forked from innovacion/searchbox
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0fa82cff7d
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embedding-
| Author | SHA1 | Date | |
|---|---|---|---|
|
8a7918e3d8 |
@@ -5,6 +5,7 @@ description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.13"
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dependencies = [
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"openai>=2.0.0",
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"qdrant-client==1.13",
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"vault-settings>=0.1.0",
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]
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@@ -11,10 +11,6 @@ from .engine import Backend, get_engine
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from .models import Chunk, Condition
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class QueryError(ValueError):
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"""Raised when query parameters are invalid."""
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class EmbedderNotConfiguredError(ValueError):
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"""Raised when embedder is required but not configured."""
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@@ -79,8 +75,7 @@ class Client:
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async def semantic_search(
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self,
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query: str | list[float] | None = None,
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embedding: list[float] | None = None,
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query: str | list[float],
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limit: int = 10,
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conditions: list[Condition] | None = None,
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threshold: float | None = None,
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@@ -88,8 +83,7 @@ class Client:
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"""Perform semantic search using vector similarity.
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Args:
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query: Text query to embed (requires embedder to be configured)
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embedding: Pre-computed query vector as a list of floats
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query: Text query to embed (requires embedder) or pre-computed vector
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limit: Maximum number of results to return (default: 10)
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conditions: Optional list of filter conditions to apply
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threshold: Optional minimum similarity score threshold
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@@ -98,23 +92,13 @@ class Client:
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List of search results with chunk IDs, scores, and metadata
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Raises:
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ValueError: If neither query nor embedding is provided, or if query
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is provided but no embedder is configured
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EmbedderNotConfiguredError: If query is a string but no embedder is configured
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"""
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if query is None and embedding is None:
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msg = "Either 'query' or 'embedding' must be provided"
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raise QueryError(msg)
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if query is not None and embedding is not None:
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msg = "Only one of 'query' or 'embedding' should be provided"
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raise QueryError(msg)
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# Handle query string
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if query is not None:
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# Handle query parameter
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if isinstance(query, str):
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if self.embedder is None:
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msg = "Cannot use 'query' parameter without an embedder"
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msg = "Cannot use text query without an embedder"
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raise EmbedderNotConfiguredError(msg)
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embedding = self.embedder.embed(query)
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else:
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@@ -17,6 +17,10 @@ class Settings(VaultSettings):
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qdrant_url: The URL endpoint for the vector database server (e.g., Qdrant).
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qdrant_api_key: Optional API key for authenticating with the vector database.
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If None, the connection will be made without authentication.
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azure_openai_endpoint: Azure OpenAI endpoint URL.
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azure_openai_api_key: Azure OpenAI API key.
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azure_openai_api_version: Azure OpenAI API version (e.g., "2024-02-01").
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azure_openai_embedding_model: Azure OpenAI embedding model name.
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Example:
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>>> settings = Settings()
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@@ -34,3 +38,7 @@ class Settings(VaultSettings):
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qdrant_url: str
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qdrant_api_key: str | None = None
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azure_openai_endpoint: str | None = None
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azure_openai_api_key: str | None = None
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azure_openai_api_version: str | None = None
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azure_openai_embedding_model: str | None = None
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@@ -1 +1,115 @@
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"""Embedder class using Azure AI Foundry."""
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"""Embedder package.
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This package provides an abstract embedder interface and concrete implementations
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for different embedding service providers. It uses a factory pattern with caching
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to provide efficient embedder instantiation.
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The package includes:
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- Abstract BaseEmbedder class
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- AzureEmbedder implementation for Azure OpenAI
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- EmbedderBackend enum for specifying embedder types
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- Factory function with overloaded type hints for type safety
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Example:
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>>> from searchbox.embedder import get_embedder, EmbedderBackend
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>>> embedder = get_embedder(EmbedderBackend.AZURE)
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>>> embedding = embedder.embed("Hello world")
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"""
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from enum import StrEnum
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from functools import cache
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from typing import Literal, overload
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from .azure import AzureEmbedder
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from .base import BaseEmbedder
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class UnknownEmbedderError(Exception):
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"""Exception raised when an unknown embedder is requested."""
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def __init__(self, backend: str):
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"""Initialize the exception with the unknown backend."""
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super().__init__(f"Unknown embedder type: {backend}")
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class EmbedderBackend(StrEnum):
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"""Enumeration of supported embedder backends.
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This enum defines the available embedder implementations that can
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be used with the embedder factory. Each backend corresponds to a specific
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embedding service provider.
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Attributes:
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AZURE: Azure OpenAI embedder backend
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Example:
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>>> backend = EmbedderBackend.AZURE
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>>> print(backend) # "azure"
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>>> embedder = get_embedder(backend)
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"""
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AZURE = "azure"
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@overload
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def get_embedder(backend: Literal["azure"]) -> AzureEmbedder: ...
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@overload
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def get_embedder(
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backend: Literal["azure"],
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model: str,
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azure_endpoint: str,
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api_key: str,
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openai_api_version: str,
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) -> AzureEmbedder: ...
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@overload
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def get_embedder(backend: EmbedderBackend, **kwargs: str) -> BaseEmbedder: ...
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@cache
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def get_embedder(backend: EmbedderBackend, **kwargs: str) -> BaseEmbedder:
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"""Get an embedder instance for the specified backend.
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This factory function creates and returns embedder instances based on the
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specified backend type. Instances are cached using functools.cache, so
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multiple calls with the same backend will return the same instance.
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Args:
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backend: The embedder backend to use. Must be an EmbedderBackend enum value.
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**kwargs: Additional keyword arguments to pass to the embedder constructor.
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Returns:
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An embedder instance implementing the BaseEmbedder interface. The specific
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type depends on the backend:
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- EmbedderBackend.AZURE returns AzureEmbedder
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Raises:
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UnknownEmbedderError: If an unknown backend type is provided.
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ValueError: If required settings are missing when using from_settings.
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Example:
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>>> embedder = get_embedder(EmbedderBackend.AZURE)
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>>> isinstance(embedder, AzureEmbedder) # True
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>>> # Type checker knows the exact type for literals:
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>>> azure_embedder = get_embedder(EmbedderBackend.AZURE) # Type: AzureEmbedder
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"""
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if backend == EmbedderBackend.AZURE:
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return AzureEmbedder(**kwargs) if kwargs else AzureEmbedder.from_settings()
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else:
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raise UnknownEmbedderError(backend)
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__all__ = [
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"BaseEmbedder",
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"AzureEmbedder",
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"EmbedderBackend",
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"get_embedder",
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"UnknownEmbedderError",
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]
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@@ -2,6 +2,7 @@
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from openai import AzureOpenAI
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from ..config import Settings
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from .base import BaseEmbedder
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@@ -52,6 +53,42 @@ class AzureEmbedder(BaseEmbedder):
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api_version=openai_api_version,
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)
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@classmethod
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def from_settings(cls) -> "AzureEmbedder":
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"""Initialize the Azure embedder from Settings.
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Returns:
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Initialized AzureEmbedder instance.
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Raises:
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ValueError: If required settings are not configured.
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"""
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settings = Settings() # type: ignore[reportCallArgs]
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if not all(
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[
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settings.azure_openai_endpoint,
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settings.azure_openai_api_key,
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settings.azure_openai_api_version,
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settings.azure_openai_embedding_model,
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]
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):
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msg = (
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"Missing required Azure OpenAI settings. "
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"Ensure AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, "
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"AZURE_OPENAI_API_VERSION, and AZURE_OPENAI_EMBEDDING_MODEL "
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"are set."
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)
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raise ValueError(msg)
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return cls(
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model=settings.azure_openai_embedding_model, # type: ignore[arg-type]
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azure_endpoint=settings.azure_openai_endpoint, # type: ignore[arg-type]
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api_key=settings.azure_openai_api_key, # type: ignore[arg-type]
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openai_api_version=settings.azure_openai_api_version, # type: ignore[arg-type]
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)
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def embed(self, text: str) -> list[float]:
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"""Generate embedding vector for the given text.
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@@ -1,13 +1,13 @@
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"""Main MCP server implementation for vector search operations.
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This module sets up and configures the FastMCP server with vector search capabilities.
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It creates a Qdrant engine instance and exposes the semantic search functionality
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It creates engine and embedder instances and exposes the semantic search functionality
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as an MCP tool.
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The server provides:
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- Semantic search tool for vector similarity queries
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- Support for various search conditions and filters
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- Integration with Qdrant vector database
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- Integration with vector databases and embedding services
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Example:
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The server is typically started using the run function from the package:
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@@ -22,38 +22,36 @@ from fastmcp import FastMCP
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from starlette.requests import Request
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from starlette.responses import JSONResponse
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from ..client import Backend, Client
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from ..embedder.azure import AzureEmbedder
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from ..client import Client
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from ..embedder import EmbedderBackend, get_embedder
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mcp = FastMCP("Searchbox MCP")
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# Initialize Azure embedder
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embedder = AzureEmbedder(
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model="",
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azure_endpoint="",
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api_key="",
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openai_api_version="",
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)
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# Initialize embedder map
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embedder_map = {
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"azure": get_embedder(EmbedderBackend.AZURE),
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}
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@mcp.tool(exclude_args=["backend", "collection", "limit", "threshold"])
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@mcp.tool(exclude_args=["backend", "collection", "embedder", "limit", "threshold"])
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async def get_information(
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query: Annotated[str, "The user query"],
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backend: str = "qdrant",
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collection: str = "default",
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embedder: str = "azure",
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limit: int = 10,
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threshold: float | None = None,
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):
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"""Search a private repository for information using semantic search.
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The query will be automatically converted to an embedding vector using
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Azure OpenAI's text-embedding-3-large model before searching.
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the specified embedder before searching.
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"""
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# Create client with embedder
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client = Client(
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backend=Backend.QDRANT if backend == "qdrant" else Backend.QDRANT,
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backend=backend, # type: ignore[arg-type]
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collection=collection,
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embedder=embedder,
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embedder=embedder_map[embedder],
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)
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# Perform semantic search with automatic embedding
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68
uv.lock
generated
68
uv.lock
generated
@@ -1,5 +1,5 @@
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version = 1
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revision = 3
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revision = 2
|
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requires-python = ">=3.13"
|
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|
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[[package]]
|
||||
@@ -295,6 +295,15 @@ wheels = [
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[[package]]
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name = "distro"
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[[package]]
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{ name = "distro" },
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{ name = "pydantic" },
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{ name = "sniffio" },
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[[package]]
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{ name = "vault-settings", specifier = ">=0.1.0" },
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Reference in New Issue
Block a user