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Stocks Insights Ai Agent
Overview :
This product is a full-stack application that leverages LLM (Large Language Models) and LangChain technology, combined with LangGraph, to retrieve and analyze stock data and news. It uses ChromaDB as a vector database to support semantic search and data visualization, providing users with deep insights into the stock market. It is primarily aimed at investors, financial analysts, and data scientists, helping them quickly acquire and analyze stock-related information to support decision-making. The product is currently open-source and free, making it suitable for users who need to efficiently process financial data and news.
Target Users :
This product is suitable for investors, financial analysts, and data scientists. Investors can use it to quickly access stock market information to aid their investment decisions; financial analysts can conduct in-depth analyses of stock data and news to support their research; data scientists can leverage its technical architecture for further data mining and model development.
Use Cases
Investors can quickly view the historical price trends and related news of specific stocks through this application, aiding investment decisions.
Financial analysts can utilize its data scraping and analysis capabilities to deeply study the financial performance and market dynamics of specific companies.
Data scientists can further develop customized financial data analysis models based on the open-source code and architecture of this application.
Features
Stock Performance Visualization: Display historical performance of selected stocks through charts
Attribute-Specific Data Retrieval: Obtain detailed information about specific stocks
News Aggregation: Provide news articles related to specific stocks or companies
Asynchronous Data Scraping: Regularly scrape news and financial data for storage in the database
LangGraph Workflow: Implement semantic search and result generation for news and stock data using RAG graphs
API Interface: Offer various APIs for retrieving stock price statistics, news, and more
Testing Framework: Use pytest for automated testing to ensure the application's stability and reliability
Observability and Tracking: Integrate LangSmith tracking to monitor LLM calls and debug processes
How to Use
1. Visit the GitHub repository page and clone or download the project code
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2. Install project dependencies, including the Python environment and relevant libraries
3. Configure the database connections, including MongoDB and PostgreSQL
4. Start the data scraping service to regularly update stock and news data
5. Use the LangGraph workflow for data querying and analysis
6. Access stock price statistics, news, and other data through the API interface
7. Utilize visualization tools to view stock performance charts
8. Extend the code as needed or integrate it into other systems
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