Chatbot developed with Python and Flask that features conversation with a virtual assistant. This uses a context based conversation and the answers are focused on a local indexation file structured with Llamaindex (GPT-index) lib which converts raw data into a vector dataset (Json) a therefore the assistant will use the dataset to provide answers. It allows to define an initial role and personification.
It make use of LlamaIndex (GPT Index) that provides a central interface to connect LLM’s (large language models) with external data. On this demo is used GPTSimpleVectorIndex to created a vector file from the local documents.
LangChain is a framework built around LLMs. The core of the library is to “chain” together different components to create more advanced use cases around LLMs.
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Define necessary parameters (OpenAi API key, ...) on file 'qa_engine.py'
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Initialize virtual environment and install dependencies, run:
virtualenv env env\Scripts\activate pip install flask python-dotenv pip install openai pip install gpt_index langchain
- Add documents to folder "content"
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Initialize the app:
flask run -
Enter "http://localhost:5000" on browser to interact with app
- v0.1
- initial build