTA Malengaea-203070071503.0740.0763.0338.0
3CentralNtchisiTA Malengaea-203070051139.0553.0586.0251.0
4CentralNtchisiTA Malengaea-203070121400.0668.0732.0284.0
\n", + "
" + ], + "text/plain": [ + " RegionName DistrictName TAName EnumerationArea TotalPopulation \\\n", + "0 Central Ntchisi TA Malenga ea-20307802 633.0 \n", + "1 Central Ntchisi TA Malenga ea-20307025 1006.0 \n", + "2 Central Ntchisi TA Malenga ea-20307007 1503.0 \n", + "3 Central Ntchisi TA Malenga ea-20307005 1139.0 \n", + "4 Central Ntchisi TA Malenga ea-20307012 1400.0 \n", + "\n", + " PopulationMale PopulationFemale NumberHouseholds \n", + "0 331.0 302.0 145.0 \n", + "1 507.0 499.0 226.0 \n", + "2 740.0 763.0 338.0 \n", + "3 553.0 586.0 251.0 \n", + "4 668.0 732.0 284.0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pop = pd.read_csv(\"mw-ea-pop.csv\")\n", + "df_pop.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "1a28fd50-a6c4-4929-8212-b2f4c9889b80", + "metadata": {}, + "source": [ + "## 2. Setup LangChain for Connecting to Database\n", + "The tool we will use is called LangChain. Its a popular tool for creating apps ontop of LLMs. During the course, we will delve more into using LangChain." + ] + }, + { + "cell_type": "markdown", + "id": "cbe6719b-2c38-49d7-8c96-622fc6900207", + "metadata": {}, + "source": [ + "### 2.1 Import LangChain Packages and Setup Connection" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e1ed50a5-adc2-4ab2-a4ca-ad9e487fa464", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.utilities import SQLDatabase\n", + "from langchain.chains import create_sql_query_chain\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\n", + "\n", + "from operator import itemgetter\n", + "\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import PromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough" + ] + }, + { + "cell_type": "markdown", + "id": "ef46f940-9e27-4e49-b84a-41001ba9a79d", + "metadata": {}, + "source": [ + "### 2.2 Create the SQL Agent and a Chain" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c44bb470-9afd-4fcd-bf67-c3cc08ddfcb9", + "metadata": {}, + "outputs": [], + "source": [ + "# Test connection to the database\n", + "db = SQLDatabase.from_uri(\"sqlite:///mydatabase.db\")\n", + "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n", + "\n", + "execute_query = QuerySQLDataBaseTool(db=db)\n", + "write_query = create_sql_query_chain(llm, db)\n", + "chain = write_query | execute_query" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2da6cdd0-012b-4fb2-8ae3-bde91d67aa20", + "metadata": {}, + "outputs": [], + "source": [ + "answer_prompt = PromptTemplate.from_template(\n", + " \"\"\"Given the following user question, corresponding SQL query, and SQL result, answer the user question.\n", + "\n", + "Question: {question}\n", + "SQL Query: {query}\n", + "SQL Result: {result}\n", + "Answer: \"\"\"\n", + ")\n", + "\n", + "answer = answer_prompt | llm | StrOutputParser()\n", + "chain = (\n", + " RunnablePassthrough.assign(query=write_query).assign(\n", + " result=itemgetter(\"query\") | execute_query\n", + " )\n", + " | answer\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "6f39f651-c3e4-4fa7-b67a-b6b8c91de57f", + "metadata": {}, + "source": [ + "## 3. Chat with the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2ff768f4-7622-43db-afbe-4155bf5eeff2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'There are 33 districts in Malawi.'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"How many districts are there in Malawi\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b6d13719-e60e-4cff-afdb-c76253a65fc1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# VERIFY THIS INFORMATION USING PYTHON\n", + "df_pop.DistrictName.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a6305316-c437-4e86-bb3a-8f75450897e6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'There are 9,042,289 women in Malawi.'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"How many women are there in Malawi\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "23e0abae-b0af-4965-9c37-00cafc30db5d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9042289.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# VERIFY THIS INFORMATION USING PYTHON\n", + "df_pop.PopulationFemale.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e18bb38b-6313-4fd8-b2b8-b079c0e21e31", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'There are 246,415 women in Salima district.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"How many women are there in Salima district\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d1f8a6ca-6d53-4eca-80ae-09eb07ad886f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "246415.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can check that the answer above is correct using Python code\n", + "df_pop.query('DistrictName == \"Salima\"')['PopulationFemale'].sum()" + ] + }, + { + "cell_type": "markdown", + "id": "358519f6-98e3-4d45-ba6d-96560c67dcab", + "metadata": {}, + "source": [ + "### Complicated question" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "96f94c49-1d06-4765-8af1-12b2d217b8da", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'Approximately 51.48% of the population in Malawi is female.'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"What percent of the population is female in Malawi?\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2033ecd1-0714-4bb0-a582-7dde4c366364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "51.482681744085504" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fem = df_pop.PopulationFemale.sum()\n", + "tot = df_pop.TotalPopulation.sum()\n", + "\n", + "fem/tot*100" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c267ea6e-6028-4716-9731-30beabf8b3f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'Based on the SQL query and result provided, we are only retrieving the population of males in the specified region (Central, Ntchisi, TA Malenga) for the last four years. We are not directly comparing the number of men over the years to determine if they are increasing. To answer the user question accurately, we would need to retrieve the population data for men in Malawi over the last four years and compare the numbers to see if there is an increase.'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"are the number of men increasing in the four last years in malawi\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "986fd189-6019-4a8a-bf3a-4fdc4f6e708e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "'The fertility rate of Malawi can be calculated by dividing the total female population by the total population. \\n\\nFor the first set of data:\\nFertility rate = total_female_population / total_population\\nFertility rate = 1303 / 2604\\nFertility rate = 0.5008\\n\\nFor the second set of data:\\nFertility rate = total_female_population / total_population\\nFertility rate = 9042289 / 17563749\\nFertility rate = 0.5143\\n\\nTherefore, the fertility rate of Malawi is approximately 0.5008 for the first set of data and 0.5143 for the second set of data.'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "user_question = \"what is the fertilely rate of Malawi(Calculate)?\"\n", + "chain.invoke({\"question\": \"{}\".format(user_question)})" + ] + }, + { + "cell_type": "markdown", + "id": "8e977b26-373b-4482-b613-bd1f70276b6f", + "metadata": {}, + "source": [ + "## 4. EXERCISE: What Question Do You Want Me to Try?\n", + "Share any question in the chat you would like me to try based on this dataset so that we see how much it can handle. \n", + "\n", + "- **Share your question on the chat**\n", + "- **I will run the question here and we will inspect the response together**" + ] + }, + { + "cell_type": "markdown", + "id": "c07e2bc8-c41c-49dd-ac6b-1a77d6c6e168", + "metadata": {}, + "source": [ + "## 5. What We will Do During the Course\n", + "During the course we will use LangChain to build our own **Ask-A-Question (AAQ)** type \n", + "of Chatbot to enable a user to chat with a dataset by asking natural language questions. \n", + "We will build an interactive app like [this](https://llm-examples.streamlit.app) using Streamlit and be able to share it with others." + ] + }, + { + "cell_type": "markdown", + "id": "3b84bb26-3dff-4d1b-af02-8edc18ac2f36", + "metadata": {}, + "source": [ + "# Deployment\n", + "1. Web app\n", + "2. WhatsApp \n", + "2. Chatbot on website of NSO or Health ministry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72accb41-83da-4781-bf06-2488db5f91d1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tunisia-may-24/2-document-classification-with-sklearn.ipynb b/notebooks/tunisia-may-24/2-document-classification-with-sklearn.ipynb new file mode 100644 index 0000000..20c458b --- /dev/null +++ b/notebooks/tunisia-may-24/2-document-classification-with-sklearn.ipynb @@ -0,0 +1,575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a Document Classification System\n", + "The NumPy (Numerical Python) library used for working iwith arrays, and the Scikit-learn library is a python library built on NumPy, SciPy and matplotlib for data analytics and machine learning. The NLTK (Natural Language Toolkit) provides access to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensuring that you have the necessary libraries\n", + "# !pip install nltk\n", + "# !pip install numpy\n", + "# !pip install scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import nltk\n", + "from nltk.corpus import reuters\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.naive_bayes import MultinomialNB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Load your data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Reuters-21578 dataset is one of the most widely used data collections for text categorization research. It is a collection of documents with news articles and the original corpus has 10,369 documents and a vocabulary of 29,930 word and has labeled categories such as \"earnings\", \"acquisitions\".. etc. You can read metadata about the dataset on [Hugging Face](https://huggingface.co/datasets/ucirvine/reuters21578)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package reuters to\n", + "[nltk_data] /Users/dunstanmatekenya/nltk_data...\n", + "[nltk_data] Package reuters is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# download the dataset\n", + "nltk.download('reuters')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the Reuters-21578 dataset\n", + "documents = reuters.fileids()\n", + "train_docs = list(filter(lambda doc: doc.startswith(\"train\"), documents))\n", + "test_docs = list(filter(lambda doc: doc.startswith(\"test\"), documents))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Prepare your data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare the data by extracting the raw text and category labels for both the training and testing documents. Assumption is that each document has only one category label, so we take only the first category label for each document." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare the data\n", + "train_data = [reuters.raw(doc_id) for doc_id in train_docs]\n", + "train_labels = [reuters.categories(doc_id)[0] for doc_id in train_docs]\n", + "test_data = [reuters.raw(doc_id) for doc_id in test_docs]\n", + "test_labels = [reuters.categories(doc_id)[0] for doc_id in test_docs]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question-How many different classes are in the training data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore some of the training examples" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Article content: COMPUTER TERMINAL SYSTEMS <CPML> COMPLETES SALE\n", + " Computer Terminal Systems Inc said\n", + " it has completed the sale of 200,000 shares of its common\n", + " stock, and warrants to acquire an additional one mln shares, to\n", + " <Sedio N.V.> of Lugano, Switzerland for 50,000 dlrs.\n", + " The company said the warrants are exercisable for five\n", + " years at a purchase price of .125 dlrs per share.\n", + " Computer Terminal said Sedio also has the right to buy\n", + " additional shares and increase its total holdings up to 40 pct\n", + " of the Computer Terminal's outstanding common stock under\n", + " certain circumstances involving change of control at the\n", + " company.\n", + " The company said if the conditions occur the warrants would\n", + " be exercisable at a price equal to 75 pct of its common stock's\n", + " market price at the time, not to exceed 1.50 dlrs per share.\n", + " Computer Terminal also said it sold the technolgy rights to\n", + " its Dot Matrix impact technology, including any future\n", + " improvements, to <Woodco Inc> of Houston, Tex. for 200,000\n", + " dlrs. But, it said it would continue to be the exclusive\n", + " worldwide licensee of the technology for Woodco.\n", + " The company said the moves were part of its reorganization\n", + " plan and would help pay current operation costs and ensure\n", + " product delivery.\n", + " Computer Terminal makes computer generated labels, forms,\n", + " tags and ticket printers and terminals.\n", + " \n", + "\n", + " n\\, Label: acq\n" + ] + } + ], + "source": [ + "print(\"Article content: {} n\\, Label: {}\".format(train_data[1], train_labels[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Vectorizing the text data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Vectorize the text data using the TfidVectorizer from scikit-learn. TF-IDF is an abbreviation for Term Frequency Inverse Document Frequency. This is very common algorithm to transform text into a meaningful representation of numbers which is used to fit machine algorithm for prediction. \n", + "- Its worth noting that nowadays, this vectorization approach is not commonly used. We will cover **word embeddings** tomorrow which is a better approach to represent words as numbers because **vector embeddings** can capture semantic meanings better.\n", + "\n", + "For the sklearn TF-IDF vectorizer, you can learn more about it [here](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Vectorize the text data\n", + "vectorizer = TfidfVectorizer(stop_words=\"english\", max_features=1000)\n", + "X_train = vectorizer.fit_transform(train_data)\n", + "X_test = vectorizer.transform(test_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question: What role are the ```stop words``` playing in the code above? You might have learned this from Prof. Mohamad Ali already." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Training a Linear Support Vector Machine (LinearSVC) classifier using the vectorized training data and corresponding label" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearSVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearSVC()" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the classifier\n", + "classifier = LinearSVC()\n", + "classifier.fit(X_train, train_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Evaluate the classifier used and calculate the accuracy score as well as some other metrics (Precision, Recall and F-1 score)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.876117919841007\n", + " precision recall f1-score support\n", + "\n", + " acq 0.95 0.96 0.96 719\n", + " alum 0.33 0.18 0.24 22\n", + " barley 1.00 0.71 0.83 14\n", + " bop 0.77 0.80 0.79 30\n", + " carcass 0.79 0.65 0.71 17\n", + " castor-oil 0.00 0.00 0.00 1\n", + " cocoa 0.94 1.00 0.97 17\n", + " coconut 0.00 0.00 0.00 2\n", + " coconut-oil 0.00 0.00 0.00 2\n", + " coffee 0.89 0.96 0.92 25\n", + " copper 0.93 0.93 0.93 15\n", + " corn 0.85 0.81 0.83 48\n", + " cotton 1.00 0.86 0.92 14\n", + " cpi 0.62 0.62 0.62 24\n", + " cpu 0.00 0.00 0.00 1\n", + " crude 0.79 0.93 0.86 182\n", + " dfl 0.00 0.00 0.00 1\n", + " dlr 0.70 0.72 0.71 43\n", + " dmk 0.00 0.00 0.00 1\n", + " earn 0.98 0.99 0.98 1083\n", + " fuel 1.00 0.22 0.36 9\n", + " gas 0.75 0.33 0.46 9\n", + " gnp 0.59 0.89 0.71 19\n", + " gold 0.96 0.96 0.96 26\n", + " grain 0.71 0.77 0.74 77\n", + " groundnut 0.00 0.00 0.00 3\n", + " heat 1.00 0.75 0.86 4\n", + " hog 1.00 0.50 0.67 4\n", + " housing 1.00 0.67 0.80 3\n", + " income 1.00 0.80 0.89 5\n", + " instal-debt 1.00 1.00 1.00 1\n", + " interest 0.78 0.76 0.77 124\n", + " ipi 1.00 1.00 1.00 11\n", + " iron-steel 0.69 0.64 0.67 14\n", + " jet 0.00 0.00 0.00 1\n", + " jobs 0.73 0.85 0.79 13\n", + " l-cattle 0.00 0.00 0.00 2\n", + " lead 0.83 0.42 0.56 12\n", + " lei 1.00 1.00 1.00 3\n", + " livestock 0.50 0.50 0.50 6\n", + " lumber 0.00 0.00 0.00 5\n", + " meal-feed 0.20 0.17 0.18 6\n", + " money-fx 0.65 0.65 0.65 96\n", + " money-supply 0.80 0.83 0.81 29\n", + " naphtha 0.00 0.00 0.00 1\n", + " nat-gas 0.64 0.54 0.58 13\n", + " nickel 0.00 0.00 0.00 1\n", + " oilseed 0.54 0.54 0.54 13\n", + " orange 0.75 0.33 0.46 9\n", + " palladium 0.00 0.00 0.00 1\n", + " palm-oil 0.67 1.00 0.80 4\n", + " pet-chem 1.00 0.50 0.67 6\n", + " platinum 0.00 0.00 0.00 3\n", + " potato 1.00 0.67 0.80 3\n", + " propane 0.00 0.00 0.00 2\n", + " rape-oil 0.00 0.00 0.00 1\n", + " reserves 1.00 0.64 0.78 14\n", + " retail 1.00 1.00 1.00 1\n", + " rice 0.00 0.00 0.00 1\n", + " rubber 0.69 1.00 0.82 9\n", + " ship 0.39 0.41 0.40 39\n", + " silver 0.00 0.00 0.00 0\n", + " soy-oil 0.00 0.00 0.00 2\n", + " soybean 0.00 0.00 0.00 2\n", + "strategic-metal 0.00 0.00 0.00 6\n", + " sugar 0.71 0.96 0.81 25\n", + " tea 0.00 0.00 0.00 3\n", + " tin 0.71 0.50 0.59 10\n", + " trade 0.70 0.93 0.80 76\n", + " veg-oil 0.54 0.64 0.58 11\n", + " wpi 0.62 0.56 0.59 9\n", + " yen 0.00 0.00 0.00 6\n", + " zinc 0.00 0.00 0.00 5\n", + "\n", + " accuracy 0.88 3019\n", + " macro avg 0.53 0.48 0.49 3019\n", + " weighted avg 0.86 0.88 0.87 3019\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "# Evaluate the classifier\n", + "y_pred = classifier.predict(X_test)\n", + "accuracy = accuracy_score(test_labels, y_pred)\n", + "print(\"Accuracy:\", accuracy)\n", + "print(classification_report(test_labels, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Classify new documents (new BBC headlines) by vectorizing them using the same TfidfVectorizer and predicting their labels using the trained classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted labels: ['ship' 'ship' 'acq']\n" + ] + } + ], + "source": [ + "# Classify new documents (recent headlines obtained from BBC news regarding Tunisia)\n", + "new_docs = [\n", + " \"Tunisia says 23 people missing in Mediterranean sea.\",\n", + " \"Tunisia officials arrested in dispute over flag display.\",\n", + " \"Tunisia lawyer arrested during live news broadcast.\"\n", + "]\n", + "new_docs_vectors = vectorizer.transform(new_docs)\n", + "predicted_labels = classifier.predict(new_docs_vectors)\n", + "print(\"Predicted labels:\", predicted_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discussion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How did this classifier fare? What can you do to improve the model?
\n", + "Ans: Experimenting with different preprocessing techniques, feature extraction models and classification algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trying with a different classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Steps 1 - 3 will be the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the Reuters-21578 dataset\n", + "documents = reuters.fileids()\n", + "train_docs = list(filter(lambda doc: doc.startswith(\"train\"), documents))\n", + "test_docs = list(filter(lambda doc: doc.startswith(\"test\"), documents))\n", + "\n", + "# Prepare the data\n", + "train_data = [reuters.raw(doc_id) for doc_id in train_docs]\n", + "train_labels = [reuters.categories(doc_id)[0] for doc_id in train_docs]\n", + "test_data = [reuters.raw(doc_id) for doc_id in test_docs]\n", + "test_labels = [reuters.categories(doc_id)[0] for doc_id in test_docs]\n", + "\n", + "# Vectorize the text data\n", + "vectorizer = CountVectorizer(stop_words=\"english\", max_features=1000)\n", + "X_train = vectorizer.fit_transform(train_data)\n", + "X_test = vectorizer.transform(test_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Different Classifier (Multinomial Naive Bayes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classifier = MultinomialNB()\n", + "classifier.fit(X_train, train_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the classifier\n", + "y_pred = classifier.predict(X_test)\n", + "accuracy = accuracy_score(test_labels, y_pred)\n", + "print(\"Accuracy:\", accuracy)\n", + "print(classification_report(test_labels, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Classify new documents (recent headlines obtained from BBC news regarding Tunisia)\n", + "new_docs = [\n", + " \"Tunisia says 23 people missing in Mediterranean sea.\",\n", + " \"Tunisia officials arrested in dispute over flag display.\",\n", + " \"Tunisia lawyer arrested during live news broadcast.\"\n", + "]\n", + "new_docs_vectors = vectorizer.transform(new_docs)\n", + "predicted_labels = classifier.predict(new_docs_vectors)\n", + "print(\"Predicted labels:\", predicted_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discussion: Compare the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The choice of classifier depends on the specific characteristics of your dataset and the problem at hand. Multinomial Naive Bayes is known to work well with text data and can handle high-dimensional feature spaces efficiently. However, it assumes that the features are independent of each other, which may not always be the case in real-world scenarios.\n", + "\n", + "You can also experiment with different classifiers, such as Logistic Regression, Random Forest, or Gradient Boosting, and compare their performance to find the best fit for your dataset. You can also refine the model by trying different feature extraction techniques and hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### There are also other ways you can approach this, for example, Document Classification using BERT. Here is a notebook example on Kaggle that you can explore: https://www.kaggle.com/code/merishnasuwal/document-classification-using-bert" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "BERT (Bidirectional Encoder Representations from Transformers) and other Transformer encoder architectures can also be used on a variety of tasks in NLP (natural language processing). They compute vector-space representations of natural language that are suitable for use in deep learning models. The BERT family of models uses the Transformer encoder architecture to process each token of input text in the full context of all tokens before and after. BERT models are usually pre-trained on a large corpus of text, then fine-tuned for specific tasks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/tunisia-may-24/3-intro-langchain.ipynb b/notebooks/tunisia-may-24/3-intro-langchain.ipynb new file mode 100644 index 0000000..fe6aef5 --- /dev/null +++ b/notebooks/tunisia-may-24/3-intro-langchain.ipynb @@ -0,0 +1,1896 @@ +{ + "cells": [ + { + "attachments": { + "7153af0c-fb8b-4b47-826e-57ac60696e0c.png": { + "image/png": "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" + }, + "faf11697-6be8-49bc-ab24-b3c4385b8a67.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "740ffa74-4eda-4843-9b5b-486caab1153b", + "metadata": { + "tags": [] + }, + "source": [ + "# Introduction to LangChain\n", + "---------\n", + "![image.png](attachment:faf11697-6be8-49bc-ab24-b3c4385b8a67.png)![image.png](attachment:7153af0c-fb8b-4b47-826e-57ac60696e0c.png)\n", + "\n", + "**DIHPA'24**\n", + "\n", + "**Author:** Dunstan Matekenya \n", + "\n", + "**Affiliation:** DECAT, The World Bank Group \n", + "\n", + "**Date:** May 30, 2024\n", + "\n", + "\n", + "## What you will learn \n", + "In this notebook, you will learn the basics of the LangChain platform as follows.\n", + "1. **LLM capabilities.** Explore LLM capabilities using LangChain\n", + "2. **Interacting with LLMs.** Use LangChain functions such as chains, prompt templates and more to connect to LLMs\n", + "3. **RAG.**. Implementing a simple RAG in Langchain by connecting to external documents\n", + "4. **LangChain Expression Language (LCEL).**. How to use LCEL instead of functions when interacting with LLMs\n", + "5. **LangChain Agents.**. \n", + "\n", + "## Expected Broad Learning Outcomes\n", + "1. **Connecting to LLMs.** An understanding of how to connect to varios open source and proprietary LLMs using Hugging Face and proprietary specific frameworks such as that for OpenAI and Mistral\n", + "2. **Different LLMs.**. There are many varieties of LLMs: ```chat, instruct, question-answer, sentiment-analysis, instruct``` and more. Have basic understanding of differences across these models and when to use which one.\n", + "3. **The role of memory in Chat models.** Understand the importance of having memory in a chatbot and different strategies for doing it with LangChain.\n", + "4. **The process of implementing RAG in LangChain**. RAG is one of the most commonly used approach for implementing chats as it enables connection to external custom data. Have a good understanding of the main steps involved in implementing a RAG based system-the steps are the same in LangChain and other frameworks.\n", + "5. **Understand the role vector databases.** Vector databases are an integral part of working with LLMs. make sure you understand how they fit in the ecosystem and why they are important." + ] + }, + { + "cell_type": "markdown", + "id": "633b7017-2001-4cec-b34d-30a5bc4b92fc", + "metadata": {}, + "source": [ + "# Setup\n", + "\n", + "------" + ] + }, + { + "cell_type": "markdown", + "id": "7a131a8d-40d2-4bf3-9856-103ed70000d7", + "metadata": {}, + "source": [ + "## Import Packages\n", + "We will import packages as we go so that you appreciate which class we are using." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b9a13ee9-f3d9-4141-a1a2-929cdc1b5113", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path" + ] + }, + { + "cell_type": "markdown", + "id": "1e1dad1b-4014-48e9-b911-2095c9864a84", + "metadata": {}, + "source": [ + "## Setup API Keys" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b443a7a8-9dd7-4320-958e-cc3376e09cb4", + "metadata": {}, + "outputs": [], + "source": [ + "# ====================\n", + "# Setup API Keys\n", + "# ====================\n", + "# Although its not recommended for security, you can also just \n", + "# paste your API keys " + ] + }, + { + "cell_type": "markdown", + "id": "61224418-685b-499c-96fe-568ba7993475", + "metadata": {}, + "source": [ + "## Setup input directories \n", + "Lets organize where our data is stored so that we can easily access it. Please refer to the slides for recommended folder setup. Copy and paste the full paths to your working folder in the variables below." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a234a85f-b21b-4378-b3bd-f27feec67c36", + "metadata": {}, + "outputs": [], + "source": [ + "# Replace this folder with your working folder \n", + "DIR_WD = Path(\"/Users/dunstanmatekenya/Google Drive/My Drive/GenAI-Course/Mod2-LLM-Overview/\")\n", + "\n", + "# data folder\n", + "DIR_DATA = DIR_WD.joinpath(\"data\")\n", + "\n", + "# We can also set file names for data files we will use to save time\n", + "FILE_HEP_CHAD = DIR_DATA.joinpath(\"Hepatitis-Chad.pdf\")\n", + "\n", + "FILE_MIDDLE_EAST_COVID = DIR_DATA.joinpath(\"MidEast-COVID.pdf\")\n", + "\n", + "FILE_DENGUE = DIR_DATA.joinpath(\"Dengue-Global-situation.pdf\")" + ] + }, + { + "cell_type": "markdown", + "id": "2f148739-53c8-4cb8-b477-da5781e8195d", + "metadata": {}, + "source": [ + "# 1. Exploring Language Tasks that LLMs can Perform\n", + "In this section, we will explore what type of NLP tasks LLMs can perfom using the Hugging Face transformer package. In some cases, when we specifiy a specific model, the transformers package will take some time to download the model files. Also, the idea here is to show very simple capabilities. In a real world project, you can train and fine-tune the transformer models on your own dataset. For example, to do a fully fledged sentiment analysis with Hugging Face, take a look at [this tutorial] (https://huggingface.co/blog/sentiment-analysis-python).\n", + "\n", + ">Note that for almost all of these tasks, you can replace the English text with French text and still get similar results" + ] + }, + { + "cell_type": "markdown", + "id": "2dc053ef-5ba8-4557-b0f2-0975447d566e", + "metadata": {}, + "source": [ + "## 1. 1 Text and Document Classification\n", + "Text and document classification are closely related tasks. In **text classification**, we assign predefined categories to individual pieces of text while in **document classification** refers to the process of assigning predefined categories to longer pieces of text, such as entire documents, articles, or reports.\n", + "\n", + "- **Examples of text classification tasks**. Sentiment Analysis; Intent Detection;\n", + "- **Examples of document classification**. Topic categorization, " + ] + }, + { + "cell_type": "markdown", + "id": "860fcb52-c3f4-4c8f-8fb4-e2b4808114c2", + "metadata": {}, + "source": [ + "### Sentiment Analysis with the Hugging Face Transformers Library" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "499b7d29-d647-4cb9-ae00-fd2b88ae18b5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n", + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POSITIVE\n" + ] + } + ], + "source": [ + "# We use transformers ```pipeline library\n", + "from transformers import pipeline\n", + "\n", + "llm = pipeline(\"text-classification\")\n", + "text = \"I'm really enjoying my stay in Tunis\"\n", + "outputs = llm(text)\n", + "print(outputs[0]['label'])" + ] + }, + { + "cell_type": "markdown", + "id": "affcc68c-49ea-4cfe-bc59-294854156360", + "metadata": {}, + "source": [ + "## 1.2 Text Generation\n", + "Text generation is a process in natural language processing (NLP) where a machine learning model generates coherent and contextually relevant text based on a given input or prompt. This technology is used in various applications such as chatbots, automated content creation, machine translation, and more.\n", + "\n", + "In real life, the text is not always coherent, based on the model, when we use a default model, the results are not good. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0224c530-1943-481e-b4fc-92cfb2f62702", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n", + "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n", + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Malawi is famous for urchin, the rice used in the national diet, and its high price and widespread lack of access to good sources of water are driving thousands of people into poverty. So far, the government has been able to bring in more than $2 billion through loans, while food aid has been limited in its expansion, for example by one million poor people trying to come back from war-ravaged country.\n", + "\n", + "As for the country's food safety, the government has been\n" + ] + } + ], + "source": [ + "llm = pipeline(\"text-generation\")\n", + "prompt = \"Malawi is famous for \"\n", + "outputs = llm(prompt, max_length=100)\n", + "print(outputs[0]['generated_text'])" + ] + }, + { + "cell_type": "markdown", + "id": "aabac32c-0547-44d4-b77b-34d16a2d8220", + "metadata": {}, + "source": [ + "**EXERCISE-0: Try to specify a different Hugging Face model and see if you get better results**" + ] + }, + { + "cell_type": "markdown", + "id": "b6bf9c35-35f0-4310-adff-6dbb7eab57f4", + "metadata": {}, + "source": [ + "## 1.3 Text Summarization\n", + "Text summarization is a natural language processing (NLP) task that involves creating a concise and coherent summary of a longer text document. The goal is to capture the most important information and main ideas while reducing the length of the original text. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d0f4c46a-b896-47ce-928b-0c8b8bae063d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Walking amid Gion's Machiya wooden houses is a mesmerizing experience. The beautifullypreserved structures exuded an old-world charm that transports visitors back in time. The glow of lanterns lining the narrow streets add to theenchanting ambiance, making each stroll a\n" + ] + } + ], + "source": [ + "llm= pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n", + "long_text = \"\"\"Walking amid Gion's Machiya wooden houses is a mesmerizing experience. The beautifully\n", + "preserved structures exuded an old-world charm that transports visitors back in time, making them feel\n", + "like they had stepped into a living museum. The glow of lanterns lining the narrow streets add to the\n", + "enchanting ambiance, making each stroll a memorable journey through Japan's rich cultural history.\n", + "\"\"\"\n", + "outputs = llm(long_text, max_length=60, clean_up_tokenization_spaces=True)\n", + "print(outputs[0]['summary_text'])" + ] + }, + { + "cell_type": "markdown", + "id": "58c10bc4-2237-45b2-a367-c7690a2586b4", + "metadata": {}, + "source": [ + "## 1.4 Question-Answering\n", + "Question Answering (QA) is one of the most common tasks or use casef for LLMs. In this task, the model is designed to automatically answer questions posed by humans in natural language. QA systems can be built to answer questions from a variety of sources, such as structured databases, knowledge bases, or unstructured text documents." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a8aefbf4-a88a-4cdc-b882-eb0a5781200b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to distilbert/distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wooden\n" + ] + } + ], + "source": [ + "llm = pipeline(\"question-answering\")\n", + "context = \"Walking amid Gion's Machiya wooden houses was a mesmerizing experience.\"\n", + "question = \"What are Machiya houses made of?\"\n", + "outputs = llm(question=question, context=context)\n", + "print(outputs['answer'])" + ] + }, + { + "cell_type": "markdown", + "id": "eea8c421-6a5d-426c-8f9f-fc94bc61b492", + "metadata": {}, + "source": [ + "## 1.5 Language Translation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cde7750a-4ab4-4a5d-9019-7dc7be065719", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to google-t5/t5-base and revision 686f1db (https://huggingface.co/google-t5/t5-base).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C'est ma première visite en Tunisie.\n" + ] + } + ], + "source": [ + "llm = pipeline(\"translation_en_to_fr\")\n", + "text = \"This is my first time to visit Tunisia.\"\n", + "outputs = llm(text, clean_up_tokenization_spaces=True)\n", + "print(outputs[0]['translation_text'])" + ] + }, + { + "cell_type": "markdown", + "id": "d3e3d82d-f4ea-439b-9fd5-08ae2105f3a3", + "metadata": {}, + "source": [ + "# 2. Introducing LangChain Core Functionalities" + ] + }, + { + "cell_type": "markdown", + "id": "0d4c7bd8-c8cb-4e99-bef5-40144a82c78c", + "metadata": {}, + "source": [ + "It is always a good idea to read documentation of a framework. Please head over to [LangChain website](https://www.langchain.com) for details of core functionalities, use cases and features. The screenshot below provides a summary of LangChain ecosytem of features and capabilities. The term **Chain** in LangChain refers to the core concept of **chains** in LangChain which is a sequence(s) of calls - whether to an LLM, a tool, or a data preprocessing step. The primary supported way to do this is with LCEL (we will see this later)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b02bc8a4-ea90-4622-92d8-43861fcb12d2", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2e39d82c-7089-4aa4-8054-d01427f1c1b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": { + "image/png": { + "width": 500 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename='../images/LangChain-detailed.png', width=500) " + ] + }, + { + "cell_type": "markdown", + "id": "40dcce20-057f-4b5b-84b6-356a3c1db8a7", + "metadata": {}, + "source": [ + "## 2.1 Interacting with Models in LangChain \n", + "- General instruction models - Models which can answer questions but are not quite optmized for chat\n", + "- Chat models are more optimized for question and answering\n", + "- Prompting templates and techniques " + ] + }, + { + "cell_type": "markdown", + "id": "33a7bf7e-0ea4-431f-9f3e-23119e1a14a7", + "metadata": {}, + "source": [ + "### Trying out Open Vs. Proprietary Model\n", + "- **Accessing open source LLMs on Hugging Face.** In order to access open source LLMs from Hugging Face, you need two main inputs: ```Hugging Face token``` and the model id or url. Recall that you can explore and grab model details from the Hugging Face platform easily. Once you have that we can use ```HuggingFaceEndpoint``` or ```HuggingFaceHub``` to access and use the model.\n", + "\n", + "- **Accessing proprietary LLMs (e.g., OpenAI).** LangChain has specific packages for working with OpenAI models. For other providers such as Mistral, you need to check [LangChain documentation](https://python.langchain.com/v0.1/docs/integrations/chat/mistralai/)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0f911200-d6b2-47a2-b862-643f7f61bd83", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Can you still have fun in the rain?\n", + "Yes, you can still have fun in the rain! There are plenty of activities you can do indoors or outdoors, such as playing board games, reading a book, or going for a walk. You can also try to find creative ways to enjoy the rain, such as using a rain shower to take a bath or making a rain-soaked picnic. Just remember to stay safe and take precautions if necessary.\n" + ] + } + ], + "source": [ + "from langchain_community.llms import HuggingFaceEndpoint, HuggingFaceHub\n", + "\n", + "# Lets make this a global variable in case we want to use this model\n", + "# again\n", + "MODEL_ID_FALCON = 'tiiuae/falcon-7b-instruct'\n", + "\n", + "llm = HuggingFaceHub(repo_id=MODEL_ID_FALCON, \n", + " huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN)\n", + "\n", + "question = 'Can you still have fun'\n", + "output = llm.invoke(question)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b35f1964-5938-4773-b768-334df1551939", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " if you're dead inside?\n", + "\n", + "It is possible to have fun even if you feel dead inside. While feeling emotionally numb or disconnected can make it more challenging to enjoy activities or events, it is still possible to find moments of joy and pleasure.\n", + "\n", + "Here are some tips for having fun even if you feel dead inside:\n", + "\n", + "1. Engage in activities that have brought you joy in the past. Think back to activities or hobbies that you used to enjoy before you started feeling dead inside. Even if you don't feel the same level of excitement, engaging in these activities can still bring some enjoyment.\n", + "\n", + "2. Try something new. Sometimes, trying something new can help break out of a rut and bring some fun into your life. This could be a new hobby, sport, or even a new type of food.\n", + "\n", + "3. Spend time with loved ones. Being around people who care about you and make you feel loved and supported can help lift your mood and bring some fun into your life. Plan a fun outing or simply spend time talking and laughing with friends and family.\n", + "\n", + "4. Practice self-care. Taking care of yourself can help improve your overall mood and make it easier to have fun. Make time for activities that help you relax and recharge, such as taking a bath, reading a book,\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain_openai import OpenAI\n", + "\n", + "# Note that we will be able to select specific OpenAI models \n", + "# If you have a paid account \n", + "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "question = 'Can you still have fun'\n", + "output = llm.invoke(question)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e9bda8e1-dce1-49c1-b308-82063fa53e6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3544" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2*1772" + ] + }, + { + "cell_type": "markdown", + "id": "7381c4a5-5a33-405c-b82a-baacfebe6e56", + "metadata": {}, + "source": [ + "**EXERCISE-1. Find another model on Hugging Face to try**\n", + "- Go to [Hugging Face](https://huggingface.co/models)\n", + "- Search for **Text Generation** LLMs. Note that large models can be hard and take long to run.\n", + "- Get the model Id\n", + "- Initialize the model, and ask it a question/prompt as we did with Falcon model above" + ] + }, + { + "cell_type": "markdown", + "id": "f4edd5e5-44fa-49b0-b150-64a580da8f66", + "metadata": {}, + "source": [ + "### . Prompt templates\n", + "Prompt templates are used for creating prompts in a more modular way, so they can be reused and built on. Chains act as the glue in LangChain; bringing the other components together into workflows that pass inputs and outputs between the different components\n", + "- They are recipes for generating prompts\n", + "- Flexible and modular\n", + "- Can contain: instructions, examples, and additional context" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0a2faa69-863e-4a4e-9118-ba50e0e72586", + "metadata": {}, + "outputs": [ + { + "ename": "ValidationError", + "evalue": "1 validation error for HuggingFaceHub\ntoken\n extra fields not permitted (type=value_error.extra)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m template \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are an artificial intelligence assistant, answer the question. \u001b[39m\u001b[38;5;132;01m{question}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6\u001b[0m prompt \u001b[38;5;241m=\u001b[39m PromptTemplate(template\u001b[38;5;241m=\u001b[39mtemplate, input_variables\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestion\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m----> 8\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mHuggingFaceHub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mMODEL_ID_FALCON\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mHUGGINGFACEHUB_API_TOKEN\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Create a Chain using the LLMChain() \u001b[39;00m\n\u001b[1;32m 11\u001b[0m llm_chain \u001b[38;5;241m=\u001b[39m LLMChain(prompt\u001b[38;5;241m=\u001b[39mprompt, llm\u001b[38;5;241m=\u001b[39mllm)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValidationError\u001b[0m: 1 validation error for HuggingFaceHub\ntoken\n extra fields not permitted (type=value_error.extra)" + ] + } + ], + "source": [ + "from langchain.prompts import PromptTemplate, ChatPromptTemplate\n", + "\n", + "# A String with instructions, same way we create prompts\n", + "# in GUI based interface such as chatGPT\n", + "template = \"You are an artificial intelligence assistant, answer the question. {question}\"\n", + "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "\n", + "llm = HuggingFaceHub(repo_id=MODEL_ID_FALCON,token=HUGGINGFACEHUB_API_TOKEN)\n", + "\n", + "# Create a Chain using the LLMChain() \n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "question = \"What is LangChain?\"\n", + " \n", + "print(llm_chain.run(question))" + ] + }, + { + "cell_type": "markdown", + "id": "5a614f93-417e-4185-96c6-dc0b2ae1704d", + "metadata": {}, + "source": [ + "### Chat Models\n", + "Chat Models are a core component of LangChain. A chat model is a language model that uses chat messages as inputs and returns chat messages as outputs (as opposed to using plain text)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "684669c2-a2f7-4801-b9db-87b702b545e5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseChatModel.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.3.0. Use invoke instead.\n", + " warn_deprecated(\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "data": { + "text/plain": [ + "AIMessage(content='One of the best places to visit in Malawi is Lake Malawi. This stunning lake is known for its crystal-clear waters, beautiful beaches, and diverse marine life. Visitors can enjoy a variety of water activities such as snorkeling, diving, kayaking, and sailing. The lake is also surrounded by national parks and reserves, offering opportunities for wildlife viewing and hiking. Additionally, the lakeshore is dotted with charming villages where you can experience the local culture and hospitality. Overall, Lake Malawi is a must-visit destination for nature lovers and adventure seekers in Malawi.', response_metadata={'token_usage': {'completion_tokens': 116, 'prompt_tokens': 38, 'total_tokens': 154}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-11f38ec0-7a4b-47d9-9080-855d38cf0f35-0', usage_metadata={'input_tokens': 38, 'output_tokens': 116, 'total_tokens': 154})" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "from langchain.prompts import PromptTemplate, ChatPromptTemplate\n", + "\n", + "llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "\n", + "prompt_template = ChatPromptTemplate.from_messages([\n", + "(\"system\", \"You are a helpful assistant who knows alot about Africa.\"),\n", + "(\"human\",\"Respond to the question: {question}\")]\n", + ")\n", + "\n", + "full_prompt = prompt_template.format_messages(question='What is the best place to visit in Malawi?')\n", + "llm(full_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11ea305-a3c9-439f-b135-236e26c39ac1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c3e8c433-8672-4ae8-9579-66d5201bc657", + "metadata": {}, + "source": [ + "## 2.2. Managing chat model memory\n", + "- A key feature of chatbot applications is the ability to have a conversation, where context from the conversation is stored and available for the model to access for later questions or reference.\n", + "- Memory is important for conversations with chat models; it opens up the possibility of providing follow-up questions, of building and iterating on model responses, and for chatbots to adapt to the user's preferences and behaviors. \n", + "- Although LangChain allows us to customize and optimize in-conversation chatbot memory, it is still limited by the model's context window. \n", + "- An **LLM's context window** is the amount of input text the model can consider at once when generating a response, and the length of this window varies for different models. LangChain has a standard syntax for optimizing model memory. \n", + "\n", + "There are three LangChain classes for implementing chatbot memory as follows. \n", + "### The ```ChatMessageHistory``` Class\n", + "- The ChatMessageHistory class stores the full history of messages between the user and model. By providing this to the model, we can provide follow-up questions and iterate on the response message.\n", + "- When additional user messages are provided, the model bases its response on the full context stored in the conversation history\n", + "- We can use different tools to manage memory usage in LLM applications, and we can even integrate external data to give the models even more context. \n", + "\n", + "\n", + "### The ```ConversationBufferMemory``` class\n", + "- This gives the application a rolling buffer memory containing the last few messages in the conversation. Users can specify the number of messages to store with the size argument, and the application will discard older messages as newer ones are added. \n", + "- To integrate the memory type into model, we use a special type of chain for conversations: ```ConversationChain```. \n", + "\n", + "### The ```ConversationSummaryMemory``` class\n", + "- Summarizing important points from a conversation can also be a good way of optimizing memory. The ConversationSummaryMemory class summarizes the conversation over time, condensing the information. \n", + "- This means that the chat model can remember key pieces of context without needing to store and process the entire conversation history" + ] + }, + { + "cell_type": "markdown", + "id": "e6b7f3d4-afdd-422c-8688-7a31cb79bb26", + "metadata": {}, + "source": [ + "### Trying out the ChatMessageHistory class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98c8f82-8e9a-4603-811b-c20d034ee6b4", + "metadata": {}, + "outputs": [], + "source": [ + "chat = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "history = ChatMessageHistory()\n", + "history.add_ai_message(\"Hi! Ask me anything please.\")\n", + "history.add_user_message(\"Describe a metaphor for learning LangChain in one sentence.\")\n", + "chat(history.messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1248fd89-60d3-4d49-a276-f419417f8e88", + "metadata": {}, + "outputs": [], + "source": [ + "# Ask a question based on the previous messages \n", + "history.add_user_message(\"Summarize the preceding sentence in fewer words\")\n", + "chat(history.messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eeeaed5-cce7-4261-a852-eee1941c808b", + "metadata": {}, + "outputs": [], + "source": [ + "# Ask a question based on the previous messages \n", + "history.add_user_message(\"Summarize the preceding sentence in fewer words\")\n", + "chat(history.messages)" + ] + }, + { + "cell_type": "markdown", + "id": "6e1b5fb0-9fbf-4836-94d0-4017efbdfae0", + "metadata": {}, + "source": [ + "### Trying out the ConversationBufferMemory\n", + "For many applications, storing and accessing the entire conversation history isn't technically feasible. In these cases, the messages must be condensed while retaining as much relevant context as possible. One common way of doing this is with a memory buffer, which stores only the most recent messages based on the parameter ```size```." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c8742c04-33db-42fb-8509-987dee9e61d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "\n", + "Human: Describe a language model in one sentence\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "Human: Describe a language model in one sentence\n", + "AI: A language model is a statistical model that is trained on a large corpus of text and is able to generate coherent and grammatically correct sentences based on the patterns and structures it has learned.\n", + "Human: Describe it again using less words\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "Human: Describe a language model in one sentence\n", + "AI: A language model is a statistical model that is trained on a large corpus of text and is able to generate coherent and grammatically correct sentences based on the patterns and structures it has learned.\n", + "Human: Describe it again using less words\n", + "AI: A language model is a computer program that can generate sentences based on patterns it has learned from a large amount of text.\n", + "Human: Describe it again fewer words but at least one word\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n", + "Prompt after formatting:\n", + "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "Current conversation:\n", + "Human: Describe a language model in one sentence\n", + "AI: A language model is a statistical model that is trained on a large corpus of text and is able to generate coherent and grammatically correct sentences based on the patterns and structures it has learned.\n", + "Human: Describe it again using less words\n", + "AI: A language model is a computer program that can generate sentences based on patterns it has learned from a large amount of text.\n", + "Human: Describe it again fewer words but at least one word\n", + "AI: A language model is a program that generates sentences from text patterns.\n", + "Human: What did I first ask you? I forgot.\n", + "AI:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "' You asked me to describe a language model in one sentence.'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.memory import ChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory\n", + "from langchain.chains import LLMChain, ConversationChain, RetrievalQA, RetrievalQAWithSourcesChain\n", + "# Create an Open AI Chat Model\n", + "chat = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Create the memory object with size set to 2\n", + "memory = ConversationBufferMemory(size=4)\n", + "buffer_chain = ConversationChain(llm=chat, memory=memory, verbose=True)\n", + "\n", + "# \n", + "buffer_chain.predict(input=\"Describe a language model in one sentence\")\n", + "buffer_chain.predict(input=\"Describe it again using less words\")\n", + "buffer_chain.predict(input=\"Describe it again fewer words but at least one word\")\n", + "buffer_chain.predict(input=\"What did I first ask you? I forgot.\")" + ] + }, + { + "cell_type": "markdown", + "id": "efd52d16-a392-4aad-83e7-2e044b6d0c43", + "metadata": {}, + "source": [ + "**EXERCISE-2. For the ```ConversationBufferMemory```, change the buffer size to 1 or 2 and see what happens**" + ] + }, + { + "cell_type": "markdown", + "id": "8e535976-cabe-4162-b8ca-e84532dc783c", + "metadata": {}, + "source": [ + "## ConversationSummaryMemory\n", + "For longer conversations, storing the entire memory, or even a long buffer memory, may not be technically feasible. In these cases, a summary memory implementation can be a good option. Summary memories summarize the conversation at each step to retain the key context for the model to use. This works by using another LLM for generating the summaries, alongside the LLM used for generating the responses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7325752-2d3d-499e-b7ae-31122b1f64d5", + "metadata": {}, + "outputs": [], + "source": [ + "# ==============================================\n", + "# PLEASE FOLLOW INSTRUCTIONS AND COMPLETE CODE\n", + "# ==============================================\n", + "\n", + "# Use openAI model from earlier as a summary model\n", + "summary_llm = YOUR CODE HERE\n", + "\n", + "# Complete code below by putting in summary model above\n", + "memory = ConversationSummaryMemory(llm=summary_llm)\n", + "\n", + "# Create a chat model to use in the Conversation chain below (refer\n", + "# previous cells where we created OpenAI chat model\n", + "chat_model = YOUR CODE HERE\n", + "\n", + "# Create a conversation chain as we did before \n", + "summary_chain = YOUR CODE HERE\n", + "\n", + "summary_chain.predict(input=\"Please tell me about Malawi.\")\n", + "summary_chain.predict(input=\"Does that affect Malawi's income?\")" + ] + }, + { + "cell_type": "markdown", + "id": "f4c250ad-6f8a-42a6-99d5-bdb7b75df1e1", + "metadata": {}, + "source": [ + "# 3. Adding External Documents to LLMs\n", + "As mentioned in the lectures, LLMs are trained on a specific dataset (often publicly available internet data) up to some point in time. Therefore, if you have some custom organization documents or data, the LLMs will not be able to provide answers based on that information. Furthermore, if there is any new information which came after the LLM was trained, the LLM will not have that information either. \n", + "\n", + "The main remedy to deal with this is to provide the LLM with external documents. Adding external documents further helps with **hallucinations** as the LLM has little opportunity to make up stuff (hallucinate) when it has access to this extra knowledge.\n", + "\n", + "In LangChain, there are three main steps to provide external documents to the LLM (essentially create a Retrieval Augmented Generation)-**RAG Chatbot**\n", + "1. Identify the data sources (documents, datasets, websites, databases etc).\n", + "\n", + "2. Load the documents into LangChain using document loaders. LangChain can work with different document sources, please see [the documentation](https://python.langchain.com/v0.1/docs/integrations/document_loaders/). \n", + "\n", + "3. Splitting the documents into chunks. \n", + "\n", + "4. Create vector embeddings and store into a vector database for retrievval" + ] + }, + { + "cell_type": "markdown", + "id": "8eb4a208-8c25-4060-bf4b-c4eb06e26557", + "metadata": {}, + "source": [ + "### 3.1 Document Loaders\n", + "LangChain has more than 160 document loaders. Some loaders are provided by 3rd parties who manage unique document formats. These include Amazon S3, Microsoft, Google Cloud, Jupyter notebooks, pandas DataFrames, unstructured HTML, YouTube audio transcripts, and more. " + ] + }, + { + "cell_type": "markdown", + "id": "9efebd7e-59c0-4aef-81a3-5d9a500d1319", + "metadata": {}, + "source": [ + "#### PDF Document Loader\n", + "- Requires installation of the ```pypdf``` package as a dependency.\n", + "- There are many different types of PDF loaders in LangChain, and there is documentation available online for each." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "fc21c844-25b1-4447-a7ea-4aff7ad450ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pypdf in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (3.8.1)\n" + ] + } + ], + "source": [ + "!pip install pypdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "417c534c-4a01-49a4-9c8f-16450dec011a", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import PyPDFLoader\n", + "loader = PyPDFLoader(str(FILE_DENGUE))\n", + "data = loader.load()\n", + "print(data[0])" + ] + }, + { + "cell_type": "markdown", + "id": "bdb6fbaf-34c0-4fc6-8570-76aa853e78a5", + "metadata": {}, + "source": [ + "**EXERCISE-3. Explore other LangChain Loaders**\n", + "\n", + "Check the LangChain [document loaders documentation](https://python.langchain.com/v0.1/docs/integrations/document_loaders/) \n", + "and also check [here](https://python.langchain.com/v0.1/docs/modules/data_connection/) for most commonly used loaders.\n", + "1. Identify 5 document loaders you find interesting. What are third party document loaders?\n", + "2. **HTML loaders**. Explore the html or webpage loaders. \n", + "3. Pick one of your favourite webpages and load it using the ```UnstructuredHTMLLoader``` loader module. Refer to the [documentation](UnstructuredHTMLLoader) on how to import the module.\n", + "4. How do you think this changes your approach to ```web-scraping```. Do you think web scraping will change or not with this new capabilities to just connect to a website and query it?" + ] + }, + { + "cell_type": "markdown", + "id": "07bde0cf-56cb-4726-a3f0-cfc839ba1d3e", + "metadata": {}, + "source": [ + "### 3.2 Preparing documents for vector database and retrieval\n", + "In this stage, there are two sub-steps:\n", + "- The document is split to enhance efficiency in storage, indexing and ultimately efficient retrieval. Furthermore, chunking also helps with ensuring the document (which act as context) can fit in the context window \n", + "- An embedding model is used to convert the documents into ```vector embeddings```\n", + "- The vectorized data is stored into a vector database." + ] + }, + { + "cell_type": "markdown", + "id": "ba116fde-3896-40d2-b921-18c2de13b56d", + "metadata": {}, + "source": [ + "#### Splitting/Chunking Documents\n", + "- Given a PDF document, one naive splitting option would be to separate the document into lines as they appear in the document. This would be simple to implement but could be problematic. Key context required for understanding one line is often found in a different line, and these lines would be processed separately, so we need another strategy which can maintain context across pieces of texts in the document-enter the **overlap concept**.\n", + "We will compare two document splitting methods from LangChain. \n", + ">- **CharacterTextSplitter** splits text based on a specified separator, looking at individual characters. This method splits based on the separator first, then evaluates chunk size and chunk overlap.\n", + ">- **RecursiveCharacterTextSplitter** attempts to split by several separators recursively until the chunks fall within the specified chunk size. There are many other methods that use natural language processing to infer meaning and split appropriately. Optimizing this is an active area of research.\n", + "\n", + "There isn't one strategy that works for all situations when it comes to splitting documents. \n", + "It's often the case of experimenting with multiple methods, and seeing which one strikes the right balance between retaining sufficient context and managing chunk size." + ] + }, + { + "cell_type": "markdown", + "id": "633a6334-4e51-4e28-b2a9-1967fd36d6b7", + "metadata": {}, + "source": [ + "##### CharacterTextSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9cabe5b1-557f-4480-83ef-9637682546c5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Created a chunk of size 52, which is longer than the specified 24\n" + ] + } + ], + "source": [ + "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n", + "quote = 'One machine can do the work of fifty ordinary humans.\\\n", + "No machine can do the work of one extraordinary human.'\n", + "\n", + "chunk_size = 24\n", + "chunk_overlap = 3\n", + "\n", + "ct_splitter = CharacterTextSplitter(separator=\".\", \n", + " chunk_overlap=chunk_overlap, chunk_size=chunk_size)\n", + "\n", + "docs = ct_splitter.split_text(quote)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2a6a8893-4f70-40db-a1a2-66055f532d3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['One machine can do the work of fifty ordinary humans',\n", + " 'No machine can do the work of one extraordinary human']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs" + ] + }, + { + "cell_type": "markdown", + "id": "08337229-f503-429b-8345-e5d987f0d774", + "metadata": {}, + "source": [ + "##### RecursiveCharacterTextSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "431f2615-19d8-45a7-b626-f78e45332534", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['One machine can do the', 'work of fifty ordinary', 'humans.No machine can', 'do the work of one', 'extraordinary human.']\n" + ] + } + ], + "source": [ + "# Using the same variables: chunk_size and chunk_overlap, instatiate RecursiveCharacterTextSplitter\n", + "rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap)\n", + "docs = rc_splitter.split_text(quote)\n", + "print(docs)" + ] + }, + { + "cell_type": "markdown", + "id": "8777067d-35d7-471d-983f-fec0a6aafbc0", + "metadata": {}, + "source": [ + "#### Load data into a vector database\n", + "At this stage, you will be faced with a decision to choose which vector database to use. \n", + "For our simple demonstration purpose, we will use [chromadb](https://www.trychroma.com), an open source vector database solution. The type of vector database solution you choose can depend on numerous factors such as:\n", + "- How large are the documents you will be processing\n", + "- How much money you have to spend on the project\n", + "- Efficiency/latency requirements for your use case, if you need to provide solution in real-time/fast, you may need a different solution\n", + "- Accuracy requirements. Sometimes there is a tradeoff between accuracy and latecy.\n", + "- Integration requirements with existing platforms. In somecases, people use ```PostgreSQL``` because they are already using it and it has enough add on extensions for vector database capabilities.\n", + "\n", + "Another decision choice is the **embedding model**- the LLM which converts the text/documents into vectors. There are many options on the market and the choice comes down to things such as:\n", + "- Available budget\n", + "- Compatibility with the LLM you are using in the generation phase. People do use a different embedding model from the generation model\n", + "> embedding_llm = Mistral, \n", + "> chat_model = ChatOpenAI\n", + "- Nature of documents, size and alot of other factors" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "79cbfc3e-d388-4410-8dff-56a46a47c53d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting sentence_transformers\n", + " Downloading sentence_transformers-3.0.0-py3-none-any.whl (224 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.7/224.7 kB\u001b[0m \u001b[31m827.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.15.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (0.23.2)\n", + "Requirement already satisfied: scipy in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (1.10.1)\n", + "Requirement already satisfied: scikit-learn in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (1.2.2)\n", + "Requirement already satisfied: torch>=1.11.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (2.1.2)\n", + "Requirement already satisfied: tqdm in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (4.65.0)\n", + "Requirement already satisfied: Pillow in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (9.4.0)\n", + "Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (4.41.2)\n", + "Requirement already satisfied: numpy in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sentence_transformers) (1.23.5)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2023.12.2)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (4.8.0)\n", + "Requirement already satisfied: packaging>=20.9 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (23.2)\n", + "Requirement already satisfied: pyyaml>=5.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (6.0.1)\n", + "Requirement already satisfied: filelock in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (3.9.0)\n", + "Requirement already satisfied: requests in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2.28.1)\n", + "Requirement already satisfied: networkx in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (2.8.4)\n", + "Requirement already satisfied: sympy in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (1.12)\n", + "Requirement already satisfied: jinja2 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (3.1.2)\n", + "Requirement already satisfied: regex!=2019.12.17 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (2022.7.9)\n", + "Requirement already satisfied: tokenizers<0.20,>=0.19 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (0.19.1)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (0.4.3)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (2.2.0)\n", + "Requirement already satisfied: joblib>=1.1.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (1.1.1)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from jinja2->torch>=1.11.0->sentence_transformers) (2.1.1)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2024.2.2)\n", + "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2.1.1)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (1.26.15)\n", + "Requirement already satisfied: idna<4,>=2.5 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (3.4)\n", + "Requirement already satisfied: mpmath>=0.19 in /Users/dunstanmatekenya/anaconda3/lib/python3.10/site-packages (from sympy->torch>=1.11.0->sentence_transformers) (1.3.0)\n", + "Installing collected packages: sentence_transformers\n", + "Successfully installed sentence_transformers-3.0.0\n" + ] + } + ], + "source": [ + "!pip install sentence_transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ad4c76d-7248-49d6-acf2-3c1193bd2dcb", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_community.vectorstores import Chroma\n", + "from langchain_community.embeddings import HuggingFaceEmbeddings\n", + "\n", + "\n", + "# Lets load the Cholera paper and then store it in a database\n", + "loader = PyPDFLoader(str(FILE_HEP_CHAD))\n", + "data = loader.load()\n", + "\n", + "chunk_size = 100\n", + "chunk_overlap = 10\n", + "\n", + "# Split with RecursiveCharacterTextSplitter\n", + "rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap)\n", + "docs = rc_splitter.split_documents(data)\n", + "\n", + "# Lets use openAI embedding model\n", + "#embedding_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_KEY)\n", + "embedding_model = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n", + "# Directory to store our database-set this to the data directory\n", + "vectordb = Chroma(persist_directory=str(DIR_DATA), embedding_function=embedding_model)\n", + "\n", + "# Store the databse\n", + "vectordb.persist()\n", + "\n", + "# Create the database\n", + "docstorage = Chroma.from_documents(docs, embedding_model)" + ] + }, + { + "cell_type": "markdown", + "id": "e30d865a-42fb-4325-b53c-84da656a0703", + "metadata": {}, + "source": [ + "**EXERCISE-4. Explore what functionality is available under the database object ```docstorage_cholera```**\n", + "- You can use ```dir(object)``` to check available attributes and functions\n", + "- Note that there many search related functions which enables you to control how user queries are searcherd when building Chatbots" + ] + }, + { + "cell_type": "markdown", + "id": "52ea226f-8de3-4c5c-a69e-9d8971b047cf", + "metadata": {}, + "source": [ + "### 3.3 Retrieval\n", + "Now that we have added our external file. Lets use the added document as context in our LLM chains and ask questions again." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5af5276c-4235-44c8-926f-652acf3d16dd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "LLM Output without using RAG-external document from WHO website\n", + "============================================================\n", + "\n", + "\n", + "As of September 2021, there are several ongoing disease outbreaks in Chad. These include:\n", + "\n", + "1. COVID-19: Chad has been experiencing a surge in COVID-19 cases since April 2021, with a peak in July. As of September 2021, there have been over 5,000 confirmed cases and over 170 deaths.\n", + "\n", + "2. Cholera: A cholera outbreak was declared in June 2021 in the Lake Chad region, affecting areas near the border with Nigeria. As of September 2021, there have been over 2,000 suspected cases and 50 deaths.\n", + "\n", + "3. Measles: Chad has been experiencing a measles outbreak since January 2020. As of September 2021, there have been over 20,000 suspected cases and over 300 deaths, mainly affecting children under the age of 5.\n", + "\n", + "4. Yellow fever: A yellow fever outbreak was declared in November 2020, affecting several regions in Chad. As of September 2021, there have been over 60 confirmed cases and 10 deaths.\n", + "\n", + "5. Meningitis: Chad is currently experiencing a meningitis outbreak, with over 2,000 suspected cases and 200 deaths reported since the beginning of 2021.\n", + "\n", + "\n", + "\n", + "============================================================\n", + "LLM Output with RAG-external document from WHO website\n", + "============================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Yes, there is currently a hepatitis E outbreak in Chad, specifically in the eastern Ouaddai province. This outbreak was last reported on May 8, 2024.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.chains import RetrievalQA\n", + "\n", + "# Create LLM as before \n", + "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Create retriever with \n", + "qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docstorage.as_retriever())\n", + "\n", + "# The question we will ask the LLM\n", + "# You can ask these questions in French and LLM will also answer in French\n", + "question = \"Are there any disease outbreaks in Chad?\"\n", + "\n", + "# Answer without RAG\n", + "output = llm.invoke(question)\n", + "print()\n", + "print(\"=\"*60)\n", + "print(\"LLM Output without using RAG-external document from WHO website\")\n", + "print(\"=\"*60)\n", + "print(output)\n", + "\n", + "# For RAG Chain, we put in the question as dictionary\n", + "print()\n", + "print(\"=\"*60)\n", + "print(\"LLM Output with RAG-external document from WHO website\")\n", + "print(\"=\"*60)\n", + "print(qa.run(question))" + ] + }, + { + "cell_type": "markdown", + "id": "3c735436-9e4a-410d-8f37-4f290cf51e1b", + "metadata": {}, + "source": [ + "**EXERCISE-5. Implement a simple RAG as we did above**\n", + "1. Use the ```FILE_MIDDLE_EAST_COVID``` file to create a new Chroma database\n", + "2. Implement a RAG chainas we did above.\n", + "3. Compare answers between a the LLM with RAG and no RAG\n", + "\n", + "**Hint.** Copy and paste the code from above and edit it." + ] + }, + { + "cell_type": "markdown", + "id": "6375cfcc-c6a9-406a-8e72-cd0a04a9b2ac", + "metadata": {}, + "source": [ + "### 3.4 Retrieval with sources reference\n", + "In reallife applications, you will have hundreds or thousands of documents. A user of your system may need to know the spurce of the answrs they are getting. Most RAG systems are able to provide details of where the information is coming from. For example, in the RAG-Malawi example, the RAG system can provide the page numbers. In this case, with LangChain, you can you can just provide information about the document where the answer came from.\n", + "\n", + "One method of mitigating the risk of LLM hallucinations from RAG is using RetrievalQAWithSourcesChain, which also returns the data source of the answer. Aside from the chain class, the code is exactly the same as RetrievalQA. However, this class returns a dictionary containing a 'sources' key and an 'answer' key. The 'sources' key refers to the file where the answer came from, which is helpful when there are many documents in the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "399ddc48-1e99-43a4-ae28-298d5427b367", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chains import RetrievalQAWithSourcesChain\n", + "\n", + "qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docstorage.as_retriever())\n", + "\n", + "results = qa({\"question\": \"Are there any disease outbreaks in Chad?\"},\n", + " return_only_outputs=True)\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "id": "d7b0f265-8583-4357-9040-8dd75429179c", + "metadata": {}, + "source": [ + "# 4. LangChain Expression Language (LCEL)\n", + "> In summary, LCEL is a different (recommended) syntax of achieving the same things we have done in LangChain\n", + "\n", + "LCEL is a key part of the LangChain toolkit. We can use it to connect prompts, models, and retrieval components using a **pipe (|)** operator rather than task-specific classes. It also lets us create complex workflows that work well in production environments. These chains have built-in support for batch processing, streaming, and asynchronous execution. This makes it easy to integrate with other LangChain tools and utilities like **LangSmith** and **LangServe**.\n", + "\n", + "A few notes about the chain with LCEL\n", + "- The ```| (pipe)``` in LCEL indicates that the output from one component will be used as the input to the next." + ] + }, + { + "cell_type": "markdown", + "id": "8229521e-790e-41b8-9fd8-159a54cae8c7", + "metadata": {}, + "source": [ + "## 4.1 A Simple Chain with LCEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88efb378-36b2-4499-a033-c3145101475e", + "metadata": {}, + "outputs": [], + "source": [ + "model = ChatOpenAI(openai_api_key=OPENAI_API_KEY)\n", + "prompt = ChatPromptTemplate.from_template(\"You are a helpful personal assistant. \\\n", + "Answer the following question: {question}\")\n", + "\n", + "# Create Chain in LCEL fashion\n", + "llm_chain = prompt | model\n", + "\n", + "# Recall how we created a chain before \n", + "#llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "\n", + "# Run using invoke\n", + "print(llm_chain.invoke(\"What is the capital of Tunisia?\"))" + ] + }, + { + "cell_type": "markdown", + "id": "1ac8949f-0ad0-4d22-8dbf-e8fd992f4065", + "metadata": {}, + "source": [ + "## 4.2 RAG with LCEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68b9b2eb-db18-49e2-b75e-2c27fb862f2e", + "metadata": {}, + "outputs": [], + "source": [ + "model = ChatOpenAI(openai_api_key = OPENAI_API_KEY)\n", + "\n", + "embedding_model = OpenAIEmbeddings(openai_api_key = OPENAI_API_KEY)\n", + "vectorstore = Chroma.from_texts([\"Dunstan stayed in Tunis, the capital of Tunisia from Sunday May 26 to Satarday May 31.\"],embedding=embedding_model)\n", + "retriever = vectorstore.as_retriever()\n", + "\n", + "template = \"\"\"Answer the question based on the context:{context}. Question: {question}\"\"\"\n", + "prompt = ChatPromptTemplate.from_template(template)\n", + "\n", + "chain = ({\"context\": retriever,\"question\": RunnablePassthrough()} | prompt | model | StrOutputParser())\n", + "chain.invoke(\"When did Dunstan visit Tunisia?\")" + ] + }, + { + "cell_type": "markdown", + "id": "c6ac7202-4bb2-446d-85bf-e55bed0f53b1", + "metadata": {}, + "source": [ + "## 4.3 More things you can do with LCEL\n", + "There are alot of things you can do with LCEL. For example,\n", + "- **Batch or Streaming**. LCEL chains can be run in ```batch``` mode or ```streaming``` mode\n", + "- **Sequential chains.**. Sequential chains utilize step-by-step processing of inputs, where the output from one step becomes the input for the next. This enables a clear and organized flow of information within the chain. They provide flexibility in constructing custom pipelines by combining different components, such as prompts, models, retrievers, and output parsers, to suit specific use cases and requirements.\n", + "- **Passing Data Across Chains.** There are many cases where your application will require the use of several chains that pass outputs between them" + ] + }, + { + "cell_type": "markdown", + "id": "826bc2fc-cb3b-4d23-abab-52451461e0c4", + "metadata": {}, + "source": [ + "### Using sequential chaining to create Python code and check it with LCEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bc0c20c-167c-454c-a87a-db01aa8f2855", + "metadata": {}, + "outputs": [], + "source": [ + "coding_prompt = PromptTemplate.from_template(\n", + " \"\"\"Write Python code to loop through the following list, printing each element: {list}\"\"\")\n", + "validate_prompt = PromptTemplate.from_template(\n", + " \"\"\"Consider the following Python code: {answer} If it doesn't use a list comprehension, update it to use one. If it does use a list comprehension, return the original code without explanation:\"\"\")\n", + "\n", + "llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Create the sequential chain\n", + "chain = ({\"answer\": coding_prompt | llm | StrOutputParser()}\n", + " | validate_prompt\n", + " | llm \n", + " | StrOutputParser() )\n", + "\n", + "# Invoke the chain with the user's question\n", + "print(chain.invoke({\"list\": \"[3, 1, 4, 1]\"}))" + ] + }, + { + "cell_type": "markdown", + "id": "2ae9fb57-5565-4bdf-a977-67f735260e51", + "metadata": {}, + "source": [ + "# 5. LangChain Agents\n", + "In LLMs and Gen AI, the idea behind agents is to use language models to determine which a sequence of actions to take to meet a pre-defined objective. Thus, the LLM is able solve complex problems or perform complex tasks by planning, determing what tools to use and what knowledge to get until the task is solved without explicit supervision.\n", + "\n", + "- Agents often use tools, which, in LangChain, are functions used by the agent to interact with the system. These tools can be high-level utilities to transform inputs, or they can be specific to a series of tasks. Agents can even use chains and other agents as tools!\n", + "- In LangChain, there different agent types. See [this documentation](https://python.langchain.com/v0.1/docs/modules/agents/agent_types/) for explanation of how the agents are categorized. \n", + "## Components of a LangChain Agent\n", + "There are four primary components to LangChain agents. \n", + "- The user input in the form of a prompt represents the initial input provided by the user. \n", + "- The definition for handling the intermediate steps explains how to handle and process actions during the agent's execution. \n", + "- The agent also needs to have a definition for the tools and model behavior to execute. \n", + "- The output parser formats the output generated by the model into the most appropriate format for the use case. Agents can be defined for specificity or high-level thought processes." + ] + }, + { + "cell_type": "markdown", + "id": "00fa06af-c231-4237-8d62-dc42ff0f59de", + "metadata": {}, + "source": [ + "## 5.1 Zero-Shot ReAct agent\n", + "ReAct stands for **Reasoning and Acting**. This simplifies the answer to infer as much context as possible. \n", + "We start by importing the initialize_agent function and AgentType for agent creation and configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1e3cf87-c98c-4dfa-9ab4-65bdee6f47df", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents import initialize_agent, AgentType, load_tools\n", + "\n", + "# Define LLM\n", + "llm = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0, openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Define what tools the agent will will use, it can be more than one tool\n", + "tools = load_tools([\"llm-math\"], llm=llm)\n", + "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n", + "agent.run(\"What is 10 multiplied by 50?\")" + ] + }, + { + "cell_type": "markdown", + "id": "aab0fb85-881a-4b00-b1a6-1cee7d7a3f75", + "metadata": {}, + "source": [ + "## 5.2 Other Agents \n", + "There are alot of other agents and tools in LangChain. For example, in order to interact with a database or structured dataset we will utilise an ```SQLAgent```" + ] + }, + { + "cell_type": "markdown", + "id": "971ae0ee-c01d-4fea-b992-110d6c7e0edf", + "metadata": {}, + "source": [ + "# 6. Evaluating LLM Outputs in LangChain\n", + "As mentioned in Lectures, its important to evaluate LLM model outputs as well as all ML based outputs fot that matter. \n", + "Although Gen AI may seem very smart, the models still make alot of mistakes. As such, evaluating AI applications is important for several reasons. \n", + "- First, it checks if the AI model can accurately interpret and respond to a variety of inputs. This is vital in applications where responses inform decision-making, and reliability is paramount. \n", + "- Evaluation also help identify the strengths and weaknesses of a model, which allows for targeted and continuous improvements, and builds trust among users and stakeholders. \n", + "- Evaluation allows us to re-align model output with human intent, getting to the ideal responses faster.\n", + "\n", + "## LangChain evaluation tools\n", + "LangChain has built-in evaluation tools for comparing model outputs based on common criteria, such as relevance and correctness. It also provides tools for defining custom criteria, which we can tailor to specific use cases. Finally, the ```QAEvalChain class``` is another tool that can be used to measure how well an AI's response answers a specific question using ground truth responses." + ] + }, + { + "cell_type": "markdown", + "id": "abf96d76-5901-4fe0-83b5-881e6e340b92", + "metadata": {}, + "source": [ + "## 6.1 LangChain Built-in Evaluation Metrics" + ] + }, + { + "cell_type": "markdown", + "id": "c7752f75-65d4-4a91-83dc-3b4740e544d9", + "metadata": {}, + "source": [ + "**EXERCISE-6: Explore Evalution Metrics in LangChain**\n", + "- run this import statement: ```from langchain.evaluation import Criteria```\n", + "- use ``list`` function pn Criteria to check the list of available functions" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ab44f6f3-2489-4670-88f4-a3361c6a7fc3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'Step 1: Identify the criterion - relevance.\\n\\nStep 2: Read the input and submission to determine if they are referring to a real quote from the text.\\n\\nStep 3: The input is asking a math question, not referring to a quote from the text.\\n\\nStep 4: The submission is referring to a different topic, the capital of New York state, and not a quote from the text.\\n\\nStep 5: Therefore, the submission does not meet the criterion of relevance.\\n\\nConclusion: The submission does not meet the criterion of relevance.', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.evaluation import load_evaluator\n", + "\n", + "\n", + "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", + "\n", + "evaluator = load_evaluator(\"criteria\", criteria=\"relevance\",llm=llm)\n", + "eval_result = evaluator.evaluate_strings(prediction=\"The capital of New York state is Albany\",input=\"What is 26 + 43?\")\n", + "print(eval_result)" + ] + }, + { + "cell_type": "markdown", + "id": "28a2c168-9b70-427d-9e0b-231212ec7699", + "metadata": {}, + "source": [ + "**EXERCISE-7: Try doing the same evaluation above with a different LLM (e.g., Mistral)**" + ] + }, + { + "cell_type": "markdown", + "id": "ce53d768-a26c-4509-948e-c464ebd20310", + "metadata": {}, + "source": [ + "## 6.2 Defining Custom Metrics\n", + "To customize the criteria, we need to evaluate the specific use case and define a dictionary named custom_criteria. This example adds simplicity, bias, clarity, and truthfulness criteria. Custom criteria work by mapping criteria names to the questions that are used to evaluate the strings. To use these new criteria, create an evaluator object, but this time, using our custom_critera." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b248976-85a5-4ef3-9998-48cf1afa9311", + "metadata": {}, + "outputs": [], + "source": [ + "custom_criteria = {\"simplicity\": \"Does the language use brevity?\",\n", + " \"bias\": \"Does the language stay free of human bias?\",\n", + " \"clarity\": \"Is the writing easy to understand?\",\n", + " \"truthfulness\": \"Is the writing honest and factual?\"}\n", + "\n", + "evaluator = load_evaluator(\"criteria\", criteria=custom_criteria,\n", + " llm=llm)\n", + "eval_result = evaluator.evaluate_strings(input=\"What is the best Italian restaurant in New York City?\",\n", + "prediction=\"That is a subjective statement and I cannot answer that.\")\n", + "print(eval_result)" + ] + }, + { + "cell_type": "markdown", + "id": "ef61e633-de72-4019-940c-7021b0e7c2e1", + "metadata": {}, + "source": [ + "## 6.3 QAEvalChain\n", + "Question-Answering (QA) is one of the most popular applications LLMs. But it is often not always obvious to determine what parameters (e.g., chunk size) or components (e.g., model choice, VectorDB) yield the best QA performance in the system we are building. The QA eval chain is an LLM chain for evaluting performance of an LLM on QA task. Refer to this detailed [LangChain blog post](https://blog.langchain.dev/auto-eval-of-question-answering-tasks/) for details about QAEvalChain." + ] + }, + { + "cell_type": "markdown", + "id": "1585ea3a-1be2-40db-9c88-774c03e220a7", + "metadata": {}, + "source": [ + "### 6.3.1 Trying out QAEvalChain\n", + "As a metric, QAEvalChain focuses on the **accuracy** and **relevance** of the response. In this chain, RAG will be used to store the document and ground truth responses, and an evaluation model instance is used to compare the semantic meaning of a model's results with the ground truth. \n", + "\n", + "First, we load our data source, in this case, a PDF document, and split it into chunks. Next, we set up the embeddings model, vector database, and LLM, and combine them in a chain. The input_key is set to \"question\", as questions will be used to query the database" + ] + }, + { + "cell_type": "markdown", + "id": "3e59bac0-ed4c-4ffe-b64c-423f88f1aab3", + "metadata": {}, + "source": [ + "### Create a RAG Retriever " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2fc52a82-7b90-4b0f-953a-32cb90beee54", + "metadata": {}, + "outputs": [], + "source": [ + "# Lets load the Cholera paper and then store it in a database\n", + "loader = PyPDFLoader(str(FILE_DENGUE))\n", + "data = loader.load()\n", + "\n", + "chunk_size = 100\n", + "chunk_overlap = 50\n", + "\n", + "# Split with RecursiveCharacterTextSplitter\n", + "rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap)\n", + "docs = rc_splitter.split_documents(data)\n", + "\n", + "# Lets use openAI embedding model\n", + "embedding_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_KEY)\n", + "\n", + "# Directory to store our database-set this to the data directory\n", + "vectordb = Chroma(persist_directory=str(DIR_DATA), embedding_function=embedding_model)\n", + "\n", + "# Store the databse\n", + "vectordb.persist()\n", + "\n", + "# Create the database\n", + "docstorage = Chroma.from_documents(docs, embedding_model)\n", + "\n", + "# LLM\n", + "llm = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", openai_api_key=OPENAI_API_KEY)\n", + "\n", + "# Define the retriever chain\n", + "qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docstorage.as_retriever(), input_key=\"question\")" + ] + }, + { + "cell_type": "markdown", + "id": "a9dddef3-e7ab-4c3e-9086-121be7b3b8a4", + "metadata": {}, + "source": [ + "## Define a Question Set as Key-Value Pairs in a Dict\n", + "This is a ground-truth dataset which a list of questions and their correct responses." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f4d95da4-f130-4a2f-b0e5-40d22146674e", + "metadata": {}, + "outputs": [], + "source": [ + "question_set = [{\"question\": \"Did dengue cases increase in 2023?\",\n", + " \"answer\": \"Yes, in 2023, there was an increase in cases globally.\"},\n", + " {\"question\": \"According to the document, which are the top four regions affected by arboviral diseases?\",\n", + " \"answer\": \"Africa is oe of the top four regions\"},\n", + " {\"question\": \"How is dengue virus transimitted to humans?\",\n", + " \"answer\": \"through the bite of infected mosquitoes\"}]" + ] + }, + { + "cell_type": "markdown", + "id": "7a509f17-4bec-4bdc-805e-321b37581a84", + "metadata": {}, + "source": [ + "## Run QAEVAL" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "5dff29e4-037b-464f-9135-3f311daf4727", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n", + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'results': ' CORRECT'}, {'results': ' INCORRECT'}, {'results': ' CORRECT'}]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to batch ingest runs: LangSmithError('Failed to POST https://api.smith.langchain.com/runs/batch in LangSmith API. HTTPError(\\'403 Client Error: Forbidden for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Forbidden\"}\\')')\n" + ] + } + ], + "source": [ + "from langchain.evaluation import QAEvalChain\n", + "predictions = qa.apply(question_set)\n", + "eval_chain = QAEvalChain.from_llm(llm)\n", + "\n", + "results = eval_chain.evaluate(question_set,predictions, question_key=\"question\",prediction_key=\"result\", answer_key='answer')\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "id": "db20cb10-2438-43b2-b9d0-df3ae73d6418", + "metadata": {}, + "source": [ + "**EXERCISE-7 (Do this in Your Groups): Run Evaluation on a Custom Eval Dataset for a RAG Chatbot QA Task**\n", + "1. Create a RAG LLM Chain as we have done before.\n", + "Please identify a PDF document to use which contains some new information that the LLMs do not have. \n", + "Note that it can be a French or English document.\n", + "2. Create 5 pairs of questions and correct answers to use to evaluate your RAG\n", + "3. Run QAEVAL on the eval dataset and report how many responses did the LLM get correct.\n", + "4. Do this again with a different LLM (e.g., Falcon or Mistral) and compare performance across models. *Note that your eval dataset remains the same.*" + ] + }, + { + "cell_type": "markdown", + "id": "20065378-5cd5-4b0f-ae9a-c2869075441a", + "metadata": {}, + "source": [ + "# 7. Summary\n", + "-----\n", + "In this notebook, we covered the basics of how to use LangChain to interact with both proprietary models from OpenAI and open source LLMs through Hugging Face library. We noted that there are two approaches to building Chains with LangChain: either using the functions or using the LCEL syntax. We covered key topics as follows: creating chains and interacting with LLMs; managing memeory of chat models; setup a RAG based chains which incorprates external documents and evaluating LLM outputs. \n", + "\n", + "What we have covered in this notebook is the tip of the ice-berg just to get you started on building LLM based applications with LangChain and other tools. There are alot of other things to learn and check.\n", + "- What are other frameworks whoch perform the same tasks as LangChain?\n", + "- LangChain Agents and LLM agents in general\n", + "- Vector databases and their role \n", + "- How to work with different document sources (e.g., websites)\n", + "- How to choose embedding models and the influence they have on generation\n", + "- Which model to use: instruct/chat/text generation\n", + "- and more " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab0ee5b1-8133-4405-98fc-e56057daece6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python3.12-audio", + "language": "python", + "name": "audio" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tunisia-may-24/README.md b/notebooks/tunisia-may-24/README.md new file mode 100644 index 0000000..38f8eff --- /dev/null +++ b/notebooks/tunisia-may-24/README.md @@ -0,0 +1,87 @@ +# Programming Activities for the Course + +This document outlines the programming activities for the course, focusing on hands-on projects to apply the concepts learned. The document is organized into two main sections: an introduction to LLM capabilities and LangChain, followed by a practical exercise on deploying a chatbot with Streamlit. + +## LLM Foundations-understanding the ML Process + +## Introducing LLM Capabilities and LangChain + +In this section, we explore the foundational capabilities of Large Language Models (LLMs) and how they can be applied in real-world scenarios. By leveraging LLMs, you can build applications such as chatbots, document analyzers, and automated support systems. + +### Understanding LLM Capabilities +LLMs are capable of generating human-like text, answering questions, summarizing content, and even performing tasks like sentiment analysis and named entity recognition. These models can process and interpret vast amounts of textual data, making them ideal for a variety of applications across domains. + +### Introducing LangChain +LangChain is a powerful framework that simplifies the process of integrating LLMs into applications. It provides modular components and utility functions to create chains (pipelines) that combine different tasks, such as prompting, data processing, and memory management, all within a cohesive system. LangChain makes it easier to build applications that require complex interactions with language models, including: + +- **Prompt Engineering**: Designing effective prompts to achieve desired responses from the LLM. +- **Data Handling**: Loading, processing, and storing large document corpora. +- **Chain Management**: Creating workflows that link multiple steps, such as data loading, prompt generation, and response handling. + +### Building Applications with LangChain +LangChain supports various use cases, including: + +1. **QA Chatbots**: Answering user questions based on specific datasets. +2. **Document Analysis**: Extracting information, summarizing content, or classifying documents. +3. **Automated Support Systems**: Handling customer service or FAQ queries. + +In this course, we will apply these capabilities to build a QA chatbot using LangChain, deploy it on Streamlit, and explore its functionality through real-world examples. + +--- + + +# Deploying a Chatbot on Streamlit +In this activity, you will use the knowledge gained from the LangChain Tutorial to explore a chatbot deployed on Streamlit. You will deploy this app on your computer and interact with it. + +## About Streamlit + +As discussed in the lectures, Streamlit is a platform that enables data scientists to deploy dynamic, data-based apps. It’s ideal for prototyping demonstration apps and sharing them with stakeholders before full-scale production deployment. + +## Initial Setup and Getting the Chatbot Files + +1. **Get OpenAI and Hugging Face API Credentials** + The chatbot uses OpenAI models, so you’ll need to sign up for an OpenAI developer account and obtain an API key. For a step-by-step guide on creating an OpenAI API key, search for instructions on ChatGPT. Similarly, create a Hugging Face account and obtain an API token. + +2. **Try the Chatbot on Streamlit Community Cloud** + Before downloading anything, you can try the chatbot on the Streamlit Community Cloud with just the OpenAI and Hugging Face keys. + +3. **Download or Clone the Project Repository** + To get the project files on your computer, either clone the GitHub repository (if familiar with Git) or download the repository as a zipped file. + +## Deploying the Streamlit App Locally + +1. **Unzip and Navigate to the Project Folder** + Once unzipped, open the project folder and follow the instructions on the GitHub page to deploy the chatbot. + +2. **Follow steps on GitHub project repository**. [Streamlit app repo](https://github.com/worldbank/RAG-Based-ChatBot-Example) + + +3. **Install Required Packages** + The `requirements.txt` file contains a list of all required packages. If you encounter a missing package error, try installing the package again (ensuring your virtual environment is activated). + +4. **Run the App Locally** + Run the app with the following command: + ```bash + streamlit run streamlit_app.py + ``` +5. **Test and Check**. When deployed locally, you can browse the files being used in the app. + +## Explore Important Scripts + +The essential components for building a chatbot with LangChain are organized into distinct, modular Python scripts. Let’s explore some of these elements. You can use VS Code or your preferred text editor for this task. + +### Loading Files +In real-life applications, you may need to load hundreds of documents, requiring a versatile function for file loading. This project includes two types of loaders: +- **`remote_loader.py`**: For loading documents from websites. +- **`local_loader.py`**: For loading documents from the local `data` folder. + +### Document Splitting +The `splitter.py` module uses the `RecursiveCharacterTextSplitter` strategy, with a chunk size of 1000 and an overlap of 0. This method helps in breaking down large documents into manageable sections for processing. + +### Prompt Chains +In the `full_chain.py`, `base_chain.py`, and `rag_chain.py` modules, you’ll find configurations for the specific LLM models and prompting strategies used. The project utilizes OpenAI chat models, with customized chains designed to guide interactions effectively. + +### Memory Management +Memory management strategies are also implemented to optimize the chatbot’s performance, particularly for long interactions or when processing large datasets. + + diff --git a/notebooks/world-bank-api.ipynb b/notebooks/world-bank-api.ipynb deleted file mode 100644 index 7e4bbf0..0000000 --- a/notebooks/world-bank-api.ipynb +++ /dev/null @@ -1,721 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", - "metadata": { - "tags": [] - }, - "source": [ - "# World Bank Indicators API Example\n", - "\n", - "> The following is an example of a [Jupyter notebook](https://jupyter.org) - a tutorial of how to retrieve data from the [World Bank Indicators API](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation) - that illustrates how to use computational content with the [template](https://worldbank.github.io/template). " - ] - }, - { - "cell_type": "markdown", - "id": "e0d992a6-f656-45ce-a025-f824901e8797", - "metadata": {}, - "source": [ - "## Requirements" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1811080b-c4c6-43cb-9e46-5cfa65d54abf", - "metadata": {}, - "outputs": [], - "source": [ - "import itertools\n", - "\n", - "import pandas\n", - "import requests\n", - "from bokeh.palettes import Spectral6\n", - "from bokeh.plotting import figure, output_notebook, show" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "fb8d2738-535e-4957-b82a-987891955a7f", - "metadata": {}, - "source": [ - "## Data Retrieval\n", - "\n", - "In this example, we retrieve **Population, total** (`SP.POP.TOTL`) from the [World Bank Indicators](https://data.worldbank.org/indicator) for [BRICS](https://infobrics.org)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c955864a-1889-4f7f-a29e-108b0534846b", - "metadata": {}, - "outputs": [], - "source": [ - "url = \"https://api.worldbank.org/v2/country/chn;bra;ind;rus;zaf/indicator/SP.POP.TOTL?format=json&per_page=10000\"" - ] - }, - { - "cell_type": "markdown", - "id": "6b5aac7c-bf80-4daa-a4a4-eebe8edc97bb", - "metadata": {}, - "source": [ - "Let's use [requests](https://requests.readthedocs.io) to send a GET request," - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8d699f28-853a-40a1-8ea9-8dd566962454", - "metadata": {}, - "outputs": [], - "source": [ - "r = requests.get(url)" - ] - }, - { - "cell_type": "markdown", - "id": "a21cf193-ec13-45b8-9726-bb960ac8586a", - "metadata": {}, - "source": [ - "Now, let's normalize and create `pandas.DataFrame` from the response," - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3dc152b2-95ba-473a-a416-2c4d5bac7622", - "metadata": {}, - "outputs": [], - "source": [ - "# normalize\n", - "data = pandas.json_normalize(r.json()[-1])\n", - "\n", - "# create dataframe\n", - "df = pandas.DataFrame.from_dict(data)" - ] - }, - { - "cell_type": "markdown", - "id": "241904c0-35b9-4e43-a3f9-f97738ea9fd1", - "metadata": {}, - "source": [ - "```{tip}\n", - "Alternatively, the World Bank API supports downloading the data as an [archive](http://api.worldbank.org/v2/country/all/indicator/SP.POP.TOTL?date=2000&source=2&downloadformat=csv). \n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "bae3462b-f49c-4b8a-badb-8f580b4fc268", - "metadata": {}, - "source": [ - "Let's take a look at the dataframe, " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c0bbef2d-495c-4140-b8ac-45ee47772142", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
countryiso3codeBRACHNINDRUSZAF
date
196073.092515667.070445.954579119.89700016.520441
196175.330008660.330456.351876121.23600016.989464
196277.599218665.770467.024193122.59100017.503133
196379.915555682.335477.933619123.96000018.042215
196482.262794698.355489.059309125.34500018.603097
..................
2018210.1665921402.7601369.003306144.47785957.339635
2019211.7828781407.7451383.112050144.40626158.087055
2020213.1963041411.1001396.387127144.07313958.801927
2021214.3262231412.3601407.563842144.13048259.392255
2022215.3134981412.1751417.173173144.23693359.893885
\n", - "

63 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - "countryiso3code BRA CHN IND RUS ZAF\n", - "date \n", - "1960 73.092515 667.070 445.954579 119.897000 16.520441\n", - "1961 75.330008 660.330 456.351876 121.236000 16.989464\n", - "1962 77.599218 665.770 467.024193 122.591000 17.503133\n", - "1963 79.915555 682.335 477.933619 123.960000 18.042215\n", - "1964 82.262794 698.355 489.059309 125.345000 18.603097\n", - "... ... ... ... ... ...\n", - "2018 210.166592 1402.760 1369.003306 144.477859 57.339635\n", - "2019 211.782878 1407.745 1383.112050 144.406261 58.087055\n", - "2020 213.196304 1411.100 1396.387127 144.073139 58.801927\n", - "2021 214.326223 1412.360 1407.563842 144.130482 59.392255\n", - "2022 215.313498 1412.175 1417.173173 144.236933 59.893885\n", - "\n", - "[63 rows x 5 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = df.pivot_table(values=\"value\", index=\"date\", columns=\"countryiso3code\")\n", - "df = df / 1e6 # scaling\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "27f26a03-6d7a-4c86-8a02-1ea341d7ac5b", - "metadata": {}, - "source": [ - "## Visualization" - ] - }, - { - "cell_type": "markdown", - "id": "38e8582b-2a51-4908-8356-76cb6158fdc3", - "metadata": {}, - "source": [ - "Let's now plot the data as a time series using [Bokeh](https://docs.bokeh.org)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "78041d94-56a6-43ff-a307-8e8a3b377858", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " function drop(id) {\n", - " const view = Bokeh.index.get_by_id(id)\n", - " if (view != null) {\n", - " view.model.document.clear()\n", - " Bokeh.index.delete(view)\n", - " }\n", - " }\n", - "\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - "\n", - " // Clean up Bokeh references\n", - " if (id != null) {\n", - " drop(id)\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim()\n", - " drop(id)\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(\"b627ae3b-1db0-4fe3-8762-32a285a44007\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"b627ae3b-1db0-4fe3-8762-32a285a44007\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"b627ae3b-1db0-4fe3-8762-32a285a44007\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"b627ae3b-1db0-4fe3-8762-32a285a44007\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " const docs_json = {\"6c86f57c-0f70-4648-a27e-ed2b73c3308b\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1001\",\"attributes\":{\"width\":700,\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1002\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1003\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1011\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1012\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1004\",\"attributes\":{\"text\":\"Population, total (World Bank)\",\"text_font_size\":\"12pt\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1040\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1034\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1035\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1036\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":[\"1960\",\"1961\",\"1962\",\"1963\",\"1964\",\"1965\",\"1966\",\"1967\",\"1968\",\"1969\",\"1970\",\"1971\",\"1972\",\"1973\",\"1974\",\"1975\",\"1976\",\"1977\",\"1978\",\"1979\",\"1980\",\"1981\",\"1982\",\"1983\",\"1984\",\"1985\",\"1986\",\"1987\",\"1988\",\"1989\",\"1990\",\"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\",\"2002\",\"2003\",\"2004\",\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"shape\":[63],\"dtype\":\"object\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[63],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1041\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1042\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1037\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#3288bd\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1038\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#3288bd\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1039\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#3288bd\",\"line_alpha\":0.2,\"line_width\":2}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1051\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1045\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1046\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1047\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":[\"1960\",\"1961\",\"1962\",\"1963\",\"1964\",\"1965\",\"1966\",\"1967\",\"1968\",\"1969\",\"1970\",\"1971\",\"1972\",\"1973\",\"1974\",\"1975\",\"1976\",\"1977\",\"1978\",\"1979\",\"1980\",\"1981\",\"1982\",\"1983\",\"1984\",\"1985\",\"1986\",\"1987\",\"1988\",\"1989\",\"1990\",\"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\",\"2002\",\"2003\",\"2004\",\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"shape\":[63],\"dtype\":\"object\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[63],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1052\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1053\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1048\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#99d594\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1049\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#99d594\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1050\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#99d594\",\"line_alpha\":0.2,\"line_width\":2}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1061\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1055\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1056\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1057\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":[\"1960\",\"1961\",\"1962\",\"1963\",\"1964\",\"1965\",\"1966\",\"1967\",\"1968\",\"1969\",\"1970\",\"1971\",\"1972\",\"1973\",\"1974\",\"1975\",\"1976\",\"1977\",\"1978\",\"1979\",\"1980\",\"1981\",\"1982\",\"1983\",\"1984\",\"1985\",\"1986\",\"1987\",\"1988\",\"1989\",\"1990\",\"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\",\"2002\",\"2003\",\"2004\",\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"shape\":[63],\"dtype\":\"object\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[63],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1062\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1063\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1058\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#e6f598\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1059\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#e6f598\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1060\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#e6f598\",\"line_alpha\":0.2,\"line_width\":2}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1071\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1065\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1066\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1067\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":[\"1960\",\"1961\",\"1962\",\"1963\",\"1964\",\"1965\",\"1966\",\"1967\",\"1968\",\"1969\",\"1970\",\"1971\",\"1972\",\"1973\",\"1974\",\"1975\",\"1976\",\"1977\",\"1978\",\"1979\",\"1980\",\"1981\",\"1982\",\"1983\",\"1984\",\"1985\",\"1986\",\"1987\",\"1988\",\"1989\",\"1990\",\"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\",\"2002\",\"2003\",\"2004\",\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"shape\":[63],\"dtype\":\"object\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[63],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1072\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1073\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1068\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#fee08b\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1069\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#fee08b\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1070\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#fee08b\",\"line_alpha\":0.2,\"line_width\":2}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1081\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1075\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1076\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1077\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":[\"1960\",\"1961\",\"1962\",\"1963\",\"1964\",\"1965\",\"1966\",\"1967\",\"1968\",\"1969\",\"1970\",\"1971\",\"1972\",\"1973\",\"1974\",\"1975\",\"1976\",\"1977\",\"1978\",\"1979\",\"1980\",\"1981\",\"1982\",\"1983\",\"1984\",\"1985\",\"1986\",\"1987\",\"1988\",\"1989\",\"1990\",\"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\",\"2002\",\"2003\",\"2004\",\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"shape\":[63],\"dtype\":\"object\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[63],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1082\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1083\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1078\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#fc8d59\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1079\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#fc8d59\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1080\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#fc8d59\",\"line_alpha\":0.2,\"line_width\":2}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1010\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1023\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1024\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1025\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1026\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1031\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1032\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1033\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1018\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1019\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1020\"},\"axis_label\":\"Population, total (in millions)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1021\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1013\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1014\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1015\"},\"axis_label\":\"Year\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1016\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1017\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1022\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1018\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1043\",\"attributes\":{\"location\":\"right\",\"click_policy\":\"mute\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1044\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"BRA\"},\"renderers\":[{\"id\":\"p1040\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1054\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"CHN\"},\"renderers\":[{\"id\":\"p1051\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1064\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"IND\"},\"renderers\":[{\"id\":\"p1061\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1074\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"RUS\"},\"renderers\":[{\"id\":\"p1071\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1084\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"ZAF\"},\"renderers\":[{\"id\":\"p1081\"}]}}]}}]}}]}};\n", - " const render_items = [{\"docid\":\"6c86f57c-0f70-4648-a27e-ed2b73c3308b\",\"roots\":{\"p1001\":\"d2127207-0be3-46fb-86fa-2b57704ab0fc\"},\"root_ids\":[\"p1001\"]}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " let attempts = 0;\n", - " const timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p1001" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "output_notebook()\n", - "\n", - "p = figure(title=\"Population, total (World Bank)\", width=700, height=600)\n", - "\n", - "# colors\n", - "colors = itertools.cycle(Spectral6)\n", - "\n", - "# plotting the line graph\n", - "for column, color in zip(df.columns, colors):\n", - " p.line(\n", - " df.index,\n", - " df[column],\n", - " legend_label=column,\n", - " color=color,\n", - " line_width=2,\n", - " )\n", - "\n", - "p.legend.location = \"right\"\n", - "p.legend.click_policy = \"mute\"\n", - "p.title.text_font_size = \"12pt\"\n", - "\n", - "p.xaxis.axis_label = \"Year\"\n", - "p.yaxis.axis_label = \"Population, total (in millions)\"\n", - "\n", - "show(p)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "vscode": { - "interpreter": { - "hash": "b6702b69e93007336b96338c5a331192f07cedff01d36d4dcfa0f842adb718ad" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/world-bank-package.ipynb b/notebooks/world-bank-package.ipynb deleted file mode 100644 index 6883570..0000000 --- a/notebooks/world-bank-package.ipynb +++ /dev/null @@ -1,281 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", - "metadata": { - "tags": [] - }, - "source": [ - "# Python Package Example\n", - "\n", - "> The following is an example of on how to use and distribute your project as a [Python package](https://packaging.python.org) using the example template. Remember mix and match to yout project's requirements. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef92b033-81e2-4c5f-b56a-63f4f7a37247", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import itertools\n", - "\n", - "from bokeh.palettes import Spectral6\n", - "from bokeh.plotting import figure, output_notebook, show" - ] - }, - { - "cell_type": "markdown", - "id": "14e89727", - "metadata": {}, - "source": [ - "## Usage" - ] - }, - { - "cell_type": "markdown", - "id": "b4c0f3e8-7756-41bb-aa21-cc2eee5ff67f", - "metadata": {}, - "source": [ - "Unlike the [previous example](https://worldbank.github.io/template/notebooks/world-bank-api.html), where the source code was contained on the Jupyter notebook itself, we (re)use a Python package - the [template](https://github.com/worldbank/template/tree/main/src/template) Python package - which will let us (re)use any attributes and methods in the following example.\n", - "\n", - "Let's start by importing `WorldBankIndicatorsAPI`, a Python API wrapper class created to facilitate the usage of the [World Bank Indicators API](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d797ef77-6ca4-4f9d-a1f8-abbfd9884b07", - "metadata": {}, - "outputs": [], - "source": [ - "from template.indicators import WorldBankIndicatorsAPI" - ] - }, - { - "cell_type": "markdown", - "id": "17f380e4-3854-4af6-940c-7afe9723a59a", - "metadata": {}, - "source": [ - "Let's continue by creating the API object. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f911a5c3-6994-45a6-a049-4b398f5890c0", - "metadata": {}, - "outputs": [], - "source": [ - "api = WorldBankIndicatorsAPI()" - ] - }, - { - "cell_type": "markdown", - "id": "7fa96741-f4cd-4504-a5f8-6467f9a2345e", - "metadata": {}, - "source": [ - "The `api` wrapper object is now ready to use! We will invoke its `query` method to retrieve data from the [World Bank Indicators API](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation). To learn how to use it, such as information about method signature, valid parameters and return value, we read `help`. Since [PEP 257](https://peps.python.org/pep-0257), Python offers *doctrings*, which are an easy and standard to create code documentation and it is a good practice adopt it. Documentating the source code is crucial to create a maintainable reliable and reproducicle code base and project.\n", - "\n", - "Let's see the `query` method's *docstring* as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb6ca314-d161-40e1-a376-a3013a0711eb", - "metadata": {}, - "outputs": [], - "source": [ - "help(api.query)" - ] - }, - { - "cell_type": "markdown", - "id": "e82fc342-165d-42d6-b3dc-7534c215ca1f", - "metadata": {}, - "source": [ - "The `query` method allows us to select an **indicator** (e.g, [World Development Indicators](https://datatopics.worldbank.org/world-development-indicators)), a list of countries and [query parameters](https://datahelpdesk.worldbank.org/knowledgebase/articles/898581#query-strings). Note that contrary to the [previous example](https://worldbank.github.io/template/notebooks/world-bank-api.html), the method expects a list of country names and converts them to [ISO 3166-1 alpha-3](https://www.iso.org/iso-3166-country-codes.html) automatically." - ] - }, - { - "cell_type": "markdown", - "id": "23b0a1eb-73c1-42e7-8903-98e362ef86de", - "metadata": {}, - "source": [ - "Let's invoke the `query` method and retrieve the results for `SP.POP.TOTL` for the [BRICS](https://infobrics.org) (as before)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7fb7daea-c5cf-42ea-b746-a565dd9ac4e1", - "metadata": {}, - "outputs": [], - "source": [ - "df = api.query(\n", - " \"SP.POP.TOTL\", country=[\"Brazil\", \"China\", \"India\", \"Russia\", \"South Africa\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "46662c1b-4c19-424b-8a61-f651cb486c5b", - "metadata": {}, - "source": [ - "**Voilà!** We just (re)used the [template](https://github.com/worldbank/template/tree/main/src/template) Python package in our example delegating the maintenance and logic, making the notebook easier to understand and reproduce. \n", - "\n", - "```{tip}\n", - "In addition, the `template` makes any Python package automatically [pip installable](https://packaging.python.org/en/latest/tutorials/installing-packages/) and accessible to *anyone* and from *anywhere*!\n", - "\n", - "To install from source:\n", - "\n", - "\tpip install git+https://github.com/worldbank/template.git\n", - "\n", - "To install from version:\n", - "\n", - "\tpip install git+https://github.com/worldbank/template.git@v0.1.0\n", - "\t\n", - "\n", - "When distributing a project release, it is strongly recommended to adhere to release management good practices. It is recommended to create checklists, adopt versioning (e.g, [semantic versioning](https://semver.org/) and to release on [Python Package Index](https://pypi.org/) (instead of GitHub).\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "80887da5-0474-48b3-8c71-fbe3dbd3a8e8", - "metadata": {}, - "source": [ - "```{tip}\n", - "The template will automatically find and install any local `src` packages as long as the `setup.cfg` file is up-to-date.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "daa4319a-8936-4195-b1fc-aad9c008325b", - "metadata": {}, - "source": [ - "```{caution}\n", - "The `template` Python package should be used for demonstration purposes only. For support, please see the [World Bank Indicators API Documentation](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation).\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "e9f14239", - "metadata": {}, - "source": [ - "Finally, let's take a look at the retrieved data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1d7cf70-bf0e-4c12-ae0d-fd26349291db", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "df = df.pivot_table(values=\"value\", index=\"date\", columns=\"country.value\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "699a0495-4f06-479c-b517-58336110547f", - "metadata": { - "tags": [ - "output_scroll" - ] - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "id": "c5daa85a-004d-4e93-be84-72d064d0b83b", - "metadata": {}, - "source": [ - "## Visualization\n", - "\n", - "As before, let's now plot the data as a time series using [Bokeh](https://docs.bokeh.org)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60219760", - "metadata": {}, - "outputs": [], - "source": [ - "output_notebook()\n", - "\n", - "# instantiating the figure object\n", - "p = figure(title=\"Population, total (World Bank)\", width=700, height=600)\n", - "\n", - "# colors\n", - "colors = itertools.cycle(Spectral6)\n", - "\n", - "# plotting the line graph\n", - "for column, color in zip(df.columns, colors):\n", - " p.line(\n", - " df.index,\n", - " df[column],\n", - " legend_label=column,\n", - " color=color,\n", - " line_width=2,\n", - " )\n", - "\n", - "p.legend.location = \"right\"\n", - "p.legend.click_policy = \"mute\"\n", - "p.title.text_font_size = \"12pt\"\n", - "\n", - "p.xaxis.axis_label = \"Year\"\n", - "p.yaxis.axis_label = \"Population, total (in millions)\"\n", - "\n", - "show(p)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "vscode": { - "interpreter": { - "hash": "b6702b69e93007336b96338c5a331192f07cedff01d36d4dcfa0f842adb718ad" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/template/__init__.py b/src/template/__init__.py deleted file mode 100644 index a535a10..0000000 --- a/src/template/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from importlib.metadata import version, PackageNotFoundError - -try: - __version__ = version("datalab") -except PackageNotFoundError: - # package is not installed - pass diff --git a/src/template/indicators.py b/src/template/indicators.py deleted file mode 100644 index b98824f..0000000 --- a/src/template/indicators.py +++ /dev/null @@ -1,83 +0,0 @@ -import pandas -import pycountry -import requests - - -class WorldBankIndicatorsAPI: - URL = "https://api.worldbank.org/v2/country" - - def _get_country_code(self, country): - """ - Using `pycountry`, return the ISO 3166-1 alpha-3 country code for corresponding query term. - - See also: - https://github.com/flyingcircusio/pycountry - - Parameters - ---------- - country : str - - Returns - ------- - str - ISO 3166-1 alpha-3 country code for corresponding query term. - - Raises - ------ - LookupError - If the query term is not a valid country. - """ - return pycountry.countries.search_fuzzy(country)[0].alpha_3 - - def _get(self, indicator, country: str = "all", params: dict = {}): - """ - Retrieve a response, valid JSON response or error, from the World Bank Indicators API. - - See also: - https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation - - Parameters - ---------- - indicator : str - country : str, optional - params : dict, optional - - Returns - ------- - requests.models.Response - Return JSON response from the World Bank Indicators API. - """ - url = f"{self.URL}/{country}/indicator/{indicator}" - - return requests.get(url, params) - - def query(self, indicator, country: list = "all", params: dict = {}): - """ - Retrieve a response, valid JSON response or error, from the World Bank Indicators API. - - See also: - https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation - - Parameters - ---------- - indicator : str - World Bank API Indicator. - country : list, optional - List of countries. The country name is converted to ISO 3166-1 alpha-3 country code. - params : dict, optional - World Bank API Indicator Query Strings. - - Returns - ------- - pandas.core.frame.DataFrame - Return a Pandas DataFrame obtained with response data from World Bank Indicators API. - """ - if isinstance(country, list): - country = ";".join([self._get_country_code(c) for c in country]) - - params.update({"format": "json", "per_page": 1000}) - - response = self._get(indicator, country, params) - data = response.json()[-1] - - return pandas.json_normalize(data) diff --git a/src/tunisia/streamlit_app.py b/src/tunisia/streamlit_app.py new file mode 100644 index 0000000..4f47b99 --- /dev/null +++ b/src/tunisia/streamlit_app.py @@ -0,0 +1,81 @@ +import streamlit as st +from langchain.llms import OpenAI +import os +from pathlib import Path +from langchain_community.llms import HuggingFaceEndpoint, HuggingFaceHub +from langchain.prompts import PromptTemplate, ChatPromptTemplate +from langchain.chains import LLMChain, ConversationChain, RetrievalQA, RetrievalQAWithSourcesChain +from langchain_openai import ChatOpenAI, OpenAI +from langchain.memory import ChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory + +# Document Loaders and Text Splitter +from langchain_community.document_loaders import PyPDFLoader, CSVLoader, HNLoader, UnstructuredHTMLLoader +from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter + +# Vector Embeddings +from langchain_openai import OpenAIEmbeddings +from langchain_community.vectorstores import Chroma + +from langchain_core.runnables import RunnablePassthrough +from langchain.schema.output_parser import StrOutputParser + +# App Logic +st.title('🦜🔗 RAG Based Chatbot') + +OPENAI_API_KEY = st.sidebar.text_input('OpenAI API Key', type='password') +DIR_WD = Path("/Users/dunstanmatekenya/Google Drive/My Drive/GenAI-Course/Mod2-LLM-Overview/") +DIR_DATA = DIR_WD.joinpath("data") +DIR_DOCS = Path("/Users/dunstanmatekenya/Google Drive/My Drive/GenAI-Course/Public Health Documents") +FILE_TU_COVID_RESPONSE = DIR_DOCS.joinpath("who_wou_apr_2024.pdf") + +def load_pdf_docs(pdf_file): + # Load the PDF + loader = PyPDFLoader(pdf_file) + data = loader.load() + + chunk_size = 400 + chunk_overlap = 100 + + rc_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap = chunk_overlap) + docs = rc_splitter.split_documents(data) + + return docs + +def add_documents2vectordb(embedding_model, pdf_file, vectordb_dir): + + # Load and split the file + docs = load_pdf_docs(pdf_file) + + # Create embeddings + embedding_model = OpenAIEmbeddings(openai_api_type=OPENAI_API_KEY) + + + vectordb = Chroma(persist_directory=vectordb_dir, embedding_function=embedding_model) + + vectordb.persist() + docstorage = Chroma.from_documents(docs, embedding_model) + + return docstorage + +def generate_response(input_text): + embedding_function = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) + docstorage = add_documents2vectordb(embedding_model=embedding_function, + pdf_file=str(FILE_TU_COVID_RESPONSE), + vectordb_dir=str(DIR_DATA)) + print('Done with Preparing Documents') + llm = OpenAI(model_name="gpt-3.5-turbo-instruct", openai_api_key=OPENAI_API_KEY, + temperature=0.7) + qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm,chain_type="stuff", + retriever=docstorage.as_retriever()) + results = qa({"question": "{}".format(input_text)}, + return_only_outputs=True) + print(results) + st.info(results) + +with st.form('my_form'): + text = st.text_area('Enter text:', 'What is the situation of drought in the Amazon forest?') + submitted = st.form_submit_button('Submit') + if not OPENAI_API_KEY.startswith('sk-'): + st.warning('Please enter your OpenAI API key!', icon='⚠') + if submitted and OPENAI_API_KEY.startswith('sk-'): + generate_response(text) \ No newline at end of file