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

vendor/litellm-pgvector/models.py

@@ -0,0 +1,86 @@
+from typing import Optional, Dict, Any, List
+from pydantic import BaseModel
+from datetime import datetime
+
+
+class VectorStoreCreateRequest(BaseModel):
+    name: str
+    file_ids: Optional[List[str]] = None
+    expires_after: Optional[Dict[str, Any]] = None
+    chunking_strategy: Optional[Dict[str, Any]] = None
+    metadata: Optional[Dict[str, Any]] = None
+
+
+class VectorStoreResponse(BaseModel):
+    id: str
+    object: str = "vector_store"
+    created_at: int
+    name: str
+    usage_bytes: int
+    file_counts: Dict[str, int]
+    status: str
+    expires_after: Optional[Dict[str, Any]] = None
+    expires_at: Optional[int] = None
+    last_active_at: Optional[int] = None
+    metadata: Optional[Dict[str, Any]] = None
+
+
+class VectorStoreSearchRequest(BaseModel):
+    query: str
+    limit: Optional[int] = 20
+    filters: Optional[Dict[str, Any]] = None
+    return_metadata: Optional[bool] = True
+
+
+class ContentChunk(BaseModel):
+    type: str = "text"
+    text: str
+
+
+class SearchResult(BaseModel):
+    file_id: str
+    filename: str
+    score: float
+    attributes: Optional[Dict[str, Any]] = None
+    content: List[ContentChunk]
+
+
+class VectorStoreSearchResponse(BaseModel):
+    object: str = "vector_store.search_results.page"
+    search_query: str
+    data: List[SearchResult]
+    has_more: bool = False
+    next_page: Optional[str] = None
+
+
+class EmbeddingCreateRequest(BaseModel):
+    content: str
+    embedding: List[float]
+    metadata: Optional[Dict[str, Any]] = None
+
+
+class EmbeddingResponse(BaseModel):
+    id: str
+    object: str = "embedding"
+    vector_store_id: str
+    content: str
+    metadata: Optional[Dict[str, Any]] = None
+    created_at: int
+
+
+class EmbeddingBatchCreateRequest(BaseModel):
+    embeddings: List[EmbeddingCreateRequest]
+
+
+class EmbeddingBatchCreateResponse(BaseModel):
+    object: str = "embedding.batch"
+    data: List[EmbeddingResponse]
+    created: int
+
+
+class VectorStoreListResponse(BaseModel):
+    object: str = "list"
+    data: List[VectorStoreResponse]
+    first_id: Optional[str] = None
+    last_id: Optional[str] = None
+    has_more: bool = False