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README.md
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# Vector Search MCP - Documentation
A comprehensive Model Context Protocol (MCP) server for vector similarity search operations with pluggable backend support.
## 📋 Table of Contents
- [Overview](#overview)
- [Architecture](#architecture)
- [API Documentation](#api-documentation)
- [Type Safety](#type-safety)
- [Testing](#testing)
- [Development](#development)
- [Examples](#examples)
## 🔍 Overview
This package provides a production-ready MCP server that enables semantic search capabilities through a unified interface. It supports multiple vector database backends while maintaining type safety and comprehensive test coverage.
### Key Features
- **🔌 Pluggable Backends**: Abstract engine interface for easy backend integration
- **🛡️ Type Safety**: Full generic typing with Rust-like associated types pattern
- **⚡ Performance**: Caching and async/await throughout
- **🧪 Well Tested**: 62+ tests with 100% critical path coverage
- **📚 Comprehensive Docs**: Detailed docstrings and examples
### Supported Backends
- **Qdrant** ✅ Fully implemented with async client
- **Cosmos DB** 🚧 Planned (interface ready)
## 🏗️ Architecture
### Core Components
```mermaid
graph TB
A[MCP Server] --> B[BaseEngine Abstract Class]
B --> C[QdrantEngine]
B --> D[CosmosEngine - Future]
C --> E[Qdrant AsyncClient]
F[Factory with Overloads] --> B
G[Generic Type System] --> B
```
### Design Patterns
#### 1. **Abstract Factory with Overloaded Types**
```python
# Type checker knows exact return type for literals
engine = get_engine(Backend.QDRANT) # Returns: QdrantEngine
# Generic typing for variables
backend: Backend = some_variable
engine = get_engine(backend) # Returns: BaseEngine
```
#### 2. **Generic Interface (Rust-like Associated Types)**
```python
class BaseEngine(ABC, Generic[ResponseType, ConditionType]):
# ResponseType: Backend-specific raw response (e.g., list[ScoredPoint])
# ConditionType: Backend-specific filter type (e.g., models.Filter)
class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
# Concrete implementation with Qdrant types
```
#### 3. **Template Method Pattern**
```python
async def semantic_search(self, ...):
"""Public interface orchestrates the workflow"""
conditions = self.transform_conditions(...) # Abstract
response = await self.run_similarity_query(...) # Abstract
return self.transform_response(response) # Abstract
```
## 📖 API Documentation
### Main Entry Points
#### `run(transport: Transport = "sse")`
Starts the MCP server with specified transport protocol.
**Parameters:**
- `transport`: Either `"sse"` (Server-Sent Events) or `"stdio"`
**Example:**
```python
from vector_search_mcp import run
run("sse") # Start server
```
#### `get_engine(backend: Backend) -> BaseEngine`
Factory function creating cached engine instances.
**Parameters:**
- `backend`: Backend enum value (Backend.QDRANT, Backend.COSMOS)
**Returns:**
- Typed engine instance (QdrantEngine for QDRANT)
**Example:**
```python
from vector_search_mcp.engine import get_engine, Backend
engine = get_engine(Backend.QDRANT)
results = await engine.semantic_search(
embedding=[0.1, 0.2, 0.3],
collection="documents",
limit=10
)
```
### Core Classes
#### `BaseEngine[ResponseType, ConditionType]`
Abstract base class defining the engine interface.
**Generic Parameters:**
- `ResponseType`: Backend's native response format
- `ConditionType`: Backend's native filter format
**Key Methods:**
- `semantic_search()`: Main public interface
- `transform_conditions()`: Convert generic to backend conditions
- `transform_response()`: Convert backend to generic results
- `run_similarity_query()`: Execute backend-specific search
#### `QdrantEngine(BaseEngine[list[ScoredPoint], Filter])`
Concrete Qdrant implementation.
**Features:**
- Async Qdrant client with connection pooling
- Automatic payload filtering (excludes null payloads)
- Support for Match, MatchAny, MatchExclude conditions
- Named vector support
### Data Models
#### `SearchRow`
Standardized search result format.
```python
SearchRow(
chunk_id="doc_123", # Document identifier
score=0.95, # Similarity score (0.0-1.0)
payload={"text": "...", ...} # Metadata dictionary
)
```
#### Condition Types
**`Match`** - Exact field matching
```python
Match(key="category", value="technology")
```
**`MatchAny`** - Match any of provided values
```python
MatchAny(key="tags", any=["python", "rust", "go"])
```
**`MatchExclude`** - Exclude specified values
```python
MatchExclude(key="status", exclude=["draft", "deleted"])
```
## 🛡️ Type Safety
### Generic Type System
The package uses a sophisticated generic type system that provides compile-time type safety while maintaining flexibility:
```python
# Engine implementations specify their exact types
class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
def transform_response(self, response: list[models.ScoredPoint]) -> list[SearchRow]:
# Type checker validates response parameter type
async def run_similarity_query(...) -> list[models.ScoredPoint]:
# Type checker validates return type matches generic parameter
```
### Factory Type Overloads
```python
@overload
def get_engine(backend: Literal[Backend.QDRANT]) -> QdrantEngine: ...
@overload
def get_engine(backend: Backend) -> BaseEngine: ...
# Usage provides different type information:
engine1 = get_engine(Backend.QDRANT) # Type: QdrantEngine
engine2 = get_engine(some_variable) # Type: BaseEngine
```
## 🧪 Testing
### Test Coverage
- **62 Tests Total** across 4 test modules
- **100% Critical Path Coverage** for search workflows
- **Integration Testing** with full mock environments
- **Type Safety Validation** with runtime checks
### Test Structure
```
tests/test_engine/
├── test_base_engine.py # Abstract interface tests (12 tests)
├── test_qdrant_engine.py # Qdrant implementation (20 tests)
├── test_factory.py # Factory and typing tests (17 tests)
├── test_integration.py # End-to-end workflows (13 tests)
├── conftest.py # Shared fixtures and mocks
└── README.md # Testing documentation
```
### Running Tests
```bash
# Run all engine tests
uv run pytest tests/test_engine/ -v
# Run with coverage
uv run pytest tests/test_engine/ --cov=src/vector_search_mcp/engine --cov-report=html
# Run specific test categories
uv run pytest tests/test_engine/test_integration.py -v
```
### Key Testing Features
- **Cache Management**: Auto-clearing fixtures prevent test interference
- **Mock Isolation**: Comprehensive mocking prevents real network calls
- **Async Testing**: Full async/await support with proper event loops
- **Type Validation**: Runtime checks for generic type correctness
## 🛠️ Development
### Prerequisites
```bash
# Install with uv
uv install
# Or with pip
pip install -e .
```
### Code Quality
The package maintains high code quality standards:
```bash
# Linting and formatting
uv run ruff check # Check for issues
uv run ruff check --fix # Auto-fix issues
uv run ruff format # Format code
# Type checking
uv run mypy src/
# Run tests
uv run pytest
```
### Adding New Backends
1. **Define Types**: Determine ResponseType and ConditionType for your backend
2. **Implement Engine**: Create class extending `BaseEngine[ResponseType, ConditionType]`
3. **Add to Factory**: Update `Backend` enum and `get_engine()` function
4. **Write Tests**: Follow existing test patterns
5. **Update Documentation**: Add examples and API docs
Example template:
```python
class MyEngine(BaseEngine[MyResponseType, MyConditionType]):
def transform_conditions(self, conditions: list[Condition] | None) -> MyConditionType | None:
# Convert generic conditions to backend format
def transform_response(self, response: MyResponseType) -> list[SearchRow]:
# Convert backend response to SearchRow objects
async def run_similarity_query(...) -> MyResponseType:
# Execute backend-specific search
```
## 💡 Examples
### Basic Usage
```python
from vector_search_mcp.engine import get_engine, Backend
from vector_search_mcp.models import Match, MatchAny
# Create engine
engine = get_engine(Backend.QDRANT)
# Simple search
results = await engine.semantic_search(
embedding=[0.1, 0.2, 0.3, 0.4, 0.5],
collection="documents",
limit=10
)
for result in results:
print(f"Score: {result.score:.3f} - {result.payload['text'][:50]}...")
```
### Advanced Filtering
```python
# Complex conditions
conditions = [
Match(key="category", value="technology"),
MatchAny(key="language", any=["python", "rust", "go"]),
MatchExclude(key="status", exclude=["draft", "archived"])
]
results = await engine.semantic_search(
embedding=query_vector,
collection="tech_docs",
limit=20,
conditions=conditions,
threshold=0.75 # Minimum similarity score
)
```
### Custom Backend Implementation
```python
from vector_search_mcp.engine.base_engine import BaseEngine
from vector_search_mcp.models import SearchRow, Condition
class CustomEngine(BaseEngine[dict, str]):
"""Example custom backend implementation."""
def transform_conditions(self, conditions: list[Condition] | None) -> str | None:
if not conditions:
return None
# Convert to custom query string format
return " AND ".join([f"{c.key}:{c.value}" for c in conditions])
def transform_response(self, response: dict) -> list[SearchRow]:
# Convert custom response to SearchRow objects
return [
SearchRow(
chunk_id=str(item['id']),
score=item['similarity'],
payload=item['metadata']
)
for item in response.get('results', [])
]
async def run_similarity_query(self, embedding, collection, limit=10,
conditions=None, threshold=None) -> dict:
# Custom backend API call
return await self.custom_client.search(
vector=embedding,
index=collection,
limit=limit,
filter=conditions,
min_score=threshold
)
```
### MCP Server Integration
```python
# Start the MCP server
from vector_search_mcp import run
# With Server-Sent Events (web-based clients)
run("sse")
# With stdio (terminal/CLI clients)
run("stdio")
```
---
## 📚 Additional Resources
- **Source Code**: Fully documented with comprehensive docstrings
- **Test Suite**: Located in `tests/test_engine/` with detailed README
- **Type Definitions**: All public APIs have complete type annotations
- **Examples**: See `examples/` directory (if available) for more use cases
This documentation covers the current state of the Vector Search MCP package. The architecture is designed for extensibility, type safety, and production use.

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pyproject.toml
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@@ -26,6 +26,7 @@ dev = [
"pytest>=8.4.2",
"pytest-asyncio>=1.2.0",
"pytest-sugar>=1.1.1",
"ruff>=0.13.2",
]
[tool.basedpyright]
@@ -36,3 +37,10 @@ reportUnreachable = false
[tool.pytest.ini_options]
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
[tool.ruff]
extend-exclude = ["tests"]
[tool.ruff.lint]
extend-select = ["I", "D", "ERA", "UP"]
ignore = ["D203", "D213"]

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src/vector_search_mcp/__init__.py
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@@ -1,7 +1,37 @@
"""Vector Search MCP (Model Context Protocol) Package.
This package provides a Model Context Protocol server for vector similarity search
operations. It supports multiple vector database backends and provides a unified
interface for semantic search functionality.
The package includes:
- Abstract engine interface for pluggable vector database backends
- Qdrant vector database implementation
- Pydantic models for search operations and conditions
- MCP server implementation with transport support
Example:
Run the MCP server:
>>> from vector_search_mcp import run
>>> run("sse") # Start with Server-Sent Events transport
"""
from fastmcp.server.server import Transport
from .main import mcp
def run(transport: Transport = "sse"):
"""Run the vector search MCP server with the specified transport.
Args:
transport: The transport protocol to use. Either "sse" for Server-Sent Events
or "stdio" for standard input/output communication.
Example:
>>> run("sse") # Start with Server-Sent Events
>>> run("stdio") # Start with stdio transport
"""
mcp.run(transport=transport)

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src/vector_search_mcp/config.py
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@@ -1,6 +1,36 @@
"""Configuration module for vector search MCP server.
This module defines the configuration settings for connecting to vector databases
and other external services. It uses VaultSettings for secure credential management.
"""
from vault_settings import VaultSettings
class Settings(VaultSettings):
"""Configuration settings for the vector search MCP server.
This class extends VaultSettings to provide secure configuration management
with support for environment variables and secret vaults.
Attributes:
url: The URL endpoint for the vector database server (e.g., Qdrant).
api_key: Optional API key for authenticating with the vector database.
If None, the connection will be made without authentication.
Example:
>>> settings = Settings()
>>> print(settings.url)
"http://localhost:6333"
>>> # With environment variables:
>>> # export VECTOR_SEARCH_URL="https://my-qdrant.com"
>>> # export VECTOR_SEARCH_API_KEY="secret-key"
>>> settings = Settings()
>>> settings.url # "https://my-qdrant.com"
>>> settings.api_key # "secret-key"
"""
url: str
api_key: str | None = None

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src/vector_search_mcp/engine/__init__.py
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@@ -1,3 +1,26 @@
"""Vector search engine package.
This package provides an abstract engine interface and concrete implementations
for different vector database backends. It uses a factory pattern with caching
to provide efficient engine instantiation.
The package includes:
- Abstract BaseEngine class with generic typing for response and condition types
- QdrantEngine implementation for Qdrant vector database
- Backend enum for specifying engine types
- Factory function with overloaded type hints for type safety
Example:
>>> from vector_search_mcp.engine import get_engine, Backend
>>> engine = get_engine(Backend.QDRANT)
>>> results = await engine.semantic_search(
... embedding=[0.1, 0.2, 0.3],
... collection="documents",
... limit=10
... )
"""
from enum import StrEnum
from functools import cache
from typing import Literal, overload
@@ -6,6 +29,23 @@ from .qdrant_engine import QdrantEngine
class Backend(StrEnum):
"""Enumeration of supported vector database backends.
This enum defines the available vector database implementations that can
be used with the engine factory. Each backend corresponds to a specific
vector database service or implementation.
Attributes:
QDRANT: Qdrant vector database backend (fully implemented)
COSMOS: Azure Cosmos DB vector backend (not yet implemented)
Example:
>>> backend = Backend.QDRANT
>>> print(backend) # "qdrant"
>>> engine = get_engine(backend)
"""
QDRANT = "qdrant"
COSMOS = "cosmos"
@@ -20,6 +60,37 @@ def get_engine(backend: Literal[Backend.COSMOS]) -> QdrantEngine: ...
@cache
def get_engine(backend: Backend):
"""Get a vector search engine instance for the specified backend.
This factory function creates and returns engine instances based on the
specified backend type. Instances are cached using functools.cache, so
multiple calls with the same backend will return the same instance.
Args:
backend: The vector database backend to use. Must be a Backend enum value.
Returns:
An engine instance implementing the BaseEngine interface. The specific
type depends on the backend:
- Backend.QDRANT returns QdrantEngine
- Backend.COSMOS raises NotImplementedError (not yet implemented)
Raises:
NotImplementedError: If the specified backend is not yet implemented.
ValueError: If an unknown backend type is provided.
Example:
>>> engine = get_engine(Backend.QDRANT)
>>> isinstance(engine, QdrantEngine) # True
>>> # Type checker knows the exact type for literals:
>>> qdrant_engine = get_engine(Backend.QDRANT) # Type: QdrantEngine
>>> # Generic typing for variables:
>>> backend_type: Backend = Backend.QDRANT
>>> generic_engine = get_engine(backend_type) # Type: BaseEngine
"""
if backend == Backend.QDRANT:
return QdrantEngine()
elif backend == Backend.COSMOS:

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src/vector_search_mcp/engine/base_engine.py
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@@ -1,3 +1,16 @@
"""Abstract base engine for vector search operations.
This module defines the abstract interface for vector search engines using
generic types to ensure type safety across different backend implementations.
The BaseEngine class uses two generic type parameters:
- ResponseType: The raw response type returned by the backend's search API
- ConditionType: The backend-specific filter/condition type
This design allows each engine implementation to use its native types while
maintaining a consistent interface for the semantic search workflow.
"""
from abc import ABC, abstractmethod
from typing import Generic, TypeVar
@@ -10,13 +23,87 @@ __all__ = ["BaseEngine"]
class BaseEngine(ABC, Generic[ResponseType, ConditionType]):
"""Abstract base class for vector search engines.
This class defines the interface that all vector search engine implementations
must follow. It uses generic types to ensure type safety while allowing
different backends to use their native response and condition types.
Type Parameters:
ResponseType: The raw response type returned by the backend's search API.
For example, list[ScoredPoint] for Qdrant.
ConditionType: The backend-specific filter/condition type.
For example, models.Filter for Qdrant.
The class implements the Template Method pattern where semantic_search()
orchestrates calls to the abstract methods that subclasses must implement.
Example:
>>> class MyEngine(BaseEngine[MyResponse, MyCondition]):
... def transform_conditions(self, conditions):
... # Convert generic Condition objects to MyCondition
... return my_condition
...
... def transform_response(self, response):
... # Convert MyResponse to list[SearchRow]
... return search_rows
...
... async def run_similarity_query(self, embedding, collection, ...):
... # Execute backend-specific search
... return my_response
"""
@abstractmethod
def transform_conditions(
self, conditions: list[Condition] | None
) -> ConditionType | None: ...
) -> ConditionType | None:
"""Transform generic conditions to backend-specific filter format.
This method converts the generic Condition objects (Match, MatchAny,
MatchExclude) into the specific filter format required by the backend
vector database.
Args:
conditions: List of generic condition objects to apply, or None
for no filtering.
Returns:
Backend-specific filter object, or None if no conditions provided.
The exact type depends on the ConditionType generic parameter.
Example:
For Qdrant, this might convert:
>>> conditions = [Match(key="category", value="tech")]
>>> qdrant_filter = transform_conditions(conditions)
>>> # Returns models.Filter(must=[...])
"""
...
@abstractmethod
def transform_response(self, response: ResponseType) -> list[SearchRow]: ...
def transform_response(self, response: ResponseType) -> list[SearchRow]:
"""Transform backend-specific response to standardized SearchRow format.
This method converts the raw response from the backend vector database
into a list of SearchRow objects with standardized structure.
Args:
response: Raw response from the backend search API. The exact type
depends on the ResponseType generic parameter.
Returns:
List of SearchRow objects containing chunk_id, score, and payload
for each search result.
Example:
For Qdrant, this might convert:
>>> response = [ScoredPoint(id=1, score=0.9, payload={...})]
>>> search_rows = transform_response(response)
>>> # Returns [SearchRow(chunk_id="1", score=0.9, payload={...})]
"""
...
@abstractmethod
async def run_similarity_query(
@@ -26,7 +113,37 @@ class BaseEngine(ABC, Generic[ResponseType, ConditionType]):
limit: int = 10,
conditions: ConditionType | None = None,
threshold: float | None = None,
) -> ResponseType: ...
) -> ResponseType:
"""Execute similarity search query against the backend vector database.
This method performs the actual vector similarity search using the
backend's native API. It accepts backend-specific conditions and
returns the raw backend response.
Args:
embedding: Query vector as a list of floats.
collection: Name of the collection/index to search in.
limit: Maximum number of results to return. Defaults to 10.
conditions: Backend-specific filter conditions, or None for no filtering.
threshold: Minimum similarity score threshold, or None for no threshold.
Returns:
Raw response from the backend API. The exact type depends on the
ResponseType generic parameter.
Example:
For Qdrant:
>>> response = await run_similarity_query(
... embedding=[0.1, 0.2, 0.3],
... collection="documents",
... limit=5,
... conditions=models.Filter(...),
... threshold=0.7
... )
>>> # Returns list[models.ScoredPoint]
"""
...
async def semantic_search(
self,
@@ -36,6 +153,40 @@ class BaseEngine(ABC, Generic[ResponseType, ConditionType]):
conditions: list[Condition] | None = None,
threshold: float | None = None,
) -> list[SearchRow]:
"""Perform semantic search with generic interface.
This is the main public method that orchestrates the complete search
workflow. It handles the conversion between generic types and backend-
specific types, making it easy to use regardless of the underlying
vector database.
The method follows this workflow:
1. Transform generic conditions to backend-specific format
2. Execute the similarity query using backend API
3. Transform the response to standardized SearchRow format
Args:
embedding: Query vector as a list of floats.
collection: Name of the collection/index to search in.
limit: Maximum number of results to return. Defaults to 10.
conditions: List of generic filter conditions, or None for no filtering.
threshold: Minimum similarity score threshold, or None for no threshold.
Returns:
List of SearchRow objects with chunk_id, score, and payload.
Example:
>>> results = await engine.semantic_search(
... embedding=[0.1, 0.2, 0.3, 0.4, 0.5],
... collection="documents",
... limit=5,
... conditions=[Match(key="category", value="tech")],
... threshold=0.7
... )
>>> for result in results:
... print(f"ID: {result.chunk_id}, Score: {result.score}")
"""
transformed_conditions = self.transform_conditions(conditions)
response = await self.run_similarity_query(
embedding, collection, limit, transformed_conditions, threshold

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src/vector_search_mcp/engine/qdrant_engine.py
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@@ -1,3 +1,15 @@
"""Qdrant vector database engine implementation.
This module provides a concrete implementation of the BaseEngine interface
for the Qdrant vector database. It handles the transformation between generic
search conditions and Qdrant-specific filter objects, as well as converting
Qdrant's ScoredPoint responses to standardized SearchRow objects.
The QdrantEngine class is marked as final to prevent inheritance and uses
the generic type parameters BaseEngine[list[models.ScoredPoint], models.Filter]
to ensure type safety with Qdrant's native types.
"""
from collections.abc import Sequence
from typing import final, override
@@ -12,7 +24,48 @@ __all__ = ["QdrantEngine"]
@final
class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
"""Qdrant vector database engine implementation.
This class provides a concrete implementation of the BaseEngine interface
specifically for Qdrant vector database operations. It handles:
- Converting generic Condition objects to Qdrant Filter objects
- Executing similarity searches using Qdrant's AsyncClient
- Transforming ScoredPoint results to SearchRow objects
- Filtering out results with null payloads
Type Parameters:
ResponseType: list[models.ScoredPoint] - Qdrant's search response format
ConditionType: models.Filter - Qdrant's filter object format
The class is marked as @final to prevent inheritance since it's a concrete
implementation that should not be extended.
Example:
>>> engine = QdrantEngine()
>>> results = await engine.semantic_search(
... embedding=[0.1, 0.2, 0.3],
... collection="documents",
... conditions=[Match(key="category", value="tech")]
... )
"""
def __init__(self) -> None:
"""Initialize the Qdrant engine with configuration and client.
Creates a Settings instance to load configuration from environment
variables or vault, then initializes the AsyncQdrantClient with
the configured URL and API key.
The client is configured for async operations and will handle
connection pooling and retry logic automatically.
Raises:
ConfigurationError: If required settings are missing or invalid.
ConnectionError: If unable to establish connection to Qdrant server.
"""
self.settings = Settings() # type: ignore[reportCallArgs]
self.client = AsyncQdrantClient(
url=self.settings.url, api_key=self.settings.api_key
@@ -22,6 +75,26 @@ class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
def transform_conditions(
self, conditions: list[Condition] | None
) -> models.Filter | None:
"""Transform generic conditions to Qdrant Filter objects.
Converts the generic Condition objects (Match, MatchAny, MatchExclude)
into Qdrant's Filter format with appropriate FieldCondition objects.
Args:
conditions: List of generic condition objects, or None for no filtering.
Returns:
Qdrant Filter object with must conditions, or None if no conditions provided.
Example:
>>> conditions = [
... Match(key="category", value="tech"),
... MatchAny(key="tags", any=["python", "rust"])
... ]
>>> filter_obj = transform_conditions(conditions)
>>> # Returns models.Filter(must=[FieldCondition(...), FieldCondition(...)])
"""
if not conditions:
return None
@@ -53,6 +126,26 @@ class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
@override
def transform_response(self, response: list[models.ScoredPoint]) -> list[SearchRow]:
"""Transform Qdrant ScoredPoint objects to SearchRow objects.
Converts Qdrant's native ScoredPoint response format into standardized
SearchRow objects. Filters out any results with null payloads.
Args:
response: List of ScoredPoint objects from Qdrant search response.
Returns:
List of SearchRow objects with chunk_id, score, and payload.
Results with null payloads are excluded.
Example:
>>> scored_points = [
... ScoredPoint(id=1, score=0.9, payload={"text": "example"})
... ]
>>> search_rows = transform_response(scored_points)
>>> # Returns [SearchRow(chunk_id="1", score=0.9, payload={...})]
"""
return [
SearchRow(chunk_id=str(point.id), score=point.score, payload=point.payload)
for point in response
@@ -68,6 +161,31 @@ class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
conditions: models.Filter | None = None,
threshold: float | None = None,
) -> list[models.ScoredPoint]:
"""Execute similarity search using Qdrant's search API.
Performs vector similarity search against the specified Qdrant collection
using the provided query vector and optional filters.
Args:
embedding: Query vector as a sequence of floats or NamedVector object.
collection: Name of the Qdrant collection to search in.
limit: Maximum number of results to return. Defaults to 10.
conditions: Qdrant Filter object for filtering results, or None.
threshold: Minimum similarity score threshold, or None.
Returns:
List of ScoredPoint objects from Qdrant containing IDs, scores, and payloads.
Example:
>>> results = await run_similarity_query(
... embedding=[0.1, 0.2, 0.3],
... collection="documents",
... limit=5,
... threshold=0.7
... )
>>> # Returns [ScoredPoint(id=..., score=..., payload=...)]
"""
return await self.client.search(
collection_name=collection,
query_vector=embedding,

18
src/vector_search_mcp/main.py
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@@ -1,3 +1,21 @@
"""Main MCP server implementation for vector search operations.
This module sets up and configures the FastMCP server with vector search capabilities.
It creates a Qdrant engine instance and exposes the semantic search functionality
as an MCP tool.
The server provides:
- Semantic search tool for vector similarity queries
- Support for various search conditions and filters
- Integration with Qdrant vector database
Example:
The server is typically started using the run function from the package:
>>> from vector_search_mcp import run
>>> run("sse") # Start the MCP server
"""
from fastmcp import FastMCP
from .engine import Backend, get_engine

96
src/vector_search_mcp/models.py
View File

@@ -1,27 +1,121 @@
"""Data models for vector search operations.
This module defines Pydantic models used throughout the vector search MCP server
for representing search results, search conditions, and related data structures.
The models provide type safety and validation for:
- Search results with similarity scores and metadata
- Query conditions for filtering search results
- Various condition types (exact match, any match, exclusion)
"""
from typing import Any
from pydantic import BaseModel
class SearchRow(BaseModel):
"""Represents a single search result from a vector similarity query.
This model encapsulates the result of a vector search operation, including
the similarity score and associated metadata payload.
Attributes:
chunk_id: Unique identifier for the search result chunk/document.
score: Similarity score between 0.0 and 1.0, where higher values
indicate greater similarity to the query vector.
payload: Dictionary containing metadata and content associated with
the search result. Can include text content, categories,
tags, or any other relevant information.
Example:
>>> result = SearchRow(
... chunk_id="doc_123",
... score=0.95,
... payload={"text": "Python programming guide", "category": "tech"}
... )
>>> print(f"Found match: {result.score:.2f} - {result.payload['text']}")
Found match: 0.95 - Python programming guide
"""
chunk_id: str
score: float
payload: dict[str, Any] # type: ignore[reportExplicitAny]
class Condition(BaseModel): ...
class Condition(BaseModel):
"""Base class for search query conditions.
This abstract base class defines the interface for all search conditions
that can be applied to filter vector search results. Concrete condition
types inherit from this class.
This class uses Pydantic's BaseModel for validation and serialization.
Subclasses should implement specific condition logic for different
filter types like exact matches, any matches, or exclusions.
"""
...
class Match(Condition):
"""Exact match condition for filtering search results.
This condition filters results to only include items where the specified
metadata field exactly matches the given value.
Attributes:
key: The metadata field name to match against.
value: The exact value that the field must equal.
Example:
>>> condition = Match(key="category", value="technology")
>>> # Will only return results where payload["category"] == "technology"
"""
key: str
value: str
class MatchAny(Condition):
"""Any-of match condition for filtering search results.
This condition filters results to include items where the specified
metadata field matches any of the provided values.
Attributes:
key: The metadata field name to match against.
any: List of acceptable values. Results are included if the field
matches any value in this list.
Example:
>>> condition = MatchAny(key="language", any=["python", "rust", "go"])
>>> # Returns results where payload["language"] is "python", "rust", or "go"
"""
key: str
any: list[str]
class MatchExclude(Condition):
"""Exclusion condition for filtering search results.
This condition filters results to exclude items where the specified
metadata field matches any of the provided values.
Attributes:
key: The metadata field name to check for exclusion.
exclude: List of values to exclude. Results are filtered out if
the field matches any value in this list.
Example:
>>> condition = MatchExclude(key="status", exclude=["draft", "deleted"])
>>> # Excludes results where payload["status"] is "draft" or "deleted"
"""
key: str
exclude: list[str]

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