fix(litellm-pgvector): vendor the source instead of a remote git build context

The server's Docker/BuildKit couldn't do a git-context build of a public
github.com repo (fails with "could not read Username ... terminal prompts
disabled" — an auth-shaped error for what should be an anonymous clone).
Rather than debug that, vendor litellm-pgvector's small source tree
directly (vendor/litellm-pgvector/, see VENDORED.md for provenance/update
steps) and build from the local path.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

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()