Merge pull request #7 from qdrant/dev

mcp-server-qdrant 0.5.2

.github/workflows/pypi-publish.yaml

@@ -0,0 +1,42 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: PyPI Publish
+
+on:
+  workflow_dispatch:
+  push:
+    # Pattern matched against refs/tags
+    tags:
+      - 'v*'           # Push events to every version tag
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.10.x'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install uv
+        uv sync
+
+    - name: Build package
+      run: uv build
+
+    - name: Publish package
+      run: uv publish
+      with:
+        UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}

README.md

@@ -1,4 +1,5 @@
 # mcp-server-qdrant: A Qdrant MCP server
+[![smithery badge](https://smithery.ai/badge/mcp-server-qdrant)](https://smithery.ai/protocol/mcp-server-qdrant)
 
 > The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
 
@@ -38,6 +39,14 @@ uv run mcp-server-qdrant \
   --fastembed-model-name "sentence-transformers/all-MiniLM-L6-v2"
 ```
 
+### Installing via Smithery
+
+To install Qdrant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/protocol/mcp-server-qdrant):
+
+```bash
+npx @smithery/cli install mcp-server-qdrant --client claude
+```
+
 ## Usage with Claude Desktop
 
 To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your `claude_desktop_config.json`:
@@ -69,14 +78,38 @@ By default, the server will use the `sentence-transformers/all-MiniLM-L6-v2` emb
 For the time being, only [FastEmbed](https://qdrant.github.io/fastembed/) models are supported, and you can change it
 by passing the `--fastembed-model-name` argument to the server.
 
-### Environment Variables
+### Using the local mode of Qdrant
+
+To use a local mode of Qdrant, you can specify the path to the database using the `--qdrant-local-path` argument:
+
+```json
+{
+  "qdrant": {
+    "command": "uvx",
+    "args": [
+      "mcp-server-qdrant", 
+      "--qdrant-local-path",
+      "/path/to/qdrant/database",
+      "--collection-name",
+      "your_collection_name"
+    ]
+  }
+}
+```
+
+It will run Qdrant local mode inside the same process as the MCP server. Although it is not recommended for production.
+
+## Environment Variables
 
 The configuration of the server can be also done using environment variables:
 
-- `QDRANT_URL`: URL of the Qdrant server
+- `QDRANT_URL`: URL of the Qdrant server, e.g. `http://localhost:6333`
 - `QDRANT_API_KEY`: API key for the Qdrant server
 - `COLLECTION_NAME`: Name of the collection to use
 - `FASTEMBED_MODEL_NAME`: Name of the FastEmbed model to use
+- `QDRANT_LOCAL_PATH`: Path to the local Qdrant database
+
+You cannot provide `QDRANT_URL` and `QDRANT_LOCAL_PATH` at the same time.
 
 ## License
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "mcp-server-qdrant"
-version = "0.5.1"
+version = "0.5.2"
 description = "MCP server for retrieving context from a Qdrant vector database"
 readme = "README.md"
 requires-python = ">=3.10"

src/mcp_server_qdrant/qdrant.py

@@ -9,23 +9,25 @@ class QdrantConnector:
     :param qdrant_api_key: The API key to use for the Qdrant server.
     :param collection_name: The name of the collection to use.
     :param fastembed_model_name: The name of the FastEmbed model to use.
+    :param qdrant_local_path: The path to the storage directory for the Qdrant client, if local mode is used.
     """
 
     def __init__(
         self,
-        qdrant_url: str,
+        qdrant_url: Optional[str],
         qdrant_api_key: Optional[str],
         collection_name: str,
         fastembed_model_name: str,
+        qdrant_local_path: Optional[str] = None,
     ):
-        self._qdrant_url = qdrant_url.rstrip("/")
+        self._qdrant_url = qdrant_url.rstrip("/") if qdrant_url else None
         self._qdrant_api_key = qdrant_api_key
         self._collection_name = collection_name
         self._fastembed_model_name = fastembed_model_name
         # For the time being, FastEmbed models are the only supported ones.
         # A list of all available models can be found here:
         # https://qdrant.github.io/fastembed/examples/Supported_Models/
-        self._client = AsyncQdrantClient(qdrant_url, api_key=qdrant_api_key)
+        self._client = AsyncQdrantClient(location=qdrant_url, api_key=qdrant_api_key, path=qdrant_local_path)
         self._client.set_model(fastembed_model_name)
 
     async def store_memory(self, information: str):
@@ -40,10 +42,14 @@ async def store_memory(self, information: str):
 
     async def find_memories(self, query: str) -> list[str]:
         """
-        Find memories in the Qdrant collection.
+        Find memories in the Qdrant collection. If there are no memories found, an empty list is returned.
         :param query: The query to use for the search.
         :return: A list of memories found.
         """
+        collection_exists = await self._client.collection_exists(self._collection_name)
+        if not collection_exists:
+            return []
+
         search_results = await self._client.query(
             self._collection_name,
             query_text=query,

src/mcp_server_qdrant/server.py

@@ -12,22 +12,24 @@
 
 
 def serve(
-    qdrant_url: str,
+    qdrant_url: Optional[str],
     qdrant_api_key: Optional[str],
     collection_name: str,
     fastembed_model_name: str,
+    qdrant_local_path: Optional[str] = None,
 ) -> Server:
     """
     Instantiate the server and configure tools to store and find memories in Qdrant.
     :param qdrant_url: The URL of the Qdrant server.
     :param qdrant_api_key: The API key to use for the Qdrant server.
     :param collection_name: The name of the collection to use.
     :param fastembed_model_name: The name of the FastEmbed model to use.
+    :param qdrant_local_path: The path to the storage directory for the Qdrant client, if local mode is used.
     """
     server = Server("qdrant")
 
     qdrant = QdrantConnector(
-        qdrant_url, qdrant_api_key, collection_name, fastembed_model_name
+        qdrant_url, qdrant_api_key, collection_name, fastembed_model_name, qdrant_local_path
     )
 
     @server.list_tools()
@@ -112,7 +114,7 @@ async def handle_tool_call(
 @click.option(
     "--qdrant-url",
     envvar="QDRANT_URL",
-    required=True,
+    required=False,
     help="Qdrant URL",
 )
 @click.option(
@@ -134,19 +136,31 @@ async def handle_tool_call(
     help="FastEmbed model name",
     default="sentence-transformers/all-MiniLM-L6-v2",
 )
+@click.option(
+    "--qdrant-local-path",
+    envvar="QDRANT_LOCAL_PATH",
+    required=False,
+    help="Qdrant local path",
+)
 def main(
-    qdrant_url: str,
+    qdrant_url: Optional[str],
     qdrant_api_key: str,
     collection_name: Optional[str],
     fastembed_model_name: str,
+    qdrant_local_path: Optional[str],
 ):
+    # XOR of url and local path, since we accept only one of them
+    if not (bool(qdrant_url) ^ bool(qdrant_local_path)):
+        raise ValueError("Exactly one of qdrant-url or qdrant-local-path must be provided")
+
     async def _run():
         async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
             server = serve(
                 qdrant_url,
                 qdrant_api_key,
                 collection_name,
                 fastembed_model_name,
+                qdrant_local_path,
             )
             await server.run(
                 read_stream,