Chatbot with LangChain & RAG

This project is an end-to-end AI-powered chatbot that leverages LangChain and Retrieval-Augmented Generation (RAG) to deliver contextually accurate and insightful responses by combining LLMs (Large Language Models) with custom data sources.

🚀 Features

• 🔗 LangChain Integration: Modular, flexible chain management for LLM workflows.
• 📄 RAG Pipeline: Combines LLMs with document retrieval to answer queries grounded in real data.
• 🧠 LLM-Powered Chatbot: Uses state-of-the-art language models for human-like conversations.
• 🔎 Vector Search: Efficient document search via vector embeddings.
• 📂 Custom Knowledge Base: Query your own documents (PDFs, text files, web pages, etc.).
• ☁️ Cloud-Friendly: Can be deployed on cloud platforms (e.g., AWS, GCP) with minimal adjustments.
• ⚙️ Configurable & Extensible: Easy to plug in other LLMs, vector stores, or document loaders.

---

📂 Project Structure

├── data/                     # Source documents for retrieval
│   └── sample_docs/          # Example PDFs, text files, etc.
├── notebooks/                # Jupyter notebooks for experimentation
├── src/
│   ├── chains/               # LangChain chain definitions
│   ├── retriever/            # Document loader & vector index code
│   ├── chatbot.py            # Main chatbot app entry point
│   └── config.py             # Configuration for paths, models, etc.
├── requirements.txt          # Python dependencies
└── README.md                 # This file


⸻

🧰 Tech Stack
	•	LangChain: Chain orchestration, memory, tools.
	•	LLMs: OpenAI GPT, Llama, or other models (configurable).
	•	Vector Store: FAISS / ChromaDB / Pinecone (plug-in support).
	•	Embeddings: OpenAI, HuggingFace, or custom models.
	•	RAG Pipeline: Retrieval + LLM Response.
	•	Python 3.10+

⸻

📝 How It Works
	1.	Document Ingestion: Load documents from data/ and convert to text.
	2.	Embedding & Indexing: Convert texts into vector embeddings and store in a vector database.
	3.	User Query: User inputs a natural language query.
	4.	Retrieval: Top-k relevant documents are retrieved from the vector store.
	5.	LLM Response: LangChain combines the query and retrieved documents, then passes them to the LLM for final answer generation.
	6.	Chat Response: The chatbot returns the response to the user.

⸻

📦 Installation
	1.	Clone this repo:

git clone https://github.com/your_username/your_repo.git
cd your_repo

	2.	Create a virtual environment:

python3 -m venv venv
source venv/bin/activate

	3.	Install dependencies:

pip install -r requirements.txt


⸻

⚙️ Configuration

Edit src/config.py to adjust:
	•	LLM Provider: OpenAI, HuggingFace, etc.
	•	Embedding Model: Choose the embedding method.
	•	Vector Store: FAISS, ChromaDB, or others.
	•	Retriever Settings: Top-k results, etc.

⸻

🏃‍♀️ Run the Chatbot

python src/chatbot.py

Then, enter your questions interactively in the terminal.

⸻

🧪 Example Query Flow

> User: How does the LangChain memory module work?
> Bot: LangChain’s memory module enables conversation-aware chains by storing past interactions...


⸻

📄 Supported File Types
	•	PDF (.pdf)
	•	Markdown (.md)
	•	Text (.txt)
	•	Web pages (.html)
	•	JSON, CSV, etc. (extendable)

⸻

💡 Key Concepts
	•	Retrieval-Augmented Generation (RAG):
Combines search and generation to ground LLM answers in real-world data.
	•	LangChain Chains:
Modular building blocks to connect LLMs, tools, retrievers, and memory.
	•	Embeddings:
Dense vector representations of text for similarity search.

⸻

📝 Future Improvements
	•	✅ Multi-turn Chat with Memory
	•	✅ Web-based Frontend (e.g., Streamlit / FastAPI)
	•	✅ API Endpoint for Programmatic Access
	•	✅ Advanced Search Ranking & Filtering
	•	✅ Multi-Source Document Loading

⸻

🤝 Contributing
	1.	Fork this repo.
	2.	Create a new branch: git checkout -b feature/my-feature
	3.	Commit changes: git commit -m 'Add my feature'
	4.	Push to branch: git push origin feature/my-feature
	5.	Submit a pull request.

⸻

📜 License

MIT License

⸻

📬 Contact

If you have any questions or suggestions, feel free to reach out!

Email: fangsiyi0089@gmail.com