feat: add configurable search parameters for RAG, knowledge, and memory (#3531)

- Add limit and score_threshold to BaseRagConfig, propagate to clients  
- Update default search params in RAG storage, knowledge, and memory (limit=5, threshold=0.6)  
- Fix linting (ruff, mypy, PERF203) and refactor save logic  
- Update tests for new defaults and ChromaDB behavior

src/crewai/rag/qdrant/client.py

@@ -6,8 +6,8 @@ from typing_extensions import Unpack
 
 from crewai.rag.core.base_client import (
     BaseClient,
-    BaseCollectionParams,
     BaseCollectionAddParams,
+    BaseCollectionParams,
     BaseCollectionSearchParams,
 )
 from crewai.rag.core.exceptions import ClientMethodMismatchError
@@ -18,11 +18,11 @@ from crewai.rag.qdrant.types import (
     QdrantCollectionCreateParams,
 )
 from crewai.rag.qdrant.utils import (
+    _create_point_from_document,
+    _get_collection_params,
     _is_async_client,
     _is_async_embedding_function,
     _is_sync_client,
-    _create_point_from_document,
-    _get_collection_params,
     _prepare_search_params,
     _process_search_results,
 )
@@ -38,21 +38,29 @@ class QdrantClient(BaseClient):
     Attributes:
         client: Qdrant client instance (QdrantClient or AsyncQdrantClient).
         embedding_function: Function to generate embeddings for documents.
+        default_limit: Default number of results to return in searches.
+        default_score_threshold: Default minimum score for search results.
     """
 
     def __init__(
         self,
         client: QdrantClientType,
         embedding_function: EmbeddingFunction | AsyncEmbeddingFunction,
+        default_limit: int = 5,
+        default_score_threshold: float = 0.6,
     ) -> None:
         """Initialize QdrantClient with client and embedding function.
 
         Args:
             client: Pre-configured Qdrant client instance.
             embedding_function: Embedding function for text to vector conversion.
+            default_limit: Default number of results to return in searches.
+            default_score_threshold: Default minimum score for search results.
         """
         self.client = client
         self.embedding_function = embedding_function
+        self.default_limit = default_limit
+        self.default_score_threshold = default_score_threshold
 
     def create_collection(self, **kwargs: Unpack[QdrantCollectionCreateParams]) -> None:
         """Create a new collection in Qdrant.
@@ -332,9 +340,9 @@ class QdrantClient(BaseClient):
 
         collection_name = kwargs["collection_name"]
         query = kwargs["query"]
-        limit = kwargs.get("limit", 10)
+        limit = kwargs.get("limit", self.default_limit)
         metadata_filter = kwargs.get("metadata_filter")
-        score_threshold = kwargs.get("score_threshold")
+        score_threshold = kwargs.get("score_threshold", self.default_score_threshold)
 
         if not self.client.collection_exists(collection_name):
             raise ValueError(f"Collection '{collection_name}' does not exist")
@@ -387,9 +395,9 @@ class QdrantClient(BaseClient):
 
         collection_name = kwargs["collection_name"]
         query = kwargs["query"]
-        limit = kwargs.get("limit", 10)
+        limit = kwargs.get("limit", self.default_limit)
         metadata_filter = kwargs.get("metadata_filter")
-        score_threshold = kwargs.get("score_threshold")
+        score_threshold = kwargs.get("score_threshold", self.default_score_threshold)
 
         if not await self.client.collection_exists(collection_name):
             raise ValueError(f"Collection '{collection_name}' does not exist")