This repository provides a series of advanced LLM-based chatbots focused on Retrieval Augmented Generation (RAG) and Question Answering (Q&A) across diverse data sources. It targets developers and researchers looking to build sophisticated conversational AI systems that can interact with vector databases, graph databases, SQL, CSV, and XLSX files, offering guidance for both Azure OpenAI and OpenAI APIs.

How It Works

The projects leverage frameworks like LangGraph, LangChain, and OpenAI to create agentic systems. These systems can integrate RAG for unstructured data, SQL agents for structured data, and graph agents for knowledge graphs. The architecture emphasizes modularity, with distinct projects demonstrating specific capabilities such as customer support with multiple tools, intelligent Q&A over large databases, and natural language interaction with tabular and graph data.

Quick Start & Requirements

• Installation: Projects generally follow a standard Python project structure, likely installable via pip after cloning. Specific database drivers and LLM API keys (OpenAI/Azure OpenAI) are required.
• Dependencies: Python, LangChain, LangGraph, OpenAI/Azure OpenAI SDKs, database connectors (e.g., SQLAlchemy, Neo4j driver), vector database clients (e.g., ChromaDB), and potentially Gradio for UIs.
• Resources: Requires API access keys for LLMs. Database interactions may require specific database instances (e.g., Neo4j).
• Documentation: Each project includes a link to a corresponding YouTube video for detailed explanations.

Highlighted Details

• Demonstrates building complex agentic systems with LangGraph, including multi-tool integration and workflow orchestration.
• Facilitates natural language interaction with SQL, CSV, XLSX, and Neo4j graph databases.
• Explores RAG techniques applied to both unstructured text and tabular/graph data.
• Provides examples of constructing knowledge graphs from tabular data using LLMs.

Maintenance & Community

The repository is maintained by Farzad-R. Links to community channels or roadmaps are not explicitly provided in the README.

Licensing & Compatibility

The repository's licensing is not specified in the provided README. Compatibility for commercial use or closed-source linking would depend on the underlying libraries used and any explicit license declarations within the repository.

Limitations & Caveats

The projects are presented as guides and demonstrations, implying they may require adaptation for production environments. Key Note 3 advises caution when interacting with sensitive databases, recommending read-only access to prevent data manipulation. Familiarity with SQL, Cypher, and Pandas is recommended for richer interactions.