feat: upgrade chromadb to v1.1.0, improve types

- update imports and include handling for chromadb v1.1.0  
- fix mypy and typing_compat issues (required, typeddict, voyageai)  
- refine embedderconfig typing and allow base provider instances  
- handle mem0 as special case for external memory storage  
- bump tools and clean up redundant deps

pyproject.toml

@@ -21,9 +21,8 @@ dependencies = [
     "opentelemetry-sdk>=1.30.0",
     "opentelemetry-exporter-otlp-proto-http>=1.30.0",
     # Data Handling
-    "chromadb>=0.5.23",
+    "chromadb~=1.1.0",
     "tokenizers>=0.20.3",
-    "onnxruntime==1.22.0",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
     # Authentication and Security
@@ -49,7 +48,9 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.73.0"]
+tools = [
+    "crewai-tools>=0.74.0",
+]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
@@ -195,3 +196,4 @@ exclude = [
 "docs/**",
 "docs/",
 ]
+

src/crewai/agent.py

@@ -1,17 +1,10 @@
 import shutil
 import subprocess
 import time
+from collections.abc import Callable, Sequence
 from typing import (
     Any,
-    Callable,
-    Dict,
-    List,
     Literal,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    Union,
 )
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -19,12 +12,31 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+    AgentExecutionStartedEvent,
+)
+from crewai.events.types.knowledge_events import (
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
+from crewai.events.types.memory_events import (
+    MemoryRetrievalCompletedEvent,
+    MemoryRetrievalStartedEvent,
+)
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
+from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security import Fingerprint
 from crewai.task import Task
 from crewai.tools import BaseTool
@@ -38,24 +50,6 @@ from crewai.utilities.agent_utils import (
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
-from crewai.events.types.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.memory_events import (
-    MemoryRetrievalStartedEvent,
-    MemoryRetrievalCompletedEvent,
-)
-from crewai.events.types.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -87,36 +81,36 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
-    max_execution_time: Optional[int] = Field(
+    max_execution_time: int | None = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
     )
     agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
-    step_callback: Optional[Any] = Field(
+    step_callback: Any | None = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    use_system_prompt: Optional[bool] = Field(
+    use_system_prompt: bool | None = Field(
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
+    llm: str | InstanceOf[BaseLLM] | Any = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    function_calling_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
         description="Language model that will run the agent.", default=None
     )
-    system_template: Optional[str] = Field(
+    system_template: str | None = Field(
         default=None, description="System format for the agent."
     )
-    prompt_template: Optional[str] = Field(
+    prompt_template: str | None = Field(
         default=None, description="Prompt format for the agent."
     )
-    response_template: Optional[str] = Field(
+    response_template: str | None = Field(
         default=None, description="Response format for the agent."
     )
-    allow_code_execution: Optional[bool] = Field(
+    allow_code_execution: bool | None = Field(
         default=False, description="Enable code execution for the agent."
     )
     respect_context_window: bool = Field(
@@ -147,31 +141,31 @@ class Agent(BaseAgent):
         default=False,
         description="Whether the agent should reflect and create a plan before executing a task.",
     )
-    max_reasoning_attempts: Optional[int] = Field(
+    max_reasoning_attempts: int | None = Field(
         default=None,
         description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
-    embedder: Optional[Dict[str, Any]] = Field(
+    embedder: EmbedderConfig | None = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
-    agent_knowledge_context: Optional[str] = Field(
+    agent_knowledge_context: str | None = Field(
         default=None,
         description="Knowledge context for the agent.",
     )
-    crew_knowledge_context: Optional[str] = Field(
+    crew_knowledge_context: str | None = Field(
         default=None,
         description="Knowledge context for the crew.",
     )
-    knowledge_search_query: Optional[str] = Field(
+    knowledge_search_query: str | None = Field(
         default=None,
         description="Knowledge search query for the agent dynamically generated by the agent.",
     )
-    from_repository: Optional[str] = Field(
+    from_repository: str | None = Field(
         default=None,
         description="The Agent's role to be used from your repository.",
     )
-    guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
+    guardrail: Callable[[Any], tuple[bool, Any]] | str | None = Field(
         default=None,
         description="Function or string description of a guardrail to validate agent output",
     )
@@ -180,7 +174,7 @@ class Agent(BaseAgent):
     )
 
     @model_validator(mode="before")
-    def validate_from_repository(cls, v):
+    def validate_from_repository(cls, v): # noqa: N805
         if v is not None and (from_repository := v.get("from_repository")):
             return load_agent_from_repository(from_repository) | v
         return v
@@ -208,7 +202,7 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
         try:
             if self.embedder is None and crew_embedder:
                 self.embedder = crew_embedder
@@ -224,7 +218,7 @@ class Agent(BaseAgent):
                     )
                     self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
-            raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
+            raise ValueError(f"Invalid Knowledge Configuration: {e!s}") from e
 
     def _is_any_available_memory(self) -> bool:
         """Check if any memory is available."""
@@ -244,8 +238,8 @@ class Agent(BaseAgent):
     def execute_task(
         self,
         task: Task,
-        context: Optional[str] = None,
+        context: str | None = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: list[BaseTool] | None = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -278,11 +272,9 @@ class Agent(BaseAgent):
                 task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
             except Exception as e:
                 if hasattr(self, "_logger"):
-                    self._logger.log(
+                    self._logger.log("error", f"Error during reasoning process: {e!s}")
-                        "error", f"Error during reasoning process: {str(e)}"
-                    )
                 else:
-                    print(f"Error during reasoning process: {str(e)}")
+                    print(f"Error during reasoning process: {e!s}")
 
         self._inject_date_to_task(task)
 
@@ -335,7 +327,7 @@ class Agent(BaseAgent):
                 agent=self,
                 task=task,
             )
-            memory = contextual_memory.build_context_for_task(task, context)
+            memory = contextual_memory.build_context_for_task(task, context)  # type: ignore[arg-type]
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
@@ -525,14 +517,14 @@ class Agent(BaseAgent):
 
             try:
                 return future.result(timeout=timeout)
-            except concurrent.futures.TimeoutError:
+            except concurrent.futures.TimeoutError as e:
                 future.cancel()
                 raise TimeoutError(
                     f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task."
-                )
+                ) from e
             except Exception as e:
                 future.cancel()
-                raise RuntimeError(f"Task execution failed: {str(e)}")
+                raise RuntimeError(f"Task execution failed: {e!s}") from e
 
     def _execute_without_timeout(self, task_prompt: str, task: Task) -> str:
         """Execute a task without a timeout.
@@ -554,14 +546,14 @@ class Agent(BaseAgent):
         )["output"]
 
     def create_agent_executor(
-        self, tools: Optional[List[BaseTool]] = None, task=None
+        self, tools: list[BaseTool] | None = None, task=None
     ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        raw_tools: List[BaseTool] = tools or self.tools or []
+        raw_tools: list[BaseTool] = tools or self.tools or []
         parsed_tools = parse_tools(raw_tools)
 
         prompt = Prompts(
@@ -587,7 +579,7 @@ class Agent(BaseAgent):
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
-            prompt=prompt,
+            prompt=prompt,  # type: ignore[arg-type]
             original_tools=raw_tools,
             stop_words=stop_words,
             max_iter=self.max_iter,
@@ -603,10 +595,9 @@ class Agent(BaseAgent):
             callbacks=[TokenCalcHandler(self._token_process)],
         )
 
-    def get_delegation_tools(self, agents: List[BaseAgent]):
+    def get_delegation_tools(self, agents: list[BaseAgent]):
         agent_tools = AgentTools(agents=agents)
-        tools = agent_tools.tools()
+        return agent_tools.tools()
-        return tools
 
     def get_multimodal_tools(self) -> Sequence[BaseTool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
@@ -654,7 +645,7 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
-    def _render_text_description(self, tools: List[Any]) -> str:
+    def _render_text_description(self, tools: list[Any]) -> str:
         """Render the tool name and description in plain text.
 
         Output will be in the format of:
@@ -664,15 +655,13 @@ class Agent(BaseAgent):
             search: This tool is used for search
             calculator: This tool is used for math
         """
-        description = "\n".join(
+        return "\n".join(
             [
                 f"Tool name: {tool.name}\nTool description:\n{tool.description}"
                 for tool in tools
             ]
         )
 
-        return description
-
     def _inject_date_to_task(self, task):
         """Inject the current date into the task description if inject_date is enabled."""
         if self.inject_date:
@@ -696,13 +685,13 @@ class Agent(BaseAgent):
                 if not is_valid:
                     raise ValueError(f"Invalid date format: {self.date_format}")
 
-                current_date: str = datetime.now().strftime(self.date_format)
+                current_date = datetime.now().strftime(self.date_format)
                 task.description += f"\n\nCurrent Date: {current_date}"
             except Exception as e:
                 if hasattr(self, "_logger"):
-                    self._logger.log("warning", f"Failed to inject date: {str(e)}")
+                    self._logger.log("warning", f"Failed to inject date: {e!s}")
                 else:
-                    print(f"Warning: Failed to inject date: {str(e)}")
+                    print(f"Warning: Failed to inject date: {e!s}")
 
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
@@ -713,15 +702,15 @@ class Agent(BaseAgent):
 
         try:
             subprocess.run(
-                ["docker", "info"],
+                ["/usr/bin/docker", "info"],
                 check=True,
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
             )
-        except subprocess.CalledProcessError:
+        except subprocess.CalledProcessError as e:
             raise RuntimeError(
                 f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
-            )
+            ) from e
 
     def __repr__(self):
         return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"
@@ -796,8 +785,8 @@ class Agent(BaseAgent):
 
     def kickoff(
         self,
-        messages: Union[str, List[Dict[str, str]]],
+        messages: str | list[dict[str, str]],
-        response_format: Optional[Type[Any]] = None,
+        response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent with the given messages using a LiteAgent instance.
@@ -836,8 +825,8 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: Union[str, List[Dict[str, str]]],
+        messages: str | list[dict[str, str]],
-        response_format: Optional[Type[Any]] = None,
+        response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent asynchronously with the given messages using a LiteAgent instance.

src/crewai/agents/agent_builder/base_agent.py

@@ -22,6 +22,7 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
@@ -359,5 +360,5 @@ class BaseAgent(ABC, BaseModel):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: dict[str, Any] | None = None):
+    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
         pass

src/crewai/crew.py

@@ -59,6 +59,7 @@ from crewai.memory.external.external_memory import ExternalMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.process import Process
+from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.rag.types import SearchResult
 from crewai.security import Fingerprint, SecurityConfig
 from crewai.task import Task
@@ -168,7 +169,7 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="An Instance of the ExternalMemory to be used by the Crew",
     )
-    embedder: dict | None = Field(
+    embedder: EmbedderConfig | None = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
@@ -622,7 +623,8 @@ class Crew(FlowTrackable, BaseModel):
                         training_data=training_data, agent_id=str(agent.id)
                     )
                     CrewTrainingHandler(filename).save_trained_data(
-                        agent_id=str(agent.role), trained_data=result.model_dump()
+                        agent_id=str(agent.role),
+                        trained_data=result.model_dump(),  # type: ignore[arg-type]
                     )
 
             crewai_event_bus.emit(
@@ -1057,7 +1059,10 @@ class Crew(FlowTrackable, BaseModel):
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
             self._file_handler.log(
-                task_name=task.name, task=task.description, agent=role, status="started"
+                task_name=task.name,  # type: ignore[arg-type]
+                task=task.description,
+                agent=role,
+                status="started",
             )
 
     def _update_manager_tools(
@@ -1086,7 +1091,7 @@ class Crew(FlowTrackable, BaseModel):
         role = task.agent.role if task.agent is not None else "None"
         if self.output_log_file:
             self._file_handler.log(
-                task_name=task.name,
+                task_name=task.name,  # type: ignore[arg-type]
                 task=task.description,
                 agent=role,
                 status="completed",

src/crewai/knowledge/knowledge.py

@@ -1,10 +1,10 @@
 import os
-from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.rag.types import SearchResult
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
@@ -16,20 +16,20 @@ class Knowledge(BaseModel):
     Args:
         sources: list[BaseKnowledgeSource] = Field(default_factory=list)
         storage: KnowledgeStorage | None = Field(default=None)
-        embedder: dict[str, Any] | None = None
+        embedder: EmbedderConfig | None = None
     """
 
     sources: list[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage | None = Field(default=None)
-    embedder: dict[str, Any] | None = None
+    embedder: EmbedderConfig | None = None
     collection_name: str | None = None
 
     def __init__(
         self,
         collection_name: str,
         sources: list[BaseKnowledgeSource],
-        embedder: dict[str, Any] | None = None,
+        embedder: EmbedderConfig | None = None,
         storage: KnowledgeStorage | None = None,
         **data,
     ):

src/crewai/knowledge/storage/knowledge_storage.py

@@ -24,7 +24,10 @@ class KnowledgeStorage(BaseKnowledgeStorage):
 
     def __init__(
         self,
-        embedder: ProviderSpec | BaseEmbeddingsProvider | None = None,
+        embedder: ProviderSpec
+        | BaseEmbeddingsProvider
+        | type[BaseEmbeddingsProvider]
+        | None = None,
         collection_name: str | None = None,
     ) -> None:
         self.collection_name = collection_name
@@ -37,7 +40,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         )
 
         if embedder:
-            embedding_function = build_embedder(embedder)
+            embedding_function = build_embedder(embedder)  # type: ignore[arg-type]
             config = ChromaDBConfig(
                 embedding_function=cast(
                     ChromaEmbeddingFunctionWrapper, embedding_function

src/crewai/memory/entity/entity_memory.py

@@ -27,7 +27,10 @@ class EntityMemory(Memory):
     _memory_provider: str | None = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        memory_provider = embedder_config.get("provider") if embedder_config else None
+        memory_provider = None
+        if embedder_config and isinstance(embedder_config, dict):
+            memory_provider = embedder_config.get("provider")
+
         if memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
@@ -35,7 +38,11 @@ class EntityMemory(Memory):
                 raise ImportError(
                     "Mem0 is not installed. Please install it with `pip install mem0ai`."
                 ) from e
-            config = embedder_config.get("config") if embedder_config else None
+            config = (
+                embedder_config.get("config")
+                if embedder_config and isinstance(embedder_config, dict)
+                else None
+            )
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (

src/crewai/memory/external/external_memory.py

@@ -13,6 +13,7 @@ from crewai.events.types.memory_events import (
 from crewai.memory.external.external_memory_item import ExternalMemoryItem
 from crewai.memory.memory import Memory
 from crewai.memory.storage.interface import Storage
+from crewai.rag.embeddings.types import ProviderSpec
 
 if TYPE_CHECKING:
     from crewai.memory.storage.mem0_storage import Mem0Storage
@@ -35,7 +36,9 @@ class ExternalMemory(Memory):
         }
 
     @staticmethod
-    def create_storage(crew: Any, embedder_config: dict[str, Any] | None) -> Storage:
+    def create_storage(
+        crew: Any, embedder_config: dict[str, Any] | ProviderSpec | None
+    ) -> Storage:
         if not embedder_config:
             raise ValueError("embedder_config is required")
 
@@ -159,6 +162,6 @@ class ExternalMemory(Memory):
         super().set_crew(crew)
 
         if not self.storage:
-            self.storage = self.create_storage(crew, self.embedder_config)
+            self.storage = self.create_storage(crew, self.embedder_config)  # type: ignore[arg-type]
 
         return self

src/crewai/memory/memory.py

@@ -2,6 +2,8 @@ from typing import TYPE_CHECKING, Any, Optional
 
 from pydantic import BaseModel
 
+from crewai.rag.embeddings.types import EmbedderConfig
+
 if TYPE_CHECKING:
     from crewai.agent import Agent
     from crewai.task import Task
@@ -12,7 +14,7 @@ class Memory(BaseModel):
     Base class for memory, now supporting agent tags and generic metadata.
     """
 
-    embedder_config: dict[str, Any] | None = None
+    embedder_config: EmbedderConfig | dict[str, Any] | None = None
     crew: Any | None = None
 
     storage: Any

src/crewai/memory/short_term/short_term_memory.py

@@ -29,7 +29,10 @@ class ShortTermMemory(Memory):
     _memory_provider: str | None = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        memory_provider = embedder_config.get("provider") if embedder_config else None
+        memory_provider = None
+        if embedder_config and isinstance(embedder_config, dict):
+            memory_provider = embedder_config.get("provider")
+
         if memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
@@ -37,7 +40,11 @@ class ShortTermMemory(Memory):
                 raise ImportError(
                     "Mem0 is not installed. Please install it with `pip install mem0ai`."
                 ) from e
-            config = embedder_config.get("config") if embedder_config else None
+            config = (
+                embedder_config.get("config")
+                if embedder_config and isinstance(embedder_config, dict)
+                else None
+            )
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (

src/crewai/rag/chromadb/utils.py

@@ -10,7 +10,6 @@ from chromadb.api.models.AsyncCollection import AsyncCollection
 from chromadb.api.models.Collection import Collection
 from chromadb.api.types import (
     Include,
-    IncludeEnum,
     QueryResult,
 )
 
@@ -142,9 +141,12 @@ def _extract_search_params(
         score_threshold=kwargs.get("score_threshold"),
         where=kwargs.get("where"),
         where_document=kwargs.get("where_document"),
-        include=kwargs.get(
+        include=cast(
-            "include",
+            Include,
-            [IncludeEnum.metadatas, IncludeEnum.documents, IncludeEnum.distances],
+            kwargs.get(
+                "include",
+                ["metadatas", "documents", "distances"],
+            ),
         ),
     )
 
@@ -193,7 +195,7 @@ def _convert_chromadb_results_to_search_results(
     """
     search_results: list[SearchResult] = []
 
-    include_strings = [item.value for item in include] if include else []
+    include_strings = list(include) if include else []
 
     ids = results["ids"][0] if results.get("ids") else []
 

src/crewai/rag/embeddings/providers/aws/bedrock.py

@@ -1,5 +1,7 @@
 """Amazon Bedrock embeddings provider."""
 
+from typing import Any
+
 from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
     AmazonBedrockEmbeddingFunction,
 )
@@ -7,15 +9,8 @@ from pydantic import Field
 
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 
-try:
-    from boto3.session import Session  # type: ignore[import-untyped]
-except ImportError as exc:
-    raise ImportError(
-        "boto3 is required for amazon-bedrock embeddings. Install it with: uv add boto3"
-    ) from exc
 
-
+def create_aws_session() -> Any:
-def create_aws_session() -> Session:
     """Create an AWS session for Bedrock.
 
     Returns:
@@ -53,6 +48,6 @@ class BedrockProvider(BaseEmbeddingsProvider[AmazonBedrockEmbeddingFunction]):
         description="Model name to use for embeddings",
         validation_alias="BEDROCK_MODEL_NAME",
     )
-    session: Session = Field(
+    session: Any = Field(
         default_factory=create_aws_session, description="AWS session object"
     )

src/crewai/rag/embeddings/providers/aws/types.py

@@ -1,6 +1,8 @@
 """Type definitions for AWS embedding providers."""
 
-from typing import Annotated, Any, Literal, TypedDict
+from typing import Annotated, Any, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class BedrockProviderConfig(TypedDict, total=False):
@@ -10,8 +12,8 @@ class BedrockProviderConfig(TypedDict, total=False):
     session: Any
 
 
-class BedrockProviderSpec(TypedDict):
+class BedrockProviderSpec(TypedDict, total=False):
     """Bedrock provider specification."""
 
-    provider: Literal["amazon-bedrock"]
+    provider: Required[Literal["amazon-bedrock"]]
     config: BedrockProviderConfig

src/crewai/rag/embeddings/providers/cohere/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Cohere embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class CohereProviderConfig(TypedDict, total=False):
@@ -10,8 +12,8 @@ class CohereProviderConfig(TypedDict, total=False):
     model_name: Annotated[str, "large"]
 
 
-class CohereProviderSpec(TypedDict):
+class CohereProviderSpec(TypedDict, total=False):
     """Cohere provider specification."""
 
-    provider: Literal["cohere"]
+    provider: Required[Literal["cohere"]]
     config: CohereProviderConfig

src/crewai/rag/embeddings/providers/custom/types.py

@@ -1,8 +1,9 @@
 """Type definitions for custom embedding providers."""
 
-from typing import Literal, TypedDict
+from typing import Literal
 
 from chromadb.api.types import EmbeddingFunction
+from typing_extensions import Required, TypedDict
 
 
 class CustomProviderConfig(TypedDict, total=False):
@@ -11,8 +12,8 @@ class CustomProviderConfig(TypedDict, total=False):
     embedding_callable: type[EmbeddingFunction]
 
 
-class CustomProviderSpec(TypedDict):
+class CustomProviderSpec(TypedDict, total=False):
     """Custom provider specification."""
 
-    provider: Literal["custom"]
+    provider: Required[Literal["custom"]]
     config: CustomProviderConfig

src/crewai/rag/embeddings/providers/google/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Google embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class GenerativeAiProviderConfig(TypedDict, total=False):
@@ -27,8 +29,8 @@ class VertexAIProviderConfig(TypedDict, total=False):
     region: Annotated[str, "us-central1"]
 
 
-class VertexAIProviderSpec(TypedDict):
+class VertexAIProviderSpec(TypedDict, total=False):
     """Vertex AI provider specification."""
 
-    provider: Literal["google-vertex"]
+    provider: Required[Literal["google-vertex"]]
     config: VertexAIProviderConfig

src/crewai/rag/embeddings/providers/huggingface/types.py

@@ -1,6 +1,8 @@
 """Type definitions for HuggingFace embedding providers."""
 
-from typing import Literal, TypedDict
+from typing import Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class HuggingFaceProviderConfig(TypedDict, total=False):
@@ -9,8 +11,8 @@ class HuggingFaceProviderConfig(TypedDict, total=False):
     url: str
 
 
-class HuggingFaceProviderSpec(TypedDict):
+class HuggingFaceProviderSpec(TypedDict, total=False):
     """HuggingFace provider specification."""
 
-    provider: Literal["huggingface"]
+    provider: Required[Literal["huggingface"]]
     config: HuggingFaceProviderConfig

src/crewai/rag/embeddings/providers/ibm/embedding_callable.py

@@ -2,11 +2,6 @@
 
 from typing import cast
 
-import ibm_watsonx_ai.foundation_models as watson_models  # type: ignore[import-not-found, import-untyped]
-from ibm_watsonx_ai import Credentials  # type: ignore[import-not-found, import-untyped]
-from ibm_watsonx_ai.metanames import (  # type: ignore[import-not-found, import-untyped]
-    EmbedTextParamsMetaNames as EmbedParams,
-)
 from typing_extensions import Unpack
 
 from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
@@ -34,6 +29,21 @@ class WatsonEmbeddingFunction(EmbeddingFunction[Documents]):
         Returns:
             List of embedding vectors.
         """
+        try:
+            import ibm_watsonx_ai.foundation_models as watson_models  # type: ignore[import-not-found, import-untyped]
+            from ibm_watsonx_ai import (
+                Credentials,  # type: ignore[import-not-found, import-untyped]
+            )
+            from ibm_watsonx_ai.metanames import (  # type: ignore[import-not-found, import-untyped]
+                EmbedTextParamsMetaNames as EmbedParams,
+            )
+
+        except ImportError as e:
+            raise ImportError(
+                "ibm-watsonx-ai is required for watson embeddings. "
+                "Install it with: uv add ibm-watsonx-ai"
+            ) from e
+
         if isinstance(input, str):
             input = [input]
 

src/crewai/rag/embeddings/providers/ibm/types.py

@@ -1,6 +1,8 @@
 """Type definitions for IBM Watson embedding providers."""
 
-from typing import Annotated, Any, Literal, TypedDict
+from typing import Annotated, Any, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class WatsonProviderConfig(TypedDict, total=False):
@@ -35,8 +37,8 @@ class WatsonProviderConfig(TypedDict, total=False):
     proxies: dict
 
 
-class WatsonProviderSpec(TypedDict):
+class WatsonProviderSpec(TypedDict, total=False):
     """Watson provider specification."""
 
-    provider: Literal["watson"]
+    provider: Required[Literal["watson"]]
     config: WatsonProviderConfig

src/crewai/rag/embeddings/providers/ibm/watson.py

@@ -1,9 +1,7 @@
 """IBM Watson embeddings provider."""
 
-from ibm_watsonx_ai import (  # type: ignore[import-not-found,import-untyped]
+from typing import Any
-    APIClient,
+
-    Credentials,
-)
 from pydantic import Field, model_validator
 from typing_extensions import Self
 
@@ -28,9 +26,7 @@ class WatsonProvider(BaseEmbeddingsProvider[WatsonEmbeddingFunction]):
     params: dict[str, str | dict[str, str]] | None = Field(
         default=None, description="Additional parameters"
     )
-    credentials: Credentials | None = Field(
+    credentials: Any | None = Field(default=None, description="Watson credentials")
-        default=None, description="Watson credentials"
-    )
     project_id: str | None = Field(
         default=None,
         description="Watson project ID",
@@ -39,7 +35,7 @@ class WatsonProvider(BaseEmbeddingsProvider[WatsonEmbeddingFunction]):
     space_id: str | None = Field(
         default=None, description="Watson space ID", validation_alias="WATSON_SPACE_ID"
     )
-    api_client: APIClient | None = Field(default=None, description="Watson API client")
+    api_client: Any | None = Field(default=None, description="Watson API client")
     verify: bool | str | None = Field(
         default=None, description="SSL verification", validation_alias="WATSON_VERIFY"
     )

src/crewai/rag/embeddings/providers/instructor/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Instructor embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class InstructorProviderConfig(TypedDict, total=False):
@@ -11,8 +13,8 @@ class InstructorProviderConfig(TypedDict, total=False):
     instruction: str
 
 
-class InstructorProviderSpec(TypedDict):
+class InstructorProviderSpec(TypedDict, total=False):
     """Instructor provider specification."""
 
-    provider: Literal["instructor"]
+    provider: Required[Literal["instructor"]]
     config: InstructorProviderConfig

src/crewai/rag/embeddings/providers/jina/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Jina embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class JinaProviderConfig(TypedDict, total=False):
@@ -10,8 +12,8 @@ class JinaProviderConfig(TypedDict, total=False):
     model_name: Annotated[str, "jina-embeddings-v2-base-en"]
 
 
-class JinaProviderSpec(TypedDict):
+class JinaProviderSpec(TypedDict, total=False):
     """Jina provider specification."""
 
-    provider: Literal["jina"]
+    provider: Required[Literal["jina"]]
     config: JinaProviderConfig

src/crewai/rag/embeddings/providers/microsoft/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Microsoft Azure embedding providers."""
 
-from typing import Annotated, Any, Literal, TypedDict
+from typing import Annotated, Any, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class AzureProviderConfig(TypedDict, total=False):
@@ -17,8 +19,8 @@ class AzureProviderConfig(TypedDict, total=False):
     organization_id: str
 
 
-class AzureProviderSpec(TypedDict):
+class AzureProviderSpec(TypedDict, total=False):
     """Azure provider specification."""
 
-    provider: Literal["azure"]
+    provider: Required[Literal["azure"]]
     config: AzureProviderConfig

src/crewai/rag/embeddings/providers/ollama/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Ollama embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class OllamaProviderConfig(TypedDict, total=False):
@@ -10,8 +12,8 @@ class OllamaProviderConfig(TypedDict, total=False):
     model_name: str
 
 
-class OllamaProviderSpec(TypedDict):
+class OllamaProviderSpec(TypedDict, total=False):
     """Ollama provider specification."""
 
-    provider: Literal["ollama"]
+    provider: Required[Literal["ollama"]]
     config: OllamaProviderConfig

src/crewai/rag/embeddings/providers/onnx/types.py

@@ -1,6 +1,8 @@
 """Type definitions for ONNX embedding providers."""
 
-from typing import Literal, TypedDict
+from typing import Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class ONNXProviderConfig(TypedDict, total=False):
@@ -9,8 +11,8 @@ class ONNXProviderConfig(TypedDict, total=False):
     preferred_providers: list[str]
 
 
-class ONNXProviderSpec(TypedDict):
+class ONNXProviderSpec(TypedDict, total=False):
     """ONNX provider specification."""
 
-    provider: Literal["onnx"]
+    provider: Required[Literal["onnx"]]
     config: ONNXProviderConfig

src/crewai/rag/embeddings/providers/openai/openai_provider.py

@@ -17,8 +17,8 @@ class OpenAIProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
         default=OpenAIEmbeddingFunction,
         description="OpenAI embedding function class",
     )
-    api_key: str = Field(
+    api_key: str | None = Field(
-        description="OpenAI API key", validation_alias="OPENAI_API_KEY"
+        default=None, description="OpenAI API key", validation_alias="OPENAI_API_KEY"
     )
     model_name: str = Field(
         default="text-embedding-ada-002",

src/crewai/rag/embeddings/providers/openai/types.py

@@ -1,6 +1,8 @@
 """Type definitions for OpenAI embedding providers."""
 
-from typing import Annotated, Any, Literal, TypedDict
+from typing import Annotated, Any, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class OpenAIProviderConfig(TypedDict, total=False):
@@ -17,8 +19,8 @@ class OpenAIProviderConfig(TypedDict, total=False):
     organization_id: str
 
 
-class OpenAIProviderSpec(TypedDict):
+class OpenAIProviderSpec(TypedDict, total=False):
     """OpenAI provider specification."""
 
-    provider: Literal["openai"]
+    provider: Required[Literal["openai"]]
     config: OpenAIProviderConfig

src/crewai/rag/embeddings/providers/openclip/types.py

@@ -1,6 +1,8 @@
 """Type definitions for OpenCLIP embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class OpenCLIPProviderConfig(TypedDict, total=False):
@@ -14,5 +16,5 @@ class OpenCLIPProviderConfig(TypedDict, total=False):
 class OpenCLIPProviderSpec(TypedDict):
     """OpenCLIP provider specification."""
 
-    provider: Literal["openclip"]
+    provider: Required[Literal["openclip"]]
     config: OpenCLIPProviderConfig

src/crewai/rag/embeddings/providers/roboflow/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Roboflow embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class RoboflowProviderConfig(TypedDict, total=False):
@@ -13,5 +15,5 @@ class RoboflowProviderConfig(TypedDict, total=False):
 class RoboflowProviderSpec(TypedDict):
     """Roboflow provider specification."""
 
-    provider: Literal["roboflow"]
+    provider: Required[Literal["roboflow"]]
     config: RoboflowProviderConfig

src/crewai/rag/embeddings/providers/sentence_transformer/types.py

@@ -1,6 +1,8 @@
 """Type definitions for SentenceTransformer embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class SentenceTransformerProviderConfig(TypedDict, total=False):
@@ -14,5 +16,5 @@ class SentenceTransformerProviderConfig(TypedDict, total=False):
 class SentenceTransformerProviderSpec(TypedDict):
     """SentenceTransformer provider specification."""
 
-    provider: Literal["sentence-transformer"]
+    provider: Required[Literal["sentence-transformer"]]
     config: SentenceTransformerProviderConfig

src/crewai/rag/embeddings/providers/text2vec/types.py

@@ -1,6 +1,8 @@
 """Type definitions for Text2Vec embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class Text2VecProviderConfig(TypedDict, total=False):
@@ -12,5 +14,5 @@ class Text2VecProviderConfig(TypedDict, total=False):
 class Text2VecProviderSpec(TypedDict):
     """Text2Vec provider specification."""
 
-    provider: Literal["text2vec"]
+    provider: Required[Literal["text2vec"]]
     config: Text2VecProviderConfig

src/crewai/rag/embeddings/providers/voyageai/embedding_callable.py

@@ -2,7 +2,6 @@
 
 from typing import cast
 
-import voyageai
 from typing_extensions import Unpack
 
 from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
@@ -19,6 +18,14 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction[Documents]):
         Args:
             **kwargs: Configuration parameters for VoyageAI.
         """
+        try:
+            import voyageai  # type: ignore[import-not-found]
+
+        except ImportError as e:
+            raise ImportError(
+                "voyageai is required for voyageai embeddings. "
+                "Install it with: uv add voyageai"
+            ) from e
         self._config = kwargs
         self._client = voyageai.Client(
             api_key=kwargs["api_key"],
@@ -35,6 +42,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction[Documents]):
         Returns:
             List of embedding vectors.
         """
+
         if isinstance(input, str):
             input = [input]
 

src/crewai/rag/embeddings/providers/voyageai/types.py

@@ -1,6 +1,8 @@
 """Type definitions for VoyageAI embedding providers."""
 
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
 
 
 class VoyageAIProviderConfig(TypedDict, total=False):
@@ -19,5 +21,5 @@ class VoyageAIProviderConfig(TypedDict, total=False):
 class VoyageAIProviderSpec(TypedDict):
     """VoyageAI provider specification."""
 
-    provider: Literal["voyageai"]
+    provider: Required[Literal["voyageai"]]
     config: VoyageAIProviderConfig

src/crewai/rag/embeddings/types.py

@@ -1,7 +1,8 @@
 """Type definitions for the embeddings module."""
 
-from typing import Literal
+from typing import Literal, TypeAlias
 
+from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
 from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
 from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
@@ -66,3 +67,7 @@ AllowedEmbeddingProviders = Literal[
     "voyageai",
     "watson",
 ]
+
+EmbedderConfig: TypeAlias = (
+    ProviderSpec | BaseEmbeddingsProvider | type[BaseEmbeddingsProvider]
+)

uv.lock

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-source = { registry = "https://pypi.org/simple" }
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-]
-
 [[package]]
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 version = "3.0.0"
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-version = "0.7.6"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
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-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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