mcp-server-qdrant-enhanced: A Qdrant MCP Server

July 14, 2025 · View on GitHub

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Overview

A Model Context Protocol server for storing and retrieving semantic memories using the Qdrant vector search engine. It provides intelligent semantic memory with automatic embedding model management and multiple deployment modes.

✨ Key Features

🤖 Automatic Model Selection - No manual embedding model configuration needed
🔄 Per-Collection Model Memory - Each collection remembers its embedding model
🐳 Multiple Deployment Modes - Memory, local file, auto-managed Docker, or external
📊 Advanced Search - Hybrid search with similarity scores and filtering
🛠️ Collection Management - Create, configure, and manage collections
🎯 Distance Metric Support - Cosine, dot product, Euclidean, and Manhattan
📈 Batch Operations - Efficient bulk storage and retrieval

Components

Tools (13 Total)

Core Storage & Search

qdrant-store - Store information in Qdrant database
- information (string): Information to store
- metadata (JSON): Optional metadata
- collection_name (string): Target collection (if no default set)
qdrant-find - Retrieve relevant information from database
- query (string): Search query
- collection_name (string): Collection to search (if no default set)

Collection Management

list_collections - List all available Qdrant collections
get_collection_info - Get detailed collection statistics and configuration
- collection_name (string): Collection to inspect
create_collection - Create new collection with custom settings
- collection_name (string): Name for new collection
- vector_size (int): Vector dimensions (e.g., 384, 768, 1024)
- distance (string): Distance metric (cosine, dot, euclidean, manhattan)
- embedding_model (string): Optional embedding model to use
delete_collection - Delete collection permanently
- collection_name (string): Collection to delete
- confirm (bool): Required confirmation flag

Dynamic Embedding Models

list_embedding_models - List all available embedding models with specs
set_collection_embedding_model - Assign embedding model to collection
- collection_name (string): Target collection
- model_name (string): Embedding model name

Advanced Search & Operations

hybrid_search - Advanced search with similarity scores and filtering
- query (string): Search query
- collection_name (string): Collection to search
- limit (int): Maximum results (default: 10)
- min_score (float): Minimum similarity threshold
- include_scores (bool): Include similarity scores in results
scroll_collection - Browse collection contents with pagination
- collection_name (string): Collection to browse
- limit (int): Entries per page (default: 20)
- offset (string): Pagination offset (point ID)
batch_store - Store multiple entries efficiently
- entries (list): List of entries with content, metadata, and optional IDs
- collection_name (string): Target collection

Resources

qdrant://collections - Live overview of all collections with statistics
qdrant://collection/{collection_name}/schema - Detailed schema and configuration for specific collection

Supported Embedding Models

The server now supports 12+ embedding models with automatic model management:

Compact Models (384D):

sentence-transformers/all-MiniLM-L6-v2 - Lightweight, fast general use
sentence-transformers/all-MiniLM-L12-v2 - Better quality than L6
BAAI/bge-small-en-v1.5 - Optimized for English
thenlper/gte-small - General text embeddings
intfloat/e5-small-v2 - E5 family, efficient

Balanced Models (768D):

sentence-transformers/all-mpnet-base-v2 - High quality, balanced
BAAI/bge-base-en-v1.5 - Better English embeddings
thenlper/gte-base - General text embeddings
intfloat/e5-base-v2 - E5 family, balanced

High-Quality Models (1024D):

BAAI/bge-large-en-v1.5 - Highest quality English
thenlper/gte-large - Large general embeddings
intfloat/e5-large-v2 - E5 family, highest quality

For advanced features with 13 tools and intelligent model management, use the configuration below.

🚀 Quick Start

Installation Options

Option 1: Development Setup (Recommended for customization)

Clone and use the repository directly:

git clone https://github.com/your-repo/mcp-server-qdrant.git
cd mcp-server-qdrant

Use this Claude Desktop config:

{
  "mcpServers": {
    "mcp-server-qdrant": {
      "command": "uv",
      "args": [
        "--directory",
        "/home/your-username/path/to/mcp-server-qdrant/src",
        "run",
        "mcp-server-qdrant"
      ],
      "env": {
        "QDRANT_MODE": "docker",
        "QDRANT_AUTO_DOCKER": "true",
        "QDRANT_API_KEY": "",
        "QDRANT_ENABLE_COLLECTION_MANAGEMENT": "true",
        "QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS": "true",
        "QDRANT_ENABLE_RESOURCES": "true"
      }
    }
  }
}

Option 2: Global Installation

Install globally using pipx or uvx:

# Using pipx (recommended)
pipx install mcp-server-qdrant

# Or using uvx
uvx install mcp-server-qdrant

Use this Claude Desktop config:

{
  "mcpServers": {
    "mcp-server-qdrant-enhanced": {
      "command": "uv",
      "args": [
        "--directory",
        "/home/ty/Repositories/mcp-server-qdrant/src",
        "run",
        "mcp-server-qdrant"
      ],
      "env": {
        "QDRANT_MODE": "docker",
        "QDRANT_AUTO_DOCKER": "true",
        "QDRANT_API_KEY": "",
        "QDRANT_ENABLE_COLLECTION_MANAGEMENT": "true",
        "QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS": "true",
        "QDRANT_ENABLE_RESOURCES": "true"
      }
    }
  }
}

Deployment Modes

Choose the deployment mode that best fits your needs:

🧠 Memory Mode (Fastest)

Perfect for testing and development:

export QDRANT_MODE=memory
uv run mcp-server-qdrant --transport sse

💾 Local File Mode (Persistent)

Data persists between restarts:

export QDRANT_MODE=local
uv run mcp-server-qdrant --transport sse

🐳 Auto-Managed Docker (Recommended)

Full Qdrant features with automatic setup:

export QDRANT_MODE=docker
export QDRANT_AUTO_DOCKER=true
uv run mcp-server-qdrant --transport sse

🌐 External Qdrant (Production)

Connect to existing Qdrant instance:

export QDRANT_URL=http://your-qdrant-server:6333
uv run mcp-server-qdrant --transport sse

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-server-qdrant": {
      "command": "uv",
      "args": [
        "--directory",
        "/home/your-username/Repositories/mcp-server-qdrant/src",
        "run",
        "mcp-server-qdrant"
      ],
      "env": {
        "QDRANT_MODE": "docker",
        "QDRANT_AUTO_DOCKER": "true",
        "QDRANT_API_KEY": "",
        "QDRANT_ENABLE_COLLECTION_MANAGEMENT": "true",
        "QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS": "true",
        "QDRANT_ENABLE_RESOURCES": "true"
      }
    }
  }
}

LM Studio Configuration

Add to your LM Studio MCP settings:

{
  "mcpServers": {
    "mcp-server-qdrant": {
      "command": "uv",
      "args": [
        "--directory",
        "/home/your-username/Repositories/mcp-server-qdrant/src",
        "run",
        "mcp-server-qdrant",
        "--transport",
        "stdio"
      ],
      "env": {
        "QDRANT_MODE": "embedded",
        "QDRANT_COLLECTION_NAME": "lm_studio_memories",
        "QDRANT_ENABLE_COLLECTION_MANAGEMENT": "true",
        "QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS": "true",
        "QDRANT_ENABLE_RESOURCES": "true"
      }
    }
  }
}

Alternative using installed package:

{
  "mcpServers": {
    "mcp-server-qdrant": {
      "command": "uvx",
      "args": [
        "mcp-server-qdrant",
        "--transport",
        "stdio"
      ],
      "env": {
        "QDRANT_MODE": "embedded",
        "QDRANT_COLLECTION_NAME": "lm_studio_memories",
        "QDRANT_ENABLE_COLLECTION_MANAGEMENT": "true",
        "QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS": "true"
      }
    }
  }
}

Environment Variables

The configuration of the server is done using environment variables:

Core Configuration

Name	Description	Default Value
`QDRANT_URL`	URL of the Qdrant server	None
`QDRANT_API_KEY`	API key for the Qdrant server	None
`COLLECTION_NAME`	Name of the default collection to use (optional for multi-collection mode)	None
`QDRANT_LOCAL_PATH`	Path to the local Qdrant database (alternative to `QDRANT_URL`)	None
`EMBEDDING_PROVIDER`	Embedding provider to use (currently only "fastembed" is supported)	`fastembed`

Note

Automatic Model Selection: EMBEDDING_MODEL is no longer required. The system automatically selects appropriate models and remembers them per collection.

Enhanced Features Configuration

Name	Description	Default Value
`QDRANT_ENABLE_COLLECTION_MANAGEMENT`	Enable collection management tools	`true`
`QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS`	Enable dynamic embedding model assignment per collection	`true`
`QDRANT_ENABLE_RESOURCES`	Enable MCP resources for collection information	`true`
`QDRANT_MAX_BATCH_SIZE`	Maximum number of entries per batch operation	`100`
`QDRANT_SEARCH_LIMIT`	Default maximum search results	`10`
`QDRANT_READ_ONLY`	Enable read-only mode (disables write operations)	`false`
`QDRANT_ALLOW_ARBITRARY_FILTER`	Allow arbitrary filtering in search	`false`

Tool Descriptions (Customizable)

Name	Description	Default Value
`TOOL_STORE_DESCRIPTION`	Custom description for the store tool	See defaults
`TOOL_FIND_DESCRIPTION`	Custom description for the find tool	See defaults
`TOOL_BATCH_STORE_DESCRIPTION`	Custom description for batch store tool	See defaults
`TOOL_LIST_COLLECTIONS_DESCRIPTION`	Custom description for list collections tool	See defaults
`TOOL_CREATE_COLLECTION_DESCRIPTION`	Custom description for create collection tool	See defaults
`TOOL_DELETE_COLLECTION_DESCRIPTION`	Custom description for delete collection tool	See defaults
`TOOL_HYBRID_SEARCH_DESCRIPTION`	Custom description for hybrid search tool	See defaults
`TOOL_SCROLL_DESCRIPTION`	Custom description for scroll collection tool	See defaults
`TOOL_LIST_EMBEDDING_MODELS_DESCRIPTION`	Custom description for list models tool	See defaults

All tool descriptions have sensible defaults defined in settings.py.

OLD- Note: You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.

Port Management

The enhanced server includes intelligent port management to avoid conflicts:

Automatic Port Detection (Recommended)

The server automatically detects if the default port (8000) is busy and finds an alternative:

{
  "env": {
    "FASTMCP_PORT": "8000"
  }
}

If port 8000 is busy, the server will automatically use ports 8001, 8002, etc. You'll see:

⚠️  Port 8000 was busy. MCP server will use port 8001
🚀 MCP Server starting on http://localhost:8001
📡 SSE endpoint: http://localhost:8001/sse

Manual Port Override

To force a specific port (disables auto-detection):

{
  "env": {
    "FASTMCP_PORT": "9000"
  }
}

No Port Specified (Full Auto-Detection)

Omit FASTMCP_PORT entirely for complete automatic port assignment:

{
  "env": {
    "QDRANT_URL": "http://localhost:6333"
  }
}

The server will scan ports 8000-8099 and use the first available port.

Usage Examples

Multi-Collection Workflow

# List available collections
collections = await list_collections()

# Create a specialized collection for code snippets
await create_collection(
    collection_name="code_snippets",
    vector_size=768,
    distance="cosine"
)

# Create another collection for documentation
await create_collection(
    collection_name="documentation",
    vector_size=384
)

# Store data in different collections
# The system automatically selects and remembers appropriate embedding models
await store("Python function for sorting", "code_snippets")
await store("API documentation for REST endpoints", "documentation")

Advanced Search with Scoring

# Perform hybrid search with similarity scores
results = await hybrid_search(
    query="authentication methods",
    collection_name="documentation",
    limit=5,
    min_score=0.7,
    include_scores=True
)

Batch Operations

# Store multiple entries efficiently
entries = [
    {"content": "Function to validate emails", "metadata": {"type": "utility"}},
    {"content": "Database connection helper", "metadata": {"type": "database"}},
    {"content": "Error handling middleware", "metadata": {"type": "middleware"}}
]
await batch_store(entries, "code_snippets")

Collection Management

# Get detailed collection information
info = await get_collection_info("code_snippets")

# Browse collection contents
await scroll_collection("code_snippets", limit=10)

# View available embedding models
await list_embedding_models()

# Check which model a collection is using
# The system automatically tracks this information

Important

Command-line arguments are not supported anymore! Please use environment variables for all configuration.

FastMCP Environment Variables

Since mcp-server-qdrant is based on FastMCP, it also supports all the FastMCP environment variables. The most important ones are listed below:

Environment Variable	Description	Default Value
`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_PORT`	Port to run the server on (auto-detects if busy)	`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- Old

It should remain backward compatible but not sure

Using uvx

When using uvx no specific installation is needed to directly run mcp-server-qdrant.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant

Transport Protocols

The server supports different transport protocols that can be specified using the --transport flag:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
uvx mcp-server-qdrant --transport sse

Supported transport protocols:

stdio (default): Standard input/output transport, might only be used by local MCP clients
sse: Server-Sent Events transport, perfect for remote clients
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.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
FASTMCP_PORT=1234 \
uvx mcp-server-qdrant --transport sse

OLD- Using Docker

A Dockerfile is available for building and running the MCP server:

# Build the container
docker build -t mcp-server-qdrant .

# Run the container
docker run -p 8000:8000 \
  -e QDRANT_URL="http://your-qdrant-server:6333" \
  -e QDRANT_API_KEY="your-api-key" \
  -e COLLECTION_NAME="your-collection" \
  mcp-server-qdrant

OLD- Installing via Smithery

Warning

The Smithery installation installs the original limited version with only basic store/find functionality, not this enhanced fork with collection management and dynamic embedding models.

To install the original basic version via Smithery:

npx @smithery/cli install mcp-server-qdrant --client claude

Single Collection Mode (Backward Compatible)

For compatibility with existing setups using a single default collection:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
      "QDRANT_API_KEY": "your_api_key",
      "COLLECTION_NAME": "your-collection-name"
    }
  }
}

Local Qdrant Setup

For local development with enhanced features:

{
  "mcp-server-qdrant": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/your/mcp-server-qdrant/src",
      "run",
      "mcp-server-qdrant"
    ],
    "env": {
      "QDRANT_LOCAL_PATH": "/tmp/qdrant_storage",
      "QDRANT_ENABLE_COLLECTION_MANAGEMENT": "true",
      "QDRANT_ENABLE_DYNAMIC_EMBEDDING_MODELS": "true",
      "QDRANT_ENABLE_RESOURCES": "true"
    }
  }
}

This MCP server will automatically create collections and select appropriate embedding models as needed.

Support for other tools

This MCP server can be used with any MCP-compatible client. For example, you can use it with Cursor and VS Code, 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:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="code-snippets" \
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. \
The value of 'metadata' is a Python dictionary with strings as keys. \
Use this whenever you generate some code snippet." \
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 SSE transport protocol. The description on how to add an MCP server to Cursor can be found in the Cursor documentation. If you are running Cursor/Windsurf locally, you can use the following URL:

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:

Store code snippets, documentation, and implementation details
Retrieve relevant code examples based on semantic search
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 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

Add the MCP server to Claude Code:

# 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

Verify the server was added:
```
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:

Use the qdrant-store tool to store code snippets with descriptions.
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.

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:

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).

{
  "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": "uvx",
        "args": ["mcp-server-qdrant"],
        "env": {
          "QDRANT_URL": "${input:qdrantUrl}",
          "QDRANT_API_KEY": "${input:qdrantApiKey}",
          "COLLECTION_NAME": "${input:collectionName}"
        }
      }
    }
  }
}

Or if you prefer using Docker, add this configuration instead:

{
  "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:

{
  "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:

{
  "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 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.

QDRANT_URL=":memory:" COLLECTION_NAME="test" \
fastmcp dev src/mcp_server_qdrant/server.py

Once started, open your browser to http://localhost:5173 to access the inspector interface.

License

This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the 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.

🤖 How Automatic Model Selection Works

The server features intelligent automatic model selection:

First Use: When you first store data in a collection, the server uses a default embedding model
Model Recording: The server automatically records which model was used for that collection
Consistency: All future operations on that collection use the same exact model
Per-Collection: Each collection can use a different model based on its specific needs
Distance Tracking: Each collection also remembers its distance metric (cosine, dot, euclidean, manhattan)
No Configuration: No need to manually specify embedding models in your config

This ensures perfect consistency - each collection always uses the same embedding model it was created with, preventing dimension mismatches and maintaining semantic coherence.

Auto-Managed Docker Features

When using QDRANT_MODE=docker with QDRANT_AUTO_DOCKER=true:

🔍 Smart Container Detection - Checks if Qdrant container already exists
🔄 Port Conflict Resolution - Automatically finds available ports
💾 Data Persistence - Mounts ./qdrant_data for persistent storage
🧹 Graceful Cleanup - Stops container when server exits
⚡ Health Checking - Waits for Qdrant API to be ready before proceeding