---
license: apache-2.0
tags:
  - edge-impulse
  - rag
  - qwen
  - faiss
  - documentation
  - tinyml
pipeline_tag: text-generation
---

# Edge Impulse Docs RAG for Qwen

This workspace builds a local Retrieval-Augmented Generation index over the Edge Impulse docs, then queries it with a small Qwen model. It is designed as the training/prototyping repo for an Edge Impulse docs-aware assistant that can later be plugged into the Hugging Face MCP demo or another agent.

## Quick Start

```powershell
cd C:\Users\Eoin\git\edgeimpulse=docs-rag-qwen
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -r requirements.txt
python -m ipykernel install --user --name edgeimpulse-docs-rag-qwen --display-name "Edge Impulse Docs RAG Qwen"
```

Fetch the docs:

```powershell
python scripts\fetch_docs.py --source https://docs.edgeimpulse.com/llms.txt
```

Build the FAISS index. This includes Markdown, text, and PDF files under `data/raw_docs`, including `data/raw_docs/pdfs/*.pdf`:

```powershell
python scripts\build_index.py --docs data\raw_docs --out data\index
```

Ask a question:

```powershell
python scripts\ask.py "How do I create an Edge Impulse JWT token?"
python scripts\ask.py "How do I start a training job from the Studio API?"
```

Run a local API:

```powershell
python scripts\serve.py --host 127.0.0.1 --port 8080
```

Then query it:

```powershell
curl -X POST http://127.0.0.1:8080/ask -H "Content-Type: application/json" -d "{\"question\":\"How do I deploy to Arduino?\"}"
```

## Notebook

Open [notebooks/edge_impulse_docs_rag_qwen.ipynb](notebooks/edge_impulse_docs_rag_qwen.ipynb) and select the `Edge Impulse Docs RAG Qwen` kernel.

The notebook runs the same workflow as the scripts:

1. Fetch Edge Impulse docs into `data/raw_docs`.
2. Chunk and embed Markdown/text docs and PDFs with `sentence-transformers/all-MiniLM-L6-v2`.
3. Save a FAISS cosine-similarity index in `data/index`.
4. Retrieve relevant chunks for a question.
5. Generate a grounded answer with Qwen.

## Defaults

- Base model: `Qwen/Qwen2.5-Coder-0.5B-Instruct`
- Optional LoRA adapter: set `--adapter eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct`
- Embedding model: `sentence-transformers/all-MiniLM-L6-v2`
- Index: FAISS `IndexFlatIP` with L2-normalized vectors
- Docs source: `https://docs.edgeimpulse.com/llms.txt`

## Notes

RAG does not train the model weights. It trains/builds the retrieval index. If you later fine-tune a Qwen adapter, keep this RAG layer anyway so the assistant can answer from current docs and PDFs without a new model training run.