refactor: simplify rag client initialization (#3401)

* Simplified Qdrant and ChromaDB client initialization
* Refactored factory structure and updated tests accordingly

src/crewai/rag/chromadb/client.py

@@ -40,8 +40,19 @@ class ChromaDBClient(BaseClient):
         embedding_function: Function to generate embeddings for documents.
     """
 
-    client: ChromaDBClientType
-    embedding_function: ChromaEmbeddingFunction[Embeddable]
+    def __init__(
+        self,
+        client: ChromaDBClientType,
+        embedding_function: ChromaEmbeddingFunction[Embeddable],
+    ) -> None:
+        """Initialize ChromaDBClient with client and embedding function.
+
+        Args:
+            client: Pre-configured ChromaDB client instance.
+            embedding_function: Embedding function for text to vector conversion.
+        """
+        self.client = client
+        self.embedding_function = embedding_function
 
     def create_collection(
         self, **kwargs: Unpack[ChromaDBCollectionCreateParams]

src/crewai/rag/chromadb/factory.py

@@ -15,12 +15,10 @@ def create_client(config: ChromaDBConfig) -> ChromaDBClient:
     Returns:
         Configured ChromaDBClient instance.
     """
-    chromadb_client = Client(
-        settings=config.settings, tenant=config.tenant, database=config.database
+
+    return ChromaDBClient(
+        client=Client(
+            settings=config.settings, tenant=config.tenant, database=config.database
+        ),
+        embedding_function=config.embedding_function,
     )
-
-    client = ChromaDBClient()
-    client.client = chromadb_client
-    client.embedding_function = config.embedding_function
-
-    return client

src/crewai/rag/config/utils.py

@@ -11,7 +11,7 @@ from crewai.rag.config.constants import (
     DEFAULT_RAG_CONFIG_PATH,
     DEFAULT_RAG_CONFIG_CLASS,
 )
-from crewai.rag.config.factory import create_client
+from crewai.rag.factory import create_client
 
 
 class RagContext(BaseModel):

src/crewai/rag/qdrant/client.py

@@ -2,8 +2,6 @@
 
 from typing import Any, cast
 
-from fastembed import TextEmbedding
-from qdrant_client import QdrantClient as SyncQdrantClientBase
 from typing_extensions import Unpack
 
 from crewai.rag.core.base_client import (
@@ -16,7 +14,6 @@ from crewai.rag.core.exceptions import ClientMethodMismatchError
 from crewai.rag.qdrant.types import (
     AsyncEmbeddingFunction,
     EmbeddingFunction,
-    QdrantClientParams,
     QdrantClientType,
     QdrantCollectionCreateParams,
 )
@@ -43,40 +40,19 @@ class QdrantClient(BaseClient):
         embedding_function: Function to generate embeddings for documents.
     """
 
-    client: QdrantClientType
-    embedding_function: EmbeddingFunction | AsyncEmbeddingFunction
-
     def __init__(
         self,
-        client: QdrantClientType | None = None,
-        embedding_function: EmbeddingFunction | AsyncEmbeddingFunction | None = None,
-        **kwargs: Unpack[QdrantClientParams],
+        client: QdrantClientType,
+        embedding_function: EmbeddingFunction | AsyncEmbeddingFunction,
     ) -> None:
-        """Initialize QdrantClient with optional client and embedding function.
+        """Initialize QdrantClient with client and embedding function.
 
         Args:
-            client: Optional pre-configured Qdrant client instance.
-            embedding_function: Optional embedding function. If not provided,
-                uses FastEmbed's BAAI/bge-small-en-v1.5 model.
-            **kwargs: Additional arguments for QdrantClient creation.
+            client: Pre-configured Qdrant client instance.
+            embedding_function: Embedding function for text to vector conversion.
         """
-        if client is not None:
-            self.client = client
-        else:
-            location = kwargs.get("location", ":memory:")
-            client_kwargs = {k: v for k, v in kwargs.items() if k != "location"}
-            self.client = SyncQdrantClientBase(location, **cast(Any, client_kwargs))
-
-        if embedding_function is not None:
-            self.embedding_function = embedding_function
-        else:
-            _embedder = TextEmbedding("BAAI/bge-small-en-v1.5")
-
-            def _embed_fn(text: str) -> list[float]:
-                embeddings = list(_embedder.embed([text]))
-                return [float(x) for x in embeddings[0]] if embeddings else []
-
-            self.embedding_function = _embed_fn
+        self.client = client
+        self.embedding_function = embedding_function
 
     def create_collection(self, **kwargs: Unpack[QdrantCollectionCreateParams]) -> None:
         """Create a new collection in Qdrant.
@@ -284,7 +260,7 @@ class QdrantClient(BaseClient):
             point = _create_point_from_document(doc, embedding)
             points.append(point)
 
-        self.client.upsert(collection_name=collection_name, points=points, wait=True)
+        self.client.upsert(collection_name=collection_name, points=points)
 
     async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
         """Add documents with their embeddings to a collection asynchronously.
@@ -325,9 +301,7 @@ class QdrantClient(BaseClient):
             point = _create_point_from_document(doc, embedding)
             points.append(point)
 
-        await self.client.upsert(
-            collection_name=collection_name, points=points, wait=True
-        )
+        await self.client.upsert(collection_name=collection_name, points=points)
 
     def search(
         self, **kwargs: Unpack[BaseCollectionSearchParams]

src/crewai/rag/qdrant/types.py

@@ -86,7 +86,11 @@ class AsyncEmbeddingFunction(Protocol):
 
 
 class QdrantClientParams(TypedDict, total=False):
-    """Parameters for QdrantClient initialization."""
+    """Parameters for QdrantClient initialization.
+
+    Notes:
+        Need to implement in factory or remove.
+    """
 
     location: str | None
     url: str | None