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

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

🔥 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.

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🔹 New Features in This Version

• GraphRAG Integration: Builds a Knowledge Graph from your documents for more contextual and relational understanding.
• Chat Memory History Awareness: Maintains context by referencing 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.

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Upcoming Features

You can select a model from the UI interface (any Ollama model).

Example: Users can choose between models like mistral, gemma, or llama3 from a dropdown menu.

The chat section suggests relevant questions based on the document.

Example: If the document is about "Machine Learning Basics," suggested questions could be: What is supervised learning? How does gradient descent work? What are the common evaluation metrics for ML models?

Different pipelines are available for various RAG (Retrieval-Augmented Generation) methods.

Example: Basic RAG Pipeline: Uses FAISS for retrieval and a simple prompt format. Advanced RAG Pipeline: Uses ChromaDB with metadata filtering for more precise document retrieval.

Different data stores and similarity search techniques are used.

Example: Data Stores: PostgreSQL, Pinecone, Weaviate, ChromaDB. Similarity Search Techniques: Cosine similarity, Euclidean distance, Jaccard similarity.

Installation & Setup*

There are a few ways to install and run the DeepSeek RAG Chatbot:

1. Simplified Installation (Recommended using install.sh)
2. Traditional (Manual Python/venv) Installation
3. Docker Installation (ideal for containerized deployments)

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1️⃣ Simplified Installation (Recommended using install.sh)

This is the easiest way to get started. The install.sh script automates the setup process.

1. Clone the Repository:

   git clone https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot.git
   cd DeepSeek-RAG-Chatbot

2. Run the Installation Script: Make sure the script is executable, then run it:

   chmod +x install.sh
   ./install.sh

   This script will:

   • Check for Ollama and install it if it's not found.
   • Install Python dependencies from requirements.txt.
   • Create or update a .env file with the necessary environment variables, including the model huihui-ai/Qwen3-1.7B-abliterated.
   • Pull the specified Ollama model.

3. Activate Environment Variables: After the script completes, source the .env file to load the environment variables into your current shell session:

   source .env

4. Run the Chatbot: Launch the Streamlit app:

   streamlit run app.py

   Open your browser at http://localhost:8501 to access the chatbot UI.

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2️⃣ Traditional (Manual Python/venv) Installation

Step A: Clone the Repository & Install Dependencies

git clone https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot.git
cd DeepSeek-RAG-Chatbot

# Create a virtual environment
python -m venv venv

# Activate your environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Upgrade pip (optional, but recommended)
pip install --upgrade pip

# Install project dependencies
pip install -r requirements.txt

Step B: Download & Set Up Ollama

1. Download Ollama → https://ollama.com/
2. Pull the required models:

   ollama pull huihui-ai/Qwen3-1.7B-abliterated 
   ollama pull nomic-embed-text
   

   Note: The install.sh script handles model pulling automatically. If installing manually and you want to use a different model, update MODEL or EMBEDDINGS_MODEL in your environment variables or create a .env file accordingly.

Step C: Run the Chatbot

1. Make sure Ollama is running on your system:

   ollama serve
   

2. Launch the Streamlit app:

   streamlit run app.py
   

3. Open your browser at http://localhost:8501 to access the chatbot UI.

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2️⃣ Docker Installation

A) Single-Container Approach (Ollama on Your Host)

If Ollama is already installed on your host machine and listening at localhost:11434, do the following:

1. Build & Run:

   docker-compose build
   docker-compose up
   

2. The app is now served at http://localhost:8501. Ollama runs on your host, and the container accesses it via the specified URL.

B) Two-Container Approach (Ollama in Docker)

If you prefer everything in Docker:

version: "3.8"

services:
  ollama:
    image: ghcr.io/jmorganca/ollama:latest
    container_name: ollama
    ports:
      - "11434:11434"

  deepgraph-rag-service:
    container_name: deepgraph-rag-service
    build: .
    ports:
      - "8501:8501"
    environment:
      - OLLAMA_API_URL=http://ollama:11434
      - MODEL=huihui-ai/Qwen3-1.7B-abliterated
      - EMBEDDINGS_MODEL=nomic-embed-text:latest
      - CROSS_ENCODER_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
    depends_on:
      - ollama

Then:

docker-compose build
docker-compose up

Both Ollama and the chatbot run in Docker. Access the chatbot at http://localhost:8501.

But consider step A) for comfort..

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How the Chatbot Works

1. Upload Documents: Add PDFs, DOCX, or TXT files via the sidebar.
2. Hybrid Retrieval: Combines BM25 and FAISS to fetch the most relevant text chunks.
3. GraphRAG Processing: Builds a Knowledge Graph from your documents to understand relationships and context.
4. Neural Reranking: Uses a Cross-Encoder model for reordering the retrieved chunks by relevance.
5. Query Expansion (HyDE): Generates hypothetical answers to expand your query for better recall.
6. Chat Memory History Integration: Maintains context by referencing previous user messages.
7. DeepSeek-7B Generation: Produces the final answer based on top-ranked chunks.

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🔹 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	Customizable + Themed Sidebar
Chat History	Not Utilized	Full Memory Integration
Error Handling	Basic	Improved with Bug Fixes

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📌 Contributing

• Fork this repo, submit pull requests, or open issues for new features or bug fixes.
• We love hearing community suggestions on how to extend or improve the chatbot.

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🔗 Connect & Share Your Thoughts!

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

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Enjoy building knowledge graphs, maintaining conversation memory, and harnessing powerful local LLM inference—all from your own machine.
The future of retrieval-augmented AI is here—no internet required!