---
license: apache-2.0
base_model: edgeimpulse/edgeimpulse-docs-qwen-0.5b
pipeline_tag: text-generation
library_name: transformers
tags:
  - edge-impulse
  - rag
  - retrieval-augmented-generation
  - faiss
  - qwen
  - documentation
  - tinyml
  - edge-ai
---

# Edge Impulse Docs — RAG Assistant

A retrieval-augmented assistant for the [Edge Impulse](https://edgeimpulse.com)
documentation. It grounds every answer in a prebuilt vector index of the docs and
generates with the small quantized model
[`edgeimpulse/edgeimpulse-docs-qwen-0.5b`](https://huggingface.co/edgeimpulse/edgeimpulse-docs-qwen-0.5b),
so it runs comfortably on a laptop.

- **Retrieval:** FAISS (inner-product) over `data/index`, embedded with
  `sentence-transformers/all-MiniLM-L6-v2` (384-dim, the same model the index was
  built with).
- **Generation:** the 0.5B GGUF, served through any OpenAI-compatible endpoint
  (llama.cpp `llama-server` or Ollama). No training stack required.
- **Grounded + cited:** answers are constrained to the retrieved context and each
  response lists its source documents.

This repo ships only what you need to **run** the assistant — the prebuilt index
and the inference code. The raw document corpus and the index-building pipeline
are not included.

## Contents

| File | Purpose |
| --- | --- |
| `data/index/edge_impulse_docs.faiss` | FAISS inner-product index of the docs |
| `data/index/chunks.pkl` | Chunk text + source metadata (aligned to the index) |
| `data/index/metadata.json` | Embedding model + index parameters |
| `rag.py` | Retrieval + grounded generation (CLI + importable) |
| `serve.py` | Minimal Flask HTTP API (`POST /ask`) |
| `requirements.txt` | Runtime dependencies |

## Quickstart

**1. Install dependencies and download this repo**

```bash
pip install -r requirements.txt
hf download edgeimpulse/edgeimpulse-docs-rag --local-dir edgeimpulse-docs-rag
cd edgeimpulse-docs-rag
```

**2. Start the generator** (pick one)

llama.cpp:

```bash
hf download edgeimpulse/edgeimpulse-docs-qwen-0.5b qwen-edgeai-q4_k_m.gguf --local-dir .
llama-server -m qwen-edgeai-q4_k_m.gguf -c 4096 --port 8080 --jinja
```

Ollama:

```bash
ollama run hf.co/edgeimpulse/edgeimpulse-docs-qwen-0.5b
# then point rag.py at Ollama's OpenAI-compatible port:
export RAG_API_BASE=http://127.0.0.1:11434/v1
export RAG_MODEL=hf.co/edgeimpulse/edgeimpulse-docs-qwen-0.5b
```

**3. Ask a question**

```bash
python rag.py "How do I deploy a model to run on a Linux target as an .eim file?"
```

Only see what was retrieved (no generation):

```bash
python rag.py "How do I create an API key?" --no-generate
```

Serve it over HTTP:

```bash
python serve.py --host 0.0.0.0 --port 8000
curl -s localhost:8000/ask -H 'content-type: application/json' \
  -d '{"question": "What is the data forwarder?"}'
```

## Configuration

`rag.py` reads these environment variables (all optional):

| Variable | Default | Meaning |
| --- | --- | --- |
| `RAG_INDEX_DIR` | `data/index` | Location of the FAISS index + chunks |
| `RAG_API_BASE` | `http://127.0.0.1:8080/v1` | OpenAI-compatible generation endpoint |
| `RAG_MODEL` | `edgeimpulse/edgeimpulse-docs-qwen-0.5b` | Model name passed to the endpoint |

## How it works

```
question ──▶ MiniLM embed ──▶ FAISS top-k ──▶ context + question
                                                    │
                                                    ▼
                             edgeimpulse-docs-qwen-0.5b (llama.cpp / Ollama)
                                                    │
                                                    ▼
                                    grounded answer + cited sources
```

The generator is a small model, so retrieval quality matters: the assistant is
most accurate when the right chunk is retrieved, and it may be terse or repeat
itself on out-of-scope questions. Sampling defaults (`temperature 0.3`,
`repeat_penalty 1.2`) are tuned to keep it from looping.

## Related

- Generator model: [`edgeimpulse/edgeimpulse-docs-qwen-0.5b`](https://huggingface.co/edgeimpulse/edgeimpulse-docs-qwen-0.5b)
- API-scoped variant: [`edgeimpulse/edgeimpulse-api-docs-rag`](https://huggingface.co/edgeimpulse/edgeimpulse-api-docs-rag)

## License

Apache-2.0. Documentation content belongs to Edge Impulse.