Add filter with metadata using restricts

README.md

@@ -6,7 +6,24 @@ An MCP (Model Context Protocol) server that exposes a `knowledge_search` tool fo
 
 1. A natural-language query is embedded using a Gemini embedding model.
 2. The embedding is sent to a Vertex AI Matching Engine index endpoint to find nearest neighbors.
-3. The matched document contents are fetched from a GCS bucket and returned to the caller.
+3. Optional filters (restricts) can be applied to search only specific source folders.
+4. The matched document contents are fetched from a GCS bucket and returned to the caller.
+
+## Filtering by Source Folder
+
+The `knowledge_search` tool supports filtering results by source folder:
+
+```python
+# Search all folders
+knowledge_search(query="what is a savings account?")
+
+# Search only in specific folders
+knowledge_search(
+    query="what is a savings account?",
+    source_folders=["Educacion Financiera", "Productos y Servicios"]
+)
+```
+
 
 ## Prerequisites
 

agent.py

@@ -57,9 +57,20 @@ async def async_main() -> None:
         model="gemini-2.0-flash",
         name="knowledge_agent",
         instruction=(
-            "You are a helpful assistant with access to a knowledge base. "
+            "You are a helpful assistant with access to a knowledge base organized by folders. "
-            "Use the knowledge_search tool to find relevant information "
+            "Use the knowledge_search tool to find relevant information when the user asks questions.\n\n"
-            "when the user asks questions. Summarize the results clearly."
+            "Available folders in the knowledge base:\n"
+            "- 'Educacion Financiera': Educational content about finance, savings, investments, financial concepts\n"
+            "- 'Funcionalidades de la App Movil': Mobile app features, functionality, usage instructions\n"
+            "- 'Productos y Servicios': Bank products and services, accounts, procedures\n\n"
+            "IMPORTANT: When the user asks about a specific topic, analyze which folders are relevant "
+            "and use the source_folders parameter to filter results for more precise answers.\n\n"
+            "Examples:\n"
+            "- User asks about 'cuenta de ahorros' → Use source_folders=['Educacion Financiera', 'Productos y Servicios']\n"
+            "- User asks about 'cómo usar la app móvil' → Use source_folders=['Funcionalidades de App Movil']\n"
+            "- User asks about 'transferencias en la app' → Use source_folders=['Funcionalidades de App Movil', 'Productos y Servicios']\n"
+            "- User asks general question → Don't use source_folders (search all)\n\n"
+            "Summarize the results clearly in Spanish."
         ),
         tools=[toolset],
     )

main.py

@@ -204,6 +204,7 @@ class GoogleCloudVectorSearch:
         deployed_index_id: str,
         query: Sequence[float],
         limit: int,
+        restricts: list[dict[str, list[str]]] | None = None,
     ) -> list[SearchResult]:
         """Run an async similarity search via the REST API.
 
@@ -229,14 +230,18 @@ class GoogleCloudVectorSearch:
             f"/locations/{self.location}"
             f"/indexEndpoints/{endpoint_id}:findNeighbors"
         )
+        query_payload = {
+            "datapoint": {"feature_vector": list(query)},
+            "neighbor_count": limit,
+        }
+
+        # Add restricts if provided
+        if restricts:
+            query_payload["restricts"] = restricts
+
         payload = {
             "deployed_index_id": deployed_index_id,
-            "queries": [
+            "queries": [query_payload],
-                {
-                    "datapoint": {"feature_vector": list(query)},
-                    "neighbor_count": limit,
-                },
-            ],
         }
 
         headers = await self._async_get_auth_headers()
@@ -385,12 +390,16 @@ mcp = FastMCP(
 async def knowledge_search(
     query: str,
     ctx: Context,
+    source_folders: list[str] | None = None,
 ) -> str:
     """Search a knowledge base using a natural-language query.
 
     Args:
         query: The text query to search for.
         ctx: MCP request context (injected automatically).
+        source_folders: Optional list of source folder paths to filter results.
+                       If provided, only documents from these folders will be returned.
+                       Example: ["Educacion Financiera", "Productos y Servicios"]
 
     Returns:
         A formatted string containing matched documents with id and content.
@@ -413,13 +422,31 @@ async def knowledge_search(
     embedding = response.embeddings[0].values
     t_embed = time.perf_counter()
 
+    # Build restricts for source folder filtering if provided
+    restricts = None
+    if source_folders:
+        restricts = [
+            {
+                "namespace": "source_folder",
+                "allow": source_folders,
+            }
+        ]
+        logger.info(f"Filtering by source_folders: {source_folders}")
+    else:
+        logger.info("No filtering - searching all folders")
+
     search_results = await app.vector_search.async_run_query(
         deployed_index_id=app.settings.deployed_index_id,
         query=embedding,
         limit=app.settings.search_limit,
+        restricts=restricts,
     )
     t_search = time.perf_counter()
 
+    # Log raw results from Vertex AI before similarity filtering
+    logger.info(f"Raw results from Vertex AI (before similarity filter): {len(search_results)} chunks")
+    logger.info(f"Raw chunk IDs: {[s['id'] for s in search_results]}")
+
     # Apply similarity filtering
     if search_results:
         max_sim = max(r["distance"] for r in search_results)