Config reference

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Top-level Keys

• sources: connector list for ingestion jobs
• embedding: embedding provider + model config
• inference: LLM provider + model config for rephrase/chat tasks
• vector_store: chunking and pgvector/HNSW settings

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sources

- type: "s3" # or mediawiki / serpapi / etc connector type
  name: "account1"
  config:
    # connector-specific fields

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Common Connector Fields

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embedding

embedding:
  provider: local # local | openrouter | openai
  model_config: sentence-transformers/all-mpnet-base-v2
  embedding_dim: 768

• provider: embedding backend
• model_config: model id/name used by provider
• embedding_dim: vector dimension returned by model

WARNING: once embedding dimension is set, it cannot be changed without rebuilding the index.

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inference

inference:
  provider: openrouter # openrouter | openai
  model_config: openai/gpt-oss-20b:free

• provider: inference backend
• model_config: chat/rephrase model id

NOTE: inference backend is not used for vector search, only for the rephrase endpoint.

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vector_store

vector_store:
  table_name: embeddings
  hybrid_search: true
  chunk_size: 512
  chunk_overlap: 50
  hnsw:
    hnsw_m: 16
    hnsw_ef_construction: 64
    hnsw_ef_search: 40
    hnsw_dist_method: vector_cosine_ops

• table_name: embeddings table name
• hybrid_search: vector + keyword retrieval toggle
• chunk_size: chunk size for indexing
• chunk_overlap: overlap between adjacent chunks
• hnsw.hnsw_m: graph neighbors per node
• hnsw.hnsw_ef_construction: index build quality/speed tradeoff
• hnsw.hnsw_ef_search: search recall/speed tradeoff
• hnsw.hnsw_dist_method: vector distance operator