stocks insights ai agent
Basic Information
This repository implements a full stack Stock Data Insights application that demonstrates Agentic Retrieval-Augmented Generation (RAG) workflows to extract insights from news and financial data for companies and the broader stock market. It integrates Large Language Models with vector and relational storage to support semantic retrieval and structured queries. The project shows how to asynchronously scrape news into MongoDB and synchronize documents to ChromaDB for semantic search, while financial time series are stored in PostgreSQL. LangChain, LangChain Expression Language (LCEL), LangGraph and ChromaDB are used to build agentic workflows. The codebase includes LangGraph RAG graphs for news retrieval, SQL generation and charting, an OpenAPI specification for REST endpoints, test cases using pytest, and observability integration via LangSmith for tracing LLM calls.
Links
App Details
-
$$$$ -
$$$$