LLM POC Project

This project demonstrates integration of various AI capabilities using LangChain, including:

• Text-based RAG (Retrieval-Augmented Generation)
• Knowledge Graph integration with Neo4j
• SQL database querying
• Web search capabilities
• Multi-tool orchestration

Features

• Text input processing with vector embeddings
• Knowledge graph creation and querying
• Database integration for structured data
• Web search capability via SerpAPI
• Streamlit-based user interface

Setup Instructions

1. Install dependencies:

   pip install -r requirements.txt
   

2. Set up API keys:

   • Create a secrets.toml file in the .streamlit directory with:

     GOOGLE_API_KEY = "your-google-api-key"
     LANGSMITH_API_KEY = "your-langsmith-api-key" # Optional
     NEO4J_URI = "your-neo4j-uri"
     NEO4J_USERNAME = "your-neo4j-username"
     NEO4J_PASSWORD = "your-neo4j-password"
     SERPAPI_API_KEY = "your-serpapi-key"
     

3. Run the application:

   streamlit run app.py
   

Usage

1. Text Data: Enter text or use the sample text. Click "Process Text Data" to create embeddings and knowledge graph.

2. Database: Upload an Excel file or use the sample database. Click "Process Database" to load data into SQLite.

3. Web Search: Enable web search for external information retrieval.

4. Query Selection: Select which data sources to use for answering queries.

5. Ask Questions: Type your query in the text box and get answers from the selected data sources.

Architecture

The application uses a modular architecture:

• Streamlit Frontend: User interface and interaction
• LangChain: Orchestration of various components
• FAISS: Vector storage for text embeddings
• Neo4j: Knowledge graph storage and querying
• SQLite: Relational database for structured data
• SerpAPI: Web search capabilities

Project Structure

├── app.py                 # Main Streamlit application
├── utils.py               # Helper Library
├── requirements.txt       # Project dependencies
├── flow_diagram.png       # System architecture diagram
└── README.md              # Project documentation

Project Flow

Future Improvements

• Support for PDF and document processing
• Integration with more external tools
• Enhanced visualization capabilities
• User authentication and permissions
• Improved performance and caching