Fix compatibility with the previous versions

src/mcp_server_qdrant/embeddings/base.py

@@ -14,3 +14,8 @@ class EmbeddingProvider(ABC):
     async def embed_query(self, query: str) -> List[float]:
         """Embed a query into a vector."""
         pass
+
+    @abstractmethod
+    def get_vector_name(self) -> str:
+        """Get the name of the vector for the Qdrant collection."""
+        pass

src/mcp_server_qdrant/embeddings/fastembed.py

@@ -35,3 +35,11 @@ class FastEmbedProvider(EmbeddingProvider):
             None, lambda: list(self.embedding_model.query_embed([query]))
         )
         return embeddings[0].tolist()
+
+    def get_vector_name(self) -> str:
+        """
+        Return the name of the vector for the Qdrant collection.
+        Important: This is compatible with the FastEmbed logic used before 0.6.0.
+        """
+        model_name = self.embedding_model.model_name.split("/")[-1].lower()
+        return f"fast-{model_name}"

src/mcp_server_qdrant/qdrant.py

@@ -1,3 +1,4 @@
+import uuid
 from typing import Optional
 
 from qdrant_client import AsyncQdrantClient, models
@@ -40,12 +41,16 @@ class QdrantConnector:
             sample_vector = await self._embedding_provider.embed_query("sample text")
             vector_size = len(sample_vector)
 
+            # Use the vector name as defined in the embedding provider
+            vector_name = self._embedding_provider.get_vector_name()
             await self._client.create_collection(
                 collection_name=self._collection_name,
-                vectors_config=models.VectorParams(
-                    size=vector_size,
-                    distance=models.Distance.COSINE,
-                ),
+                vectors_config={
+                    vector_name: models.VectorParams(
+                        size=vector_size,
+                        distance=models.Distance.COSINE,
+                    )
+                },
             )
 
     async def store_memory(self, information: str):
@@ -59,12 +64,13 @@ class QdrantConnector:
         embeddings = await self._embedding_provider.embed_documents([information])
 
         # Add to Qdrant
+        vector_name = self._embedding_provider.get_vector_name()
         await self._client.upsert(
             collection_name=self._collection_name,
             points=[
                 models.PointStruct(
-                    id=hash(information),  # Simple hash as ID
-                    vector=embeddings[0],
+                    id=uuid.uuid4().hex,
+                    vector={vector_name: embeddings[0]},
                     payload={"document": information},
                 )
             ],
@@ -82,11 +88,12 @@ class QdrantConnector:
 
         # Embed the query
         query_vector = await self._embedding_provider.embed_query(query)
+        vector_name = self._embedding_provider.get_vector_name()
 
         # Search in Qdrant
         search_results = await self._client.search(
             collection_name=self._collection_name,
-            query_vector=query_vector,
+            query_vector=models.NamedVector(name=vector_name, vector=query_vector),
             limit=10,
         )
 

src/mcp_server_qdrant/server.py

@@ -1,4 +1,5 @@
 import asyncio
+import importlib.metadata
 from typing import Optional
 
 import click
@@ -11,6 +12,15 @@ from .embeddings.factory import create_embedding_provider
 from .qdrant import QdrantConnector
 
 
+def get_package_version() -> str:
+    """Get the package version using importlib.metadata."""
+    try:
+        return importlib.metadata.version("mcp-server-qdrant")
+    except importlib.metadata.PackageNotFoundError:
+        # Fall back to a default version if package is not installed
+        return "0.0.0"
+
+
 def serve(
     qdrant_url: Optional[str],
     qdrant_api_key: Optional[str],
@@ -140,6 +150,13 @@ def serve(
     required=True,
     help="Collection name",
 )
+@click.option(
+    "--fastembed-model-name",
+    envvar="FASTEMBED_MODEL_NAME",
+    required=False,
+    help="FastEmbed model name",
+    default="sentence-transformers/all-MiniLM-L6-v2",
+)
 @click.option(
     "--embedding-provider",
     envvar="EMBEDDING_PROVIDER",
@@ -165,6 +182,7 @@ def main(
     qdrant_url: Optional[str],
     qdrant_api_key: str,
     collection_name: Optional[str],
+    fastembed_model_name: Optional[str],
     embedding_provider: str,
     embedding_model: str,
     qdrant_local_path: Optional[str],
@@ -175,6 +193,14 @@ def main(
             "Exactly one of qdrant-url or qdrant-local-path must be provided"
         )
 
+    # Warn if fastembed_model_name is provided, as this is going to be deprecated
+    if fastembed_model_name:
+        click.echo(
+            "Warning: --fastembed-model-name parameter is deprecated and will be removed in a future version. "
+            "Please use --embedding-provider and --embedding-model instead",
+            err=True,
+        )
+
     async def _run():
         async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
             server = serve(
@@ -190,7 +216,7 @@ def main(
                 write_stream,
                 InitializationOptions(
                     server_name="qdrant",
-                    server_version="0.5.1",
+                    server_version=get_package_version(),
                     capabilities=server.get_capabilities(
                         notification_options=NotificationOptions(),
                         experimental_capabilities={},