Revert "feat(knowledgebase): replace litellm-pgvector connector with memory-retrieval"

This reverts commit abeadc49c8.

Restores vendor/litellm-pgvector/ and the vector_store_registry wiring
(in-band file_search tool-call support) at the user's request, after
re-confirming against docs.litellm.ai/docs/completion/knowledgebase and
litellm-pgvector's own README that pg_vector is still not an in-process
vector_store_registry backend -- it requires this same standalone
connector service either way, so there is no simpler 'native' path that
was missed. Trading back in: 793 lines of vendored code, the untested
Prisma migration, and the git-context build risk noted in VENDORED.md
(all flagged as unverified against real hardware in issue #24), in
exchange for the file_search in-band tool call memory-retrieval did not
support.

Conflicts resolved on top of later commits (Redis, update.sh key-minting
fold-in):
- .env.example / docs/memory-knowledgebase.md: kept the auto-mint-via-
  update.sh language, renamed MEMORY_RETRIEVAL_* back to
  LITELLM_PGVECTOR_*.
- scripts/generate-secrets.sh: left deleted -- its job was folded into
  update.sh in 24d749b, unrelated to this revert.
- scripts/update.sh: renamed the MEMORY_RETRIEVAL_* secret/mint calls to
  LITELLM_PGVECTOR_* to match.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018WHfjWrSEcGhCoeu6dQfDa

docker-compose.yml

@@ -238,30 +238,38 @@ services:
       retries: 10
 
   # LiteLLM's native knowledgebase/vector-store feature has no Qdrant backend
-  # (the qdrant service above only serves Open WebUI's own RAG/Memory), so
-  # this small in-repo service wraps langchain_postgres.PGVector directly
-  # against pgvector-db instead — simpler than a vendored third-party
-  # connector (see docs/research/langchain-pgvector-vs-litellm-pgvector.md,
-  # which replaced the earlier litellm-pgvector approach). It still calls
-  # back into litellm for embeddings, same gateway boundary as everything
-  # else in this stack.
-  memory-retrieval:
+  # (the qdrant service above only serves Open WebUI's own RAG/Memory) — this
+  # companion service (github.com/BerriAI/litellm-pgvector) is the only
+  # self-hosted path. No published image exists yet, so this builds from a
+  # vendored copy in vendor/litellm-pgvector/ (see that dir's README) rather
+  # than a remote git build context — the server's Docker/BuildKit couldn't
+  # do an authenticated-looking clone of a public github.com repo (fails
+  # with "could not read Username ... terminal prompts disabled"), and
+  # vendoring sidesteps needing that debugged. See
+  # docs/research/litellm-knowledgebase.md.
+  # ponytail: unverified against real hardware — Prisma migration behavior on
+  # first boot and the exact vector_store_registry field names for the
+  # pg_vector provider need a live smoke test. See issue #24.
+  litellm-pgvector:
     build:
-      context: ./services/memory-retrieval
-    container_name: memory-retrieval
+      context: ./vendor/litellm-pgvector
+    container_name: litellm-pgvector
     depends_on:
       pgvector-db:
         condition: service_healthy
       litellm:
         condition: service_healthy
     environment:
-      - DATABASE_URL=postgresql+psycopg://litellm_pgvector:${PGVECTOR_DB_PASSWORD}@pgvector-db:5432/litellm_pgvector
-      - LITELLM_BASE_URL=http://litellm:4000/v1
-      # A virtual key for this workload — see docs/proxy-key-onboarding.md.
-      - LITELLM_API_KEY=${MEMORY_RETRIEVAL_EMBEDDING_KEY}
-      - EMBEDDING_MODEL=local-embedding
-      - SERVER_API_KEY=${MEMORY_RETRIEVAL_API_KEY}
-      - COLLECTION_NAME=memory-and-notes
+      - DATABASE_URL=postgresql://litellm_pgvector:${PGVECTOR_DB_PASSWORD}@pgvector-db:5432/litellm_pgvector
+      - SERVER_API_KEY=${LITELLM_PGVECTOR_API_KEY}
+      # Calls back into litellm for embeddings, same pattern as any other
+      # workload — see docs/proxy-key-onboarding.md for issuing this key.
+      - EMBEDDING__MODEL=local-embedding
+      - EMBEDDING__BASE_URL=http://litellm:4000
+      - EMBEDDING__API_KEY=${LITELLM_PGVECTOR_EMBEDDING_KEY}
+      - EMBEDDING__DIMENSIONS=768
+    expose:
+      - "8000"
     restart: unless-stopped
     networks: [ai-stack]