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Storage: PostgreSQL (chunk rows + manifests) and Neo4j

  • PostgreSQL

    Chunk rows with provenance, chunk summaries, caches, and the generation manifest.

  • Qdrant

    Per-corpus Qdrant generations hold the dense and sparse chunk vectors both retrieval legs query.

  • Neo4j

    Generation-scoped entities and relationships linked to chunks (FROM_CHUNK), plus GDS Leiden communities.

Get started Configuration API

One Postgres, Many Corpora

Partition by repo_id/corpus_id in schema to keep corpus isolation at the data layer.

TSConfig

indexing.postgres_ts_config resolves the text search config (simple or a stemmer language) based on tokenizer settings.

Neo4j Isolation

neo4j_database_mode=per_corpus avoids expensive cross-corpus filters but requires Enterprise Edition.

PostgreSQL Client Responsibilities

  • Upsert chunk rows with metadata, content, and provenance
  • Store the generation manifest (Qdrant collection + Neo4j graph id) and the dense/sparse contracts
  • Serve chunk hydration for every retrieval leg

Neo4j Client Responsibilities

  • Resolve the corpus database (per mode)
  • Store generation-scoped entities, relationships, and FROM_CHUNK chunk links
  • Derive GDS Leiden communities at index time when graph_storage.include_communities is on
  • Serve entity traversal seeded from the manifest's Qdrant generation

Concept diagram (where each artifact lives only — the retrieval flow is on the generated retrieval-pipeline page):

flowchart LR
    CH["Chunks + provenance"] --> PG["PostgreSQL"]
    MAN["Generation manifest"] --> PG
    VEC["Dense + sparse vectors"] --> QD["Qdrant"]
    ENT["Entities"] --> NEO["Neo4j"]
    REL["Relationships"] --> NEO
    COM["GDS Leiden communities"] --> NEO

Configuration Hooks

Section Field(s) Meaning
indexing postgres_url DSN for Postgres
graph_storage neo4j_uri, neo4j_user, neo4j_password Neo4j connectivity
graph_storage neo4j_database_mode, neo4j_database_prefix DB isolation strategy
graph_indexing enabled, build_code_graph Derived graph policy selection 
# Resolve Neo4j database name for a corpus (1)!
from server.models.tribrid_config_model import GraphStorageConfig
print(GraphStorageConfig().resolve_database("dev_corpus"))

# Connectivity checks are via readiness
curl -sS http://localhost:8000/ready | jq .

// Client: no direct DB use — rely on API readiness
  1. Uses prefix + sanitized corpus id in per_corpus mode
Qdrant vectors

Dense and sparse vectors live in per-corpus Qdrant generations; the corpus manifest names the physical collection and the contracts they were built under.