REST API (raglight serve)

Deploy your RAG pipeline as a REST API without writing any code.

REST API — raglight serve

raglight serve starts a FastAPI server that exposes your RAG pipeline as a REST API. The entire configuration is driven by environment variables — no Python code required.

The interactive API documentation (Swagger UI) is automatically available at http://localhost:8000/docs once the server is running.

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Quick start

pip install raglight
raglight serve

The server starts on http://0.0.0.0:8000 by default and prints the active configuration on startup.

CLI options

Option	Default	Description
--host	0.0.0.0	Host to bind
--port	8000	Port to listen on
--reload	false	Enable auto-reload (development)
--workers	1	Number of Uvicorn worker processes

raglight serve --host 127.0.0.1 --port 8080 --workers 4

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Configuration

All settings are read from RAGLIGHT_* environment variables. Copy the example file and adjust the values:

cp examples/serve_example/.env.example .env
raglight serve

Variable	Default	Description
RAGLIGHT_LLM_MODEL	llama3	LLM model name
RAGLIGHT_LLM_PROVIDER	Ollama	LLM provider
RAGLIGHT_LLM_API_BASE	http://localhost:11434	LLM API base URL
RAGLIGHT_EMBEDDINGS_MODEL	all-MiniLM-L6-v2	Embeddings model name
RAGLIGHT_EMBEDDINGS_PROVIDER	HuggingFace	Embeddings provider
RAGLIGHT_EMBEDDINGS_API_BASE	http://localhost:11434	Embeddings API base URL
RAGLIGHT_PERSIST_DIR	./raglight_db	Local ChromaDB persistence directory
RAGLIGHT_COLLECTION	default	ChromaDB collection name
RAGLIGHT_K	5	Number of documents retrieved per query
RAGLIGHT_SYSTEM_PROMPT	(default prompt)	Custom system prompt for the LLM
RAGLIGHT_CHROMA_HOST	—	Remote Chroma host (leave unset for local)
RAGLIGHT_CHROMA_PORT	—	Remote Chroma port

Valid provider values match the Settings constants: Ollama, Mistral, OpenAI, LmStudio, GoogleGemini for LLMs — and HuggingFace, Ollama, OpenAI, GoogleGemini for embeddings.

Example: Mistral + remote Chroma

.env

RAGLIGHT_LLM_PROVIDER=Mistral
RAGLIGHT_LLM_MODEL=mistral-small-latest
RAGLIGHT_EMBEDDINGS_PROVIDER=HuggingFace
RAGLIGHT_EMBEDDINGS_MODEL=all-MiniLM-L6-v2
RAGLIGHT_CHROMA_HOST=chromadb
RAGLIGHT_CHROMA_PORT=8000
RAGLIGHT_COLLECTION=production

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Endpoints

GET /health

Returns the server status.

curl http://localhost:8000/health
# {"status": "ok"}

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POST /generate

Ask a question to the RAG pipeline.

Request body

{ "question": "What is RAGLight?" }

Response

{ "answer": "RAGLight is a lightweight Python framework for RAG..." }

curl -X POST http://localhost:8000/generate \
  -H "Content-Type: application/json" \
  -d '{"question": "What is RAGLight?"}'

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POST /ingest

Index documents into the vector store. Supports three sources — combinable in a single call.

Request body

{
  "data_path": "./my_documents",
  "file_paths": ["/absolute/path/to/file.pdf"],
  "github_url": "https://github.com/Bessouat40/RAGLight",
  "github_branch": "main"
}

All fields are optional, but at least one must be provided.

Local folder

curl -X POST http://localhost:8000/ingest \
  -H "Content-Type: application/json" \
  -d '{"data_path": "./docs"}'

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POST /ingest/upload

Upload files directly from the client via multipart/form-data. Use this when the server and the files are on different machines.

The field name must be files. Multiple files can be sent in a single request.

curl -X POST http://localhost:8000/ingest/upload \
  -F "files=@./report.pdf" \
  -F "files=@./notes.txt"

Uploaded files are written to a temporary directory, processed, indexed into ChromaDB, then deleted. Nothing is stored permanently on disk besides the embeddings.

Response

{ "message": "Ingested 2 file(s): report.pdf, notes.txt" }

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GET /collections

List the available ChromaDB collections.

curl http://localhost:8000/collections
# {"collections": ["default", "project_x"]}

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Deploy with Docker Compose

The fastest path to a production deployment:

1. Copy the example env file

   cd examples/serve_example
   cp .env.example .env
   

   Edit .env and set your LLM provider, model, and any API keys.

2. Start the stack

   docker-compose up
   

   The API is available at http://localhost:8000.

3. Ingest your documents

   curl -X POST http://localhost:8000/ingest/upload \
     -F "files=@./my_document.pdf"
   
4. Query the API

   curl -X POST http://localhost:8000/generate \
     -H "Content-Type: application/json" \
     -d '{"question": "Summarize the document"}'
   

The docker-compose.yml includes extra_hosts: host.docker.internal:host-gateway so the container can reach an Ollama instance running on the host machine.

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Summary

Endpoint	Description
GET /health	Server liveness check
POST /generate	Ask a question to the RAG pipeline
POST /ingest	Index a folder, file paths, or GitHub repo
POST /ingest/upload	Upload files directly (multipart/form-data)
GET /collections	List available ChromaDB collections