Configuration

All configuration is done through environment variables. Variables are validated at startup; the server fails fast with a clear message if a required variable is missing.

Environment variables

Variable	Default	Description
MCP_API_KEY	(required in HTTP)	Bearer token for REST + MCP over HTTP. Ignored in stdio mode.
EMBEDDINGS_BASE_URL	http://localhost:11434/v1	OpenAI-compatible /v1/embeddings endpoint.
EMBEDDINGS_API_KEY	(none)	Bearer token for the embeddings API.
EMBEDDINGS_MODEL	bge-m3	Embedding model name.
EMBEDDINGS_DIMENSION	1024	Fixed vector dimension for the embedding column (bge-m3 = 1024). Used by the PostgreSQL backend to size the pgvector column.
KB_ROOT	./kbs (stdio) / /data/kbs (http)	Root directory for knowledge base folders.
SCAN_INTERVAL	300	Scan interval in seconds (0 = disabled). HTTP mode only.
PORT	8000	HTTP listen port. HTTP mode only.
DB_PATH	./rag.db (stdio) / /data/index/rag.db (http)	SQLite database path.
STORE_BACKEND	sqlite	sqlite (default, standalone) or postgres (requires a reachable Postgres).
DATABASE_URL	(none)	Postgres connection string. Takes precedence over the individual PG_* vars.
PG_HOST	localhost	Postgres host.
PG_PORT	5432	Postgres port.
PG_DATABASE	raghub	Postgres database name.
PG_USER	(none)	Postgres user.
PG_PASSWORD	(none)	Postgres password.
PG_SSL	false	Enable TLS for the Postgres connection.
CORS_ORIGINS	(none)	Allowed CORS origins (comma-separated). Empty = disables the CORS restriction.
RAG_TRANSPORT	stdio	stdio or http. The --http flag wins.
VERSION	1.0.0	Reported version.

Recommended embedding models

Model	Notes
bge-m3	Multilingual (FR/EN), 1024d, best open-source retrieval, CPU-friendly. Recommended.
nomic-embed-text	Lighter, English-focused.
text-embedding-3-small	OpenAI API.

Any OpenAI-compatible /v1/embeddings endpoint works — Ollama, Bifrost, OpenAI, LM Studio…

Storage backends

Two backends implement the same Store interface. SQLite is the default and runs standalone; PostgreSQL is opt-in.

SQLite (default)

A single file (DB_PATH) with FTS5 full-text search. No database server required — nothing else to start.

PostgreSQL (pgvector)

Opt-in for deployments that prefer a server-side store. Embeds the pgvector extension (vector column + ts_rank full-text search). Point the server at a reachable Postgres with the vector extension:

STORE_BACKEND=postgres \
DATABASE_URL=postgres://raghub:raghub@localhost:5432/raghub \
node dist/index.js

The schema and the vector extension are created idempotently on first connect. Set EMBEDDINGS_DIMENSION to match your model before ingesting — the column is sized once at migration time.

Tests : the PostgreSQL backend is exercised without any server via PGlite, a real Postgres engine compiled to WASM with the pgvector extension bundled, instantiated in-memory for the duration of the suite (see src/e2e/pgstore.e2e.test.ts).

Transport modes

• stdio (default): serve MCP tools on stdin/stdout for a local agent. No scan loop, no REST.
• http (--http / RAG_TRANSPORT=http): REST API + streamable-http MCP on PORT.

In stdio mode, logs go to stderr (stdout is reserved for JSON-RPC); there is no periodic scan.