Add hybrid search support

README.md

@@ -133,13 +133,13 @@ Example usage through MCP:
 
 ### Search Functionality
 
-The server provides a flexible search tool that can search across one or all indices:
+The server provides a flexible search tool that can search across one or all indices with support for hybrid search (combining keyword and semantic search):
 
 - `search`: Search through Meilisearch indices with optional parameters
 
 Example usage through MCP:
 ```json
-// Search in a specific index
+// Traditional keyword search in a specific index
 {
   "name": "search",
   "arguments": {
@@ -149,6 +149,31 @@ Example usage through MCP:
   }
 }
 
+// Hybrid search combining keyword and semantic search
+{
+  "name": "search",
+  "arguments": {
+    "query": "artificial intelligence",
+    "indexUid": "documents",
+    "hybrid": {
+      "semanticRatio": 0.7,
+      "embedder": "default"
+    },
+    "limit": 20
+  }
+}
+
+// Semantic search with custom vector
+{
+  "name": "search",
+  "arguments": {
+    "query": "machine learning",
+    "indexUid": "articles",
+    "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
+    "retrieveVectors": true
+  }
+}
+
 // Search across all indices
 {
   "name": "search",
@@ -167,6 +192,13 @@ Available search parameters:
 - `offset`: Number of results to skip (optional, default: 0)
 - `filter`: Filter expression (optional)
 - `sort`: Sorting rules (optional)
+- `hybrid`: Hybrid search configuration (optional)
+  - `semanticRatio`: Balance between keyword (0.0) and semantic (1.0) search (optional, default: 0.5)
+  - `embedder`: Name of the configured embedder to use (required when using hybrid)
+- `vector`: Custom vector for semantic search (optional)
+- `retrieveVectors`: Include vector data in search results (optional)
+
+**Note**: To use hybrid search features, you need to have an embedder configured in your Meilisearch index settings. Refer to the [Meilisearch documentation on vector search](https://www.meilisearch.com/docs/learn/vector_search/vector_search_basics) for configuration details.
 
 ### Running the Server
 
@@ -210,7 +242,7 @@ npx @modelcontextprotocol/inspector python -m src.meilisearch_mcp
 - `add-documents`: Add or update documents in an index
 
 ### Search
-- `search`: Flexible search across single or multiple indices with filtering and sorting options
+- `search`: Flexible search across single or multiple indices with support for hybrid search (keyword + semantic), custom vectors, and filtering/sorting options
 
 ### Settings Management
 - `get-settings`: View current settings for an index

pyproject.toml

@@ -4,7 +4,7 @@ version = "0.5.0"
 description = "MCP server for Meilisearch"
 requires-python = ">=3.10"
 dependencies = [
-    "meilisearch>=0.33.0",
+    "meilisearch>=0.34.0",
     "mcp>=0.1.0",
     "httpx>=0.24.0",
     "pydantic>=2.0.0"

src/meilisearch_mcp/client.py

@@ -52,6 +52,9 @@ def search(
         offset: Optional[int] = 0,
         filter: Optional[str] = None,
         sort: Optional[List[str]] = None,
+        hybrid: Optional[Dict[str, Any]] = None,
+        vector: Optional[List[float]] = None,
+        retrieve_vectors: Optional[bool] = None,
         **kwargs,
     ) -> Dict[str, Any]:
         """
@@ -70,6 +73,12 @@ def search(
                 search_params["filter"] = filter
             if sort is not None:
                 search_params["sort"] = sort
+            if hybrid is not None:
+                search_params["hybrid"] = hybrid
+            if vector is not None:
+                search_params["vector"] = vector
+            if retrieve_vectors is not None:
+                search_params["retrieveVectors"] = retrieve_vectors
 
             # Add any additional parameters
             search_params.update({k: v for k, v in kwargs.items() if v is not None})

src/meilisearch_mcp/server.py

@@ -211,7 +211,7 @@ async def handle_list_tools() -> list[types.Tool]:
                 ),
                 types.Tool(
                     name="search",
-                    description="Search through Meilisearch indices. If indexUid is not provided, it will search across all indices.",
+                    description="Search through Meilisearch indices with support for hybrid search (combining keyword and semantic search). If indexUid is not provided, it will search across all indices.",
                     inputSchema={
                         "type": "object",
                         "properties": {
@@ -224,6 +224,32 @@ async def handle_list_tools() -> list[types.Tool]:
                                 "type": "array",
                                 "items": {"type": "string"},
                             },
+                            "hybrid": {
+                                "type": "object",
+                                "properties": {
+                                    "semanticRatio": {
+                                        "type": "number",
+                                        "minimum": 0.0,
+                                        "maximum": 1.0,
+                                        "description": "Balance between keyword (0.0) and semantic (1.0) search",
+                                    },
+                                    "embedder": {
+                                        "type": "string",
+                                        "description": "Name of the configured embedder to use",
+                                    },
+                                },
+                                "required": ["embedder"],
+                                "additionalProperties": False,
+                            },
+                            "vector": {
+                                "type": "array",
+                                "items": {"type": "number"},
+                                "description": "Custom vector for semantic search",
+                            },
+                            "retrieveVectors": {
+                                "type": "boolean",
+                                "description": "Include vector data in search results",
+                            },
                         },
                         "required": ["query"],
                         "additionalProperties": False,
@@ -498,6 +524,9 @@ async def handle_call_tool(
                         offset=arguments.get("offset"),
                         filter=arguments.get("filter"),
                         sort=arguments.get("sort"),
+                        hybrid=arguments.get("hybrid"),
+                        vector=arguments.get("vector"),
+                        retrieve_vectors=arguments.get("retrieveVectors"),
                     )
 
                     # Format the results for better readability

tests/test_mcp_client.py

@@ -612,6 +612,161 @@ async def test_delete_index_integration_workflow(self, mcp_server):
         assert "Error:" in search_after_text
 
 
+class TestHybridSearch:
+    """Test hybrid search functionality with semantic search support"""
+
+    async def test_search_tool_has_hybrid_parameters(self, mcp_server):
+        """Test that search tool schema includes hybrid search parameters"""
+        tools = await simulate_list_tools(mcp_server)
+
+        # Find the search tool
+        search_tool = next((t for t in tools if t.name == "search"), None)
+        assert search_tool is not None, "Search tool not found"
+
+        # Check that hybrid parameters are in the schema
+        properties = search_tool.inputSchema["properties"]
+
+        # Check hybrid object parameter
+        assert "hybrid" in properties
+        hybrid_schema = properties["hybrid"]
+        assert hybrid_schema["type"] == "object"
+        assert "semanticRatio" in hybrid_schema["properties"]
+        assert "embedder" in hybrid_schema["properties"]
+        assert hybrid_schema["properties"]["semanticRatio"]["type"] == "number"
+        assert hybrid_schema["properties"]["semanticRatio"]["minimum"] == 0.0
+        assert hybrid_schema["properties"]["semanticRatio"]["maximum"] == 1.0
+        assert hybrid_schema["properties"]["embedder"]["type"] == "string"
+        assert hybrid_schema["required"] == ["embedder"]
+
+        # Check vector parameter
+        assert "vector" in properties
+        vector_schema = properties["vector"]
+        assert vector_schema["type"] == "array"
+        assert vector_schema["items"]["type"] == "number"
+
+        # Check retrieveVectors parameter
+        assert "retrieveVectors" in properties
+        assert properties["retrieveVectors"]["type"] == "boolean"
+
+    @pytest.mark.skip(reason="Requires Meilisearch instance with embedders configured")
+    async def test_search_with_hybrid_parameters(self, mcp_server):
+        """Test that search accepts and processes hybrid search parameters"""
+        # Create test index
+        index_name = generate_unique_index_name("hybrid_search")
+        await simulate_mcp_call(
+            mcp_server, "create-index", {"uid": index_name, "primaryKey": "id"}
+        )
+
+        # Add test documents
+        documents = [
+            {
+                "id": 1,
+                "title": "Python Programming",
+                "description": "Learn Python basics",
+            },
+            {"id": 2, "title": "JavaScript Guide", "description": "Modern JS features"},
+            {"id": 3, "title": "Machine Learning", "description": "AI and ML concepts"},
+        ]
+        await simulate_mcp_call(
+            mcp_server,
+            "add-documents",
+            {"indexUid": index_name, "documents": documents},
+        )
+        await wait_for_indexing()
+
+        # Note: This test simulates the API call structure but won't actually
+        # perform semantic search without a configured embedder in Meilisearch
+
+        # Test search with hybrid parameters
+        search_params = {
+            "query": "programming",
+            "indexUid": index_name,
+            "hybrid": {"semanticRatio": 0.7, "embedder": "default"},
+            "limit": 5,
+        }
+
+        # The search should accept these parameters without error
+        response = await simulate_mcp_call(mcp_server, "search", search_params)
+        response_text = assert_text_content_response(response, "Search results")
+
+        # Even if embedder is not configured, the API should handle the request
+        assert "Search results for 'programming'" in response_text
+
+        # Cleanup
+        await simulate_mcp_call(mcp_server, "delete-index", {"uid": index_name})
+
+    @pytest.mark.skip(reason="Requires Meilisearch instance with embedders configured")
+    async def test_search_with_vector_parameter(self, mcp_server):
+        """Test that search accepts vector parameter"""
+        # Create test index
+        index_name = generate_unique_index_name("vector_search")
+        await simulate_mcp_call(
+            mcp_server, "create-index", {"uid": index_name, "primaryKey": "id"}
+        )
+
+        # Add test documents
+        documents = [{"id": 1, "content": "Test document"}]
+        await simulate_mcp_call(
+            mcp_server,
+            "add-documents",
+            {"indexUid": index_name, "documents": documents},
+        )
+        await wait_for_indexing()
+
+        # Test search with vector parameter
+        search_params = {
+            "query": "test",
+            "indexUid": index_name,
+            "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
+            "retrieveVectors": True,
+        }
+
+        # The search should accept these parameters without error
+        response = await simulate_mcp_call(mcp_server, "search", search_params)
+        response_text = assert_text_content_response(response, "Search results")
+        assert "Search results for 'test'" in response_text
+
+        # Cleanup
+        await simulate_mcp_call(mcp_server, "delete-index", {"uid": index_name})
+
+    @pytest.mark.skip(reason="Requires Meilisearch instance with embedders configured")
+    async def test_search_semantic_only(self, mcp_server):
+        """Test semantic-only search with semanticRatio=1.0"""
+        # Create test index
+        index_name = generate_unique_index_name("semantic_only")
+        await simulate_mcp_call(
+            mcp_server, "create-index", {"uid": index_name, "primaryKey": "id"}
+        )
+
+        # Add test documents
+        documents = [
+            {"id": 1, "content": "Artificial intelligence and machine learning"}
+        ]
+        await simulate_mcp_call(
+            mcp_server,
+            "add-documents",
+            {"indexUid": index_name, "documents": documents},
+        )
+        await wait_for_indexing()
+
+        # Test semantic-only search
+        search_params = {
+            "query": "AI ML",
+            "indexUid": index_name,
+            "hybrid": {
+                "semanticRatio": 1.0,  # Pure semantic search
+                "embedder": "default",
+            },
+        }
+
+        response = await simulate_mcp_call(mcp_server, "search", search_params)
+        response_text = assert_text_content_response(response, "Search results")
+        assert "Search results for 'AI ML'" in response_text
+
+        # Cleanup
+        await simulate_mcp_call(mcp_server, "delete-index", {"uid": index_name})
+
+
 class TestIssue27OpenAISchemaCompatibility:
     """Test for issue #27 - Fix JSON schemas for OpenAI Agent SDK compatibility"""