Add filter with metadata using restricts

Reviewed-on: #3

Dockerfile

@@ -1,25 +0,0 @@
-FROM python:3.12-slim AS builder
-
-COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /usr/local/bin/
-
-WORKDIR /app
-
-COPY pyproject.toml uv.lock ./
-RUN uv sync --no-dev --frozen --no-install-project
-
-COPY main.py .
-
-FROM python:3.12-slim
-
-WORKDIR /app
-
-COPY --from=builder /app /app
-
-ENV PATH="/app/.venv/bin:$PATH"
-
-# Cloud Run injects PORT (defaults to 8080)
-ENV PORT=8080
-EXPOSE ${PORT}
-
-# Shell form so ${PORT} is expanded at runtime
-CMD python main.py --transport sse --port ${PORT}

DockerfileConnector

@@ -0,0 +1,14 @@
+FROM quay.ocp.banorte.com/golden/python-312:latest
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /usr/local/bin/
+
+WORKDIR /app
+
+COPY pyproject.toml uv.lock ./
+RUN uv sync --no-dev --frozen
+
+COPY main.py .
+
+ENV PATH="/app/.venv/bin:$PATH"
+
+CMD ["uv", "run", "python", "main.py", "--transport", "sse", "--port", "8000"]

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

@@ -5,6 +5,7 @@ import argparse
 import asyncio
 import io
 import logging
+import os
 from collections.abc import AsyncIterator, Sequence
 from contextlib import asynccontextmanager
 from dataclasses import dataclass
@@ -16,7 +17,7 @@ from gcloud.aio.storage import Storage
 from google import genai
 from google.genai import types as genai_types
 from mcp.server.fastmcp import Context, FastMCP
-from pydantic_settings import BaseSettings
+from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, YamlConfigSettingsSource
 
 logger = logging.getLogger(__name__)
 
@@ -203,6 +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.
 
@@ -228,14 +230,18 @@ class GoogleCloudVectorSearch:
             f"/locations/{self.location}"
             f"/indexEndpoints/{endpoint_id}:findNeighbors"
         )
-        payload = {
+        query_payload = {
-            "deployed_index_id": deployed_index_id,
-            "queries": [
-                {
             "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": [query_payload],
         }
 
         headers = await self._async_get_auth_headers()
@@ -282,10 +288,29 @@ class GoogleCloudVectorSearch:
 # ---------------------------------------------------------------------------
 
 
-class Settings(BaseSettings):
+def _parse_args() -> argparse.Namespace:
-    """Server configuration populated from environment variables."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--transport",
+        choices=["stdio", "sse"],
+        default="stdio",
+    )
+    parser.add_argument("--host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, default=8080)
+    parser.add_argument(
+        "--config",
+        default=os.environ.get("CONFIG_FILE", "config.yaml"),
+    )
+    return parser.parse_args()
 
-    model_config = {"env_file": ".env"}
+
+_args = _parse_args()
+
+
+class Settings(BaseSettings):
+    """Server configuration populated from env vars and a YAML config file."""
+
+    model_config = {"env_file": ".env", "yaml_file": _args.config}
 
     project_id: str
     location: str
@@ -297,6 +322,23 @@ class Settings(BaseSettings):
     embedding_model: str = "gemini-embedding-001"
     search_limit: int = 10
 
+    @classmethod
+    def settings_customise_sources(
+        cls,
+        settings_cls: type[BaseSettings],
+        init_settings: PydanticBaseSettingsSource,
+        env_settings: PydanticBaseSettingsSource,
+        dotenv_settings: PydanticBaseSettingsSource,
+        file_secret_settings: PydanticBaseSettingsSource,
+    ) -> tuple[PydanticBaseSettingsSource, ...]:
+        return (
+            init_settings,
+            env_settings,
+            dotenv_settings,
+            YamlConfigSettingsSource(settings_cls),
+            file_secret_settings,
+        )
+
 
 @dataclass
 class AppContext:
@@ -334,19 +376,6 @@ async def lifespan(_server: FastMCP) -> AsyncIterator[AppContext]:
     )
 
 
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--transport",
-        choices=["stdio", "sse"],
-        default="stdio",
-    )
-    parser.add_argument("--host", default="0.0.0.0")
-    parser.add_argument("--port", type=int, default=8080)
-    return parser.parse_args()
-
-
-_args = _parse_args()
 cfg = Settings.model_validate({})
 
 mcp = FastMCP(
@@ -361,12 +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.
@@ -389,13 +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)

pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "google-genai>=1.64.0",
     "mcp[cli]>=1.26.0",
     "pydantic-settings>=2.9.1",
+    "pyyaml>=6.0",
 ]
 
 [dependency-groups]

uv.lock

@@ -1356,6 +1356,7 @@ dependencies = [
     { name = "google-genai" },
     { name = "mcp", extra = ["cli"] },
     { name = "pydantic-settings" },
+    { name = "pyyaml" },
 ]
 
 [package.dev-dependencies]
@@ -1374,6 +1375,7 @@ requires-dist = [
     { name = "google-genai", specifier = ">=1.64.0" },
     { name = "mcp", extras = ["cli"], specifier = ">=1.26.0" },
     { name = "pydantic-settings", specifier = ">=2.9.1" },
+    { name = "pyyaml", specifier = ">=6.0" },
 ]
 
 [package.metadata.requires-dev]