fix: resolve mypy type annotation issues in storage and telemetry modules

- Add proper type parameters for EmbeddingFunction generics
- Fix ChromaDB query response handling with proper type checking
- Add missing return type annotations to telemetry methods
- Fix trace listener type annotations and imports
- Handle potential None values in nested list indexing
- Improve type safety in RAG and knowledge storage modules

src/crewai/events/listeners/tracing/trace_listener.py

@@ -99,7 +99,7 @@ class TraceCollectionListener(BaseEventListener):
             return
 
         super().__init__()
-        self.batch_manager = batch_manager or TraceBatchManager()
+        self.batch_manager = batch_manager or TraceBatchManager()  # type: ignore[call-arg]
         self._initialized = True
 
     def _check_authenticated(self) -> bool:
@@ -324,7 +324,7 @@ class TraceCollectionListener(BaseEventListener):
 
     def _initialize_batch(
         self, user_context: dict[str, str], execution_metadata: dict[str, Any]
-    ):
+    ) -> None:
         """Initialize trace batch if ephemeral"""
         if not self._check_authenticated():
             self.batch_manager.initialize_batch(
@@ -426,7 +426,7 @@ class TraceCollectionListener(BaseEventListener):
 
     # TODO: move to utils
     def _safe_serialize_to_dict(
-        self, obj, exclude: set[str] | None = None
+        self, obj: Any, exclude: set[str] | None = None
     ) -> dict[str, Any]:
         """Safely serialize an object to a dictionary for event data."""
         try:

src/crewai/knowledge/storage/knowledge_storage.py

@@ -3,6 +3,7 @@ import logging
 import os
 import shutil
 import warnings
+from collections.abc import Mapping
 from typing import Any, Optional, Union
 
 import chromadb
@@ -30,7 +31,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     collection: Optional[chromadb.Collection] = None
     collection_name: Optional[str] = "knowledge"
     app: Optional[ClientAPI] = None
-    embedder: Optional[EmbeddingFunction] = None
+    embedder: Optional[EmbeddingFunction[Any]] = None
 
     def __init__(
         self,
@@ -57,17 +58,61 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                     where=filter,
                 )
                 results = []
-                for i in range(len(fetched["ids"][0])):
-                    result = {
-                        "id": fetched["ids"][0][i],
-                        "metadata": fetched["metadatas"][0][i],
-                        "context": fetched["documents"][0][i],
-                        "score": fetched["distances"][0][i],
-                    }
-                    if (
-                        result["score"] <= score_threshold
-                    ):  # Note: distances are smaller when more similar
-                        results.append(result)
+                if (
+                    fetched
+                    and "ids" in fetched
+                    and fetched["ids"]
+                    and len(fetched["ids"]) > 0
+                ):
+                    ids_list = (
+                        fetched["ids"][0]
+                        if isinstance(fetched["ids"][0], list)
+                        else fetched["ids"]
+                    )
+                    for i in range(len(ids_list)):
+                        # Handle metadatas
+                        metadata = {}
+                        if fetched.get("metadatas") and len(fetched["metadatas"]) > 0:
+                            metadata_list = (
+                                fetched["metadatas"][0]
+                                if isinstance(fetched["metadatas"][0], list)
+                                else fetched["metadatas"]
+                            )
+                            if i < len(metadata_list):
+                                metadata = metadata_list[i]
+
+                        # Handle documents
+                        context = ""
+                        if fetched.get("documents") and len(fetched["documents"]) > 0:
+                            docs_list = (
+                                fetched["documents"][0]
+                                if isinstance(fetched["documents"][0], list)
+                                else fetched["documents"]
+                            )
+                            if i < len(docs_list):
+                                context = docs_list[i]
+
+                        # Handle distances
+                        score = 1.0
+                        if fetched.get("distances") and len(fetched["distances"]) > 0:
+                            dist_list = (
+                                fetched["distances"][0]
+                                if isinstance(fetched["distances"][0], list)
+                                else fetched["distances"]
+                            )
+                            if i < len(dist_list):
+                                score = dist_list[i]
+
+                        result = {
+                            "id": ids_list[i],
+                            "metadata": metadata,
+                            "context": context,
+                            "score": score,
+                        }
+
+                        # Check score threshold - distances are smaller when more similar
+                        if isinstance(score, (int, float)) and score <= score_threshold:
+                            results.append(result)
                 return results
             else:
                 raise Exception("Collection not initialized")
@@ -150,7 +195,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
 
             # If we have no metadata at all, set it to None
             final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
-                None if all(m is None for m in filtered_metadata) else filtered_metadata
+                None if all(m is None for m in filtered_metadata) else filtered_metadata  # type: ignore[assignment]
             )
 
             self.collection.upsert(

src/crewai/memory/storage/rag_storage.py

@@ -23,7 +23,7 @@ class RAGStorage(BaseRAGStorage):
     """
 
     app: ClientAPI | None = None
-    embedder_config: EmbeddingFunction | None = None  # type: ignore[assignment]
+    embedder_config: EmbeddingFunction[Any] | None = None
 
     def __init__(
         self,
@@ -41,14 +41,22 @@ class RAGStorage(BaseRAGStorage):
         self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
-
+        self._original_embedder_config = (
+            embedder_config  # Store for later use in _set_embedder_config
+        )
         self.allow_reset = allow_reset
         self.path = path
         self._initialize_app()
 
     def _set_embedder_config(self) -> None:
         configurator = EmbeddingConfigurator()
-        self.embedder_config = configurator.configure_embedder(self.embedder_config)
+        # Pass the original embedder_config from __init__, not self.embedder_config
+        if hasattr(self, "_original_embedder_config"):
+            self.embedder_config = configurator.configure_embedder(
+                self._original_embedder_config
+            )
+        else:
+            self.embedder_config = configurator.configure_embedder(None)
 
     def _initialize_app(self) -> None:
         from chromadb.config import Settings
@@ -118,23 +126,60 @@ class RAGStorage(BaseRAGStorage):
                 response = self.collection.query(query_texts=query, n_results=limit)
 
             results = []
-            if response and "ids" in response and response["ids"]:
-                for i in range(len(response["ids"][0])):
+            if (
+                response
+                and "ids" in response
+                and response["ids"]
+                and len(response["ids"]) > 0
+            ):
+                ids_list = (
+                    response["ids"][0]
+                    if isinstance(response["ids"][0], list)
+                    else response["ids"]
+                )
+                for i in range(len(ids_list)):
+                    # Handle metadatas
+                    metadata = {}
+                    if response.get("metadatas") and len(response["metadatas"]) > 0:
+                        metadata_list = (
+                            response["metadatas"][0]
+                            if isinstance(response["metadatas"][0], list)
+                            else response["metadatas"]
+                        )
+                        if i < len(metadata_list):
+                            metadata = metadata_list[i]
+
+                    # Handle documents
+                    context = ""
+                    if response.get("documents") and len(response["documents"]) > 0:
+                        docs_list = (
+                            response["documents"][0]
+                            if isinstance(response["documents"][0], list)
+                            else response["documents"]
+                        )
+                        if i < len(docs_list):
+                            context = docs_list[i]
+
+                    # Handle distances
+                    score = 1.0
+                    if response.get("distances") and len(response["distances"]) > 0:
+                        dist_list = (
+                            response["distances"][0]
+                            if isinstance(response["distances"][0], list)
+                            else response["distances"]
+                        )
+                        if i < len(dist_list):
+                            score = dist_list[i]
+
                     result = {
-                        "id": response["ids"][0][i],
-                        "metadata": response["metadatas"][0][i]
-                        if response.get("metadatas")
-                        else {},
-                        "context": response["documents"][0][i]
-                        if response.get("documents")
-                        else "",
-                        "score": response["distances"][0][i]
-                        if response.get("distances")
-                        else 1.0,
+                        "id": ids_list[i],
+                        "metadata": metadata,
+                        "context": context,
+                        "score": score,
                     }
-                    if (
-                        result["score"] <= score_threshold
-                    ):  # Note: distances are smaller when more similar
+
+                    # Check score threshold - distances are smaller when more similar
+                    if isinstance(score, (int, float)) and score <= score_threshold:
                         results.append(result)
 
             return results
@@ -168,7 +213,7 @@ class RAGStorage(BaseRAGStorage):
                     f"An error occurred while resetting the {self.type} memory: {e}"
                 )
 
-    def _create_default_embedding_function(self):
+    def _create_default_embedding_function(self) -> EmbeddingFunction[Any]:
         from chromadb.utils.embedding_functions.openai_embedding_function import (
             OpenAIEmbeddingFunction,
         )

src/crewai/telemetry/telemetry.py

@@ -534,7 +534,7 @@ class Telemetry:
 
     def tool_usage_error(
         self, llm: Any, agent: Any = None, tool_name: Optional[str] = None
-    ):
+    ) -> None:
         """Records when a tool usage results in an error.
 
         Args:
@@ -572,7 +572,7 @@ class Telemetry:
 
     def individual_test_result_span(
         self, crew: Crew, quality: float, exec_time: int, model_name: str
-    ):
+    ) -> None:
         """Records individual test results for a crew execution.
 
         Args:
@@ -607,7 +607,7 @@ class Telemetry:
         iterations: int,
         inputs: dict[str, Any] | None,
         model_name: str,
-    ):
+    ) -> None:
         """Records the execution of a test suite for a crew.
 
         Args:
@@ -792,7 +792,8 @@ class Telemetry:
 
         if crew.share_crew:
             self._safe_telemetry_operation(operation)
-            return operation()
+            result = operation()
+            return result  # type: ignore[no-any-return]
         return None
 
     def end_crew(self, crew: Any, final_string_output: str) -> None: