https://github.com/azeemk210/pdf-rag-chatbot
This repository contains a Retrieval-Augmented Generation (RAG) chatbot powered by DeepSeek and Ollama, designed to process PDF documents and provide intelligent answers based on uploaded content.
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.5%) to scientific vocabulary
Repository
This repository contains a Retrieval-Augmented Generation (RAG) chatbot powered by DeepSeek and Ollama, designed to process PDF documents and provide intelligent answers based on uploaded content.
Basic Info
- Host: GitHub
- Owner: azeemk210
- Language: Python
- Default Branch: master
- Size: 6.84 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
📚 Local DeepSeek RAG
🚀 Installation & Setup Guide
This guide explains how to install, set up, and use the Local DeepSeek RAG system on your machine.
📌 1. Prerequisites
- Python 3.10+
- Git
- Conda (Optional but recommended)
🔧 2. Clone the Repository
```bash
Clone the repo
git clone https://github.com/azeemk210/pdf-rag-chatbot.git cd pdf-rag-chatbot ```
🛠 3. Create and Activate Virtual Environment
Using Conda (Recommended)
bash
conda create --name deepseek_rag python=3.10 -y
conda activate deepseek_rag
Using Virtualenv (Alternative)
bash
python -m venv deepseek_rag
source deepseek_rag/bin/activate # On macOS/Linux
deepseek_rag\Scripts\activate # On Windows
🔽 4. Install Dependencies
bash
pip install -r requirements.txt
🏗 5. Install & Set Up Ollama
Step 1: Download & Install Ollama
- Windows/macOS/Linux: Download Ollama from the official site:
Step 2: Download llama2 model
bash
ollama run llama2
Step 3: Start Ollama Server in Terminal 1
bash
ollama serve
⚡ 6. Run the Application in Terminal 2
bash
python localrag.py
- Open the Gradio UI in your browser:
http://127.0.0.1:7860 - Upload PDFs and start asking questions!
📝 7. Usage
Uploading Documents
- Click on "📂 Upload PDF Documents" and select your files.
- Click Initialize System to process the documents.
Asking Questions
- Enter a question related to the document in the text box.
- Click Ask 🤖 and view the response in the chat.
Downloading Conversation History
- Click 💾 Download Chat History to save your Q&A session.
❌ 8. Troubleshooting
Ollama Not Connecting?
bash
$env:OLLAMA_HOST="http://127.0.0.1:11436"
ollama serve
Ensure it's running and accessible at http://127.0.0.1:11436 or update:
```python
uncomment this line in localrag.py
os.environ["OLLAMA_HOST"] = "http://127.0.0.1:11436"
```
Wrong Document Answers?
- Delete old embeddings:
bash rm -r chroma_db - Restart the system and re-upload PDFs.
🤝 Contributing
Feel free to fork this repository and submit pull requests!
Owner
- Name: azeemk210
- Login: azeemk210
- Kind: user
- Repositories: 1
- Profile: https://github.com/azeemk210
GitHub Events
Total
- Push event: 3
- Fork event: 1
- Create event: 2
Last Year
- Push event: 3
- Fork event: 1
- Create event: 2
Dependencies
- chroma-hnswlib ==0.7.6
- chromadb ==0.6.3
- fastapi ==0.115.8
- gradio ==5.15.0
- langchain ==0.3.17
- langchain-community ==0.3.16
- langchain-ollama ==0.2.3
- numpy >=1.22.5,<2.0
- ollama ==0.4.7
- pandas ==2.2.3
- pypdf ==5.2.0
- python-dotenv ==1.0.1
- requests ==2.32.3
- rich ==13.9.4
- tenacity ==9.0.0
- typing-extensions ==4.12.2
- uvicorn ==0.34.0