Se agrego la implementacion de Embedding

.k8s/deployment.yaml

@@ -22,3 +22,6 @@ spec:
                   image: gitea.ia-innovacion.work/innovacion/searchbox-mcp:latest
                   ports:
                       - containerPort: 8000
+                  envFrom:
+                    - secretRef:
+                        name: vault-readonly-token

.mise/config.toml

@@ -0,0 +1,5 @@
+[tasks.check]
+run = ["uv run ruff check --fix", "uv run ruff format"]
+
+[tasks.test]
+run = "uv run pytest --cov"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "searchbox"
-version = "0.1.0"
+version = "0.1.1"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"

src/searchbox/client.py

@@ -6,10 +6,19 @@ operations across different backend implementations.
 
 from typing import final
 
+from .embedder.base import BaseEmbedder
 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."""
+
+
 @final
 class Client:
     """High-level client for vector search operations.
@@ -20,20 +29,29 @@ class Client:
     Args:
         backend: The vector search backend to use (e.g., Backend.QDRANT)
         collection: Name of the collection to operate on
+        embedder: Optional embedder for converting text queries to vectors
 
     """
 
-    def __init__(self, backend: Backend, collection: str, **kwargs: str):
+    def __init__(
+        self,
+        backend: Backend,
+        collection: str,
+        embedder: BaseEmbedder | None = None,
+        **kwargs: str,
+    ):
         """Initialize the client with a specific backend and collection.
 
         Args:
             backend: The vector search backend to use
             collection: Name of the collection to operate on
+            embedder: Optional embedder for automatic query embedding
             **kwargs: Additional keyword arguments to pass to the backend
 
         """
         self.engine = get_engine(backend, **kwargs)
         self.collection = collection
+        self.embedder = embedder
 
     async def create_index(self, size: int) -> bool:
         """Create a vector index with the specified dimension size.
@@ -61,7 +79,8 @@ class Client:
 
     async def semantic_search(
         self,
-        embedding: list[float],
+        query: str | list[float] | None = None,
+        embedding: list[float] | None = None,
         limit: int = 10,
         conditions: list[Condition] | None = None,
         threshold: float | None = None,
@@ -69,7 +88,8 @@ class Client:
         """Perform semantic search using vector similarity.
 
         Args:
-            embedding: Query vector as a list of floats
+            query: Text query to embed (requires embedder to be configured)
+            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
@@ -77,7 +97,30 @@ class Client:
         Returns:
             List of search results with chunk IDs, scores, and metadata
 
+        Raises:
+            ValueError: If neither query nor embedding is provided, or if query
+                       is provided but no embedder is configured
+
         """
+        if query is None and embedding is None:
+            msg = "Either 'query' or 'embedding' must be provided"
+            raise QueryError(msg)
+
+        if query is not None and embedding is not None:
+            msg = "Only one of 'query' or 'embedding' should be provided"
+            raise QueryError(msg)
+
+        # Handle query string
+        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/embedder/__init__.py

@@ -0,0 +1 @@
+"""Embedder class using Azure AI Foundry."""

src/searchbox/embedder/azure.py

@@ -0,0 +1,69 @@
+"""Embedder class using Azure AI Foundry."""
+
+from openai import AzureOpenAI
+
+from .base import BaseEmbedder
+
+
+class AzureEmbedder(BaseEmbedder):
+    """Embedder implementation using Azure OpenAI Service.
+
+    Provides text embedding generation through Azure's OpenAI API endpoint.
+    Compatible with any Azure OpenAI embedding model (text-embedding-ada-002,
+    text-embedding-3-small, text-embedding-3-large, etc.).
+
+    Args:
+        model: The embedding model name (e.g., "text-embedding-3-large")
+        azure_endpoint: Azure OpenAI endpoint URL
+        api_key: Azure OpenAI API key
+        openai_api_version: API version (e.g., "2024-02-01")
+
+    Example:
+        >>> embedder = AzureEmbedder(
+        ...     model="text-embedding-3-large",
+        ...     azure_endpoint="https://chatocp.openai.azure.com/",
+        ...     api_key="your-api-key",
+        ...     openai_api_version="2024-02-01"
+        ... )
+        >>> embedding = embedder.embed("Hello world")
+
+    """
+
+    def __init__(
+        self,
+        model: str,
+        azure_endpoint: str,
+        api_key: str,
+        openai_api_version: str,
+    ):
+        """Initialize the Azure OpenAI embedder.
+
+        Args:
+            model: The embedding model name (e.g., "text-embedding-3-large")
+            azure_endpoint: Azure OpenAI endpoint URL
+            api_key: Azure OpenAI API key
+            openai_api_version: API version (e.g., "2024-02-01")
+
+        """
+        self.model = model
+        self.client = AzureOpenAI(
+            azure_endpoint=azure_endpoint,
+            api_key=api_key,
+            api_version=openai_api_version,
+        )
+
+    def embed(self, text: str) -> list[float]:
+        """Generate embedding vector for the given text.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            List of floats representing the embedding vector
+
+        """
+        response = self.client.embeddings.create(
+            model=self.model,
+            input=text,
+        )
+        return response.data[0].embedding

src/searchbox/embedder/base.py

@@ -0,0 +1,50 @@
+"""Base embedder interface for text embedding models.
+
+This module defines the abstract base class that all embedder implementations
+must inherit from, ensuring a consistent interface across different embedding
+providers (Azure OpenAI, FastEmbed, OpenAI, Cohere, etc.).
+"""
+
+from abc import ABC, abstractmethod
+
+
+class BaseEmbedder(ABC):
+    """Abstract base class for text embedding models.
+
+    This class defines the interface that all embedder implementations must follow,
+    allowing the system to work with any embedding model provider through a
+    unified API.
+
+    Implementations should inherit from this class and provide concrete
+    implementations of the embed() method for their specific embedding service.
+
+    Example:
+        >>> class MyEmbedder(BaseEmbedder):
+        ...     def embed(self, text: str) -> list[float]:
+        ...         # Implementation specific to your embedding service
+        ...         return [0.1, 0.2, 0.3, ...]
+
+    """
+
+    @abstractmethod
+    def embed(self, text: str) -> list[float]:
+        """Generate embedding vector for the given text.
+
+        This method must be implemented by all concrete embedder classes to
+        convert input text into a dense vector representation.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            A list of floats representing the embedding vector. The dimension
+            of the vector depends on the specific embedding model being used.
+
+        Example:
+            >>> embedder = SomeEmbedder()
+            >>> vector = embedder.embed("Hello world")
+            >>> len(vector)
+            1536
+
+        """
+        ...

src/searchbox/mcp_server/__init__.py

@@ -17,12 +17,10 @@ Example:
 
 """
 
-from fastmcp.server.server import Transport
-
 from .server import mcp
 
 
-def run(transport: Transport = "sse"):  # pragma: no cover
+def run():  # pragma: no cover
     """Run the vector search MCP server with the specified transport.
 
     Args:
@@ -34,4 +32,4 @@ def run(transport: Transport = "sse"):  # pragma: no cover
         >>> run("stdio")  # Start with stdio transport
 
     """
-    mcp.run(transport=transport)
+    mcp.run(transport="sse", host="0.0.0.0", port=8000)

src/searchbox/mcp_server/server.py

@@ -19,33 +19,54 @@ Example:
 from typing import Annotated
 
 from fastmcp import FastMCP
+from starlette.requests import Request
+from starlette.responses import JSONResponse
 
-from ..engine import get_engine
+from ..client import Backend, Client
+from ..embedder.azure import AzureEmbedder
 
 mcp = FastMCP("Searchbox MCP")
 
-engine_map = {"qdrant": get_engine("qdrant")}
+# Initialize Azure embedder
+embedder = AzureEmbedder(
+    model="",
+    azure_endpoint="",
+    api_key="",
+    openai_api_version="",
+)
 
 
-@mcp.tool(exclude_args=["backend", "embedding", "collection", "limit", "threshold"])
+@mcp.tool(exclude_args=["backend", "collection", "limit", "threshold"])
 async def get_information(
     query: Annotated[str, "The user query"],
     backend: str = "qdrant",
-    embedding: list[float] = [],
     collection: str = "default",
     limit: int = 10,
     threshold: float | None = None,
 ):
-    """Search a private repository for information."""
-    _ = query
+    """Search a private repository for information using semantic search.
 
-    engine = engine_map[backend]
-
-    result = await engine.semantic_search(
-        embedding=embedding,
+    The query will be automatically converted to an embedding vector using
+    Azure OpenAI's text-embedding-3-large model before searching.
+    """
+    # Create client with embedder
+    client = Client(
+        backend=Backend.QDRANT if backend == "qdrant" else Backend.QDRANT,
         collection=collection,
+        embedder=embedder,
+    )
+
+    # Perform semantic search with automatic embedding
+    result = await client.semantic_search(
+        query=query,
         limit=limit,
         threshold=threshold,
     )
 
     return result
+
+
+@mcp.custom_route("/health", methods=["GET"])
+async def health_check(_request: Request):
+    """Health check endpoint."""
+    return JSONResponse({"status": "ok", "service": "searchbox-mcp"})

tests/test_mcp/test_qdrant_mcp.py

@@ -1,12 +1,10 @@
 import json
+
 import pytest
 from fastmcp import Client
-from fastembed import TextEmbedding
 
 from searchbox.mcp_server.server import mcp
 
-embedding_model = TextEmbedding()
-
 
 @pytest.fixture
 async def mcp_client():
@@ -15,19 +13,18 @@ async def mcp_client():
 
 
 async def test_mcp_qdrant_backend(mcp_client):
-    embedding = list(embedding_model.embed("Quien es el mas guapo"))[0].tolist()
-
+    """Test MCP server with automatic Azure embedding."""
     result = await mcp_client.call_tool(
         name="get_information",
         arguments={
-            "query": "dummy value",
-            "collection": "dummy_collection",
-            "embedding": embedding,
+            "query": "Quien es el mas guapo",
+            "collection": "azure_collection",
         },
     )
 
     content = json.loads(result.content[0].text)[0]
 
-    assert content["chunk_id"] == "0"
-    assert content["score"] >= 0.7
-    assert content["payload"] == {"text": "Rick es el mas guapo"}
+    assert content["score"] >= 0.65
+    assert content["payload"]["page_content"] == "Rick es el mas guapo"
+    assert content["payload"]["filename"] == "test.txt"
+    assert content["payload"]["page"] == 1

uv.lock

@@ -1527,7 +1527,7 @@ wheels = [
 
 [[package]]
 name = "searchbox"
-version = "0.1.0"
+version = "0.1.1"
 source = { editable = "." }
 dependencies = [
     { name = "qdrant-client" },