https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot
100 % FREE, Private (No Internet) DeepSeek’s Advanced RAG: Boost Your RAG Chatbot: Hybrid Retrieval (BM25 + FAISS) + Neural Reranking + HyDe🚀
https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot
Last synced: about 1 month ago
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100 % FREE, Private (No Internet) DeepSeek’s Advanced RAG: Boost Your RAG Chatbot: Hybrid Retrieval (BM25 + FAISS) + Neural Reranking + HyDe🚀
- Host: GitHub
- URL: https://github.com/SaiAkhil066/DeepSeek-RAG-Chatbot
- Owner: SaiAkhil066
- License: mit
- Created: 2025-01-30T10:15:46.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-28T15:51:36.000Z (about 1 month ago)
- Last Synced: 2025-03-28T16:38:57.474Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 53.7 KB
- Stars: 1,383
- Watchers: 22
- Forks: 175
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚀 **DeepSeek RAG Chatbot 3.0 – Now with GraphRAG & Chat History Integration!**
**(100% Free, Private (No Internet), and Local PC Installation)**
[](https://www.youtube.com/watch?v=xDGLub5JPFE "Watch on YouTube")
🔥 **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:** 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**.
---
## **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**
You can install and run the **DeepSeek RAG Chatbot** in one of two ways:
1. **Traditional (Python/venv) Installation**
2. **Docker Installation** (ideal for containerized deployments)
---
## **1️⃣ Traditional (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/](https://ollama.com/)
2. **Pull the required models**:
```
ollama pull deepseek-r1:7b
ollama pull nomic-embed-text
```
*Note: If you want to use a different model, update `MODEL` or `EMBEDDINGS_MODEL` in your environment variables or `.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](http://localhost:8501)** to access the chatbot UI.
---
## **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](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=deepseek-r1:7b
- 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](http://localhost:8501)**.
### **But consider step A) for comfort..**
---
# **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.
---
## **🔹 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** |
---
## **📌 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.
---
### **🔗 Connect & Share Your Thoughts!**
Got feedback or suggestions? Let’s discuss on [**Reddit**](https://www.reddit.com/user/akhilpanja/)! 🚀💡
---
**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!_