feat(memory): add self-hosted PostgreSQL/pgvector memory service

[Nanduu24]

Link to Issue or Description of Change

• Closes: FEAT: Add a self-hosted PostgreSQL/pgvector semantic MemoryService #7273
• Related: [Feature Request] DakeraMemoryService — self-hosted, non-GCP persistent agent memory #6254, feat(memory): add SQLite memory service #4116

Problem:

ADK's memory services do not cover persistent, semantic memory on
infrastructure the user controls. InMemoryMemoryService is keyword-only and
"for prototyping purpose only" (loses data on restart), while
VertexAiMemoryBankService and VertexAiRagMemoryService require Google Cloud.
Teams running on-premise, air-gapped, or under data-residency constraints have
no built-in option, and the pending SQLite memory service (#4116) provides
persistence but exact-match retrieval rather than semantic search.

Solution:

Add PgVectorMemoryService under google.adk.integrations.pgvector, a
BaseMemoryService backed by PostgreSQL + pgvector:

• add_session_to_memory / add_events_to_memory embed each event's text and
  upsert it (idempotent per event) into a table scoped by (app_name, user_id).
• add_memory supports direct writes of explicit MemoryEntry items.
• search_memory embeds the query and returns the nearest memories by cosine
  distance using a pgvector HNSW index, with an optional distance_threshold.
• Embeddings use the google-genai client by default; an injectable embedder
  makes the service provider-agnostic.
• Ships as an optional extra: pip install "google-adk[pgvector]".

It follows the existing integrations/redis pattern (pydantic config plus an
injectable connection pool and embedder), so the store runs entirely on a
database the user owns and stays unit-testable without a live database. The
table, vector extension, and indexes are created on first use. Happy to align
the table layout with #4116 if a shared schema is preferred.

Testing Plan

Unit Tests:

• I have added or updated unit tests for my change.
• All unit tests pass locally.

New tests in tests/unittests/integrations/pgvector/ run against an in-process
fake connection pool and a deterministic bag-of-words embedder, so they exercise
ingestion, idempotent upserts, semantic ranking, per-(app_name, user_id)
scoping, the distance threshold, empty-query handling, skipping text-less
events, the direct-write path, serialization round-trips, and the
missing-driver / missing-dsn errors — all without psycopg, pgvector, or any
network access.

$ python -m pytest tests/unittests/integrations/pgvector -q
13 passed

The suite passes both with the optional driver installed and with it absent
(matching CI). Lint/format on the changed files: pyink and isort clean,
pylint 10.00/10.

Manual End-to-End (E2E) Tests:

Verified against a live PostgreSQL 17 + pgvector 0.8.6 database using the
real psycopg async driver (a deterministic local embedder stood in for the
embedding API). Confirmed: session ingestion, semantic ranking (a "billing"
query returns the billing turn; a "basketball" query returns the basketball
turn), cross-instance persistence (a fresh service reads the stored rows back),
(app_name, user_id) scoping, idempotent re-ingestion (row count stays at 3
after a double ingest), and the created schema (a vector(64) column and an
HNSW index). To reproduce:

docker run -d --name adk-pgvector -e POSTGRES_PASSWORD=pw -p 5432:5432 pgvector/pgvector:pg16
pip install "google-adk[pgvector]"
export GOOGLE_API_KEY=...   # for gemini-embedding-001

import asyncio
from google.adk.integrations.pgvector import (
    PgVectorMemoryService, PgVectorMemoryServiceConfig,
)
from google.adk.events.event import Event
from google.adk.sessions.session import Session
from google.genai import types

async def main():
    svc = PgVectorMemoryService(PgVectorMemoryServiceConfig(
        dsn="postgresql://postgres:pw@localhost:5432/postgres"))
    session = Session(app_name="demo", user_id="u1", id="s1", events=[
        Event(author="user", timestamp=1.0,
              content=types.Content(parts=[types.Part(
                  text="We decided to move billing to net-30 terms.")])),
    ])
    await svc.add_session_to_memory(session)
    res = await svc.search_memory(app_name="demo", user_id="u1",
                                  query="what did we decide about billing?")
    print([m.content.parts[0].text for m in res.memories])

asyncio.run(main())

Checklist

• I have read the CONTRIBUTING.md document.
• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.
• I have added tests that prove my fix is effective or that my feature works.
• New and existing unit tests pass locally with my changes.
• I have manually tested my changes end-to-end.
• Any dependent changes have been merged and published in downstream modules.

Additional context

Design mirrors the integrations/redis session service and the google-genai
embedding usage in tools/spanner and integrations/mongodb. The new optional
extra pgvector pins psycopg[binary], psycopg-pool, and pgvector, and is
included in the all extra.