Instructions to use edgeimpulse/edgeimpulse-api-docs-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edgeimpulse/edgeimpulse-api-docs-rag with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="edgeimpulse/edgeimpulse-api-docs-rag")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("edgeimpulse/edgeimpulse-api-docs-rag", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use edgeimpulse/edgeimpulse-api-docs-rag with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "edgeimpulse/edgeimpulse-api-docs-rag"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edgeimpulse/edgeimpulse-api-docs-rag",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/edgeimpulse/edgeimpulse-api-docs-rag

SGLang

How to use edgeimpulse/edgeimpulse-api-docs-rag with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "edgeimpulse/edgeimpulse-api-docs-rag" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edgeimpulse/edgeimpulse-api-docs-rag",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "edgeimpulse/edgeimpulse-api-docs-rag" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edgeimpulse/edgeimpulse-api-docs-rag",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use edgeimpulse/edgeimpulse-api-docs-rag with Docker Model Runner:
```
docker model run hf.co/edgeimpulse/edgeimpulse-api-docs-rag
```

edgeimpulse-api-docs-rag / README.md

eoinedge

Add API-docs-scoped Edge Impulse RAG assistant (prebuilt index + inference)

4de1825 verified 4 days ago

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	---
	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
	- api
	- documentation
	- tinyml
	- edge-ai
	---

	# Edge Impulse API Docs — RAG Assistant

	A focused retrieval-augmented assistant for the Edge Impulse API reference
	(Studio API, ingestion API, remote-management API). Same runtime as the full
	[`edgeimpulse/edgeimpulse-docs-rag`](https://huggingface.co/edgeimpulse/edgeimpulse-docs-rag)
	assistant, but the knowledge base is restricted to the API documentation so
	retrieval stays tightly on-topic for integration and automation questions.

	- Scope: Edge Impulse API reference only (`apis`, `apis_studio`,
	`apis_ingestion`, `apis_remote-management`).
	- Retrieval: FAISS (inner-product) over `data/index`, embedded with
	`sentence-transformers/all-MiniLM-L6-v2` (384-dim).
	- Generation: [`edgeimpulse/edgeimpulse-docs-qwen-0.5b`](https://huggingface.co/edgeimpulse/edgeimpulse-docs-qwen-0.5b)
	via any OpenAI-compatible endpoint (llama.cpp or Ollama).

	This repo ships only the prebuilt (API-scoped) index and the inference code — no
	raw corpus and no index-building pipeline.

	## Contents

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

	## Quickstart

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

	# start the generator (llama.cpp)
	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

	# ask an API question
	python rag.py "How do I create an Edge Impulse API key?"
	python rag.py "How do I upload data with the ingestion API?" --no-generate
	```

	Serve 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": "How do I classify an image via the API?"}'
	```

	## Configuration

	`rag.py` honours `RAG_INDEX_DIR`, `RAG_API_BASE`, and `RAG_MODEL` (see the full
	assistant's card for details). For Ollama:

	```bash
	export RAG_API_BASE=http://127.0.0.1:11434/v1
	export RAG_MODEL=hf.co/edgeimpulse/edgeimpulse-docs-qwen-0.5b
	```

	## Notes

	Because the corpus is intentionally narrow, questions outside the API reference
	may return "not in context" — that is by design. For general docs questions
	(devices, deployment targets, ML concepts) use the full
	[`edgeimpulse/edgeimpulse-docs-rag`](https://huggingface.co/edgeimpulse/edgeimpulse-docs-rag)
	assistant instead.

	## License

	Apache-2.0. Documentation content belongs to Edge Impulse.