marcopan
796a39d893
Ports vectorstore/{rest_client,rest_writer,store,reader,embeddings,records},
evidence/model (leaf dep of records), and cli/_guards (require_vector_write_allowed
workstation write-guard). Renamed psdwp3->nsp, verbatim.
VectorRestClient gains an api_key property so reader/writer clients carry their
distinct keys visibly (spec D11: vector_reader / vector_writer on the same endpoint).
scripts/create_vector_reader_rpc.sql is NEW: the reader RPCs (search_similar,
list_tables) lived server-side in Supabase and were never versioned. Authored now
mirroring the writer allowlist pattern (table allowlist, security definer, revoke
from anon/authenticated, grant to vector_reader only). Writer RPC ported verbatim.
L1: test_vector_dual_key (7 tests) pins the dual-key construction + the workstation
write-guard (exit 4 without writer key).
68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
"""Lettura del pgvector dietro un'unica interfaccia `.search(query_vec, top_n, kinds)`, così
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`search.combined_search` resta agnostico al transport. Due implementazioni:
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- `RestSearcher` → produzione: similarity search via REST (`search_similar`).
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- `DirectSearcher` → dev/test: connessione diretta a Postgres/pgvector.
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Entrambe mappano i `kind` sulle tabelle per-dominio dello schema `vectors`.
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"""
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from sqlalchemy import Engine
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from nsp.vectorstore.rest_client import VectorRestClient
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from nsp.vectorstore.store import VectorHit, VectorStore, hit_from_metadata
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# kind PsdWp3 → tabella dello schema `vectors`.
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KIND_TO_TABLE = {
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"schema_table": "schema_records",
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"schema_column": "schema_records",
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"evidence": "evidence",
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"memory": "memory",
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}
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ALL_TABLES = ["schema_records", "evidence", "memory"]
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def tables_for_kinds(kinds: list[str] | None) -> list[str]:
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"""Tabelle da interrogare per i kind richiesti (tutte se kinds è vuoto/None)."""
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if not kinds:
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return list(ALL_TABLES)
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return sorted({KIND_TO_TABLE[k] for k in kinds if k in KIND_TO_TABLE})
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def _merge(hits: list[VectorHit], top_n: int) -> list[VectorHit]:
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return sorted(hits, key=lambda h: h.similarity, reverse=True)[:top_n]
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class RestSearcher:
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"""Similarity search via REST: una chiamata `search_similar` per tabella, poi fusione."""
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def __init__(self, client: VectorRestClient):
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self.client = client
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def search(
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self, query_vec: list[float], top_n: int = 10, kinds: list[str] | None = None
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) -> list[VectorHit]:
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hits: list[VectorHit] = []
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for table in tables_for_kinds(kinds):
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for row in self.client.search_similar(table, query_vec, top_n):
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hits.append(hit_from_metadata(row.get("similarity", 0.0), row.get("metadata")))
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return _merge(hits, top_n)
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class DirectSearcher:
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"""Similarity search diretta su Postgres/pgvector, interrogando le tabelle per-dominio."""
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def __init__(self, engine: Engine, schema: str = "vectors", dim: int = 768):
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self.engine = engine
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self.schema = schema
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self.dim = dim
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def search(
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self, query_vec: list[float], top_n: int = 10, kinds: list[str] | None = None
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) -> list[VectorHit]:
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hits: list[VectorHit] = []
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for table in tables_for_kinds(kinds):
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store = VectorStore(self.engine, schema=self.schema, table=table, dim=self.dim)
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hits.extend(store.search(query_vec, top_n=top_n))
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return _merge(hits, top_n)
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