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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use edgeimpulse/edgeimpulse-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-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-docs-rag",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use edgeimpulse/edgeimpulse-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-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-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-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-docs-rag",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

edgeimpulse-docs-rag / serve.py

eoinedge

Add full Edge Impulse docs RAG assistant (prebuilt index + inference)

8c14673 verified 5 days ago

Raw

History Blame Contribute Delete

2.04 kB

	"""Minimal HTTP server for the Edge Impulse docs RAG assistant.

	python serve.py --host 0.0.0.0 --port 8000

	POST /ask {"question": "...", "k": 4} -> {"answer": "..."}
	GET /health -> {"ok": true}
	"""
	from __future__ import annotations

	import argparse
	from pathlib import Path

	from flask import Flask, jsonify, request

	from rag import DEFAULT_API_BASE, DEFAULT_INDEX_DIR, DEFAULT_MODEL, ask


	def create_app(index_dir: Path, api_base: str, model: str, k: int) -> Flask:
	app = Flask(__name__)

	@app.get("/health")
	def health():
	return jsonify({"ok": True})

	@app.post("/ask")
	def ask_route():
	payload = request.get_json(silent=True) or {}
	question = str(payload.get("question", "")).strip()
	if not question:
	return jsonify({"error": "question is required"}), 400
	try:
	answer = ask(
	question,
	index_dir=index_dir,
	k=int(payload.get("k", k)),
	max_new_tokens=int(payload.get("max_new_tokens", 320)),
	api_base=api_base,
	model=model,
	)
	return jsonify({"answer": answer})
	except Exception as exc: # noqa: BLE001 - surface the error to the client
	return jsonify({"error": str(exc)}), 500

	return app


	def main() -> None:
	parser = argparse.ArgumentParser(description="Serve the Edge Impulse docs RAG assistant.")
	parser.add_argument("--host", default="127.0.0.1")
	parser.add_argument("--port", type=int, default=8000)
	parser.add_argument("--index-dir", type=Path, default=DEFAULT_INDEX_DIR)
	parser.add_argument("--api-base", default=DEFAULT_API_BASE)
	parser.add_argument("--model", default=DEFAULT_MODEL)
	parser.add_argument("--k", type=int, default=4)
	args = parser.parse_args()
	app = create_app(args.index_dir, args.api_base, args.model, args.k)
	app.run(host=args.host, port=args.port)


	if __name__ == "__main__":
	main()