ci: Pin all gh actions to commit SHAs (#120)

pydantic 2.12.0+ has breaking changes with fastmcp 2.8.0. Pin to compatible version range.

.github/workflows/pre-commit.yaml

@@ -9,8 +9,8 @@ jobs:
   main:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
-    - uses: actions/setup-python@v4
+    - uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
       with:
         python-version: 3.x
-    - uses: pre-commit/action@v3.0.1
+    - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1

.github/workflows/pypi-publish.yaml

@@ -24,10 +24,10 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@ee0669bd1cc54295c223e0bb666b733df41de1c5 # v2.7.0
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@e9aba2c848f5ebd159c070c61ea2c4e2b122355e # v2.3.4
       with:
         python-version: '3.10.x'
 

.github/workflows/pytest.yaml

@@ -16,10 +16,10 @@ jobs:
     name: Python ${{ matrix.python-version }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
         with:
           python-version: ${{ matrix.python-version }}
 

README.md

@@ -63,7 +63,7 @@ important ones are listed below:
 |---------------------------------------|-----------------------------------------------------------|---------------|
 | `FASTMCP_DEBUG`                       | Enable debug mode                                         | `false`       |
 | `FASTMCP_LOG_LEVEL`                   | Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | `INFO`        |
-| `FASTMCP_HOST`                        | Host address to bind the server to                        | `0.0.0.0`     |
+| `FASTMCP_HOST`                        | Host address to bind the server to                        | `127.0.0.1`   |
 | `FASTMCP_PORT`                        | Port to run the server on                                 | `8000`        |
 | `FASTMCP_WARN_ON_DUPLICATE_RESOURCES` | Show warnings for duplicate resources                     | `true`        |
 | `FASTMCP_WARN_ON_DUPLICATE_TOOLS`     | Show warnings for duplicate tools                         | `true`        |
@@ -121,12 +121,17 @@ docker build -t mcp-server-qdrant .
 
 # Run the container
 docker run -p 8000:8000 \
+  -e FASTMCP_HOST="0.0.0.0" \
   -e QDRANT_URL="http://your-qdrant-server:6333" \
   -e QDRANT_API_KEY="your-api-key" \
   -e COLLECTION_NAME="your-collection" \
   mcp-server-qdrant
 ```
 
+> [!TIP]
+> Please note that we set `FASTMCP_HOST="0.0.0.0"` to make the server listen on all network interfaces. This is
+> necessary when running the server in a Docker container.
+
 ### Installing via Smithery
 
 To install Qdrant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/protocol/mcp-server-qdrant):

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "mcp-server-qdrant"
-version = "0.7.1"
+version = "0.8.1"
 description = "MCP server for retrieving context from a Qdrant vector database"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -8,8 +8,8 @@ license = "Apache-2.0"
 dependencies = [
     "fastembed>=0.6.0",
     "qdrant-client>=1.12.0",
-    "pydantic>=2.10.6",
+    "pydantic>=2.10.6,<2.12.0",
-    "fastmcp>=2.5.1",
+    "fastmcp==2.7.0",
 ]
 
 [build-system]
@@ -18,6 +18,7 @@ build-backend = "hatchling.build"
 
 [tool.uv]
 dev-dependencies = [
+    "ipdb>=0.13.13",
     "isort>=6.0.1",
     "mypy>=1.9.0",
     "pre-commit>=4.1.0",

src/mcp_server_qdrant/common/filters.py

@@ -0,0 +1,194 @@
+from typing import Any
+
+from qdrant_client import models
+
+from mcp_server_qdrant.qdrant import ArbitraryFilter
+from mcp_server_qdrant.settings import METADATA_PATH, FilterableField
+
+
+def make_filter(
+    filterable_fields: dict[str, FilterableField], values: dict[str, Any]
+) -> ArbitraryFilter:
+    must_conditions = []
+    must_not_conditions = []
+
+    for raw_field_name, field_value in values.items():
+        if raw_field_name not in filterable_fields:
+            raise ValueError(f"Field {raw_field_name} is not a filterable field")
+
+        field = filterable_fields[raw_field_name]
+
+        if field_value is None:
+            if field.required:
+                raise ValueError(f"Field {raw_field_name} is required")
+            else:
+                continue
+
+        field_name = f"{METADATA_PATH}.{raw_field_name}"
+
+        if field.field_type == "keyword":
+            if field.condition == "==":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchValue(value=field_value)
+                    )
+                )
+            elif field.condition == "!=":
+                must_not_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchValue(value=field_value)
+                    )
+                )
+            elif field.condition == "any":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchAny(any=field_value)
+                    )
+                )
+            elif field.condition == "except":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name,
+                        match=models.MatchExcept(**{"except": field_value}),
+                    )
+                )
+            elif field.condition is not None:
+                raise ValueError(
+                    f"Invalid condition {field.condition} for keyword field {field_name}"
+                )
+
+        elif field.field_type == "integer":
+            if field.condition == "==":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchValue(value=field_value)
+                    )
+                )
+            elif field.condition == "!=":
+                must_not_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchValue(value=field_value)
+                    )
+                )
+            elif field.condition == ">":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(gt=field_value)
+                    )
+                )
+            elif field.condition == ">=":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(gte=field_value)
+                    )
+                )
+            elif field.condition == "<":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(lt=field_value)
+                    )
+                )
+            elif field.condition == "<=":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(lte=field_value)
+                    )
+                )
+            elif field.condition == "any":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchAny(any=field_value)
+                    )
+                )
+            elif field.condition == "except":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name,
+                        match=models.MatchExcept(**{"except": field_value}),
+                    )
+                )
+            elif field.condition is not None:
+                raise ValueError(
+                    f"Invalid condition {field.condition} for integer field {field_name}"
+                )
+
+        elif field.field_type == "float":
+            # For float values, we only support range comparisons
+            if field.condition == ">":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(gt=field_value)
+                    )
+                )
+            elif field.condition == ">=":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(gte=field_value)
+                    )
+                )
+            elif field.condition == "<":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(lt=field_value)
+                    )
+                )
+            elif field.condition == "<=":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, range=models.Range(lte=field_value)
+                    )
+                )
+            elif field.condition is not None:
+                raise ValueError(
+                    f"Invalid condition {field.condition} for float field {field_name}. "
+                    "Only range comparisons (>, >=, <, <=) are supported for float values."
+                )
+
+        elif field.field_type == "boolean":
+            if field.condition == "==":
+                must_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchValue(value=field_value)
+                    )
+                )
+            elif field.condition == "!=":
+                must_not_conditions.append(
+                    models.FieldCondition(
+                        key=field_name, match=models.MatchValue(value=field_value)
+                    )
+                )
+            elif field.condition is not None:
+                raise ValueError(
+                    f"Invalid condition {field.condition} for boolean field {field_name}"
+                )
+
+        else:
+            raise ValueError(
+                f"Unsupported field type {field.field_type} for field {field_name}"
+            )
+
+    return models.Filter(
+        must=must_conditions, must_not=must_not_conditions
+    ).model_dump()
+
+
+def make_indexes(
+    filterable_fields: dict[str, FilterableField],
+) -> dict[str, models.PayloadSchemaType]:
+    indexes = {}
+
+    for field_name, field in filterable_fields.items():
+        if field.field_type == "keyword":
+            indexes[f"{METADATA_PATH}.{field_name}"] = models.PayloadSchemaType.KEYWORD
+        elif field.field_type == "integer":
+            indexes[f"{METADATA_PATH}.{field_name}"] = models.PayloadSchemaType.INTEGER
+        elif field.field_type == "float":
+            indexes[f"{METADATA_PATH}.{field_name}"] = models.PayloadSchemaType.FLOAT
+        elif field.field_type == "boolean":
+            indexes[f"{METADATA_PATH}.{field_name}"] = models.PayloadSchemaType.BOOL
+        else:
+            raise ValueError(
+                f"Unsupported field type {field.field_type} for field {field_name}"
+            )
+
+    return indexes

src/mcp_server_qdrant/common/wrap_filters.py

@@ -0,0 +1,150 @@
+import inspect
+from functools import wraps
+from typing import Annotated, Callable, Optional
+
+from pydantic import Field
+
+from mcp_server_qdrant.common.filters import make_filter
+from mcp_server_qdrant.settings import FilterableField
+
+
+def wrap_filters(
+    original_func: Callable, filterable_fields: dict[str, FilterableField]
+) -> Callable:
+    """
+    Wraps the original_func function: replaces `filter` parameter with multiple parameters defined by `filterable_fields`.
+    """
+
+    sig = inspect.signature(original_func)
+
+    @wraps(original_func)
+    def wrapper(*args, **kwargs):
+        # Start with fixed values
+        filter_values = {}
+
+        for field_name in filterable_fields:
+            if field_name in kwargs:
+                filter_values[field_name] = kwargs.pop(field_name)
+
+        query_filter = make_filter(filterable_fields, filter_values)
+
+        return original_func(**kwargs, query_filter=query_filter)
+
+    # Replace `query_filter` signature with parameters from `filterable_fields`
+
+    param_names = []
+
+    for param_name in sig.parameters:
+        if param_name == "query_filter":
+            continue
+        param_names.append(param_name)
+
+    new_params = [sig.parameters[param_name] for param_name in param_names]
+    required_new_params = []
+    optional_new_params = []
+
+    # Create a new signature parameters from `filterable_fields`
+    for field in filterable_fields.values():
+        field_name = field.name
+        field_type: type
+        if field.field_type == "keyword":
+            field_type = str
+        elif field.field_type == "integer":
+            field_type = int
+        elif field.field_type == "float":
+            field_type = float
+        elif field.field_type == "boolean":
+            field_type = bool
+        else:
+            raise ValueError(f"Unsupported field type: {field.field_type}")
+
+        if field.condition in {"any", "except"}:
+            if field_type not in {str, int}:
+                raise ValueError(
+                    f'Only "keyword" and "integer" types are supported for "{field.condition}" condition'
+                )
+            field_type = list[field_type]  # type: ignore
+
+        if field.required:
+            annotation = Annotated[field_type, Field(description=field.description)]  # type: ignore
+            parameter = inspect.Parameter(
+                name=field_name,
+                kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                annotation=annotation,
+            )
+            required_new_params.append(parameter)
+        else:
+            annotation = Annotated[  # type: ignore
+                Optional[field_type], Field(description=field.description)
+            ]
+            parameter = inspect.Parameter(
+                name=field_name,
+                kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                default=None,
+                annotation=annotation,
+            )
+            optional_new_params.append(parameter)
+
+    new_params.extend(required_new_params)
+    new_params.extend(optional_new_params)
+
+    # Set the new __signature__ for introspection
+    new_signature = sig.replace(parameters=new_params)
+    wrapper.__signature__ = new_signature  # type: ignore
+
+    # Set the new __annotations__ for introspection
+    new_annotations = {}
+    for param in new_signature.parameters.values():
+        if param.annotation != inspect.Parameter.empty:
+            new_annotations[param.name] = param.annotation
+
+    # Add return type annotation if it exists
+    if new_signature.return_annotation != inspect.Parameter.empty:
+        new_annotations["return"] = new_signature.return_annotation
+
+    wrapper.__annotations__ = new_annotations
+
+    return wrapper
+
+
+if __name__ == "__main__":
+    from pydantic._internal._typing_extra import get_function_type_hints
+    from qdrant_client import models
+
+    def find(
+        query: Annotated[str, Field(description="What to search for")],
+        collection_name: Annotated[
+            str, Field(description="The collection to search in")
+        ],
+        query_filter: Optional[models.Filter] = None,
+    ) -> list[str]:
+        print("query", query)
+        print("collection_name", collection_name)
+        print("query_filter", query_filter)
+        return ["mypy rules"]
+
+    wrapped_find = wrap_filters(
+        find,
+        {
+            "color": FilterableField(
+                name="color",
+                description="The color of the object",
+                field_type="keyword",
+                condition="==",
+            ),
+            "size": FilterableField(
+                name="size",
+                description="The size of the object",
+                field_type="keyword",
+                condition="==",
+                required=True,
+            ),
+        },
+    )
+
+    wrapped_find(query="dress", collection_name="test", color="red")
+
+    print("get_function_type_hints(find)", get_function_type_hints(find))
+    print(
+        "get_function_type_hints(wrapped_find)", get_function_type_hints(wrapped_find)
+    )

src/mcp_server_qdrant/embeddings/base.py

@@ -1,17 +1,16 @@
 from abc import ABC, abstractmethod
-from typing import List
 
 
 class EmbeddingProvider(ABC):
     """Abstract base class for embedding providers."""
 
     @abstractmethod
-    async def embed_documents(self, documents: List[str]) -> List[List[float]]:
+    async def embed_documents(self, documents: list[str]) -> list[list[float]]:
         """Embed a list of documents into vectors."""
         pass
 
     @abstractmethod
-    async def embed_query(self, query: str) -> List[float]:
+    async def embed_query(self, query: str) -> list[float]:
         """Embed a query into a vector."""
         pass
 

src/mcp_server_qdrant/embeddings/fastembed.py

@@ -1,5 +1,4 @@
 import asyncio
-from typing import List
 
 from fastembed import TextEmbedding
 from fastembed.common.model_description import DenseModelDescription
@@ -17,7 +16,7 @@ class FastEmbedProvider(EmbeddingProvider):
         self.model_name = model_name
         self.embedding_model = TextEmbedding(model_name)
 
-    async def embed_documents(self, documents: List[str]) -> List[List[float]]:
+    async def embed_documents(self, documents: list[str]) -> list[list[float]]:
         """Embed a list of documents into vectors."""
         # Run in a thread pool since FastEmbed is synchronous
         loop = asyncio.get_event_loop()
@@ -26,7 +25,7 @@ class FastEmbedProvider(EmbeddingProvider):
         )
         return [embedding.tolist() for embedding in embeddings]
 
-    async def embed_query(self, query: str) -> List[float]:
+    async def embed_query(self, query: str) -> list[float]:
         """Embed a query into a vector."""
         # Run in a thread pool since FastEmbed is synchronous
         loop = asyncio.get_event_loop()

src/mcp_server_qdrant/mcp_server.py

@@ -1,12 +1,17 @@
 import json
 import logging
-from typing import Any, List, Optional
+from typing import Annotated, Any, Optional
 
 from fastmcp import Context, FastMCP
+from pydantic import Field
+from qdrant_client import models
 
+from mcp_server_qdrant.common.filters import make_indexes
 from mcp_server_qdrant.common.func_tools import make_partial_function
+from mcp_server_qdrant.common.wrap_filters import wrap_filters
+from mcp_server_qdrant.embeddings.base import EmbeddingProvider
 from mcp_server_qdrant.embeddings.factory import create_embedding_provider
-from mcp_server_qdrant.qdrant import Entry, Metadata, QdrantConnector
+from mcp_server_qdrant.qdrant import ArbitraryFilter, Entry, Metadata, QdrantConnector
 from mcp_server_qdrant.settings import (
     EmbeddingProviderSettings,
     QdrantSettings,
@@ -27,22 +32,46 @@ class QdrantMCPServer(FastMCP):
         self,
         tool_settings: ToolSettings,
         qdrant_settings: QdrantSettings,
-        embedding_provider_settings: EmbeddingProviderSettings,
+        embedding_provider_settings: Optional[EmbeddingProviderSettings] = None,
+        embedding_provider: Optional[EmbeddingProvider] = None,
         name: str = "mcp-server-qdrant",
         instructions: str | None = None,
         **settings: Any,
     ):
         self.tool_settings = tool_settings
         self.qdrant_settings = qdrant_settings
-        self.embedding_provider_settings = embedding_provider_settings
 
-        self.embedding_provider = create_embedding_provider(embedding_provider_settings)
+        if embedding_provider_settings and embedding_provider:
+            raise ValueError(
+                "Cannot provide both embedding_provider_settings and embedding_provider"
+            )
+
+        if not embedding_provider_settings and not embedding_provider:
+            raise ValueError(
+                "Must provide either embedding_provider_settings or embedding_provider"
+            )
+
+        self.embedding_provider_settings: Optional[EmbeddingProviderSettings] = None
+        self.embedding_provider: Optional[EmbeddingProvider] = None
+
+        if embedding_provider_settings:
+            self.embedding_provider_settings = embedding_provider_settings
+            self.embedding_provider = create_embedding_provider(
+                embedding_provider_settings
+            )
+        else:
+            self.embedding_provider_settings = None
+            self.embedding_provider = embedding_provider
+
+        assert self.embedding_provider is not None, "Embedding provider is required"
+
         self.qdrant_connector = QdrantConnector(
             qdrant_settings.location,
             qdrant_settings.api_key,
             qdrant_settings.collection_name,
             self.embedding_provider,
             qdrant_settings.local_path,
+            make_indexes(qdrant_settings.filterable_fields_dict()),
         )
 
         super().__init__(name=name, instructions=instructions, **settings)
@@ -63,12 +92,19 @@ class QdrantMCPServer(FastMCP):
 
         async def store(
             ctx: Context,
-            information: str,
+            information: Annotated[str, Field(description="Text to store")],
-            collection_name: str,
+            collection_name: Annotated[
+                str, Field(description="The collection to store the information in")
+            ],
             # The `metadata` parameter is defined as non-optional, but it can be None.
             # If we set it to be optional, some of the MCP clients, like Cursor, cannot
             # handle the optional parameter correctly.
-            metadata: Optional[Metadata] = None,  # type: ignore
+            metadata: Annotated[
+                Metadata | None,
+                Field(
+                    description="Extra metadata stored along with memorised information. Any json is accepted."
+                ),
+            ] = None,
         ) -> str:
             """
             Store some information in Qdrant.
@@ -90,30 +126,37 @@ class QdrantMCPServer(FastMCP):
 
         async def find(
             ctx: Context,
-            query: str,
+            query: Annotated[str, Field(description="What to search for")],
-            collection_name: str,
+            collection_name: Annotated[
-        ) -> List[str]:
+                str, Field(description="The collection to search in")
+            ],
+            query_filter: ArbitraryFilter | None = None,
+        ) -> list[str] | None:
             """
             Find memories in Qdrant.
             :param ctx: The context for the request.
             :param query: The query to use for the search.
             :param collection_name: The name of the collection to search in, optional. If not provided,
                                     the default collection is used.
-            :return: A list of entries found.
+            :param query_filter: The filter to apply to the query.
+            :return: A list of entries found or None.
             """
+
+            # Log query_filter
+            await ctx.debug(f"Query filter: {query_filter}")
+
+            query_filter = models.Filter(**query_filter) if query_filter else None
+
             await ctx.debug(f"Finding results for query {query}")
-            if collection_name:
-                await ctx.debug(
-                    f"Overriding the collection name with {collection_name}"
-                )
 
             entries = await self.qdrant_connector.search(
                 query,
                 collection_name=collection_name,
                 limit=self.qdrant_settings.search_limit,
+                query_filter=query_filter,
             )
             if not entries:
-                return [f"No information found for the query '{query}'"]
+                return None
             content = [
                 f"Results for the query '{query}'",
             ]
@@ -124,6 +167,15 @@ class QdrantMCPServer(FastMCP):
         find_foo = find
         store_foo = store
 
+        filterable_conditions = (
+            self.qdrant_settings.filterable_fields_dict_with_conditions()
+        )
+
+        if len(filterable_conditions) > 0:
+            find_foo = wrap_filters(find_foo, filterable_conditions)
+        elif not self.qdrant_settings.allow_arbitrary_filter:
+            find_foo = make_partial_function(find_foo, {"query_filter": None})
+
         if self.qdrant_settings.collection_name:
             find_foo = make_partial_function(
                 find_foo, {"collection_name": self.qdrant_settings.collection_name}
@@ -132,7 +184,7 @@ class QdrantMCPServer(FastMCP):
                 store_foo, {"collection_name": self.qdrant_settings.collection_name}
             )
 
-        self.add_tool(
+        self.tool(
             find_foo,
             name="qdrant-find",
             description=self.tool_settings.tool_find_description,
@@ -140,7 +192,7 @@ class QdrantMCPServer(FastMCP):
 
         if not self.qdrant_settings.read_only:
             # Those methods can modify the database
-            self.add_tool(
+            self.tool(
                 store_foo,
                 name="qdrant-store",
                 description=self.tool_settings.tool_store_description,

src/mcp_server_qdrant/qdrant.py

@@ -1,15 +1,17 @@
 import logging
 import uuid
-from typing import Any, Dict, Optional
+from typing import Any
 
 from pydantic import BaseModel
 from qdrant_client import AsyncQdrantClient, models
 
 from mcp_server_qdrant.embeddings.base import EmbeddingProvider
+from mcp_server_qdrant.settings import METADATA_PATH
 
 logger = logging.getLogger(__name__)
 
-Metadata = Dict[str, Any]
+Metadata = dict[str, Any]
+ArbitraryFilter = dict[str, Any]
 
 
 class Entry(BaseModel):
@@ -18,7 +20,7 @@ class Entry(BaseModel):
     """
 
     content: str
-    metadata: Optional[Metadata] = None
+    metadata: Metadata | None = None
 
 
 class QdrantConnector:
@@ -34,11 +36,12 @@ class QdrantConnector:
 
     def __init__(
         self,
-        qdrant_url: Optional[str],
+        qdrant_url: str | None,
-        qdrant_api_key: Optional[str],
+        qdrant_api_key: str | None,
-        collection_name: Optional[str],
+        collection_name: str | None,
         embedding_provider: EmbeddingProvider,
-        qdrant_local_path: Optional[str] = None,
+        qdrant_local_path: str | None = None,
+        field_indexes: dict[str, models.PayloadSchemaType] | None = None,
     ):
         self._qdrant_url = qdrant_url.rstrip("/") if qdrant_url else None
         self._qdrant_api_key = qdrant_api_key
@@ -47,6 +50,7 @@ class QdrantConnector:
         self._client = AsyncQdrantClient(
             location=qdrant_url, api_key=qdrant_api_key, path=qdrant_local_path
         )
+        self._field_indexes = field_indexes
 
     async def get_collection_names(self) -> list[str]:
         """
@@ -56,7 +60,7 @@ class QdrantConnector:
         response = await self._client.get_collections()
         return [collection.name for collection in response.collections]
 
-    async def store(self, entry: Entry, *, collection_name: Optional[str] = None):
+    async def store(self, entry: Entry, *, collection_name: str | None = None):
         """
         Store some information in the Qdrant collection, along with the specified metadata.
         :param entry: The entry to store in the Qdrant collection.
@@ -74,7 +78,7 @@ class QdrantConnector:
 
         # Add to Qdrant
         vector_name = self._embedding_provider.get_vector_name()
-        payload = {"document": entry.content, "metadata": entry.metadata}
+        payload = {"document": entry.content, METADATA_PATH: entry.metadata}
         await self._client.upsert(
             collection_name=collection_name,
             points=[
@@ -87,7 +91,12 @@ class QdrantConnector:
         )
 
     async def search(
-        self, query: str, *, collection_name: Optional[str] = None, limit: int = 10
+        self,
+        query: str,
+        *,
+        collection_name: str | None = None,
+        limit: int = 10,
+        query_filter: models.Filter | None = None,
     ) -> list[Entry]:
         """
         Find points in the Qdrant collection. If there are no entries found, an empty list is returned.
@@ -95,6 +104,8 @@ class QdrantConnector:
         :param collection_name: The name of the collection to search in, optional. If not provided,
                                 the default collection is used.
         :param limit: The maximum number of entries to return.
+        :param query_filter: The filter to apply to the query, if any.
+
         :return: A list of entries found.
         """
         collection_name = collection_name or self._default_collection_name
@@ -115,6 +126,7 @@ class QdrantConnector:
             query=query_vector,
             using=vector_name,
             limit=limit,
+            query_filter=query_filter,
         )
 
         return [
@@ -146,3 +158,13 @@ class QdrantConnector:
                     )
                 },
             )
+
+            # Create payload indexes if configured
+
+            if self._field_indexes:
+                for field_name, field_type in self._field_indexes.items():
+                    await self._client.create_payload_index(
+                        collection_name=collection_name,
+                        field_name=field_name,
+                        field_schema=field_type,
+                    )

src/mcp_server_qdrant/settings.py

@@ -1,6 +1,6 @@
-from typing import Optional
+from typing import Literal
 
-from pydantic import Field
+from pydantic import BaseModel, Field, model_validator
 from pydantic_settings import BaseSettings
 
 from mcp_server_qdrant.embeddings.types import EmbeddingProviderType
@@ -15,6 +15,8 @@ DEFAULT_TOOL_FIND_DESCRIPTION = (
     " - Get some personal information about the user"
 )
 
+METADATA_PATH = "metadata"
+
 
 class ToolSettings(BaseSettings):
     """
@@ -46,18 +48,68 @@ class EmbeddingProviderSettings(BaseSettings):
     )
 
 
+class FilterableField(BaseModel):
+    name: str = Field(description="The name of the field payload field to filter on")
+    description: str = Field(
+        description="A description for the field used in the tool description"
+    )
+    field_type: Literal["keyword", "integer", "float", "boolean"] = Field(
+        description="The type of the field"
+    )
+    condition: Literal["==", "!=", ">", ">=", "<", "<=", "any", "except"] | None = (
+        Field(
+            default=None,
+            description=(
+                "The condition to use for the filter. If not provided, the field will be indexed, but no "
+                "filter argument will be exposed to MCP tool."
+            ),
+        )
+    )
+    required: bool = Field(
+        default=False,
+        description="Whether the field is required for the filter.",
+    )
+
+
 class QdrantSettings(BaseSettings):
     """
     Configuration for the Qdrant connector.
     """
 
-    location: Optional[str] = Field(default=None, validation_alias="QDRANT_URL")
+    location: str | None = Field(default=None, validation_alias="QDRANT_URL")
-    api_key: Optional[str] = Field(default=None, validation_alias="QDRANT_API_KEY")
+    api_key: str | None = Field(default=None, validation_alias="QDRANT_API_KEY")
-    collection_name: Optional[str] = Field(
+    collection_name: str | None = Field(
         default=None, validation_alias="COLLECTION_NAME"
     )
-    local_path: Optional[str] = Field(
+    local_path: str | None = Field(default=None, validation_alias="QDRANT_LOCAL_PATH")
-        default=None, validation_alias="QDRANT_LOCAL_PATH"
-    )
     search_limit: int = Field(default=10, validation_alias="QDRANT_SEARCH_LIMIT")
     read_only: bool = Field(default=False, validation_alias="QDRANT_READ_ONLY")
+
+    filterable_fields: list[FilterableField] | None = Field(default=None)
+
+    allow_arbitrary_filter: bool = Field(
+        default=False, validation_alias="QDRANT_ALLOW_ARBITRARY_FILTER"
+    )
+
+    def filterable_fields_dict(self) -> dict[str, FilterableField]:
+        if self.filterable_fields is None:
+            return {}
+        return {field.name: field for field in self.filterable_fields}
+
+    def filterable_fields_dict_with_conditions(self) -> dict[str, FilterableField]:
+        if self.filterable_fields is None:
+            return {}
+        return {
+            field.name: field
+            for field in self.filterable_fields
+            if field.condition is not None
+        }
+
+    @model_validator(mode="after")
+    def check_local_path_conflict(self) -> "QdrantSettings":
+        if self.local_path:
+            if self.location is not None or self.api_key is not None:
+                raise ValueError(
+                    "If 'local_path' is set, 'location' and 'api_key' must be None."
+                )
+        return self

tests/test_settings.py

@@ -1,5 +1,4 @@
-import os
+import pytest
-from unittest.mock import patch
 
 from mcp_server_qdrant.embeddings.types import EmbeddingProviderType
 from mcp_server_qdrant.settings import (
@@ -18,34 +17,51 @@ class TestQdrantSettings:
         # Should not raise error because there are no required fields
         QdrantSettings()
 
-    @patch.dict(
+    def test_minimal_config(self, monkeypatch):
-        os.environ,
-        {"QDRANT_URL": "http://localhost:6333", "COLLECTION_NAME": "test_collection"},
-    )
-    def test_minimal_config(self):
         """Test loading minimal configuration from environment variables."""
+        monkeypatch.setenv("QDRANT_URL", "http://localhost:6333")
+        monkeypatch.setenv("COLLECTION_NAME", "test_collection")
+
         settings = QdrantSettings()
         assert settings.location == "http://localhost:6333"
         assert settings.collection_name == "test_collection"
         assert settings.api_key is None
         assert settings.local_path is None
 
-    @patch.dict(
+    def test_full_config(self, monkeypatch):
-        os.environ,
-        {
-            "QDRANT_URL": "http://qdrant.example.com:6333",
-            "QDRANT_API_KEY": "test_api_key",
-            "COLLECTION_NAME": "my_memories",
-            "QDRANT_LOCAL_PATH": "/tmp/qdrant",
-        },
-    )
-    def test_full_config(self):
         """Test loading full configuration from environment variables."""
+        monkeypatch.setenv("QDRANT_URL", "http://qdrant.example.com:6333")
+        monkeypatch.setenv("QDRANT_API_KEY", "test_api_key")
+        monkeypatch.setenv("COLLECTION_NAME", "my_memories")
+        monkeypatch.setenv("QDRANT_SEARCH_LIMIT", "15")
+        monkeypatch.setenv("QDRANT_READ_ONLY", "1")
+
         settings = QdrantSettings()
         assert settings.location == "http://qdrant.example.com:6333"
         assert settings.api_key == "test_api_key"
         assert settings.collection_name == "my_memories"
-        assert settings.local_path == "/tmp/qdrant"
+        assert settings.search_limit == 15
+        assert settings.read_only is True
+
+    def test_local_path_config(self, monkeypatch):
+        """Test loading local path configuration from environment variables."""
+        monkeypatch.setenv("QDRANT_LOCAL_PATH", "/path/to/local/qdrant")
+
+        settings = QdrantSettings()
+        assert settings.local_path == "/path/to/local/qdrant"
+
+    def test_local_path_is_exclusive_with_url(self, monkeypatch):
+        """Test that local path cannot be set if Qdrant URL is provided."""
+        monkeypatch.setenv("QDRANT_URL", "http://localhost:6333")
+        monkeypatch.setenv("QDRANT_LOCAL_PATH", "/path/to/local/qdrant")
+
+        with pytest.raises(ValueError):
+            QdrantSettings()
+
+        monkeypatch.delenv("QDRANT_URL", raising=False)
+        monkeypatch.setenv("QDRANT_API_KEY", "test_api_key")
+        with pytest.raises(ValueError):
+            QdrantSettings()
 
 
 class TestEmbeddingProviderSettings:
@@ -55,12 +71,9 @@ class TestEmbeddingProviderSettings:
         assert settings.provider_type == EmbeddingProviderType.FASTEMBED
         assert settings.model_name == "sentence-transformers/all-MiniLM-L6-v2"
 
-    @patch.dict(
+    def test_custom_values(self, monkeypatch):
-        os.environ,
-        {"EMBEDDING_MODEL": "custom_model"},
-    )
-    def test_custom_values(self):
         """Test loading custom values from environment variables."""
+        monkeypatch.setenv("EMBEDDING_MODEL", "custom_model")
         settings = EmbeddingProviderSettings()
         assert settings.provider_type == EmbeddingProviderType.FASTEMBED
         assert settings.model_name == "custom_model"
@@ -73,35 +86,24 @@ class TestToolSettings:
         assert settings.tool_store_description == DEFAULT_TOOL_STORE_DESCRIPTION
         assert settings.tool_find_description == DEFAULT_TOOL_FIND_DESCRIPTION
 
-    @patch.dict(
+    def test_custom_store_description(self, monkeypatch):
-        os.environ,
-        {"TOOL_STORE_DESCRIPTION": "Custom store description"},
-    )
-    def test_custom_store_description(self):
         """Test loading custom store description from environment variable."""
+        monkeypatch.setenv("TOOL_STORE_DESCRIPTION", "Custom store description")
         settings = ToolSettings()
         assert settings.tool_store_description == "Custom store description"
         assert settings.tool_find_description == DEFAULT_TOOL_FIND_DESCRIPTION
 
-    @patch.dict(
+    def test_custom_find_description(self, monkeypatch):
-        os.environ,
-        {"TOOL_FIND_DESCRIPTION": "Custom find description"},
-    )
-    def test_custom_find_description(self):
         """Test loading custom find description from environment variable."""
+        monkeypatch.setenv("TOOL_FIND_DESCRIPTION", "Custom find description")
         settings = ToolSettings()
         assert settings.tool_store_description == DEFAULT_TOOL_STORE_DESCRIPTION
         assert settings.tool_find_description == "Custom find description"
 
-    @patch.dict(
+    def test_all_custom_values(self, monkeypatch):
-        os.environ,
-        {
-            "TOOL_STORE_DESCRIPTION": "Custom store description",
-            "TOOL_FIND_DESCRIPTION": "Custom find description",
-        },
-    )
-    def test_all_custom_values(self):
         """Test loading all custom values from environment variables."""
+        monkeypatch.setenv("TOOL_STORE_DESCRIPTION", "Custom store description")
+        monkeypatch.setenv("TOOL_FIND_DESCRIPTION", "Custom find description")
         settings = ToolSettings()
         assert settings.tool_store_description == "Custom store description"
         assert settings.tool_find_description == "Custom find description"