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c17b23378e
docsgpt/core/settings.py had grown to 258 fields in one 600-line class, touched by about two commits a week, with related settings scattered (GitHub ingest caps inside the embeddings block, API keys in four places, the OpenAI Responses knobs 100 lines from the other OpenAI fields). It is now a package: one module per domain (auth, llm, embeddings, retrieval, vectorstores, database, workers, ingestion, ocr, storage, connectors, server, events, agents, guardrails, scheduler, sandbox, speech), each a SettingsGroup owning its fields and validators, composed by multiple inheritance into the same flat Settings class. Every attribute name, type, default, alias and constraint is unchanged, so settings.NAME reads, .env files and test monkeypatches all keep working; the import path docsgpt.core.settings is the package. Settings.normalize_api_key is kept as a classmethod for callers that reuse it. The comment above or beside each field became its Field(description=...), so the definitions are visible to tooling; the next commit generates the docs reference from them. Pitfall recorded for future groups: pydantic collects validators by method name across the MRO, so two groups naming a validator the same would silently keep only one. Each group's validator has a unique name.
90 lines
4.3 KiB
Python
90 lines
4.3 KiB
Python
"""Connection settings for each vector store backend."""
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from __future__ import annotations
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from typing import Optional
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from pydantic import Field, field_validator
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from docsgpt.core.db_uri import normalize_pgvector_connection_string
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from docsgpt.core.paths import home_dir
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from docsgpt.core.settings._shared import SettingsGroup, normalize_secret
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class VectorStoreSettings(SettingsGroup):
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"""Per-backend connection details; only the backend named by VECTOR_STORE is read."""
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MONGO_URI: Optional[str] = Field(
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default=None,
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description=(
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"Only consulted when VECTOR_STORE=mongodb or when running scripts/db/backfill.py; user data lives "
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"in Postgres."
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),
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)
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# Elasticsearch.
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ELASTIC_CLOUD_ID: Optional[str] = Field(default=None, description="Elastic Cloud id.")
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ELASTIC_USERNAME: Optional[str] = Field(default=None, description="Elasticsearch username.")
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ELASTIC_PASSWORD: Optional[str] = Field(default=None, description="Elasticsearch password.")
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ELASTIC_URL: Optional[str] = Field(default=None, description="Elasticsearch URL.")
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ELASTIC_INDEX: Optional[str] = Field(default="docsgpt", description="Elasticsearch index name.")
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# Qdrant.
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QDRANT_COLLECTION_NAME: Optional[str] = Field(default="docsgpt", description="Qdrant collection name.")
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QDRANT_LOCATION: Optional[str] = Field(default=None, description="Qdrant location (':memory:' or a URL).")
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QDRANT_URL: Optional[str] = Field(default=None, description="Qdrant server URL.")
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QDRANT_PORT: Optional[int] = Field(default=6333, description="Qdrant REST port.")
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QDRANT_GRPC_PORT: int = Field(default=6334, description="Qdrant gRPC port.")
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QDRANT_PREFER_GRPC: bool = Field(default=False, description="Use gRPC instead of REST where possible.")
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QDRANT_HTTPS: Optional[bool] = Field(default=None, description="Use HTTPS for the Qdrant connection.")
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QDRANT_API_KEY: Optional[str] = Field(default=None, description="Qdrant API key.")
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QDRANT_PREFIX: Optional[str] = Field(default=None, description="URL prefix for a Qdrant behind a proxy.")
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QDRANT_TIMEOUT: Optional[float] = Field(default=None, description="Qdrant request timeout in seconds.")
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QDRANT_HOST: Optional[str] = Field(default=None, description="Qdrant host (alternative to QDRANT_URL).")
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QDRANT_PATH: Optional[str] = Field(default=None, description="Path for an embedded on-disk Qdrant.")
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QDRANT_DISTANCE_FUNC: str = Field(default="Cosine", description="Qdrant distance function.")
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# PGVector.
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PGVECTOR_CONNECTION_STRING: Optional[str] = Field(
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default=None,
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description=(
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"pgvector connection string. postgres://, postgresql:// and postgresql+psycopg:// are all accepted "
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"and normalized internally for psycopg.connect(). Unset falls back to POSTGRES_URI."
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),
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)
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PGVECTOR_POOL_MAX_SIZE: int = Field(
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default=8, description="Per-process connection pool size; 0 uses one direct connection per store."
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)
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PGVECTOR_IVFFLAT_PROBES: Optional[int] = Field(
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default=None,
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description="IVFFlat probes; unset derives sqrt(lists) from the index. Higher means better recall, more scan.",
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)
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# Milvus.
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MILVUS_COLLECTION_NAME: Optional[str] = Field(default="docsgpt", description="Milvus collection name.")
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MILVUS_URI: Optional[str] = Field(
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default_factory=lambda: str(home_dir() / "milvus_local.db"),
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description=(
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"Milvus server URI. The default is a milvus-lite (embedded) database file under the data home, "
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"like the other local stores."
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),
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)
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MILVUS_TOKEN: Optional[str] = Field(default="", description="Milvus auth token.")
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# LanceDB.
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LANCEDB_PATH: str = Field(
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default_factory=lambda: str(home_dir() / "data" / "lancedb"),
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description="LanceDB local data directory.",
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)
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LANCEDB_TABLE_NAME: Optional[str] = Field(default="docsgpts", description="LanceDB table for stored vectors.")
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@field_validator("PGVECTOR_CONNECTION_STRING", mode="before")
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@classmethod
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def _normalize_pgvector_connection_string(cls, v):
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return normalize_pgvector_connection_string(v)
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@field_validator("QDRANT_API_KEY", mode="before")
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@classmethod
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def _normalize_vectorstore_secrets(cls, v):
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return normalize_secret(v)
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