https://github.com/astrabert/ragcoon
Agentic RAG to help you build a startup🚀
https://github.com/astrabert/ragcoon
agentic-rag groq llamaindex mesop qdrant qwq-32b rag startup
Last synced: 7 months ago
JSON representation
Agentic RAG to help you build a startup🚀
- Host: GitHub
- URL: https://github.com/astrabert/ragcoon
- Owner: AstraBert
- License: mit
- Created: 2025-03-11T18:20:28.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-11T22:24:53.000Z (about 1 year ago)
- Last Synced: 2025-03-13T18:51:14.956Z (about 1 year ago)
- Topics: agentic-rag, groq, llamaindex, mesop, qdrant, qwq-32b, rag, startup
- Language: Python
- Homepage:
- Size: 352 KB
- Stars: 10
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
RAGcoon🦝
Agentic RAG to help you build your startup
If you find RAGcoon userful, please consider to donate and support the project:
## Install and launch🚀
The first step, common to both the Docker and the source code setup approaches, is to clone the repository and access it:
```bash
git clone https://github.com/AstraBert/ragcoon.git
```
Once there, you can choose one of the two following approaches:
### Docker (recommended)🐋
> _Required: [Docker](https://docs.docker.com/desktop/) and [docker compose](https://docs.docker.com/compose/)_
- Add the `groq_api_key` in the [`.env.example`](.env.example)file and modify the name of the file to `.env`. Get this key:
+ [On Groq Console](https://console.groq.com/keys)
```bash
mv .env.example .env
```
- Launch the Docker application:
```bash
# If you are on Linux/macOS
bash start_services.sh
# If you are on Windows
.\start_services.ps1
```
Or, if you prefer:
```bash
docker compose up qdrant -d
docker compose up frontend -d
docker compose up backend -d
```
You will see the frontend application running on `http://localhost:8001`/ and you will be able to use it. Depending on your connection and on your hardware, the set up might take some time (up to 30 mins to set up) - but this is only for the first time your run it!
### Source code🖥️
> _Required: [Docker](https://docs.docker.com/desktop/), [docker compose](https://docs.docker.com/compose/) and [conda](https://anaconda.org/anaconda/conda)_
- Add the `groq_api_key` in the [`.env.example`](.env.example)file and modify the name of the file to `.env` in the `scripts` folder. Get this key:
+ [On Groq Console](https://console.groq.com/keys)
```bash
mv .env.example scripts/.env
```
- Set up RAGcoon using the dedicated script:
```bash
# For MacOs/Linux users
bash setup.sh
# For Windows users
.\setup.ps1
```
- Or you can do it manually, if you prefer:
```bash
docker compose up qdrant -d
conda env create -f environment.yml
conda activate ragcoon
```
- Now launch the frontend application
```bash
gunicorn frontend:me --bind 0.0.0.0:8001
```
- And then, from another terminal window, go into `scripts` and launch the backend:
```bash
uvicorn main:app --host 0.0.0.0 --port 8000
```
You will see the application running on `http://localhost:8001` and you will be able to use it.
## How it works
The main workflow is handled by a Query Agent, built upon the ReAct architecture. The agent exploits, as a base LLM, the latest reasoning model by Qwen, i.e. [`QwQ-32B`](https://console.groq.com/docs/model/qwen-qwq-32b), provisioned by [Groq](https://groq.com/).
1. The question coming from the frontend (developed with [Mesop](https://google.github.io/mesop/) - running on `http://localhost:8001`) is sent into a POST request to the [FastAPI](https://fastapi.tiangolo.com/)-managed API endpoint on `http://localhost:8000/chat`.
2. When the Agent is prompted with the user's question, it tries to retrieved relevant context routing the query to one of three query engines:
- If the query is simple and specific, it goes for a direct hybrid retrieval, exploiting both a dense ([`Alibaba-NLP/gte-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)) and a sparse ([`Qdrant/bm25`](https://huggingface.co/Qdrant/bm25)) retriever
- If the query is general and vague, it first creates an hypothetical document, which is embedded and used for retrieval
- If the query is complex and involves searching for nested information, the query is decomposed into several sub-queries, and the retrieval is performed for all of them, with a summarization step in the end
3. The agent evaluates the context using [`llama-3.3-70B-versatile`](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md), provisioned through Groq. If the context is deemed relevant, the Agent proceeds, otherwise it goes back to retrieval, trying a different method.
4. The agent produces a candidate answer
5. The agent evaluates the faithfulness and relevancy of the candidate response, in light of the retrieved context, using [LlamaIndex evaluation methods](https://docs.llamaindex.ai/en/stable/module_guides/evaluating/)
6. If the response is faithful and relevant, the agent returns the response, otherwise it gets back at generating a new one.
## Contributing
Contributions are always welcome! Follow the contributions guidelines reported [here](CONTRIBUTING.md).
## License and rights of usage
The software is provided under MIT [license](./LICENSE).