Resuelto los problemas comentados, se agrego al Vault keys adicionales, junto a mejores practicas

pyproject.toml

@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
+    "openai>=2.0.0",
     "qdrant-client==1.13",
     "vault-settings>=0.1.0",
 ]

src/searchbox/client.py

@@ -11,10 +11,6 @@ from .engine import Backend, get_engine
 from .models import Chunk, Condition
 
 
-class QueryError(ValueError):
-    """Raised when query parameters are invalid."""
-
-
 class EmbedderNotConfiguredError(ValueError):
     """Raised when embedder is required but not configured."""
 
@@ -79,8 +75,7 @@ class Client:
 
     async def semantic_search(
         self,
-        query: str | list[float] | None = None,
+        query: str | list[float],
-        embedding: list[float] | None = None,
         limit: int = 10,
         conditions: list[Condition] | None = None,
         threshold: float | None = None,
@@ -88,8 +83,7 @@ class Client:
         """Perform semantic search using vector similarity.
 
         Args:
-            query: Text query to embed (requires embedder to be configured)
+            query: Text query to embed (requires embedder) or pre-computed vector
-            embedding: Pre-computed query vector as a list of floats
             limit: Maximum number of results to return (default: 10)
             conditions: Optional list of filter conditions to apply
             threshold: Optional minimum similarity score threshold
@@ -98,28 +92,18 @@ class Client:
             List of search results with chunk IDs, scores, and metadata
 
         Raises:
-            ValueError: If neither query nor embedding is provided, or if query
+            EmbedderNotConfiguredError: If query is a string but no embedder is configured
-                       is provided but no embedder is configured
 
         """
-        if query is None and embedding is None:
+        # Handle query parameter
-            msg = "Either 'query' or 'embedding' must be provided"
+        if isinstance(query, str):
-            raise QueryError(msg)
+            if self.embedder is None:
-
+                msg = "Cannot use text query without an embedder"
-        if query is not None and embedding is not None:
+                raise EmbedderNotConfiguredError(msg)
-            msg = "Only one of 'query' or 'embedding' should be provided"
+            embedding = self.embedder.embed(query)
-            raise QueryError(msg)
+        else:
-
+            # query is already a list[float]
-        # Handle query string
+            embedding = query
-        if query is not None:
-            if isinstance(query, str):
-                if self.embedder is None:
-                    msg = "Cannot use 'query' parameter without an embedder"
-                    raise EmbedderNotConfiguredError(msg)
-                embedding = self.embedder.embed(query)
-            else:
-                # query is already a list[float]
-                embedding = query
 
         return await self.engine.semantic_search(
             embedding, self.collection, limit, conditions, threshold

src/searchbox/config.py

@@ -17,6 +17,10 @@ class Settings(VaultSettings):
         qdrant_url: The URL endpoint for the vector database server (e.g., Qdrant).
         qdrant_api_key: Optional API key for authenticating with the vector database.
                 If None, the connection will be made without authentication.
+        azure_openai_endpoint: Azure OpenAI endpoint URL.
+        azure_openai_api_key: Azure OpenAI API key.
+        azure_openai_api_version: Azure OpenAI API version (e.g., "2024-02-01").
+        azure_openai_embedding_model: Azure OpenAI embedding model name.
 
     Example:
         >>> settings = Settings()
@@ -34,3 +38,7 @@ class Settings(VaultSettings):
 
     qdrant_url: str
     qdrant_api_key: str | None = None
+    azure_openai_endpoint: str | None = None
+    azure_openai_api_key: str | None = None
+    azure_openai_api_version: str | None = None
+    azure_openai_embedding_model: str | None = None

src/searchbox/embedder/__init__.py

@@ -1 +1,115 @@
-"""Embedder class using Azure AI Foundry."""
+"""Embedder package.
+
+This package provides an abstract embedder interface and concrete implementations
+for different embedding service providers. It uses a factory pattern with caching
+to provide efficient embedder instantiation.
+
+The package includes:
+- Abstract BaseEmbedder class
+- AzureEmbedder implementation for Azure OpenAI
+- EmbedderBackend enum for specifying embedder types
+- Factory function with overloaded type hints for type safety
+
+Example:
+    >>> from searchbox.embedder import get_embedder, EmbedderBackend
+    >>> embedder = get_embedder(EmbedderBackend.AZURE)
+    >>> embedding = embedder.embed("Hello world")
+
+"""
+
+from enum import StrEnum
+from functools import cache
+from typing import Literal, overload
+
+from .azure import AzureEmbedder
+from .base import BaseEmbedder
+
+
+class UnknownEmbedderError(Exception):
+    """Exception raised when an unknown embedder is requested."""
+
+    def __init__(self, backend: str):
+        """Initialize the exception with the unknown backend."""
+        super().__init__(f"Unknown embedder type: {backend}")
+
+
+class EmbedderBackend(StrEnum):
+    """Enumeration of supported embedder backends.
+
+    This enum defines the available embedder implementations that can
+    be used with the embedder factory. Each backend corresponds to a specific
+    embedding service provider.
+
+    Attributes:
+        AZURE: Azure OpenAI embedder backend
+
+    Example:
+        >>> backend = EmbedderBackend.AZURE
+        >>> print(backend)  # "azure"
+        >>> embedder = get_embedder(backend)
+
+    """
+
+    AZURE = "azure"
+
+
+@overload
+def get_embedder(backend: Literal["azure"]) -> AzureEmbedder: ...
+
+
+@overload
+def get_embedder(
+    backend: Literal["azure"],
+    model: str,
+    azure_endpoint: str,
+    api_key: str,
+    openai_api_version: str,
+) -> AzureEmbedder: ...
+
+
+@overload
+def get_embedder(backend: EmbedderBackend, **kwargs: str) -> BaseEmbedder: ...
+
+
+@cache
+def get_embedder(backend: EmbedderBackend, **kwargs: str) -> BaseEmbedder:
+    """Get an embedder instance for the specified backend.
+
+    This factory function creates and returns embedder 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 embedder backend to use. Must be an EmbedderBackend enum value.
+        **kwargs: Additional keyword arguments to pass to the embedder constructor.
+
+    Returns:
+        An embedder instance implementing the BaseEmbedder interface. The specific
+        type depends on the backend:
+        - EmbedderBackend.AZURE returns AzureEmbedder
+
+    Raises:
+        UnknownEmbedderError: If an unknown backend type is provided.
+        ValueError: If required settings are missing when using from_settings.
+
+    Example:
+        >>> embedder = get_embedder(EmbedderBackend.AZURE)
+        >>> isinstance(embedder, AzureEmbedder)  # True
+
+        >>> # Type checker knows the exact type for literals:
+        >>> azure_embedder = get_embedder(EmbedderBackend.AZURE)  # Type: AzureEmbedder
+
+    """
+    if backend == EmbedderBackend.AZURE:
+        return AzureEmbedder(**kwargs) if kwargs else AzureEmbedder.from_settings()
+    else:
+        raise UnknownEmbedderError(backend)
+
+
+__all__ = [
+    "BaseEmbedder",
+    "AzureEmbedder",
+    "EmbedderBackend",
+    "get_embedder",
+    "UnknownEmbedderError",
+]

src/searchbox/embedder/azure.py

@@ -2,6 +2,7 @@
 
 from openai import AzureOpenAI
 
+from ..config import Settings
 from .base import BaseEmbedder
 
 
@@ -52,6 +53,42 @@ class AzureEmbedder(BaseEmbedder):
             api_version=openai_api_version,
         )
 
+    @classmethod
+    def from_settings(cls) -> "AzureEmbedder":
+        """Initialize the Azure embedder from Settings.
+
+        Returns:
+            Initialized AzureEmbedder instance.
+
+        Raises:
+            ValueError: If required settings are not configured.
+
+        """
+        settings = Settings()  # type: ignore[reportCallArgs]
+
+        if not all(
+            [
+                settings.azure_openai_endpoint,
+                settings.azure_openai_api_key,
+                settings.azure_openai_api_version,
+                settings.azure_openai_embedding_model,
+            ]
+        ):
+            msg = (
+                "Missing required Azure OpenAI settings. "
+                "Ensure AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, "
+                "AZURE_OPENAI_API_VERSION, and AZURE_OPENAI_EMBEDDING_MODEL "
+                "are set."
+            )
+            raise ValueError(msg)
+
+        return cls(
+            model=settings.azure_openai_embedding_model,  # type: ignore[arg-type]
+            azure_endpoint=settings.azure_openai_endpoint,  # type: ignore[arg-type]
+            api_key=settings.azure_openai_api_key,  # type: ignore[arg-type]
+            openai_api_version=settings.azure_openai_api_version,  # type: ignore[arg-type]
+        )
+
     def embed(self, text: str) -> list[float]:
         """Generate embedding vector for the given text.
 

src/searchbox/mcp_server/server.py

@@ -1,13 +1,13 @@
 """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
+It creates engine and embedder instances 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
+- Integration with vector databases and embedding services
 
 Example:
     The server is typically started using the run function from the package:
@@ -22,38 +22,36 @@ from fastmcp import FastMCP
 from starlette.requests import Request
 from starlette.responses import JSONResponse
 
-from ..client import Backend, Client
+from ..client import Client
-from ..embedder.azure import AzureEmbedder
+from ..embedder import EmbedderBackend, get_embedder
 
 mcp = FastMCP("Searchbox MCP")
 
-# Initialize Azure embedder
+# Initialize embedder map
-embedder = AzureEmbedder(
+embedder_map = {
-    model="",
+    "azure": get_embedder(EmbedderBackend.AZURE),
-    azure_endpoint="",
+}
-    api_key="",
-    openai_api_version="",
-)
 
 
-@mcp.tool(exclude_args=["backend", "collection", "limit", "threshold"])
+@mcp.tool(exclude_args=["backend", "collection", "embedder", "limit", "threshold"])
 async def get_information(
     query: Annotated[str, "The user query"],
     backend: str = "qdrant",
     collection: str = "default",
+    embedder: str = "azure",
     limit: int = 10,
     threshold: float | None = None,
 ):
     """Search a private repository for information using semantic search.
 
     The query will be automatically converted to an embedding vector using
-    Azure OpenAI's text-embedding-3-large model before searching.
+    the specified embedder before searching.
     """
     # Create client with embedder
     client = Client(
-        backend=Backend.QDRANT if backend == "qdrant" else Backend.QDRANT,
+        backend=backend,  # type: ignore[arg-type]
         collection=collection,
-        embedder=embedder,
+        embedder=embedder_map[embedder],
     )
 
     # Perform semantic search with automatic embedding

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.13"
 
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 ]
 
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+]
+
 [[package]]
 name = "dnspython"
 version = "2.8.0"
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 source = { editable = "." }
 dependencies = [
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 [package.metadata]
 requires-dist = [
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