Update README: document hybrid search and project tagging features

Rewrite README to highlight the two fork-specific features:
- BM25 hybrid search (dense + sparse vectors with RRF)
- Automatic project tagging with metadata.project index

Also update the environment variables table with all current options.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

README.md

@@ -1,149 +1,170 @@
-# mcp-server-qdrant: A Qdrant MCP server
+# mcp-server-qdrant: Hybrid Search Fork
 
-[![smithery badge](https://smithery.ai/badge/mcp-server-qdrant)](https://smithery.ai/protocol/mcp-server-qdrant)
+> Forked from [qdrant/mcp-server-qdrant](https://github.com/qdrant/mcp-server-qdrant) — the official MCP server for Qdrant.
 
-> The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables
+An [MCP](https://modelcontextprotocol.io/introduction) server for [Qdrant](https://qdrant.tech/) vector search engine that acts as a **semantic memory layer** for LLM applications.
-> 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.
 
-This repository is an example of how to create a MCP server for [Qdrant](https://qdrant.tech/), a vector search engine.
+This fork adds two features on top of the upstream:
 
-## Overview
+1. **Hybrid Search** — combines dense (semantic) and sparse (BM25 keyword) vectors using Reciprocal Rank Fusion for significantly better recall
+2. **Project Tagging** — automatic `project` metadata on every stored memory, with a payload index for efficient filtering
 
-An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine.
+Everything else remains fully compatible with the upstream.
-It acts as a semantic memory layer on top of the Qdrant database.
 
-## Components
+---
 
-### Tools
+## What's Different in This Fork
 
-1. `qdrant-store`
+### Hybrid Search (Dense + BM25 Sparse with RRF)
-   - Store some information in the Qdrant database
+
-   - Input:
+The upstream server uses **dense vectors only** (semantic similarity). This works well for paraphrased queries but can miss results when the user searches for exact terms, names, or identifiers.
-     - `information` (string): Information to store
+
-     - `metadata` (JSON): Optional metadata to store
+This fork adds **BM25 sparse vectors** alongside the dense ones. At query time, both vector spaces are searched independently and results are fused using **Reciprocal Rank Fusion (RRF)** — a proven technique that combines rankings without requiring score calibration.
-     - `collection_name` (string): Name of the collection to store the information in. This field is required if there are no default collection name.
+
-                                   If there is a default collection name, this field is not enabled.
+**How it works:**
-   - Returns: Confirmation message
+
-2. `qdrant-find`
+```
-   - Retrieve relevant information from the Qdrant database
+Store: document → [dense embedding] + [BM25 sparse embedding] → Qdrant
-   - Input:
+Search: query → prefetch(dense, top-k) + prefetch(BM25, top-k) → RRF fusion → final results
-     - `query` (string): Query to use for searching
+```
-     - `collection_name` (string): Name of the collection to store the information in. This field is required if there are no default collection name.
+
-                                   If there is a default collection name, this field is not enabled.
+- Dense vectors capture **semantic meaning** (synonyms, paraphrases, context)
-   - Returns: Information stored in the Qdrant database as separate messages
+- BM25 sparse vectors excel at **exact keyword matching** (names, IDs, error codes)
+- RRF fusion gives you the best of both worlds
+
+**Enable it** with a single environment variable:
+
+```bash
+HYBRID_SEARCH=true
+```
+
+> [!NOTE]
+> Hybrid search uses the `Qdrant/bm25` model from [FastEmbed](https://qdrant.github.io/fastembed/) for sparse embeddings. The model is downloaded automatically on first use (~50 MB). The IDF modifier is applied to upweight rare terms in the corpus.
+
+> [!IMPORTANT]
+> Enabling hybrid search on an existing collection requires re-creating it, as the sparse vector configuration must be set at collection creation time. Back up your data before switching.
+
+### Project Tagging
+
+The `qdrant-store` tool now accepts a `project` parameter (default: `"global"`). This value is automatically injected into the metadata of every stored record and indexed as a keyword field for efficient filtering.
+
+This is useful when multiple projects share the same Qdrant collection — you can tag memories with the project name and filter by it later.
+
+```
+qdrant-store(information="...", project="my-project")
+→ metadata: {"project": "my-project", ...}
+```
+
+A payload index on `metadata.project` is created automatically when the collection is first set up.
+
+---
+
+## Tools
+
+### `qdrant-store`
+
+Store information in the Qdrant database.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `information` | string | yes | Text to store |
+| `project` | string | no | Project name to tag this memory with. Default: `"global"`. Use the project name (e.g. `"my-app"`) for project-specific knowledge, or `"global"` for cross-project knowledge. |
+| `metadata` | JSON | no | Extra metadata stored alongside the information |
+| `collection_name` | string | depends | Collection name. Required if no default is configured. |
+
+### `qdrant-find`
+
+Retrieve relevant information from the Qdrant database.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `query` | string | yes | What to search for |
+| `collection_name` | string | depends | Collection name. Required if no default is configured. |
 
 ## Environment Variables
 
-The configuration of the server is done using environment variables:
+| Name | Description | Default |
-
+|------|-------------|---------|
-| Name                     | Description                                                         | Default Value                                                     |
+| `QDRANT_URL` | URL of the Qdrant server | None |
-|--------------------------|---------------------------------------------------------------------|-------------------------------------------------------------------|
+| `QDRANT_API_KEY` | API key for the Qdrant server | None |
-| `QDRANT_URL`             | URL of the Qdrant server                                            | None                                                              |
+| `QDRANT_LOCAL_PATH` | Path to local Qdrant database (alternative to `QDRANT_URL`) | None |
-| `QDRANT_API_KEY`         | API key for the Qdrant server                                       | None                                                              |
+| `COLLECTION_NAME` | Default collection name | None |
-| `COLLECTION_NAME`        | Name of the default collection to use.                              | None                                                              |
+| `EMBEDDING_PROVIDER` | Embedding provider (currently only `fastembed`) | `fastembed` |
-| `QDRANT_LOCAL_PATH`      | Path to the local Qdrant database (alternative to `QDRANT_URL`)     | None                                                              |
+| `EMBEDDING_MODEL` | Embedding model name | `sentence-transformers/all-MiniLM-L6-v2` |
-| `EMBEDDING_PROVIDER`     | Embedding provider to use (currently only "fastembed" is supported) | `fastembed`                                                       |
+| **`HYBRID_SEARCH`** | **Enable hybrid search (dense + BM25 sparse with RRF)** | **`false`** |
-| `EMBEDDING_MODEL`        | Name of the embedding model to use                                  | `sentence-transformers/all-MiniLM-L6-v2`                          |
+| `QDRANT_SEARCH_LIMIT` | Maximum number of results per search | `10` |
-| `TOOL_STORE_DESCRIPTION` | Custom description for the store tool                               | See default in [`settings.py`](src/mcp_server_qdrant/settings.py) |
+| `QDRANT_READ_ONLY` | Disable write operations (store tool) | `false` |
-| `TOOL_FIND_DESCRIPTION`  | Custom description for the find tool                                | See default in [`settings.py`](src/mcp_server_qdrant/settings.py) |
+| `QDRANT_ALLOW_ARBITRARY_FILTER` | Allow arbitrary filter objects in find queries | `false` |
-
+| `TOOL_STORE_DESCRIPTION` | Custom description for the store tool | See [`settings.py`](src/mcp_server_qdrant/settings.py) |
-Note: You cannot provide both `QDRANT_URL` and `QDRANT_LOCAL_PATH` at the same time.
+| `TOOL_FIND_DESCRIPTION` | Custom description for the find tool | See [`settings.py`](src/mcp_server_qdrant/settings.py) |
 
 > [!IMPORTANT]
-> Command-line arguments are not supported anymore! Please use environment variables for all configuration.
+> You cannot provide both `QDRANT_URL` and `QDRANT_LOCAL_PATH` at the same time.
 
 ### FastMCP Environment Variables
 
-Since `mcp-server-qdrant` is based on FastMCP, it also supports all the FastMCP environment variables. The most
+Since `mcp-server-qdrant` is based on FastMCP, it also supports all FastMCP environment variables:
-important ones are listed below:
 
-| Environment Variable                  | Description                                               | Default Value |
+| Name | Description | Default |
-|---------------------------------------|-----------------------------------------------------------|---------------|
+|------|-------------|---------|
-| `FASTMCP_DEBUG`                       | Enable debug mode                                         | `false`       |
+| `FASTMCP_DEBUG` | Enable debug mode | `false` |
-| `FASTMCP_LOG_LEVEL`                   | Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | `INFO`        |
+| `FASTMCP_LOG_LEVEL` | Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | `INFO` |
-| `FASTMCP_HOST`                        | Host address to bind the server to                        | `127.0.0.1`   |
+| `FASTMCP_HOST` | Host address to bind to | `127.0.0.1` |
-| `FASTMCP_PORT`                        | Port to run the server on                                 | `8000`        |
+| `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`        |
-| `FASTMCP_WARN_ON_DUPLICATE_PROMPTS`   | Show warnings for duplicate prompts                       | `true`        |
-| `FASTMCP_DEPENDENCIES`                | List of dependencies to install in the server environment | `[]`          |
 
 ## Installation
 
 ### Using uvx
 
-When using [`uvx`](https://docs.astral.sh/uv/guides/tools/#running-tools) no specific installation is needed to directly run *mcp-server-qdrant*.
+No installation needed with [`uvx`](https://docs.astral.sh/uv/guides/tools/#running-tools):
 
 ```shell
 QDRANT_URL="http://localhost:6333" \
 COLLECTION_NAME="my-collection" \
-EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
+HYBRID_SEARCH=true \
 uvx mcp-server-qdrant
 ```
 
 #### Transport Protocols
 
-The server supports different transport protocols that can be specified using the `--transport` flag:
-
 ```shell
+# SSE transport (for remote clients)
 QDRANT_URL="http://localhost:6333" \
 COLLECTION_NAME="my-collection" \
+HYBRID_SEARCH=true \
 uvx mcp-server-qdrant --transport sse
 ```
 
-Supported transport protocols:
+Supported transports:
-
+- `stdio` (default) — for local MCP clients
-- `stdio` (default): Standard input/output transport, might only be used by local MCP clients
+- `sse` — Server-Sent Events, for remote clients
-- `sse`: Server-Sent Events transport, perfect for remote clients
+- `streamable-http` — streamable HTTP, newer alternative to SSE
-- `streamable-http`: Streamable HTTP transport, perfect for remote clients, more recent than SSE
-
-The default transport is `stdio` if not specified.
-
-When SSE transport is used, the server will listen on the specified port and wait for incoming connections. The default
-port is 8000, however it can be changed using the `FASTMCP_PORT` environment variable.
-
-```shell
-QDRANT_URL="http://localhost:6333" \
-COLLECTION_NAME="my-collection" \
-FASTMCP_PORT=1234 \
-uvx mcp-server-qdrant --transport sse
-```
 
 ### Using Docker
 
-A Dockerfile is available for building and running the MCP server:
-
 ```bash
-# Build the container
 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" \
+  -e HYBRID_SEARCH=true \
   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):
-
 ```bash
 npx @smithery/cli install mcp-server-qdrant --client claude
 ```
 
-### Manual configuration of Claude Desktop
+## Usage with MCP Clients
 
-To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your
+### Claude Desktop
-`claude_desktop_config.json`:
+
+Add to `claude_desktop_config.json`:
 
 ```json
 {
@@ -151,171 +172,64 @@ To use this server with the Claude Desktop app, add the following configuration
     "command": "uvx",
     "args": ["mcp-server-qdrant"],
     "env": {
-      "QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
+      "QDRANT_URL": "https://your-qdrant-instance:6333",
       "QDRANT_API_KEY": "your_api_key",
-      "COLLECTION_NAME": "your-collection-name",
+      "COLLECTION_NAME": "your-collection",
-      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
+      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2",
+      "HYBRID_SEARCH": "true"
     }
   }
 }
 ```
 
-For local Qdrant mode:
+### Claude Code
 
-```json
+```shell
-{
+claude mcp add qdrant-memory \
-  "qdrant": {
+  -e QDRANT_URL="http://localhost:6333" \
-    "command": "uvx",
+  -e COLLECTION_NAME="my-memory" \
-    "args": ["mcp-server-qdrant"],
+  -e HYBRID_SEARCH="true" \
-    "env": {
+  -- uvx mcp-server-qdrant
-      "QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
-      "COLLECTION_NAME": "your-collection-name",
-      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
-    }
-  }
-}
 ```
 
-This MCP server will automatically create a collection with the specified name if it doesn't exist.
+Verify:
 
-By default, the server will use the `sentence-transformers/all-MiniLM-L6-v2` embedding model to encode memories.
+```shell
-For the time being, only [FastEmbed](https://qdrant.github.io/fastembed/) models are supported.
+claude mcp list
+```
 
-## Support for other tools
+### Cursor / Windsurf
 
-This MCP server can be used with any MCP-compatible client. For example, you can use it with
+Run the server with SSE transport and custom tool descriptions for code search:
-[Cursor](https://docs.cursor.com/context/model-context-protocol) and [VS Code](https://code.visualstudio.com/docs), which provide built-in support for the Model Context
-Protocol.
-
-### Using with Cursor/Windsurf
-
-You can configure this MCP server to work as a code search tool for Cursor or Windsurf by customizing the tool
-descriptions:
 
 ```bash
 QDRANT_URL="http://localhost:6333" \
 COLLECTION_NAME="code-snippets" \
+HYBRID_SEARCH=true \
 TOOL_STORE_DESCRIPTION="Store reusable code snippets for later retrieval. \
 The 'information' parameter should contain a natural language description of what the code does, \
-while the actual code should be included in the 'metadata' parameter as a 'code' property. \
+while the actual code should be included in the 'metadata' parameter as a 'code' property." \
-The value of 'metadata' is a Python dictionary with strings as keys. \
+TOOL_FIND_DESCRIPTION="Search for relevant code snippets based on natural language descriptions." \
-Use this whenever you generate some code snippet." \
+uvx mcp-server-qdrant --transport sse
-TOOL_FIND_DESCRIPTION="Search for relevant code snippets based on natural language descriptions. \
-The 'query' parameter should describe what you're looking for, \
-and the tool will return the most relevant code snippets. \
-Use this when you need to find existing code snippets for reuse or reference." \
-uvx mcp-server-qdrant --transport sse # Enable SSE transport
 ```
 
-In Cursor/Windsurf, you can then configure the MCP server in your settings by pointing to this running server using
+Then point Cursor/Windsurf to `http://localhost:8000/sse`.
-SSE transport protocol. The description on how to add an MCP server to Cursor can be found in the [Cursor
-documentation](https://docs.cursor.com/context/model-context-protocol#adding-an-mcp-server-to-cursor). If you are
-running Cursor/Windsurf locally, you can use the following URL:
 
-```
+### VS Code
-http://localhost:8000/sse
-```
-
-> [!TIP]
-> We suggest SSE transport as a preferred way to connect Cursor/Windsurf to the MCP server, as it can support remote
-> connections. That makes it easy to share the server with your team or use it in a cloud environment.
-
-This configuration transforms the Qdrant MCP server into a specialized code search tool that can:
-
-1. Store code snippets, documentation, and implementation details
-2. Retrieve relevant code examples based on semantic search
-3. Help developers find specific implementations or usage patterns
-
-You can populate the database by storing natural language descriptions of code snippets (in the `information` parameter)
-along with the actual code (in the `metadata.code` property), and then search for them using natural language queries
-that describe what you're looking for.
-
-> [!NOTE]
-> The tool descriptions provided above are examples and may need to be customized for your specific use case. Consider
-> adjusting the descriptions to better match your team's workflow and the specific types of code snippets you want to
-> store and retrieve.
-
-**If you have successfully installed the `mcp-server-qdrant`, but still can't get it to work with Cursor, please
-consider creating the [Cursor rules](https://docs.cursor.com/context/rules-for-ai) so the MCP tools are always used when
-the agent produces a new code snippet.** You can restrict the rules to only work for certain file types, to avoid using
-the MCP server for the documentation or other types of content.
-
-### Using with Claude Code
-
-You can enhance Claude Code's capabilities by connecting it to this MCP server, enabling semantic search over your
-existing codebase.
-
-#### Setting up mcp-server-qdrant
-
-1. Add the MCP server to Claude Code:
-
-    ```shell
-    # Add mcp-server-qdrant configured for code search
-    claude mcp add code-search \
-    -e QDRANT_URL="http://localhost:6333" \
-    -e COLLECTION_NAME="code-repository" \
-    -e EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
-    -e TOOL_STORE_DESCRIPTION="Store code snippets with descriptions. The 'information' parameter should contain a natural language description of what the code does, while the actual code should be included in the 'metadata' parameter as a 'code' property." \
-    -e TOOL_FIND_DESCRIPTION="Search for relevant code snippets using natural language. The 'query' parameter should describe the functionality you're looking for." \
-    -- uvx mcp-server-qdrant
-    ```
-
-2. Verify the server was added:
-
-    ```shell
-    claude mcp list
-    ```
-
-#### Using Semantic Code Search in Claude Code
-
-Tool descriptions, specified in `TOOL_STORE_DESCRIPTION` and `TOOL_FIND_DESCRIPTION`, guide Claude Code on how to use
-the MCP server. The ones provided above are examples and may need to be customized for your specific use case. However,
-Claude Code should be already able to:
-
-1. Use the `qdrant-store` tool to store code snippets with descriptions.
-2. Use the `qdrant-find` tool to search for relevant code snippets using natural language.
-
-### Run MCP server in Development Mode
-
-The MCP server can be run in development mode using the `mcp dev` command. This will start the server and open the MCP
-inspector in your browser.
-
-```shell
-COLLECTION_NAME=mcp-dev fastmcp dev src/mcp_server_qdrant/server.py
-```
-
-### Using with VS Code
 
 For one-click installation, click one of the install buttons below:
 
 [![Install with UVX in VS Code](https://img.shields.io/badge/VS_Code-UVX-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D) [![Install with UVX in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-UVX-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D&quality=insiders)
 
-[![Install with Docker in VS Code](https://img.shields.io/badge/VS_Code-Docker-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22docker%22%2C%22args%22%3A%5B%22run%22%2C%22-p%22%2C%228000%3A8000%22%2C%22-i%22%2C%22--rm%22%2C%22-e%22%2C%22QDRANT_URL%22%2C%22-e%22%2C%22QDRANT_API_KEY%22%2C%22-e%22%2C%22COLLECTION_NAME%22%2C%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D) [![Install with Docker in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Docker-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22docker%22%2C%22args%22%3A%5B%22run%22%2C%22-p%22%2C%228000%3A8000%22%2C%22-i%22%2C%22--rm%22%2C%22-e%22%2C%22QDRANT_URL%22%2C%22-e%22%2C%22QDRANT_API_KEY%22%2C%22-e%22%2C%22COLLECTION_NAME%22%2C%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D&quality=insiders)
+Or add manually to VS Code settings (`Ctrl+Shift+P` → `Preferences: Open User Settings (JSON)`):
-
-#### Manual Installation
-
-Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing `Ctrl + Shift + P` and typing `Preferences: Open User Settings (JSON)`.
 
 ```json
 {
   "mcp": {
     "inputs": [
-      {
+      {"type": "promptString", "id": "qdrantUrl", "description": "Qdrant URL"},
-        "type": "promptString",
+      {"type": "promptString", "id": "qdrantApiKey", "description": "Qdrant API Key", "password": true},
-        "id": "qdrantUrl",
+      {"type": "promptString", "id": "collectionName", "description": "Collection Name"}
-        "description": "Qdrant URL"
-      },
-      {
-        "type": "promptString",
-        "id": "qdrantApiKey",
-        "description": "Qdrant API Key",
-        "password": true
-      },
-      {
-        "type": "promptString",
-        "id": "collectionName",
-        "description": "Collection Name"
-      }
     ],
     "servers": {
       "qdrant": {
@@ -324,7 +238,8 @@ Add the following JSON block to your User Settings (JSON) file in VS Code. You c
         "env": {
           "QDRANT_URL": "${input:qdrantUrl}",
           "QDRANT_API_KEY": "${input:qdrantApiKey}",
-          "COLLECTION_NAME": "${input:collectionName}"
+          "COLLECTION_NAME": "${input:collectionName}",
+          "HYBRID_SEARCH": "true"
         }
       }
     }
@@ -332,154 +247,40 @@ Add the following JSON block to your User Settings (JSON) file in VS Code. You c
 }
 ```
 
-Or if you prefer using Docker, add this configuration instead:
+## Development
 
-```json
+Run in development mode with the MCP inspector:
-{
-  "mcp": {
-    "inputs": [
-      {
-        "type": "promptString",
-        "id": "qdrantUrl",
-        "description": "Qdrant URL"
-      },
-      {
-        "type": "promptString",
-        "id": "qdrantApiKey",
-        "description": "Qdrant API Key",
-        "password": true
-      },
-      {
-        "type": "promptString",
-        "id": "collectionName",
-        "description": "Collection Name"
-      }
-    ],
-    "servers": {
-      "qdrant": {
-        "command": "docker",
-        "args": [
-          "run",
-          "-p", "8000:8000",
-          "-i",
-          "--rm",
-          "-e", "QDRANT_URL",
-          "-e", "QDRANT_API_KEY",
-          "-e", "COLLECTION_NAME",
-          "mcp-server-qdrant"
-        ],
-        "env": {
-          "QDRANT_URL": "${input:qdrantUrl}",
-          "QDRANT_API_KEY": "${input:qdrantApiKey}",
-          "COLLECTION_NAME": "${input:collectionName}"
-        }
-      }
-    }
-  }
-}
-```
-
-Alternatively, you can create a `.vscode/mcp.json` file in your workspace with the following content:
-
-```json
-{
-  "inputs": [
-    {
-      "type": "promptString",
-      "id": "qdrantUrl",
-      "description": "Qdrant URL"
-    },
-    {
-      "type": "promptString",
-      "id": "qdrantApiKey",
-      "description": "Qdrant API Key",
-      "password": true
-    },
-    {
-      "type": "promptString",
-      "id": "collectionName",
-      "description": "Collection Name"
-    }
-  ],
-  "servers": {
-    "qdrant": {
-      "command": "uvx",
-      "args": ["mcp-server-qdrant"],
-      "env": {
-        "QDRANT_URL": "${input:qdrantUrl}",
-        "QDRANT_API_KEY": "${input:qdrantApiKey}",
-        "COLLECTION_NAME": "${input:collectionName}"
-      }
-    }
-  }
-}
-```
-
-For workspace configuration with Docker, use this in `.vscode/mcp.json`:
-
-```json
-{
-  "inputs": [
-    {
-      "type": "promptString",
-      "id": "qdrantUrl",
-      "description": "Qdrant URL"
-    },
-    {
-      "type": "promptString",
-      "id": "qdrantApiKey",
-      "description": "Qdrant API Key",
-      "password": true
-    },
-    {
-      "type": "promptString",
-      "id": "collectionName",
-      "description": "Collection Name"
-    }
-  ],
-  "servers": {
-    "qdrant": {
-      "command": "docker",
-      "args": [
-        "run",
-        "-p", "8000:8000",
-        "-i",
-        "--rm",
-        "-e", "QDRANT_URL",
-        "-e", "QDRANT_API_KEY",
-        "-e", "COLLECTION_NAME",
-        "mcp-server-qdrant"
-      ],
-      "env": {
-        "QDRANT_URL": "${input:qdrantUrl}",
-        "QDRANT_API_KEY": "${input:qdrantApiKey}",
-        "COLLECTION_NAME": "${input:collectionName}"
-      }
-    }
-  }
-}
-```
-
-## Contributing
-
-If you have suggestions for how mcp-server-qdrant could be improved, or want to report a bug, open an issue!
-We'd love all and any contributions.
-
-### Testing `mcp-server-qdrant` locally
-
-The [MCP inspector](https://github.com/modelcontextprotocol/inspector) is a developer tool for testing and debugging MCP
-servers. It runs both a client UI (default port 5173) and an MCP proxy server (default port 3000). Open the client UI in
-your browser to use the inspector.
 
 ```shell
-QDRANT_URL=":memory:" COLLECTION_NAME="test" \
+COLLECTION_NAME=mcp-dev HYBRID_SEARCH=true \
 fastmcp dev src/mcp_server_qdrant/server.py
 ```
 
-Once started, open your browser to http://localhost:5173 to access the inspector interface.
+Open http://localhost:5173 to access the inspector.
+
+## How Hybrid Search Works Under the Hood
+
+When `HYBRID_SEARCH=true`:
+
+**Storing:**
+1. The document is embedded with the dense model (e.g. `all-MiniLM-L6-v2`) → semantic vector
+2. The document is also embedded with `Qdrant/bm25` → sparse vector (term frequencies with IDF)
+3. Both vectors are stored in the same Qdrant point
+
+**Searching:**
+1. The query is embedded with both models
+2. Two independent prefetch queries run in parallel:
+   - Dense vector search (cosine similarity)
+   - BM25 sparse vector search (dot product with IDF weighting)
+3. Results are fused using **Reciprocal Rank Fusion**: `score = 1/(k + rank_dense) + 1/(k + rank_sparse)`
+4. Top-k fused results are returned
+
+This approach is battle-tested in information retrieval and consistently outperforms either method alone, especially for queries that mix natural language with specific terms.
+
+## Acknowledgments
+
+This is a fork of [qdrant/mcp-server-qdrant](https://github.com/qdrant/mcp-server-qdrant). All credit for the original implementation goes to the Qdrant team.
 
 ## License
 
-This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the
+Apache License 2.0 — see [LICENSE](LICENSE) for details.
-software, subject to the terms and conditions of the Apache License 2.0. For more details, please see the LICENSE file
-in the project repository.

src/mcp_server_qdrant/embeddings/base.py

@@ -1,4 +1,13 @@
 from abc import ABC, abstractmethod
+from dataclasses import dataclass
+
+
+@dataclass
+class SparseVector:
+    """A sparse vector representation with indices and values."""
+
+    indices: list[int]
+    values: list[float]
 
 
 class EmbeddingProvider(ABC):
@@ -23,3 +32,15 @@ class EmbeddingProvider(ABC):
     def get_vector_size(self) -> int:
         """Get the size of the vector for the Qdrant collection."""
         pass
+
+    def supports_sparse(self) -> bool:
+        """Whether this provider supports sparse (BM25) embeddings."""
+        return False
+
+    async def embed_documents_sparse(self, documents: list[str]) -> list[SparseVector]:
+        """Embed documents into sparse vectors. Override if supports_sparse() is True."""
+        raise NotImplementedError
+
+    async def embed_query_sparse(self, query: str) -> SparseVector:
+        """Embed a query into a sparse vector. Override if supports_sparse() is True."""
+        raise NotImplementedError

src/mcp_server_qdrant/embeddings/factory.py

@@ -3,15 +3,18 @@ from mcp_server_qdrant.embeddings.types import EmbeddingProviderType
 from mcp_server_qdrant.settings import EmbeddingProviderSettings
 
 
-def create_embedding_provider(settings: EmbeddingProviderSettings) -> EmbeddingProvider:
+def create_embedding_provider(
+    settings: EmbeddingProviderSettings, enable_sparse: bool = False
+) -> EmbeddingProvider:
     """
     Create an embedding provider based on the specified type.
     :param settings: The settings for the embedding provider.
+    :param enable_sparse: Whether to enable sparse (BM25) embeddings.
     :return: An instance of the specified embedding provider.
     """
     if settings.provider_type == EmbeddingProviderType.FASTEMBED:
         from mcp_server_qdrant.embeddings.fastembed import FastEmbedProvider
 
-        return FastEmbedProvider(settings.model_name)
+        return FastEmbedProvider(settings.model_name, enable_sparse=enable_sparse)
     else:
         raise ValueError(f"Unsupported embedding provider: {settings.provider_type}")

src/mcp_server_qdrant/embeddings/fastembed.py

@@ -1,24 +1,31 @@
 import asyncio
 
-from fastembed import TextEmbedding
+from fastembed import SparseTextEmbedding, TextEmbedding
 from fastembed.common.model_description import DenseModelDescription
 
-from mcp_server_qdrant.embeddings.base import EmbeddingProvider
+from mcp_server_qdrant.embeddings.base import EmbeddingProvider, SparseVector
 
 
 class FastEmbedProvider(EmbeddingProvider):
     """
     FastEmbed implementation of the embedding provider.
     :param model_name: The name of the FastEmbed model to use.
+    :param enable_sparse: Whether to enable BM25 sparse embeddings for hybrid search.
     """
 
-    def __init__(self, model_name: str):
+    def __init__(self, model_name: str, enable_sparse: bool = False):
         self.model_name = model_name
         self.embedding_model = TextEmbedding(model_name)
+        self._enable_sparse = enable_sparse
+        self._sparse_model = None
+        if enable_sparse:
+            self._sparse_model = SparseTextEmbedding("Qdrant/bm25")
+
+    def supports_sparse(self) -> bool:
+        return self._enable_sparse and self._sparse_model is not None
 
     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()
         embeddings = await loop.run_in_executor(
             None, lambda: list(self.embedding_model.passage_embed(documents))
@@ -27,13 +34,37 @@ class FastEmbedProvider(EmbeddingProvider):
 
     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()
         embeddings = await loop.run_in_executor(
             None, lambda: list(self.embedding_model.query_embed([query]))
         )
         return embeddings[0].tolist()
 
+    async def embed_documents_sparse(self, documents: list[str]) -> list[SparseVector]:
+        """Embed documents into BM25 sparse vectors."""
+        loop = asyncio.get_event_loop()
+        results = await loop.run_in_executor(
+            None, lambda: list(self._sparse_model.passage_embed(documents))
+        )
+        return [
+            SparseVector(
+                indices=r.indices.tolist(),
+                values=r.values.tolist(),
+            )
+            for r in results
+        ]
+
+    async def embed_query_sparse(self, query: str) -> SparseVector:
+        """Embed a query into a BM25 sparse vector."""
+        loop = asyncio.get_event_loop()
+        results = await loop.run_in_executor(
+            None, lambda: list(self._sparse_model.query_embed([query]))
+        )
+        return SparseVector(
+            indices=results[0].indices.tolist(),
+            values=results[0].values.tolist(),
+        )
+
     def get_vector_name(self) -> str:
         """
         Return the name of the vector for the Qdrant collection.

src/mcp_server_qdrant/mcp_server.py

@@ -57,7 +57,8 @@ class QdrantMCPServer(FastMCP):
         if embedding_provider_settings:
             self.embedding_provider_settings = embedding_provider_settings
             self.embedding_provider = create_embedding_provider(
-                embedding_provider_settings
+                embedding_provider_settings,
+                enable_sparse=qdrant_settings.hybrid_search,
             )
         else:
             self.embedding_provider_settings = None
@@ -72,6 +73,7 @@ class QdrantMCPServer(FastMCP):
             self.embedding_provider,
             qdrant_settings.local_path,
             make_indexes(qdrant_settings.filterable_fields_dict()),
+            hybrid_search=qdrant_settings.hybrid_search,
         )
 
         super().__init__(name=name, instructions=instructions, **settings)
@@ -96,6 +98,13 @@ class QdrantMCPServer(FastMCP):
             collection_name: Annotated[
                 str, Field(description="The collection to store the information in")
             ],
+            project: Annotated[
+                str,
+                Field(
+                    description="Project name, e.g. devops, stereo-hysteria, voice-assistant. "
+                    "Use 'global' for cross-project knowledge (servers, network, user preferences)."
+                ),
+            ] = "global",
             # 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.
@@ -110,12 +119,17 @@ class QdrantMCPServer(FastMCP):
             Store some information in Qdrant.
             :param ctx: The context for the request.
             :param information: The information to store.
+            :param project: The project name to tag this memory with.
             :param metadata: JSON metadata to store with the information, optional.
             :param collection_name: The name of the collection to store the information in, optional. If not provided,
                                     the default collection is used.
             :return: A message indicating that the information was stored.
             """
-            await ctx.debug(f"Storing information {information} in Qdrant")
+            await ctx.debug(f"Storing information {information} in Qdrant (project={project})")
+
+            if metadata is None:
+                metadata = {}
+            metadata["project"] = project
 
             entry = Entry(content=information, metadata=metadata)
 

src/mcp_server_qdrant/qdrant.py

@@ -23,6 +23,9 @@ class Entry(BaseModel):
     metadata: Metadata | None = None
 
 
+SPARSE_VECTOR_NAME = "bm25"
+
+
 class QdrantConnector:
     """
     Encapsulates the connection to a Qdrant server and all the methods to interact with it.
@@ -32,6 +35,7 @@ class QdrantConnector:
                             the collection name to be provided.
     :param embedding_provider: The embedding provider to use.
     :param qdrant_local_path: The path to the storage directory for the Qdrant client, if local mode is used.
+    :param hybrid_search: Whether to enable hybrid search (dense + BM25 sparse vectors with RRF).
     """
 
     def __init__(
@@ -42,15 +46,19 @@ class QdrantConnector:
         embedding_provider: EmbeddingProvider,
         qdrant_local_path: str | None = None,
         field_indexes: dict[str, models.PayloadSchemaType] | None = None,
+        hybrid_search: bool = False,
     ):
         self._qdrant_url = qdrant_url.rstrip("/") if qdrant_url else None
         self._qdrant_api_key = qdrant_api_key
         self._default_collection_name = collection_name
         self._embedding_provider = embedding_provider
+        self._hybrid_search = hybrid_search and embedding_provider.supports_sparse()
         self._client = AsyncQdrantClient(
             location=qdrant_url, api_key=qdrant_api_key, path=qdrant_local_path
         )
         self._field_indexes = field_indexes
+        if self._hybrid_search:
+            logger.info("Hybrid search enabled (dense + BM25 sparse vectors with RRF)")
 
     async def get_collection_names(self) -> list[str]:
         """
@@ -72,19 +80,30 @@ class QdrantConnector:
         await self._ensure_collection_exists(collection_name)
 
         # Embed the document
-        # ToDo: instead of embedding text explicitly, use `models.Document`,
-        # it should unlock usage of server-side inference.
         embeddings = await self._embedding_provider.embed_documents([entry.content])
 
-        # Add to Qdrant
+        # Build vector dict
         vector_name = self._embedding_provider.get_vector_name()
+        vector_data: dict = {vector_name: embeddings[0]}
+
+        # Add sparse vector if hybrid search is enabled
+        if self._hybrid_search:
+            sparse_embeddings = await self._embedding_provider.embed_documents_sparse(
+                [entry.content]
+            )
+            sparse = sparse_embeddings[0]
+            vector_data[SPARSE_VECTOR_NAME] = models.SparseVector(
+                indices=sparse.indices, values=sparse.values
+            )
+
+        # Add to Qdrant
         payload = {"document": entry.content, METADATA_PATH: entry.metadata}
         await self._client.upsert(
             collection_name=collection_name,
             points=[
                 models.PointStruct(
                     id=uuid.uuid4().hex,
-                    vector={vector_name: embeddings[0]},
+                    vector=vector_data,
                     payload=payload,
                 )
             ],
@@ -113,21 +132,43 @@ class QdrantConnector:
         if not collection_exists:
             return []
 
-        # Embed the query
-        # ToDo: instead of embedding text explicitly, use `models.Document`,
-        # it should unlock usage of server-side inference.
-
         query_vector = await self._embedding_provider.embed_query(query)
         vector_name = self._embedding_provider.get_vector_name()
 
-        # Search in Qdrant
+        # Hybrid search: prefetch dense + sparse, fuse with RRF
-        search_results = await self._client.query_points(
+        if self._hybrid_search:
-            collection_name=collection_name,
+            sparse_vector = await self._embedding_provider.embed_query_sparse(query)
-            query=query_vector,
+            search_results = await self._client.query_points(
-            using=vector_name,
+                collection_name=collection_name,
-            limit=limit,
+                prefetch=[
-            query_filter=query_filter,
+                    models.Prefetch(
-        )
+                        query=query_vector,
+                        using=vector_name,
+                        limit=limit,
+                        filter=query_filter,
+                    ),
+                    models.Prefetch(
+                        query=models.SparseVector(
+                            indices=sparse_vector.indices,
+                            values=sparse_vector.values,
+                        ),
+                        using=SPARSE_VECTOR_NAME,
+                        limit=limit,
+                        filter=query_filter,
+                    ),
+                ],
+                query=models.FusionQuery(fusion=models.Fusion.RRF),
+                limit=limit,
+            )
+        else:
+            # Dense-only search (original behavior)
+            search_results = await self._client.query_points(
+                collection_name=collection_name,
+                query=query_vector,
+                using=vector_name,
+                limit=limit,
+                query_filter=query_filter,
+            )
 
         return [
             Entry(
@@ -149,6 +190,16 @@ class QdrantConnector:
 
             # Use the vector name as defined in the embedding provider
             vector_name = self._embedding_provider.get_vector_name()
+
+            # Sparse vectors config for hybrid search (BM25)
+            sparse_config = None
+            if self._hybrid_search:
+                sparse_config = {
+                    SPARSE_VECTOR_NAME: models.SparseVectorParams(
+                        modifier=models.Modifier.IDF,
+                    )
+                }
+
             await self._client.create_collection(
                 collection_name=collection_name,
                 vectors_config={
@@ -157,10 +208,17 @@ class QdrantConnector:
                         distance=models.Distance.COSINE,
                     )
                 },
+                sparse_vectors_config=sparse_config,
+            )
+
+            # Always index metadata.project for efficient filtering
+            await self._client.create_payload_index(
+                collection_name=collection_name,
+                field_name="metadata.project",
+                field_schema=models.PayloadSchemaType.KEYWORD,
             )
 
             # 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(

src/mcp_server_qdrant/settings.py

@@ -78,6 +78,7 @@ class QdrantSettings(BaseSettings):
 
     location: str | None = Field(default=None, validation_alias="QDRANT_URL")
     api_key: str | None = Field(default=None, validation_alias="QDRANT_API_KEY")
+    hybrid_search: bool = Field(default=False, validation_alias="HYBRID_SEARCH")
     collection_name: str | None = Field(
         default=None, validation_alias="COLLECTION_NAME"
     )