fix types

Author: ishaan-jaff

main.py

@@ -18,7 +18,8 @@
     EmbeddingResponse,
     EmbeddingBatchCreateRequest,
     EmbeddingBatchCreateResponse,
-    VectorStoreListResponse
+    VectorStoreListResponse,
+    ContentChunk
 )
 from config import settings
 from embedding_service import embedding_service
@@ -288,17 +289,26 @@ async def search_vector_store(
             # Cosine distance ranges from 0 (identical) to 2 (opposite)
             similarity_score = max(0, 1 - (row['distance'] / 2))
 
+            # Extract filename from metadata or use a default
+            metadata = row[fields.metadata_field] or {}
+            filename = metadata.get('filename', 'document.txt')
+            
+            content_chunks = [ContentChunk(type="text", text=row[fields.content_field])]
+            
             result = SearchResult(
-                id=row[fields.id_field],
-                content=row[fields.content_field],
+                file_id=row[fields.id_field],
+                filename=filename,
                 score=similarity_score,
-                metadata=row[fields.metadata_field] if request.return_metadata else None
+                attributes=metadata if request.return_metadata else None,
+                content=content_chunks
             )
             search_results.append(result)
 
         return VectorStoreSearchResponse(
+            search_query=request.query,
             data=search_results,
-            usage={"total_tokens": len(search_results)}
+            has_more=False,  # TODO: Implement pagination
+            next_page=None
         )
 
     except HTTPException:

models.py

@@ -32,17 +32,25 @@ class VectorStoreSearchRequest(BaseModel):
     return_metadata: Optional[bool] = True
 
 
+class ContentChunk(BaseModel):
+    type: str = "text"
+    text: str
+
+
 class SearchResult(BaseModel):
-    id: str
-    content: str
+    file_id: str
+    filename: str
     score: float
-    metadata: Optional[Dict[str, Any]] = None
+    attributes: Optional[Dict[str, Any]] = None
+    content: List[ContentChunk]
 
 
 class VectorStoreSearchResponse(BaseModel):
-    object: str = "vector_store.search"
+    object: str = "vector_store.search_results.page"
+    search_query: str
     data: List[SearchResult]
-    usage: Dict[str, int]
+    has_more: bool = False
+    next_page: Optional[str] = None
 
 
 class EmbeddingCreateRequest(BaseModel):