The BSHR (Brainstorm, Search, Hypothesize, Refine) project is an innovative information processing application. It leverages the power of AI to process complex queries, combining web search and AI-driven hypothesis generation. The application iteratively refines user queries, gathers relevant information, and synthesizes insightful answers. Idea from Dave Shapiro, project developed by Joe Crowley.
- Dynamic Query Expansion: Generates evolving search queries based on initial user input and ongoing analysis.
- Web Search Integration: Utilizes the Bing Web Search API for comprehensive and current internet data retrieval.
- AI-Driven Hypothesis Generation: Employs OpenAI's GPT-4 for crafting hypotheses and insights from search results.
- Insightful Answer Synthesis: Combines all gathered data into a coherent and detailed final answer.
- Clone the Repository:
git clone https://github.com/joseph-crowley/BSHR.git cd BSHR/ - Install Dependencies:
pip install -r requirements.txt - Modify config/settings.py:
- Add your Bing Web Search API key to
BING_API_KEY. - Add your OpenAI API key to
OPENAI_API_KEY.
- Add your Bing Web Search API key to
Set up your API keys and configure endpoints in config/settings.py. Customize logging preferences in src/utils/logger.py.
Set up logs dir and Run the application:
mkdir logs
cd src/
python main.py
Input your query when prompted and the system will process and synthesize the response.
config/: Stores application settings. Add your API keys and configure endpoints here.src/main.py: Entry point of the application.src/query_processing/: Handles query generation, execution, and result updating.src/hypothesis/: Manages hypothesis generation and answer synthesis.logs/: Stores application logs.
- Modify modules in
src/query_processingandsrc/hypothesisfor enhancements. - Add new modules as needed, ensuring integration with the main application flow in
src/main.py.
Run unit tests in src/tests/ using a framework like pytest to ensure functionality and prevent regressions.
We welcome contributions. Please adhere to the project's coding style, include unit tests for new features, and document your changes.
Specify the license under which the project is released.
Execute main.py and input your query when prompted.
Observe the console output as the system processes your query, searching the web and generating hypotheses.
Logs are in logs/application.log
The application will display the final synthesized answer after completing its analysis.
- Provide detailed queries for more specific results.
- Allow time for complex queries to be fully processed and answered.
$ python main.py
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What is your query? generative AI for financial modeling and algorithmic trading
ANSWER:
Artificial Intelligence (AI) is enhancing the scope and capabilities of financial services, particularly in the realm of trading, risk management, and predictive analysis. AI-based technologies, such as generative AI and algorithmic trading, are transforming the trading landscape by automating human tasks and optimizing performance. They mimic and sometimes surpass human intelligence in making trade decisions, resulting in potentially higher profits.
For example, algorithmic trading utilizes mathematical models to make buy and sell orders in the financial markets, enabling a system that can function on auto-pilot. This automated trading not only facilitates efficiency but also matches individual trading styles, leading to customized trading applications. The emerging field of AI-powered trading integrates algorithmic trading with reinforcement learning, a model that enables AI to continually learn and adapt, thus potentially optimizing trading strategies and enhancing market power.
The use of generative AI in finance, which involves generative models that can simulate complex data patterns and make predictions, has the potential to revolutionize predictive analysis in the sector. This capability of AI can help in making stock market predictions with high accuracy and fine-tuning trading strategies. Predictive analytics, a subfield of AI, can optimize bank processes, reducing costs and resources deployed, further highlighting AI's potential in financial services.
The integration of AI in finance is not without its challenges, however. While AI can lead to significant cost savings and potential profits, such as the $1 trillion savings anticipated by algorithmic trading by 2030, it requires constant monitoring and risk assessment. The use of AI introduces the need for regulating and controlling AI-powered tools and applications, ensuring their ethical and efficient usage.
In conclusion, the adoption of AI in finance, especially in trading, predictive analysis, and risk management, offers significant potential for enhanced performance, automation, and profitability. However, the effective integration of AI in this sector also necessitates the management of risks associated with AI deployment, such as issues of control, ethics, and oversight.
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This demonstrates the use of a BSHR loop for advanced inference and hypothesis generation/testing.