https://github.com/aghoshpro/chatdocument
Chat with any document Text files (.txt), PDF files (.pdf), Word documents (.docx), Word documents (.doc), JSON files (.json), GeoJSON files (.geojson) using RAG
https://github.com/aghoshpro/chatdocument
chatbot chroma embeddings langchain llms ollam rag retrieval-augmented-generation vector-database
Last synced: 6 months ago
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Chat with any document Text files (.txt), PDF files (.pdf), Word documents (.docx), Word documents (.doc), JSON files (.json), GeoJSON files (.geojson) using RAG
- Host: GitHub
- URL: https://github.com/aghoshpro/chatdocument
- Owner: aghoshpro
- License: mit
- Created: 2025-02-09T19:10:41.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-02-17T00:42:58.000Z (11 months ago)
- Last Synced: 2025-02-17T01:27:00.378Z (11 months ago)
- Topics: chatbot, chroma, embeddings, langchain, llms, ollam, rag, retrieval-augmented-generation, vector-database
- Language: Python
- Homepage:
- Size: 3.67 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ChatDocument
Retrieval Augmented Generation (RAG) application that allows you to chat with any of your local documents in disparate formats e.g., `.txt`,`.pdf`, `.md`, `.docx`, `.doc`, `.json`,`.geojson` using Ollama LLMs and LangChain. Upload your document in the Streamlit Web UI for Q&A interaction. Have fun
## 📂 Project Structure
```
├── .streamlit/
│ └── config.toml # Streamlit configuration (OPTIONAL)
├── assets/
│ └── ui.png # Streamlit UI image
├── components/
│ ├── __init__.py
│ ├── chat.py # Chat interface implementation
│ └── upload.py # Document upload handling
├── core/
│ ├── __init__.py
│ ├── embeddings.py # Vector embeddings configuration
│ └── llm.py # Language model setup
├── data/
│ ├── vector_store/ # To store vector embeddings in chromadb
│ └── sample_docs/ # Sample documents for testing
├── utils/
│ ├── __init__.py
│ └── helpers.py # Utility functions
└── main.py # Application entry point
```
## 📚 RAG Architecture
## ✨ Features
- 📄 Multi document (`.txt`, `.pdf`, `.md`, `.docx`, `.doc`, `.json`) processing with intelligent chunking
- 🧠 Multi-query retrieval for better context understanding
- 🎯 Advanced RAG implementation using LangChain and Ollama
- 🔒 Complete local data processing - no data leaves your machine
- 📓 Jupyter notebook for experimentation
- 🖥️ Clean Streamlit UI
## 🚀 Getting Started
### 1. **Install Ollama**
- Visit [Ollama.ai](https://ollama.com) to download Ollama and install
- Open `cmd` or `terminal` and run `ollama`
- Install LLM models (locally):
- Start with `ollama pull llama3.2` as it's low sized (4GB) basic llm model tailored for general usecases
- For vector embeddings pull the following,
```bash
ollama pull mxbai-embed-large # or `nomic-embed-text`
```
- Chat with the model in `terminal`,
```bash
ollama run llama3.2 # or your preferred model
```
- Go to [Ollama Models](https://ollama.com/search) to search and pull other famous models as follows,
```bash
ollama pull dolphin3
ollama pull deepseek-r1:8b
ollama pull mistral
```
- Check the list of locally available ollama models:
```bash
ollama list
```
### 2. **Clone Repository**
- Open `cmd` or `terminal` and navigate to your preferred directory, then run the following,
```bash
git clone https://github.com/aghoshpro/ChatDocument.git
```
- Go to the ChatDocument folder using `cd ChatDocument`
### 3. **Set Up Local Environment**
- Create a virtual environment `myvenv` inside the `./ChatDocument` folder and activate it:
```bash
python -m venv myvenv
```
```bash
# Windows
.\myvenv\Scripts\activate # OR source myvenv/bin/activate (in Linux or Mac)
```
- Install dependencies:
```bash
pip install --upgrade -r requirements.txt
```
- 🧪 Experiment with code in `*.ipynb`
```sh
jupyter notebook
```
## 🕹️ Run
```bash
streamlit run main.py
```
- Select `llama3.2` as the model and start chatting.
- Content View
- WordCloud View:
## 🛠 Troubleshooting
- Ensure Ollama is running in the background
- GPU preferred for good performance if not CPU (will be slower)
- `./data/sample_docs` contains few sample documents for you to test
- Use `pip list` or `pip freeze` to check currently installed packages
## ✨Theme Configuration
- Edit `.streamlit/config.toml` for your color preferences
```toml
[theme]
primaryColor = "#FF4B4B"
backgroundColor = "#0E1117"
secondaryBackgroundColor = "#262730"
textColor = "#FAFAFA"
font = "sans serif"
```
## 🤝 Contributing
- Open issues for bugs or suggestions
- Submit pull requests
## 📑 References
### Docs
- [LangChain](https://python.langchain.com/docs/index.html)
- [Ollama](https://ollama.com/docs/index.html)
- [ChromaDB](https://www.trychroma.com/)
- [Streamlit](https://docs.streamlit.io/)
- [Folium](https://python-visualization.github.io/folium/)
- [Unstructured](https://docs.unstructured.io/platform/supported-file-types)
- [ChromaDB Tutorial Step by Step Guide](https://www.datacamp.com/tutorial/chromadb-tutorial-step-by-step-guide)
- [ChromaDB Collections](https://docs.trychroma.com/docs/collections/create-get-delete)
### Blogs
- [Finding the Best Open Source Embedding Model for RAG](https://medium.com/timescale/finding-the-best-open-source-embedding-model-for-rag-929d1656d331)
- [Enhancing Retrieval Augmented Generation with ChromaDB and SQLite](https://medium.com/@dassandipan9080/enhancing-retrieval-augmented-generation-with-chromadb-and-sqlite-c499109f8082)
- [Implementing RAG in LangChain with Chroma](https://medium.com/@callumjmac/implementing-rag-in-langchain-with-chroma-a-step-by-step-guide-16fc21815339)
- [Build Your Own RAG and Run Them Locally](https://blog.duy.dev/build-your-own-rag-and-run-them-locally/)
### Stack Overflow
- [Langchain Ollama Module Difference](https://stackoverflow.com/questions/78921530/langchain-ollama-module-difference)