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
```

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 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 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.

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

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:

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:

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 assistant instead.