54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
"""L2: tht memory save-one against real pgvector (spec D11, L2).
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Validates D11 end-to-end: a single promoted decision is upserted to the real
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pgvector via the WRITER key (not a full resync), and a subsequent search_similar
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finds the memory. L1 tested the pure save_one_memory core; here the REST writer +
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real pgvector + real embeddings are in the loop.
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Run: pytest -m l2 tests/l2/test_memory_save_one_real.py -s (needs .env + VPN + Ollama)
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"""
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from datetime import datetime
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from pathlib import Path
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import pytest
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from tht.memory import MemoryRecord, save_one_memory
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from tht.workspace import load_workspace
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pytestmark = [pytest.mark.l2]
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WORKSPACE = Path(__file__).resolve().parents[2] / "workspaces" / "tht-test.yaml"
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def test_save_one_upserts_to_real_pgvector(l2_env):
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"""save_one_memory pushes one row to the real pgvector via the writer key, and
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a subsequent search_similar retrieves it. Idempotent (re-running upserts >= 0)."""
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from tht.vectorstore.embeddings import OllamaEmbeddings
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from tht.vectorstore.rest_client import VectorRestClient
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ws = load_workspace(WORKSPACE)
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if not ws.vector_write_rest or not ws.vector_write_rest.api_key.strip():
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pytest.skip("vector_write_rest not configured (no writer key)")
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writer = VectorRestClient(ws.vector_write_rest)
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embedder = OllamaEmbeddings(ws.embeddings)
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record = MemoryRecord(
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id="mem-l2test", ts=datetime.now(), session_id="l2-self-test",
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decision_seq=999, type="concept_clarified", subject="ablazione recente",
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detail="evento di ablazione negli ultimi 15 anni",
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rationale="L2 self-test (idempotent)",
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question_context="ablazione 2025", tables=[], concepts=["ablazione recente"],
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)
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from tht.adapters.vector import ThothHttpVectorStore
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store = ThothHttpVectorStore(reader=writer, writer=writer)
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upserted = save_one_memory([record], decision_seq=999, store=store, embedder=embedder)
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assert upserted >= 0 # idempotent: 0 on unchanged, >=1 on new/updated
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# read it back via the READER key (vector_rest, path /vector/v1/)
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reader = VectorRestClient(ws.vector_rest)
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qvec = embedder.embed_query("ablazione")
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hits = reader.search_similar("memory", qvec, 10)
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ids = {h.get("metadata", {}).get("record_key", "") for h in hits}
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assert "memory:mem-l2test" in ids, "upserted memory not retrievable via search_similar"
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