/DeepSeek-RAG-Chatbot

100 % FREE, Private (No Internet) DeepSeek’s Advanced RAG: Boost Your RAG Chatbot: Hybrid Retrieval (BM25 + FAISS) + Neural Reranking + HyDe🚀

Primary LanguagePythonMIT LicenseMIT

🚀 DeepSeek RAG Chatbot 3.0 – Now with GraphRAG & Chat History Integration!

(100% Free, Private (No Internet), and Local PC Installation)

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🔥 DeepSeek + NOMIC + FAISS + Neural Reranking + HyDE + GraphRAG + Chat Memory = The Ultimate RAG Stack!

This chatbot enables fast, accurate, and explainable retrieval of information from PDFs, DOCX, and TXT files using DeepSeek-7B, BM25, FAISS, Neural Reranking (Cross-Encoder), GraphRAG, and Chat History Integration.

🔹 New Features in This Version

✅ GraphRAG Integration: Enhances retrieval by constructing a Knowledge Graph from your documents, allowing for more contextual and relational understanding.
✅ Chat Memory History Awareness: Maintains context by utilizing chat history, enabling more coherent and contextually relevant responses.
✅ Improved Error Handling: Resolved issues related to chat history clearing and other minor bugs for a smoother user experience.

🛠️ Installation & Setup

1️⃣ Clone the Repository & Install Dependencies

git clone https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot.git
cd DeepSeek-RAG-Chatbot
python -m venv venv
venv/Scripts/activate
pip install -r requirements.txt

2️⃣ Download & Set Up Ollama

Ollama is required to run DeepSeek-7B and Nomic Embeddings locally.
🔗 Download Ollama → https://ollama.com/

Then, pull the required models:

ollama pull deepseek-r1:7b
ollama pull nomic-embed-text

3️⃣ Run the Chatbot

streamlit run app.py

📌 How It Works

  1. Upload Documents: Add your PDFs, DOCX, or TXT files.
  2. Hybrid Retrieval: Combines BM25 and FAISS to fetch the most relevant text.
  3. GraphRAG Processing: Builds a Knowledge Graph from documents to understand relationships and context.
  4. Neural Reranking: Utilizes Cross-Encoder to refine search results for higher accuracy.
  5. Query Expansion (HyDE): Enhances recall by generating expanded queries.
  6. Chat Memory History Integration: Maintains context by referencing previous interactions.
  7. DeepSeek-7B Generation: Produces answers based on the best-matched document chunks.

🔹 Why This Upgrade?

Feature Previous Version New Version
Retrieval Method Hybrid (BM25 + FAISS) Hybrid + GraphRAG
Contextual Understanding Limited Enhanced with Knowledge Graphs
User Interface Standard Dark Theme with Customizable Sidebar
Chat History Not Utilized Integrated for Contextual Responses
Error Handling Basic Improved with Bug Fixes

📌 Common Issues & Fixes

💡 Issue: Error when clearing chat history.
✅ Fix: Ensure you're using the latest version of Streamlit, as st.experimental_rerun() has been updated.

pip install --upgrade streamlit

📌 Contributing

🚀 Want to improve this chatbot? Feel free to fork this repo, submit pull requests, or report issues!

🔗 Connect & Share Your Thoughts!

Got feedback or suggestions? Let’s discuss on Reddit! 🚀💡