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/embedding_service.py

@@ -0,0 +1,90 @@
+from typing import List, Optional
+from config import settings, EmbeddingConfig
+from litellm.types.utils import EmbeddingResponse
+import litellm
+import logging
+
+class EmbeddingService:
+    """Service for generating embeddings using OpenAI SDK pointed at LiteLLM proxy"""
+    
+    def __init__(self, config: Optional[EmbeddingConfig] = None):
+        self.config = config or settings.embedding
+
+
+    
+    async def generate_embedding(self, text: str) -> List[float]:
+        """
+        Generate embedding for a single text using LiteLLM proxy
+        
+        Args:
+            text: Text to embed
+            
+        Returns:
+            List of floats representing the embedding vector
+        """
+        try:
+            response: EmbeddingResponse = await litellm.aembedding(
+                model=self.config.model,
+                input=[text],
+                api_base=self.config.base_url,
+                api_key=self.config.api_key
+            )
+            logging.debug(f"Embedding response: {response}")
+            
+            # Extract embedding from response
+            embedding = response.data[0]["embedding"]
+            
+            # Validate embedding dimensions
+            if len(embedding) != self.config.dimensions:
+                raise ValueError(
+                    f"Expected embedding dimension {self.config.dimensions}, "
+                    f"got {len(embedding)}"
+                )
+            
+            return embedding
+            
+        except Exception as e:
+            raise RuntimeError(f"Failed to generate embedding: {str(e)}")
+    
+    async def generate_embeddings(self, texts: List[str]) -> List[List[float]]:
+        """
+        Generate embeddings for multiple texts
+        
+        Args:
+            texts: List of texts to embed
+            
+        Returns:
+            List of embedding vectors
+        """
+        try:
+            # Generate embeddings using LiteLLM
+            response = await litellm.aembedding(
+                model=self.config.model,
+                input=texts,
+                api_base=self.config.base_url,
+                api_key=self.config.api_key
+            )
+            
+            # Extract embeddings from response
+            embeddings = [item.embedding for item in response.data]
+            
+            # Validate embedding dimensions
+            for i, embedding in enumerate(embeddings):
+                if len(embedding) != self.config.dimensions:
+                    raise ValueError(
+                        f"Expected embedding dimension {self.config.dimensions} for text {i}, "
+                        f"got {len(embedding)}"
+                    )
+            
+            return embeddings
+            
+        except Exception as e:
+            raise RuntimeError(f"Failed to generate embeddings: {str(e)}")
+    
+    def update_config(self, new_config: EmbeddingConfig):
+        """Update the embedding configuration"""
+        self.config = new_config
+
+
+# Global embedding service instance
+embedding_service = EmbeddingService()