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https://github.com/sudarshan-koirala/RAG-chat-with-documents

Chainlit app for advanced RAG. Uses llamaparse, langchain, qdrant and models from groq.
https://github.com/sudarshan-koirala/RAG-chat-with-documents

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Chainlit app for advanced RAG. Uses llamaparse, langchain, qdrant and models from groq.

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# RAG-chat-with-documents
Chainlit app for advanced RAG. Uses llamaparse, langchain, qdrant and models from groq.

## Videos covering these topics
### [Llamaparse LlamaIndex](https://youtu.be/wRMnHbiz5ck?si=iQZV7N6-trcuBm8M)
### [Llamaparse Qdrant Gorq](https://youtu.be/w7Ap6gZFXl0?si=05AUGmRp1quTdeZl)
### [RAG With LlamaParse from LlamaIndex & LangChain 🚀](https://youtu.be/f9hvrqVvZl0?si=qnJBsAZD4hBUweiS)

### Links shown in video
- [LlamaCloud](https://cloud.llamaindex.ai/)
- [Qdrant Cloud](https://cloud.qdrant.io/)
- [Groq Cloud](https://console.groq.com/)

### create virtualenv
```
python3 -m venv .venv && source .venv/bin/activate
```

### Install packages
```
pip install -r requirements.txt
```

### Environment variables
All env variables goes to .env ( cp `example.env` to `.env` and paste required env variables)

### Run the python files (following the video to run step by step is recommended)
```
python3 ingest.py
chainlit run app.py
```

## Additional helper documents
- [LlamaIndex blogpost about Llamaparse](https://www.llamaindex.ai/blog/launching-the-first-genai-native-document-parsing-platform)
- [Advanced demo with Reranker](https://github.com/run-llama/llama_parse/blob/main/examples/demo_advanced.ipynb)
- [Parsing instructions Llamaparse](https://colab.research.google.com/drive/1dO2cwDCXjj9pS9yQDZ2vjg-0b5sRXQYo#scrollTo=dEX7Mv9V0UvM)