feat(integrations): add vector search retrieval to MongoDbMemoryService - #7573

Agent Engine packages the app object with cloudpickle, and a live
pymongo.MongoClient (sockets, locks, background threads) cannot cross
that boundary. Constructed with `connection_string`, the toolset now
drops the client at pickle time and rebuilds it on the runtime; the
query-embedding genai client is likewise rebuilt lazily from the
ambient environment on the destination. Constructing with a
caller-owned `mongo_client` raises TypeError at pickle time with
guidance, since such a client cannot be rebuilt remotely.

Pickling support for the MongoDB session and memory services is
deliberately left to the follow-up that wires those services into
Agent Engine.

Split from google#7291.

MongoDbSessionService persists sessions, events, and app:/user:-scoped
state in MongoDB, exported from google.adk.integrations.mongodb behind
the MONGODB_SESSION_SERVICE experimental feature (default on).

Concurrency model: within a process a per-session lock serializes
append_event; across runner replicas the optimistic-concurrency
revision on the session document is the only guard, and the losing
writer gets StaleSessionError. Multi-document writes run in a
transaction on replica sets / sharded clusters / Atlas; on deployments
without transaction support (standalone mongod) the verdict is cached
and writes fall back to sequential with the revision check. Optional
`ensure_indexes` creates the recommended secondary indexes on first
use. mongomock joins the test dependencies for the new unit tests.

NO_UNIT_GUIDE=The integration README documents the service, including
its document layout, concurrency model, and least-privilege setup; a
per-unit guide will follow when the experimental API stabilizes.

Split from google#7291.

MongoDbMemoryService ingests session events into a memory collection
and recalls them by keyword matching, exported from
google.adk.integrations.mongodb behind the MONGODB_MEMORY_SERVICE
experimental feature (default on). Ingestion is idempotent (memory
documents are keyed by app/user/session/event), and `ensure_indexes`
creates the app/user/keywords index on first use.

Retrieval is intentionally keyword-only: it needs no embedding model,
no Google credentials, and no vector index, so it works on any MongoDB
deployment at zero per-query cost. Vector recall is a possible
follow-up building on this integration's own search tools.

NO_UNIT_GUIDE=The integration README documents the service, including
its storage layout and least-privilege setup; a per-unit guide will
follow when the experimental API stabilizes.

Split from google#7291.

…oDB credentials out of Agent Engine packages

Three hardening fixes from the google#7291 review:

- MongoDbMemoryService.search_memory ranked nothing and returned every
  keyword match. Results are now ranked by keyword overlap (recency
  breaks ties) and capped by a new max_results constructor argument
  (default 20); ranking and limiting run server-side in the
  aggregation pipeline so a common keyword no longer returns
  everything.
- ensure_indexes defaulted to False, so users had to opt in or reads
  degraded into collection scans as data grew. It now defaults to True
  for both services; a deployment without the createIndex privilege
  logs one warning and falls back to unindexed reads instead of
  failing (unexpected index-creation errors still propagate and are
  retried on the next operation).
- A literal connection_string stored on the toolset was pickled into
  the Agent Engine deployment package in plaintext, password included.
  MongoDbToolset now accepts connection_string_env_var: only the
  variable's name is stored, and the value is resolved where the
  client is built — on the runtime after unpickling. The README's new
  Agent Engine section pairs it with Secret Manager-backed env vars
  (SecretRef) so the credential never touches the deployment spec.

Split from google#7291.

…b-hardening

# Conflicts:
#	constraints-3.11.txt
#	constraints-3.12.txt
#	constraints-3.13.txt
#	constraints-3.14.txt

…b-session-service

# Conflicts:
#	constraints-3.11.txt
#	constraints-3.12.txt
#	constraints-3.13.txt
#	constraints-3.14.txt

…n MongoDbSessionService

Review follow-up from google#7533: list_sessions fetched each user's state
with a separate find_one per user (N+1); it now issues a single $in
query against the user_states collection. Index creation on first use
is also serialized with a thread lock (double-checked flag), so
concurrent first calls no longer issue duplicate create_index calls.

Addresses gemini-code-assist review comments on google#7533.

Review follow-up from google#7533: add_session_to_memory issued one
replace_one round-trip per event; it now batches all upserts into a
single bulk_write (ordered=False). First-use index creation is
serialized with a thread lock (double-checked flag) so concurrent
startup calls no longer issue duplicate create_index calls. Tests
carry a shim for mongomock's BulkOperationBuilder, which rejects the
`sort` key pymongo 4.9+ adds to bulk operations.

Addresses gemini-code-assist review comments on google#7533.

# Conflicts:
#	src/google/adk/integrations/mongodb/_memory_service.py
#	src/google/adk/integrations/mongodb/_session_service.py

pyink reflow of the merged _ensure_indexes_once bodies, plus
constraints regenerated against upstream's 2026-10-14 cutoff (now
including the new constraints-3.15.txt) with the branch's mongomock
entries applied consistently across all five Python versions.

Answers the google#7291 review question with the promised follow-up: memory
recall is no longer keyword-only. `retrieval_mode` selects the recall
strategy:

- "keyword" (default): unchanged — dependency-free overlap matching that
  works on any MongoDB deployment.
- "vector": texts are embedded at ingest (one batch call per session)
  with the Google embedding model through the genai client, and recall
  embeds the query and runs $vectorSearch with an app/user pre-filter.
- "auto": Atlas Automated Embedding — documents carry plain text and an
  autoEmbed index makes Atlas embed documents and queries (no Google
  credentials; freshly ingested memories become recallable once Atlas
  embeds them).

ensure_indexes now creates the index the mode needs: the keyword
multikey index, or a vectorSearch index (vector field sized from
embedding_output_dimensionality, or an autoEmbed field over `text`,
both with app_name/user_id filter fields). It also creates the
memories collection first — createSearchIndexes rejects a nonexistent
collection, which a fresh deployment hits on its first ingest (caught
by the live E2E test).

All modes honor max_results; keyword mode stays the default so
existing deployments are unaffected.

Split from google#7291 follow-ups; stacked on google#7533.