fix: resolve lint and type-checker issues

- Fix RET504 lint error by removing unnecessary assignment before return
- Add proper type annotations for embedding_functions dictionary
- Import Callable and Any from typing to resolve mypy errors

Co-Authored-By: João <joao@crewai.com>

docs/en/quickstart.mdx

@@ -27,7 +27,7 @@ Follow the steps below to get Crewing! 🚣‍♂️
   <Step title="Navigate to your new crew project">
     <CodeGroup>
       ```shell Terminal
-      cd latest_ai_development
+      cd latest-ai-development
       ```
     </CodeGroup>
   </Step>

docs/ko/quickstart.mdx

@@ -27,7 +27,7 @@ mode: "wide"
   <Step title="새로운 crew 프로젝트로 이동하기">
     <CodeGroup>
       ```shell Terminal
-      cd latest_ai_development
+      cd latest-ai-development
       ```
     </CodeGroup>
   </Step>

docs/pt-BR/quickstart.mdx

@@ -27,7 +27,7 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
   <Step title="Navegue até o novo projeto da sua tripulação">
     <CodeGroup>
       ```shell Terminal
-      cd latest_ai_development
+      cd latest-ai-development
       ```
     </CodeGroup>
   </Step>

pyproject.toml

@@ -9,7 +9,7 @@ authors = [
 ]
 dependencies = [
     # Core Dependencies
-    "pydantic>=2.11.9",
+    "pydantic>=2.4.2",
     "openai>=1.13.3",
     "litellm==1.74.9",
     "instructor>=1.3.3",
@@ -21,12 +21,13 @@ dependencies = [
     "opentelemetry-sdk>=1.30.0",
     "opentelemetry-exporter-otlp-proto-http>=1.30.0",
     # Data Handling
-    "chromadb~=1.1.0",
+    "chromadb>=0.5.23",
     "tokenizers>=0.20.3",
+    "onnxruntime==1.22.0",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
     # Authentication and Security
-    "python-dotenv>=1.1.1",
+    "python-dotenv>=1.0.0",
     "pyjwt>=2.9.0",
     # Configuration and Utils
     "click>=8.1.7",
@@ -39,7 +40,6 @@ dependencies = [
     "blinker>=1.9.0",
     "json5>=0.10.0",
     "portalocker==2.7.0",
-    "pydantic-settings>=2.10.1",
 ]
 
 [project.urls]
@@ -48,9 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = [
-    "crewai-tools>=0.74.0",
-]
+tools = ["crewai-tools~=0.73.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
@@ -73,30 +71,24 @@ aisuite = [
 qdrant = [
     "qdrant-client[fastembed]>=1.14.3",
 ]
-aws = [
-    "boto3>=1.40.38",
-]
-watson = [
-    "ibm-watsonx-ai>=1.3.39",
-]
-voyageai = [
-    "voyageai>=0.3.5",
-]
 
-[dependency-groups]
-dev = [
-    "ruff>=0.13.1",
-    "mypy>=1.18.2",
+[tool.uv]
+dev-dependencies = [
+    "ruff>=0.12.11",
+    "mypy>=1.17.1",
     "pre-commit>=4.3.0",
     "bandit>=1.8.6",
-    "pytest>=8.4.2",
-    "pytest-asyncio>=1.2.0",
-    "pytest-subprocess>=1.5.3",
-    "pytest-recording>=0.13.4",
-    "pytest-randomly>=4.0.1",
-    "pytest-timeout>=2.4.0",
-    "pytest-xdist>=3.8.0",
-    "pytest-split>=0.10.0",
+    "pillow>=10.2.0",
+    "cairosvg>=2.7.1",
+    "pytest>=8.0.0",
+    "python-dotenv>=1.0.0",
+    "pytest-asyncio>=0.23.7",
+    "pytest-subprocess>=1.5.2",
+    "pytest-recording>=0.13.2",
+    "pytest-randomly>=3.16.0",
+    "pytest-timeout>=2.3.1",
+    "pytest-xdist>=3.6.1",
+    "pytest-split>=0.9.0",
     "types-requests==2.32.*",
     "types-pyyaml==6.0.*",
     "types-regex==2024.11.6.*",

src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "0.201.0"
+__version__ = "0.193.2"
 _telemetry_submitted = False
 
 

src/crewai/agent.py

@@ -1,10 +1,17 @@
 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
@@ -12,31 +19,12 @@ 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
@@ -50,6 +38,24 @@ 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
@@ -81,36 +87,36 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
-    max_execution_time: int | None = Field(
+    max_execution_time: Optional[int] = 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: Any | None = Field(
+    step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    use_system_prompt: bool | None = Field(
+    use_system_prompt: Optional[bool] = Field(
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: str | InstanceOf[BaseLLM] | Any = Field(
+    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
-    system_template: str | None = Field(
+    system_template: Optional[str] = Field(
         default=None, description="System format for the agent."
     )
-    prompt_template: str | None = Field(
+    prompt_template: Optional[str] = Field(
         default=None, description="Prompt format for the agent."
     )
-    response_template: str | None = Field(
+    response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
-    allow_code_execution: bool | None = Field(
+    allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
     respect_context_window: bool = Field(
@@ -141,31 +147,31 @@ class Agent(BaseAgent):
         default=False,
         description="Whether the agent should reflect and create a plan before executing a task.",
     )
-    max_reasoning_attempts: int | None = Field(
+    max_reasoning_attempts: Optional[int] = Field(
         default=None,
         description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
-    embedder: EmbedderConfig | None = Field(
+    embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
-    agent_knowledge_context: str | None = Field(
+    agent_knowledge_context: Optional[str] = Field(
         default=None,
         description="Knowledge context for the agent.",
     )
-    crew_knowledge_context: str | None = Field(
+    crew_knowledge_context: Optional[str] = Field(
         default=None,
         description="Knowledge context for the crew.",
     )
-    knowledge_search_query: str | None = Field(
+    knowledge_search_query: Optional[str] = Field(
         default=None,
         description="Knowledge search query for the agent dynamically generated by the agent.",
     )
-    from_repository: str | None = Field(
+    from_repository: Optional[str] = Field(
         default=None,
         description="The Agent's role to be used from your repository.",
     )
-    guardrail: Callable[[Any], tuple[bool, Any]] | str | None = Field(
+    guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
         default=None,
         description="Function or string description of a guardrail to validate agent output",
     )
@@ -174,7 +180,7 @@ class Agent(BaseAgent):
     )
 
     @model_validator(mode="before")
-    def validate_from_repository(cls, v): # noqa: N805
+    def validate_from_repository(cls, v):
         if v is not None and (from_repository := v.get("from_repository")):
             return load_agent_from_repository(from_repository) | v
         return v
@@ -202,7 +208,7 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
+    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
         try:
             if self.embedder is None and crew_embedder:
                 self.embedder = crew_embedder
@@ -218,7 +224,7 @@ class Agent(BaseAgent):
                     )
                     self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
-            raise ValueError(f"Invalid Knowledge Configuration: {e!s}") from e
+            raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
 
     def _is_any_available_memory(self) -> bool:
         """Check if any memory is available."""
@@ -238,8 +244,8 @@ class Agent(BaseAgent):
     def execute_task(
         self,
         task: Task,
-        context: str | None = None,
-        tools: list[BaseTool] | None = None,
+        context: Optional[str] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -272,9 +278,11 @@ 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("error", f"Error during reasoning process: {e!s}")
+                    self._logger.log(
+                        "error", f"Error during reasoning process: {str(e)}"
+                    )
                 else:
-                    print(f"Error during reasoning process: {e!s}")
+                    print(f"Error during reasoning process: {str(e)}")
 
         self._inject_date_to_task(task)
 
@@ -327,7 +335,7 @@ class Agent(BaseAgent):
                 agent=self,
                 task=task,
             )
-            memory = contextual_memory.build_context_for_task(task, context)  # type: ignore[arg-type]
+            memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
@@ -517,14 +525,14 @@ class Agent(BaseAgent):
 
             try:
                 return future.result(timeout=timeout)
-            except concurrent.futures.TimeoutError as e:
+            except concurrent.futures.TimeoutError:
                 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: {e!s}") from e
+                raise RuntimeError(f"Task execution failed: {str(e)}")
 
     def _execute_without_timeout(self, task_prompt: str, task: Task) -> str:
         """Execute a task without a timeout.
@@ -546,14 +554,14 @@ class Agent(BaseAgent):
         )["output"]
 
     def create_agent_executor(
-        self, tools: list[BaseTool] | None = None, task=None
+        self, tools: Optional[List[BaseTool]] = 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(
@@ -579,7 +587,7 @@ class Agent(BaseAgent):
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
-            prompt=prompt,  # type: ignore[arg-type]
+            prompt=prompt,
             original_tools=raw_tools,
             stop_words=stop_words,
             max_iter=self.max_iter,
@@ -595,9 +603,10 @@ 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)
-        return agent_tools.tools()
+        tools = agent_tools.tools()
+        return tools
 
     def get_multimodal_tools(self) -> Sequence[BaseTool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
@@ -645,7 +654,7 @@ 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:
@@ -655,13 +664,15 @@ class Agent(BaseAgent):
             search: This tool is used for search
             calculator: This tool is used for math
         """
-        return "\n".join(
+        description = "\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:
@@ -685,13 +696,13 @@ class Agent(BaseAgent):
                 if not is_valid:
                     raise ValueError(f"Invalid date format: {self.date_format}")
 
-                current_date = datetime.now().strftime(self.date_format)
+                current_date: str = 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: {e!s}")
+                    self._logger.log("warning", f"Failed to inject date: {str(e)}")
                 else:
-                    print(f"Warning: Failed to inject date: {e!s}")
+                    print(f"Warning: Failed to inject date: {str(e)}")
 
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
@@ -702,15 +713,15 @@ class Agent(BaseAgent):
 
         try:
             subprocess.run(
-                ["/usr/bin/docker", "info"],
+                ["docker", "info"],
                 check=True,
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
             )
-        except subprocess.CalledProcessError as e:
+        except subprocess.CalledProcessError:
             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})"
@@ -785,8 +796,8 @@ class Agent(BaseAgent):
 
     def kickoff(
         self,
-        messages: str | list[dict[str, str]],
-        response_format: type[Any] | None = None,
+        messages: Union[str, List[Dict[str, str]]],
+        response_format: Optional[Type[Any]] = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent with the given messages using a LiteAgent instance.
@@ -825,8 +836,8 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: str | list[dict[str, str]],
-        response_format: type[Any] | None = None,
+        messages: Union[str, List[Dict[str, str]]],
+        response_format: Optional[Type[Any]] = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent asynchronously with the given messages using a LiteAgent instance.

src/crewai/agents/agent_builder/base_agent.py

@@ -22,7 +22,6 @@ 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
@@ -360,5 +359,5 @@ class BaseAgent(ABC, BaseModel):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
+    def set_knowledge(self, crew_embedder: dict[str, Any] | None = None):
         pass

src/crewai/cli/plus_api.py

@@ -166,13 +166,3 @@ class PlusAPI:
             json=payload,
             timeout=30,
         )
-
-    def mark_trace_batch_as_failed(
-        self, trace_batch_id: str, error_message: str
-    ) -> requests.Response:
-        return self._make_request(
-            "PATCH",
-            f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}",
-            json={"status": "failed", "failure_reason": error_message},
-            timeout=30,
-        )

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.201.0,<1.0.0"
+    "crewai[tools]>=0.193.2,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.201.0,<1.0.0",
+    "crewai[tools]>=0.193.2,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.201.0"
+    "crewai[tools]>=0.193.2"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -59,7 +59,6 @@ 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
@@ -169,7 +168,7 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="An Instance of the ExternalMemory to be used by the Crew",
     )
-    embedder: EmbedderConfig | None = Field(
+    embedder: dict | None = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
@@ -623,8 +622,7 @@ 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(),  # type: ignore[arg-type]
+                        agent_id=str(agent.role), trained_data=result.model_dump()
                     )
 
             crewai_event_bus.emit(
@@ -1059,10 +1057,7 @@ 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,  # type: ignore[arg-type]
-                task=task.description,
-                agent=role,
-                status="started",
+                task_name=task.name, task=task.description, agent=role, status="started"
             )
 
     def _update_manager_tools(
@@ -1091,7 +1086,7 @@ 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,  # type: ignore[arg-type]
+                task_name=task.name,
                 task=task.description,
                 agent=role,
                 status="completed",

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

@@ -200,9 +200,6 @@ class TraceBatchManager:
         if self.event_buffer:
             events_sent_to_backend_status = self._send_events_to_backend()
             if events_sent_to_backend_status == 500:
-                self.plus_api.mark_trace_batch_as_failed(
-                    self.trace_batch_id, "Error sending events to backend"
-                )
                 return None
         self._finalize_backend_batch()
 
@@ -276,13 +273,10 @@ class TraceBatchManager:
                 logger.error(
                     f"❌ Failed to finalize trace batch: {response.status_code} - {response.text}"
                 )
-                self.plus_api.mark_trace_batch_as_failed(
-                    self.trace_batch_id, response.text
-                )
 
         except Exception as e:
             logger.error(f"❌ Error finalizing trace batch: {e}")
-            self.plus_api.mark_trace_batch_as_failed(self.trace_batch_id, str(e))
+            # TODO: send error to app marking as failed
 
     def _cleanup_batch_data(self):
         """Clean up batch data after successful finalization to free memory"""

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: EmbedderConfig | None = None
+        embedder: dict[str, Any] | None = None
     """
 
     sources: list[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage | None = Field(default=None)
-    embedder: EmbedderConfig | None = None
+    embedder: dict[str, Any] | None = None
     collection_name: str | None = None
 
     def __init__(
         self,
         collection_name: str,
         sources: list[BaseKnowledgeSource],
-        embedder: EmbedderConfig | None = None,
+        embedder: dict[str, Any] | None = None,
         storage: KnowledgeStorage | None = None,
         **data,
     ):

src/crewai/knowledge/storage/knowledge_storage.py

@@ -8,9 +8,7 @@ from crewai.rag.chromadb.config import ChromaDBConfig
 from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper
 from crewai.rag.config.utils import get_rag_client
 from crewai.rag.core.base_client import BaseClient
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.factory import build_embedder
-from crewai.rag.embeddings.types import ProviderSpec
+from crewai.rag.embeddings.factory import get_embedding_function
 from crewai.rag.factory import create_client
 from crewai.rag.types import BaseRecord, SearchResult
 from crewai.utilities.logger import Logger
@@ -24,10 +22,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
 
     def __init__(
         self,
-        embedder: ProviderSpec
-        | BaseEmbeddingsProvider
-        | type[BaseEmbeddingsProvider]
-        | None = None,
+        embedder: dict[str, Any] | None = None,
         collection_name: str | None = None,
     ) -> None:
         self.collection_name = collection_name
@@ -40,7 +35,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         )
 
         if embedder:
-            embedding_function = build_embedder(embedder)  # type: ignore[arg-type]
+            embedding_function = get_embedding_function(embedder)
             config = ChromaDBConfig(
                 embedding_function=cast(
                     ChromaEmbeddingFunctionWrapper, embedding_function

src/crewai/memory/entity/entity_memory.py

@@ -27,10 +27,7 @@ class EntityMemory(Memory):
     _memory_provider: str | None = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        memory_provider = None
-        if embedder_config and isinstance(embedder_config, dict):
-            memory_provider = embedder_config.get("provider")
-
+        memory_provider = embedder_config.get("provider") if embedder_config else None
         if memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
@@ -38,11 +35,7 @@ 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 and isinstance(embedder_config, dict)
-                else None
-            )
+            config = embedder_config.get("config") if embedder_config else None
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (

src/crewai/memory/external/external_memory.py

@@ -13,7 +13,6 @@ 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
@@ -36,9 +35,7 @@ class ExternalMemory(Memory):
         }
 
     @staticmethod
-    def create_storage(
-        crew: Any, embedder_config: dict[str, Any] | ProviderSpec | None
-    ) -> Storage:
+    def create_storage(crew: Any, embedder_config: dict[str, Any] | None) -> Storage:
         if not embedder_config:
             raise ValueError("embedder_config is required")
 
@@ -162,6 +159,6 @@ class ExternalMemory(Memory):
         super().set_crew(crew)
 
         if not self.storage:
-            self.storage = self.create_storage(crew, self.embedder_config)  # type: ignore[arg-type]
+            self.storage = self.create_storage(crew, self.embedder_config)
 
         return self

src/crewai/memory/memory.py

@@ -2,8 +2,6 @@ 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
@@ -14,7 +12,7 @@ class Memory(BaseModel):
     Base class for memory, now supporting agent tags and generic metadata.
     """
 
-    embedder_config: EmbedderConfig | dict[str, Any] | None = None
+    embedder_config: dict[str, Any] | None = None
     crew: Any | None = None
 
     storage: Any

src/crewai/memory/short_term/short_term_memory.py

@@ -29,10 +29,7 @@ class ShortTermMemory(Memory):
     _memory_provider: str | None = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        memory_provider = None
-        if embedder_config and isinstance(embedder_config, dict):
-            memory_provider = embedder_config.get("provider")
-
+        memory_provider = embedder_config.get("provider") if embedder_config else None
         if memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
@@ -40,11 +37,7 @@ 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 and isinstance(embedder_config, dict)
-                else None
-            )
+            config = embedder_config.get("config") if embedder_config else None
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (

src/crewai/memory/storage/rag_storage.py

@@ -7,9 +7,7 @@ from crewai.rag.chromadb.config import ChromaDBConfig
 from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper
 from crewai.rag.config.utils import get_rag_client
 from crewai.rag.core.base_client import BaseClient
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.factory import build_embedder
-from crewai.rag.embeddings.types import ProviderSpec
+from crewai.rag.embeddings.factory import get_embedding_function
 from crewai.rag.factory import create_client
 from crewai.rag.storage.base_rag_storage import BaseRAGStorage
 from crewai.rag.types import BaseRecord
@@ -27,7 +25,7 @@ class RAGStorage(BaseRAGStorage):
         self,
         type: str,
         allow_reset: bool = True,
-        embedder_config: ProviderSpec | BaseEmbeddingsProvider | None = None,
+        embedder_config: dict[str, Any] | None = None,
         crew: Any = None,
         path: str | None = None,
     ) -> None:
@@ -51,46 +49,12 @@ class RAGStorage(BaseRAGStorage):
         )
 
         if self.embedder_config:
-            embedding_function = build_embedder(self.embedder_config)
-
-            try:
-                _ = embedding_function(["test"])
-            except Exception as e:
-                provider = (
-                    self.embedder_config["provider"]
-                    if isinstance(self.embedder_config, dict)
-                    else self.embedder_config.__class__.__name__.replace(
-                        "Provider", ""
-                    ).lower()
-                )
-                raise ValueError(
-                    f"Failed to initialize embedder. Please check your configuration or connection.\n"
-                    f"Provider: {provider}\n"
-                    f"Error: {e}"
-                ) from e
-
-            batch_size = None
-            if (
-                isinstance(self.embedder_config, dict)
-                and "config" in self.embedder_config
-            ):
-                nested_config = self.embedder_config["config"]
-                if isinstance(nested_config, dict):
-                    batch_size = nested_config.get("batch_size")
-
-            if batch_size is not None:
-                config = ChromaDBConfig(
-                    embedding_function=cast(
-                        ChromaEmbeddingFunctionWrapper, embedding_function
-                    ),
-                    batch_size=cast(int, batch_size),
-                )
-            else:
-                config = ChromaDBConfig(
-                    embedding_function=cast(
-                        ChromaEmbeddingFunctionWrapper, embedding_function
-                    )
+            embedding_function = get_embedding_function(self.embedder_config)
+            config = ChromaDBConfig(
+                embedding_function=cast(
+                    ChromaEmbeddingFunctionWrapper, embedding_function
                 )
+            )
             self._client = create_client(config)
 
     def _get_client(self) -> BaseClient:
@@ -131,26 +95,7 @@ class RAGStorage(BaseRAGStorage):
             if metadata:
                 document["metadata"] = metadata
 
-            batch_size = None
-            if (
-                self.embedder_config
-                and isinstance(self.embedder_config, dict)
-                and "config" in self.embedder_config
-            ):
-                nested_config = self.embedder_config["config"]
-                if isinstance(nested_config, dict):
-                    batch_size = nested_config.get("batch_size")
-
-            if batch_size is not None:
-                client.add_documents(
-                    collection_name=collection_name,
-                    documents=[document],
-                    batch_size=cast(int, batch_size),
-                )
-            else:
-                client.add_documents(
-                    collection_name=collection_name, documents=[document]
-                )
+            client.add_documents(collection_name=collection_name, documents=[document])
         except Exception as e:
             logging.error(
                 f"Error during {self.type} save: {e!s}\n{traceback.format_exc()}"

src/crewai/rag/chromadb/client.py

@@ -17,7 +17,6 @@ from crewai.rag.chromadb.types import (
     ChromaDBCollectionSearchParams,
 )
 from crewai.rag.chromadb.utils import (
-    _create_batch_slice,
     _extract_search_params,
     _is_async_client,
     _is_sync_client,
@@ -53,7 +52,6 @@ class ChromaDBClient(BaseClient):
         embedding_function: ChromaEmbeddingFunction,
         default_limit: int = 5,
         default_score_threshold: float = 0.6,
-        default_batch_size: int = 100,
     ) -> None:
         """Initialize ChromaDBClient with client and embedding function.
 
@@ -62,13 +60,11 @@ class ChromaDBClient(BaseClient):
             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.
-            default_batch_size: Default batch size for adding documents.
         """
         self.client = client
         self.embedding_function = embedding_function
         self.default_limit = default_limit
         self.default_score_threshold = default_score_threshold
-        self.default_batch_size = default_batch_size
 
     def create_collection(
         self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
@@ -295,7 +291,6 @@ class ChromaDBClient(BaseClient):
                 - content: The text content (required)
                 - doc_id: Optional unique identifier (auto-generated if missing)
                 - metadata: Optional metadata dictionary
-            batch_size: Optional batch size for processing documents (default: 100)
 
         Raises:
             TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
@@ -310,7 +305,6 @@ class ChromaDBClient(BaseClient):
 
         collection_name = kwargs["collection_name"]
         documents = kwargs["documents"]
-        batch_size = kwargs.get("batch_size", self.default_batch_size)
 
         if not documents:
             raise ValueError("Documents list cannot be empty")
@@ -321,17 +315,13 @@ class ChromaDBClient(BaseClient):
         )
 
         prepared = _prepare_documents_for_chromadb(documents)
-
-        for i in range(0, len(prepared.ids), batch_size):
-            batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-                prepared=prepared, start_index=i, batch_size=batch_size
-            )
-
-            collection.upsert(
-                ids=batch_ids,
-                documents=batch_texts,
-                metadatas=batch_metadatas,
-            )
+        # ChromaDB doesn't accept empty metadata dicts, so pass None if all are empty
+        metadatas = prepared.metadatas if any(m for m in prepared.metadatas) else None
+        collection.upsert(
+            ids=prepared.ids,
+            documents=prepared.texts,
+            metadatas=metadatas,
+        )
 
     async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
         """Add documents with their embeddings to a collection asynchronously.
@@ -345,7 +335,6 @@ class ChromaDBClient(BaseClient):
                 - content: The text content (required)
                 - doc_id: Optional unique identifier (auto-generated if missing)
                 - metadata: Optional metadata dictionary
-            batch_size: Optional batch size for processing documents (default: 100)
 
         Raises:
             TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
@@ -360,7 +349,6 @@ class ChromaDBClient(BaseClient):
 
         collection_name = kwargs["collection_name"]
         documents = kwargs["documents"]
-        batch_size = kwargs.get("batch_size", self.default_batch_size)
 
         if not documents:
             raise ValueError("Documents list cannot be empty")
@@ -370,17 +358,13 @@ class ChromaDBClient(BaseClient):
             embedding_function=self.embedding_function,
         )
         prepared = _prepare_documents_for_chromadb(documents)
-
-        for i in range(0, len(prepared.ids), batch_size):
-            batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-                prepared=prepared, start_index=i, batch_size=batch_size
-            )
-
-            await collection.upsert(
-                ids=batch_ids,
-                documents=batch_texts,
-                metadatas=batch_metadatas,
-            )
+        # ChromaDB doesn't accept empty metadata dicts, so pass None if all are empty
+        metadatas = prepared.metadatas if any(m for m in prepared.metadatas) else None
+        await collection.upsert(
+            ids=prepared.ids,
+            documents=prepared.texts,
+            metadatas=metadatas,
+        )
 
     def search(
         self, **kwargs: Unpack[ChromaDBCollectionSearchParams]

src/crewai/rag/chromadb/factory.py

@@ -41,5 +41,4 @@ def create_client(config: ChromaDBConfig) -> ChromaDBClient:
         embedding_function=config.embedding_function,
         default_limit=config.limit,
         default_score_threshold=config.score_threshold,
-        default_batch_size=config.batch_size,
     )

src/crewai/rag/chromadb/utils.py

@@ -1,7 +1,6 @@
 """Utility functions for ChromaDB client implementation."""
 
 import hashlib
-import json
 from collections.abc import Mapping
 from typing import Literal, TypeGuard, cast
 
@@ -10,6 +9,7 @@ from chromadb.api.models.AsyncCollection import AsyncCollection
 from chromadb.api.models.Collection import Collection
 from chromadb.api.types import (
     Include,
+    IncludeEnum,
     QueryResult,
 )
 
@@ -72,15 +72,7 @@ def _prepare_documents_for_chromadb(
         if "doc_id" in doc:
             ids.append(doc["doc_id"])
         else:
-            content_for_hash = doc["content"]
-            metadata = doc.get("metadata")
-            if metadata:
-                metadata_str = json.dumps(metadata, sort_keys=True)
-                content_for_hash = f"{content_for_hash}|{metadata_str}"
-
-            content_hash = hashlib.blake2b(
-                content_for_hash.encode(), digest_size=32
-            ).hexdigest()
+            content_hash = hashlib.sha256(doc["content"].encode()).hexdigest()[:16]
             ids.append(content_hash)
 
         texts.append(doc["content"])
@@ -96,32 +88,6 @@ def _prepare_documents_for_chromadb(
     return PreparedDocuments(ids, texts, metadatas)
 
 
-def _create_batch_slice(
-    prepared: PreparedDocuments, start_index: int, batch_size: int
-) -> tuple[list[str], list[str], list[Mapping[str, str | int | float | bool]] | None]:
-    """Create a batch slice from prepared documents.
-
-    Args:
-        prepared: PreparedDocuments containing ids, texts, and metadatas.
-        start_index: Starting index for the batch.
-        batch_size: Size of the batch.
-
-    Returns:
-        Tuple of (batch_ids, batch_texts, batch_metadatas).
-    """
-    batch_end = min(start_index + batch_size, len(prepared.ids))
-    batch_ids = prepared.ids[start_index:batch_end]
-    batch_texts = prepared.texts[start_index:batch_end]
-    batch_metadatas = (
-        prepared.metadatas[start_index:batch_end] if prepared.metadatas else None
-    )
-
-    if batch_metadatas and not any(m for m in batch_metadatas):
-        batch_metadatas = None
-
-    return batch_ids, batch_texts, batch_metadatas
-
-
 def _extract_search_params(
     kwargs: ChromaDBCollectionSearchParams,
 ) -> ExtractedSearchParams:
@@ -141,12 +107,9 @@ def _extract_search_params(
         score_threshold=kwargs.get("score_threshold"),
         where=kwargs.get("where"),
         where_document=kwargs.get("where_document"),
-        include=cast(
-            Include,
-            kwargs.get(
-                "include",
-                ["metadatas", "documents", "distances"],
-            ),
+        include=kwargs.get(
+            "include",
+            [IncludeEnum.metadatas, IncludeEnum.documents, IncludeEnum.distances],
         ),
     )
 
@@ -195,7 +158,7 @@ def _convert_chromadb_results_to_search_results(
     """
     search_results: list[SearchResult] = []
 
-    include_strings = list(include) if include else []
+    include_strings = [item.value for item in include] if include else []
 
     ids = results["ids"][0] if results.get("ids") else []
 

src/crewai/rag/config/base.py

@@ -16,4 +16,3 @@ class BaseRagConfig:
     embedding_function: Any | None = field(default=None)
     limit: int = field(default=5)
     score_threshold: float = field(default=0.6)
-    batch_size: int = field(default=100)

src/crewai/rag/core/base_client.py

@@ -29,7 +29,7 @@ class BaseCollectionParams(TypedDict):
     ]
 
 
-class BaseCollectionAddParams(BaseCollectionParams, total=False):
+class BaseCollectionAddParams(BaseCollectionParams):
     """Parameters for adding documents to a collection.
 
     Extends BaseCollectionParams with document-specific fields.
@@ -37,11 +37,9 @@ class BaseCollectionAddParams(BaseCollectionParams, total=False):
     Attributes:
         collection_name: The name of the collection to add documents to.
         documents: List of BaseRecord dictionaries containing document data.
-        batch_size: Optional batch size for processing documents to avoid token limits.
     """
 
-    documents: Required[list[BaseRecord]]
-    batch_size: int
+    documents: list[BaseRecord]
 
 
 class BaseCollectionSearchParams(BaseCollectionParams, total=False):

src/crewai/rag/core/base_embeddings_callable.py

@@ -1,142 +0,0 @@
-"""Base embeddings callable utilities for RAG systems."""
-
-from typing import Protocol, TypeVar, runtime_checkable
-
-import numpy as np
-
-from crewai.rag.core.types import (
-    Embeddable,
-    Embedding,
-    Embeddings,
-    PyEmbedding,
-)
-
-T = TypeVar("T")
-D = TypeVar("D", bound=Embeddable, contravariant=True)
-
-
-def normalize_embeddings(
-    target: Embedding | list[Embedding] | PyEmbedding | list[PyEmbedding],
-) -> Embeddings | None:
-    """Normalize various embedding formats to a standard list of numpy arrays.
-
-    Args:
-        target: Input embeddings in various formats (list of floats, list of lists,
-                numpy array, or list of numpy arrays).
-
-    Returns:
-        Normalized embeddings as a list of numpy arrays, or None if input is None.
-
-    Raises:
-        ValueError: If embeddings are empty or in an unsupported format.
-    """
-    if isinstance(target, np.ndarray):
-        if target.ndim == 1:
-            return [target.astype(np.float32)]
-        if target.ndim == 2:
-            return [row.astype(np.float32) for row in target]
-        raise ValueError(f"Unsupported numpy array shape: {target.shape}")
-
-    first = target[0]
-    if isinstance(first, (int, float)) and not isinstance(first, bool):
-        return [np.array(target, dtype=np.float32)]
-    if isinstance(first, list):
-        return [np.array(emb, dtype=np.float32) for emb in target]
-    if isinstance(first, np.ndarray):
-        return [emb.astype(np.float32) for emb in target]  # type: ignore[union-attr]
-
-    raise ValueError(f"Unsupported embeddings format: {type(first)}")
-
-
-def maybe_cast_one_to_many(target: T | list[T] | None) -> list[T] | None:
-    """Cast a single item to a list if needed.
-
-    Args:
-        target: A single item or list of items.
-
-    Returns:
-        A list of items or None if input is None.
-    """
-    if target is None:
-        return None
-    return target if isinstance(target, list) else [target]
-
-
-def validate_embeddings(embeddings: Embeddings) -> Embeddings:
-    """Validate embeddings format and content.
-
-    Args:
-        embeddings: List of numpy arrays to validate.
-
-    Returns:
-        Validated embeddings.
-
-    Raises:
-        ValueError: If embeddings format or content is invalid.
-    """
-    if not isinstance(embeddings, list):
-        raise ValueError(
-            f"Expected embeddings to be a list, got {type(embeddings).__name__}"
-        )
-    if len(embeddings) == 0:
-        raise ValueError(
-            f"Expected embeddings to be a list with at least one item, got {len(embeddings)} embeddings"
-        )
-    if not all(isinstance(e, np.ndarray) for e in embeddings):
-        raise ValueError(
-            "Expected each embedding in the embeddings to be a numpy array"
-        )
-    for i, embedding in enumerate(embeddings):
-        if embedding.ndim == 0:
-            raise ValueError(
-                f"Expected a 1-dimensional array, got a 0-dimensional array {embedding}"
-            )
-        if embedding.size == 0:
-            raise ValueError(
-                f"Expected each embedding to be a 1-dimensional numpy array with at least 1 value. "
-                f"Got an array with no values at position {i}"
-            )
-        if not all(
-            isinstance(value, (np.integer, float, np.floating))
-            and not isinstance(value, bool)
-            for value in embedding
-        ):
-            raise ValueError(
-                f"Expected embedding to contain numeric values, got non-numeric values at position {i}"
-            )
-    return embeddings
-
-
-@runtime_checkable
-class EmbeddingFunction(Protocol[D]):
-    """Protocol for embedding functions.
-
-    Embedding functions convert input data (documents or images) into vector embeddings.
-    """
-
-    def __call__(self, input: D) -> Embeddings:
-        """Convert input data to embeddings.
-
-        Args:
-            input: Input data to embed (documents or images).
-
-        Returns:
-            List of numpy arrays representing the embeddings.
-        """
-        ...
-
-    def __init_subclass__(cls) -> None:
-        """Wrap __call__ method to normalize and validate embeddings."""
-        super().__init_subclass__()
-        original_call = cls.__call__
-
-        def wrapped_call(self: EmbeddingFunction[D], input: D) -> Embeddings:
-            result = original_call(self, input)
-            if result is None:
-                raise ValueError("Embedding function returned None")
-            normalized = normalize_embeddings(result)
-            if normalized is None:
-                raise ValueError("Normalization returned None for non-None input")
-            return validate_embeddings(normalized)
-
-        cls.__call__ = wrapped_call  # type: ignore[method-assign]

src/crewai/rag/core/base_embeddings_provider.py

@@ -1,23 +0,0 @@
-"""Base class for embedding providers."""
-
-from typing import Generic, TypeVar
-
-from pydantic import Field
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-
-T = TypeVar("T", bound=EmbeddingFunction)
-
-
-class BaseEmbeddingsProvider(BaseSettings, Generic[T]):
-    """Abstract base class for embedding providers.
-
-    This class provides a common interface for dynamically loading and building
-    embedding functions from various providers.
-    """
-
-    model_config = SettingsConfigDict(extra="allow", populate_by_name=True)
-    embedding_callable: type[T] = Field(
-        ..., description="The embedding function class to use"
-    )

src/crewai/rag/core/types.py

@@ -1,28 +0,0 @@
-"""Core type definitions for RAG systems."""
-
-from collections.abc import Sequence
-from typing import TypeVar
-
-import numpy as np
-from numpy import floating, integer, number
-from numpy.typing import NDArray
-
-T = TypeVar("T")
-
-PyEmbedding = Sequence[float] | Sequence[int]
-PyEmbeddings = list[PyEmbedding]
-Embedding = NDArray[np.int32 | np.float32]
-Embeddings = list[Embedding]
-
-Documents = list[str]
-Images = list[np.ndarray]
-Embeddable = Documents | Images
-
-ScalarType = TypeVar("ScalarType", bound=np.generic)
-IntegerType = TypeVar("IntegerType", bound=integer)
-FloatingType = TypeVar("FloatingType", bound=floating)
-NumberType = TypeVar("NumberType", bound=number)
-
-DType32 = TypeVar("DType32", np.int32, np.float32)
-DType64 = TypeVar("DType64", np.int64, np.float64)
-DTypeCommon = TypeVar("DTypeCommon", np.int32, np.int64, np.float32, np.float64)

src/crewai/rag/embeddings/configurator.py

@@ -0,0 +1,245 @@
+import os
+from typing import Any, cast
+
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from chromadb.api.types import validate_embedding_function
+
+
+class EmbeddingConfigurator:
+    def __init__(self):
+        self.embedding_functions = {
+            "openai": self._configure_openai,
+            "azure": self._configure_azure,
+            "ollama": self._configure_ollama,
+            "vertexai": self._configure_vertexai,
+            "google": self._configure_google,
+            "cohere": self._configure_cohere,
+            "voyageai": self._configure_voyageai,
+            "bedrock": self._configure_bedrock,
+            "huggingface": self._configure_huggingface,
+            "watson": self._configure_watson,
+            "custom": self._configure_custom,
+        }
+
+    def configure_embedder(
+        self,
+        embedder_config: dict[str, Any] | None = None,
+    ) -> EmbeddingFunction:
+        """Configures and returns an embedding function based on the provided config."""
+        if embedder_config is None:
+            return self._create_default_embedding_function()
+
+        provider = embedder_config.get("provider")
+        config = embedder_config.get("config", {})
+        model_name = config.get("model") if provider != "custom" else None
+
+        if provider not in self.embedding_functions:
+            raise Exception(
+                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
+            )
+
+        try:
+            embedding_function = self.embedding_functions[provider]
+        except ImportError as e:
+            missing_package = str(e).split()[-1]
+            raise ImportError(
+                f"{missing_package} is not installed. Please install it with: pip install {missing_package}"
+            ) from e
+
+        return (
+            embedding_function(config)
+            if provider == "custom"
+            else embedding_function(config, model_name)
+        )
+
+    @staticmethod
+    def _create_default_embedding_function():
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    @staticmethod
+    def _configure_openai(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+            model_name=model_name,
+            api_base=config.get("api_base", None),
+            api_type=config.get("api_type", None),
+            api_version=config.get("api_version", None),
+            default_headers=config.get("default_headers", None),
+            dimensions=config.get("dimensions", None),
+            deployment_id=config.get("deployment_id", None),
+            organization_id=config.get("organization_id", None),
+        )
+
+    @staticmethod
+    def _configure_azure(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key"),
+            api_base=config.get("api_base"),
+            api_type=config.get("api_type", "azure"),
+            api_version=config.get("api_version"),
+            model_name=model_name,
+            default_headers=config.get("default_headers"),
+            dimensions=config.get("dimensions"),
+            deployment_id=config.get("deployment_id"),
+            organization_id=config.get("organization_id"),
+        )
+
+    @staticmethod
+    def _configure_ollama(config, model_name):
+        from chromadb.utils.embedding_functions.ollama_embedding_function import (
+            OllamaEmbeddingFunction,
+        )
+
+        return OllamaEmbeddingFunction(
+            url=config.get("url", "http://localhost:11434/api/embeddings"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_vertexai(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
+
+        return GoogleVertexEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+            project_id=config.get("project_id"),
+            region=config.get("region"),
+        )
+
+    @staticmethod
+    def _configure_google(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleGenerativeAiEmbeddingFunction,
+        )
+
+        return GoogleGenerativeAiEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+            task_type=config.get("task_type"),
+        )
+
+    @staticmethod
+    def _configure_cohere(config, model_name):
+        from chromadb.utils.embedding_functions.cohere_embedding_function import (
+            CohereEmbeddingFunction,
+        )
+
+        return CohereEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_voyageai(config, model_name):
+        from chromadb.utils.embedding_functions.voyageai_embedding_function import (  # type: ignore[import-not-found]
+            VoyageAIEmbeddingFunction,
+        )
+
+        return VoyageAIEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_bedrock(config, model_name):
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+            AmazonBedrockEmbeddingFunction,
+        )
+
+        # Allow custom model_name override with backwards compatibility
+        kwargs = {"session": config.get("session")}
+        if model_name is not None:
+            kwargs["model_name"] = model_name
+        return AmazonBedrockEmbeddingFunction(**kwargs)
+
+    @staticmethod
+    def _configure_huggingface(config, model_name):
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+            HuggingFaceEmbeddingServer,
+        )
+
+        return HuggingFaceEmbeddingServer(
+            url=config.get("api_url"),
+        )
+
+    @staticmethod
+    def _configure_watson(config, model_name):
+        try:
+            import ibm_watsonx_ai.foundation_models as watson_models  # type: ignore[import-not-found]
+            from ibm_watsonx_ai import Credentials  # type: ignore[import-not-found]
+            from ibm_watsonx_ai.metanames import (  # type: ignore[import-not-found]
+                EmbedTextParamsMetaNames as EmbedParams,
+            )
+        except ImportError as e:
+            raise ImportError(
+                "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+            ) from e
+
+        class WatsonEmbeddingFunction(EmbeddingFunction):
+            def __call__(self, input: Documents) -> Embeddings:
+                if isinstance(input, str):
+                    input = [input]
+
+                embed_params = {
+                    EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                    EmbedParams.RETURN_OPTIONS: {"input_text": True},
+                }
+
+                embedding = watson_models.Embeddings(
+                    model_id=config.get("model"),
+                    params=embed_params,
+                    credentials=Credentials(
+                        api_key=config.get("api_key"), url=config.get("api_url")
+                    ),
+                    project_id=config.get("project_id"),
+                )
+
+                try:
+                    embeddings = embedding.embed_documents(input)
+                    return cast(Embeddings, embeddings)
+                except Exception as e:
+                    print("Error during Watson embedding:", e)
+                    raise e
+
+        return WatsonEmbeddingFunction()
+
+    @staticmethod
+    def _configure_custom(config):
+        custom_embedder = config.get("embedder")
+        if isinstance(custom_embedder, EmbeddingFunction):
+            try:
+                validate_embedding_function(custom_embedder)
+                return custom_embedder
+            except Exception as e:
+                raise ValueError(f"Invalid custom embedding function: {e!s}") from e
+        elif callable(custom_embedder):
+            try:
+                instance = custom_embedder()
+                if isinstance(instance, EmbeddingFunction):
+                    validate_embedding_function(instance)
+                    return instance
+                raise ValueError(
+                    "Custom embedder does not create an EmbeddingFunction instance"
+                )
+            except Exception as e:
+                raise ValueError(f"Error instantiating custom embedder: {e!s}") from e
+        else:
+            raise ValueError(
+                "Custom embedder must be an instance of `EmbeddingFunction` or a callable that creates one"
+            )

src/crewai/rag/embeddings/factory.py

@@ -1,363 +1,204 @@
-"""Factory functions for creating embedding providers and functions."""
+"""Minimal embedding function factory for CrewAI."""
 
-from __future__ import annotations
+import os
+from typing import Any, Callable
 
-from typing import TYPE_CHECKING, TypeVar, overload
+from chromadb import EmbeddingFunction
+from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+    AmazonBedrockEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.cohere_embedding_function import (
+    CohereEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.google_embedding_function import (
+    GoogleGenerativeAiEmbeddingFunction,
+    GooglePalmEmbeddingFunction,
+    GoogleVertexEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+    HuggingFaceEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.instructor_embedding_function import (
+    InstructorEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.jina_embedding_function import (
+    JinaEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.ollama_embedding_function import (
+    OllamaEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2
+from chromadb.utils.embedding_functions.open_clip_embedding_function import (
+    OpenCLIPEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.openai_embedding_function import (
+    OpenAIEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.roboflow_embedding_function import (
+    RoboflowEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import (
+    SentenceTransformerEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.text2vec_embedding_function import (
+    Text2VecEmbeddingFunction,
+)
 
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.utilities.import_utils import import_and_validate_definition
-
-if TYPE_CHECKING:
-    from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-        AmazonBedrockEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.cohere_embedding_function import (
-        CohereEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.google_embedding_function import (
-        GoogleGenerativeAiEmbeddingFunction,
-        GoogleVertexEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-        HuggingFaceEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.instructor_embedding_function import (
-        InstructorEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.jina_embedding_function import (
-        JinaEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.ollama_embedding_function import (
-        OllamaEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2
-    from chromadb.utils.embedding_functions.open_clip_embedding_function import (
-        OpenCLIPEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.openai_embedding_function import (
-        OpenAIEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.roboflow_embedding_function import (
-        RoboflowEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import (
-        SentenceTransformerEmbeddingFunction,
-    )
-    from chromadb.utils.embedding_functions.text2vec_embedding_function import (
-        Text2VecEmbeddingFunction,
-    )
-
-    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
-    from crewai.rag.embeddings.providers.google.types import (
-        GenerativeAiProviderSpec,
-        VertexAIProviderSpec,
-    )
-    from crewai.rag.embeddings.providers.huggingface.types import (
-        HuggingFaceProviderSpec,
-    )
-    from crewai.rag.embeddings.providers.ibm.embedding_callable import (
-        WatsonEmbeddingFunction,
-    )
-    from crewai.rag.embeddings.providers.ibm.types import WatsonProviderSpec
-    from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
-    from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
-    from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
-    from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec
-    from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec
-    from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
-    from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec
-    from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec
-    from crewai.rag.embeddings.providers.sentence_transformer.types import (
-        SentenceTransformerProviderSpec,
-    )
-    from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
-    from crewai.rag.embeddings.providers.voyageai.embedding_callable import (
-        VoyageAIEmbeddingFunction,
-    )
-    from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
-
-T = TypeVar("T", bound=EmbeddingFunction)
+from crewai.rag.embeddings.types import EmbeddingOptions
 
 
-PROVIDER_PATHS = {
-    "azure": "crewai.rag.embeddings.providers.microsoft.azure.AzureProvider",
-    "amazon-bedrock": "crewai.rag.embeddings.providers.aws.bedrock.BedrockProvider",
-    "cohere": "crewai.rag.embeddings.providers.cohere.cohere_provider.CohereProvider",
-    "custom": "crewai.rag.embeddings.providers.custom.custom_provider.CustomProvider",
-    "google-generativeai": "crewai.rag.embeddings.providers.google.generative_ai.GenerativeAiProvider",
-    "google-vertex": "crewai.rag.embeddings.providers.google.vertex.VertexAIProvider",
-    "huggingface": "crewai.rag.embeddings.providers.huggingface.huggingface_provider.HuggingFaceProvider",
-    "instructor": "crewai.rag.embeddings.providers.instructor.instructor_provider.InstructorProvider",
-    "jina": "crewai.rag.embeddings.providers.jina.jina_provider.JinaProvider",
-    "ollama": "crewai.rag.embeddings.providers.ollama.ollama_provider.OllamaProvider",
-    "onnx": "crewai.rag.embeddings.providers.onnx.onnx_provider.ONNXProvider",
-    "openai": "crewai.rag.embeddings.providers.openai.openai_provider.OpenAIProvider",
-    "openclip": "crewai.rag.embeddings.providers.openclip.openclip_provider.OpenCLIPProvider",
-    "roboflow": "crewai.rag.embeddings.providers.roboflow.roboflow_provider.RoboflowProvider",
-    "sentence-transformer": "crewai.rag.embeddings.providers.sentence_transformer.sentence_transformer_provider.SentenceTransformerProvider",
-    "text2vec": "crewai.rag.embeddings.providers.text2vec.text2vec_provider.Text2VecProvider",
-    "voyageai": "crewai.rag.embeddings.providers.voyageai.voyageai_provider.VoyageAIProvider",
-    "watson": "crewai.rag.embeddings.providers.ibm.watson.WatsonProvider",
-}
-
-
-def build_embedder_from_provider(provider: BaseEmbeddingsProvider[T]) -> T:
-    """Build an embedding function instance from a provider.
-
-    Args:
-        provider: The embedding provider configuration.
-
-    Returns:
-        An instance of the specified embedding function type.
-    """
-    return provider.embedding_callable(
-        **provider.model_dump(exclude={"embedding_callable"})
-    )
-
-
-@overload
-def build_embedder_from_dict(spec: AzureProviderSpec) -> OpenAIEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: BedrockProviderSpec,
-) -> AmazonBedrockEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: CohereProviderSpec) -> CohereEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: CustomProviderSpec) -> EmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: GenerativeAiProviderSpec,
-) -> GoogleGenerativeAiEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: HuggingFaceProviderSpec,
-) -> HuggingFaceEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: OllamaProviderSpec) -> OllamaEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: OpenAIProviderSpec) -> OpenAIEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: VertexAIProviderSpec,
-) -> GoogleVertexEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: VoyageAIProviderSpec,
-) -> VoyageAIEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: WatsonProviderSpec) -> WatsonEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: SentenceTransformerProviderSpec,
-) -> SentenceTransformerEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: InstructorProviderSpec,
-) -> InstructorEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: JinaProviderSpec) -> JinaEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: RoboflowProviderSpec,
-) -> RoboflowEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: OpenCLIPProviderSpec,
-) -> OpenCLIPEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(
-    spec: Text2VecProviderSpec,
-) -> Text2VecEmbeddingFunction: ...
-
-
-@overload
-def build_embedder_from_dict(spec: ONNXProviderSpec) -> ONNXMiniLM_L6_V2: ...
-
-
-def build_embedder_from_dict(spec):
-    """Build an embedding function instance from a dictionary specification.
-
-    Args:
-        spec: A dictionary with 'provider' and 'config' keys.
-              Example: {
-                  "provider": "openai",
-                  "config": {
-                      "api_key": "sk-...",
-                      "model_name": "text-embedding-3-small"
-                  }
-              }
-
-    Returns:
-        An instance of the appropriate embedding function.
-
-    Raises:
-        ValueError: If the provider is not recognized.
-    """
-    provider_name = spec["provider"]
-    if not provider_name:
-        raise ValueError("Missing 'provider' key in specification")
-
-    if provider_name not in PROVIDER_PATHS:
-        raise ValueError(
-            f"Unknown provider: {provider_name}. Available providers: {list(PROVIDER_PATHS.keys())}"
-        )
-
-    provider_path = PROVIDER_PATHS[provider_name]
+def _create_watson_embedding_function(**config_dict) -> EmbeddingFunction:
+    """Create Watson embedding function with proper error handling."""
     try:
-        provider_class = import_and_validate_definition(provider_path)
-    except (ImportError, AttributeError, ValueError) as e:
-        raise ImportError(f"Failed to import provider {provider_name}: {e}") from e
+        import ibm_watsonx_ai.foundation_models as watson_models  # type: ignore[import-not-found]
+        from ibm_watsonx_ai import Credentials  # type: ignore[import-not-found]
+        from ibm_watsonx_ai.metanames import (  # type: ignore[import-not-found]
+            EmbedTextParamsMetaNames as EmbedParams,
+        )
+    except ImportError as e:
+        raise ImportError(
+            "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+        ) from e
 
-    provider_config = spec.get("config", {})
+    class WatsonEmbeddingFunction(EmbeddingFunction):
+        def __init__(self, **kwargs):
+            self.config = kwargs
 
-    if provider_name == "custom" and "embedding_callable" not in provider_config:
-        raise ValueError("Custom provider requires 'embedding_callable' in config")
+        def __call__(self, input):
+            if isinstance(input, str):
+                input = [input]
 
-    provider = provider_class(**provider_config)
-    return build_embedder_from_provider(provider)
+            embed_params = {
+                EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                EmbedParams.RETURN_OPTIONS: {"input_text": True},
+            }
+
+            embedding = watson_models.Embeddings(
+                model_id=self.config.get("model_name") or self.config.get("model"),
+                params=embed_params,
+                credentials=Credentials(
+                    api_key=self.config.get("api_key"),
+                    url=self.config.get("api_url") or self.config.get("url")
+                ),
+                project_id=self.config.get("project_id"),
+            )
+
+            try:
+                return embedding.embed_documents(input)
+            except Exception as e:
+                raise RuntimeError(f"Error during Watson embedding: {e}") from e
+
+    return WatsonEmbeddingFunction(**config_dict)
 
 
-@overload
-def build_embedder(spec: BaseEmbeddingsProvider[T]) -> T: ...
-
-
-@overload
-def build_embedder(spec: AzureProviderSpec) -> OpenAIEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: BedrockProviderSpec) -> AmazonBedrockEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: CohereProviderSpec) -> CohereEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: CustomProviderSpec) -> EmbeddingFunction: ...
-
-
-@overload
-def build_embedder(
-    spec: GenerativeAiProviderSpec,
-) -> GoogleGenerativeAiEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: HuggingFaceProviderSpec) -> HuggingFaceEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: OllamaProviderSpec) -> OllamaEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: OpenAIProviderSpec) -> OpenAIEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: VertexAIProviderSpec) -> GoogleVertexEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: VoyageAIProviderSpec) -> VoyageAIEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: WatsonProviderSpec) -> WatsonEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(
-    spec: SentenceTransformerProviderSpec,
-) -> SentenceTransformerEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: InstructorProviderSpec) -> InstructorEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: JinaProviderSpec) -> JinaEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: RoboflowProviderSpec) -> RoboflowEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: OpenCLIPProviderSpec) -> OpenCLIPEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: Text2VecProviderSpec) -> Text2VecEmbeddingFunction: ...
-
-
-@overload
-def build_embedder(spec: ONNXProviderSpec) -> ONNXMiniLM_L6_V2: ...
-
-
-def build_embedder(spec):
-    """Build an embedding function from either a provider spec or a provider instance.
+def get_embedding_function(
+    config: EmbeddingOptions | dict | None = None,
+) -> EmbeddingFunction:
+    """Get embedding function - delegates to ChromaDB.
 
     Args:
-        spec: Either a provider specification dictionary or a provider instance.
+        config: Optional configuration - either an EmbeddingOptions object or a dict with:
+            - provider: The embedding provider to use (default: "openai")
+            - Any other provider-specific parameters
 
     Returns:
-        An embedding function instance. If a typed provider is passed, returns
-        the specific embedding function type.
+        EmbeddingFunction instance ready for use with ChromaDB
+
+    Supported providers:
+        - openai: OpenAI embeddings
+        - cohere: Cohere embeddings
+        - ollama: Ollama local embeddings
+        - huggingface: HuggingFace embeddings
+        - sentence-transformer: Local sentence transformers
+        - instructor: Instructor embeddings for specialized tasks
+        - google-palm: Google PaLM embeddings
+        - google-generativeai: Google Generative AI embeddings
+        - google-vertex: Google Vertex AI embeddings
+        - amazon-bedrock: AWS Bedrock embeddings
+        - jina: Jina AI embeddings
+        - roboflow: Roboflow embeddings for vision tasks
+        - openclip: OpenCLIP embeddings for multimodal tasks
+        - text2vec: Text2Vec embeddings
+        - onnx: ONNX MiniLM-L6-v2 (no API key needed, included with ChromaDB)
+        - watson: IBM Watson embeddings
 
     Examples:
-        # From dictionary specification
-        embedder = build_embedder({
-            "provider": "openai",
-            "config": {"api_key": "sk-..."}
-        })
+        # Use default OpenAI embedding
+        >>> embedder = get_embedding_function()
 
-        # From provider instance
-        provider = OpenAIProvider(api_key="sk-...")
-        embedder = build_embedder(provider)
+        # Use Cohere with dict
+        >>> embedder = get_embedding_function({
+        ...     "provider": "cohere",
+        ...     "api_key": "your-key",
+        ...     "model_name": "embed-english-v3.0"
+        ... })
+
+        # Use with EmbeddingOptions
+        >>> embedder = get_embedding_function(
+        ...     EmbeddingOptions(provider="sentence-transformer", model_name="all-MiniLM-L6-v2")
+        ... )
+
+        # Use local sentence transformers (no API key needed)
+        >>> embedder = get_embedding_function({
+        ...     "provider": "sentence-transformer",
+        ...     "model_name": "all-MiniLM-L6-v2"
+        ... })
+
+        # Use Ollama for local embeddings
+        >>> embedder = get_embedding_function({
+        ...     "provider": "ollama",
+        ...     "model_name": "nomic-embed-text"
+        ... })
+
+        # Use ONNX (no API key needed)
+        >>> embedder = get_embedding_function({
+        ...     "provider": "onnx"
+        ... })
+
+        # Use Watson embeddings
+        >>> embedder = get_embedding_function({
+        ...     "provider": "watson",
+        ...     "api_key": "your-watson-api-key",
+        ...     "api_url": "your-watson-url",
+        ...     "project_id": "your-project-id",
+        ...     "model_name": "ibm/slate-125m-english-rtrvr"
+        ... })
     """
-    if isinstance(spec, BaseEmbeddingsProvider):
-        return build_embedder_from_provider(spec)
-    return build_embedder_from_dict(spec)
+    if config is None:
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
 
+    # Handle EmbeddingOptions object
+    if isinstance(config, EmbeddingOptions):
+        config_dict = config.model_dump(exclude_none=True)
+    else:
+        config_dict = config.copy()
 
-# Backward compatibility alias
-get_embedding_function = build_embedder
+    provider = config_dict.pop("provider", "openai")
+
+    embedding_functions: dict[str, Callable[..., EmbeddingFunction]] = {
+        "openai": OpenAIEmbeddingFunction,
+        "cohere": CohereEmbeddingFunction,
+        "ollama": OllamaEmbeddingFunction,
+        "huggingface": HuggingFaceEmbeddingFunction,
+        "sentence-transformer": SentenceTransformerEmbeddingFunction,
+        "instructor": InstructorEmbeddingFunction,
+        "google-palm": GooglePalmEmbeddingFunction,
+        "google-generativeai": GoogleGenerativeAiEmbeddingFunction,
+        "google-vertex": GoogleVertexEmbeddingFunction,
+        "amazon-bedrock": AmazonBedrockEmbeddingFunction,
+        "jina": JinaEmbeddingFunction,
+        "roboflow": RoboflowEmbeddingFunction,
+        "openclip": OpenCLIPEmbeddingFunction,
+        "text2vec": Text2VecEmbeddingFunction,
+        "onnx": ONNXMiniLM_L6_V2,
+        "watson": _create_watson_embedding_function,
+    }
+
+    if provider not in embedding_functions:
+        raise ValueError(
+            f"Unsupported provider: {provider}. "
+            f"Available providers: {list(embedding_functions.keys())}"
+        )
+    return embedding_functions[provider](**config_dict)

src/crewai/rag/embeddings/providers/__init__.py

@@ -1 +0,0 @@
-"""Embedding provider implementations."""

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

@@ -1,13 +0,0 @@
-"""AWS embedding providers."""
-
-from crewai.rag.embeddings.providers.aws.bedrock import BedrockProvider
-from crewai.rag.embeddings.providers.aws.types import (
-    BedrockProviderConfig,
-    BedrockProviderSpec,
-)
-
-__all__ = [
-    "BedrockProvider",
-    "BedrockProviderConfig",
-    "BedrockProviderSpec",
-]

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

@@ -1,53 +0,0 @@
-"""Amazon Bedrock embeddings provider."""
-
-from typing import Any
-
-from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-    AmazonBedrockEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-def create_aws_session() -> Any:
-    """Create an AWS session for Bedrock.
-
-    Returns:
-        boto3.Session: AWS session object
-
-    Raises:
-        ImportError: If boto3 is not installed
-        ValueError: If AWS session creation fails
-    """
-    try:
-        import boto3  # type: ignore[import]
-
-        return boto3.Session()
-    except ImportError as e:
-        raise ImportError(
-            "boto3 is required for amazon-bedrock embeddings. "
-            "Install it with: uv add boto3"
-        ) from e
-    except Exception as e:
-        raise ValueError(
-            f"Failed to create AWS session for amazon-bedrock. "
-            f"Ensure AWS credentials are configured. Error: {e}"
-        ) from e
-
-
-class BedrockProvider(BaseEmbeddingsProvider[AmazonBedrockEmbeddingFunction]):
-    """Amazon Bedrock embeddings provider."""
-
-    embedding_callable: type[AmazonBedrockEmbeddingFunction] = Field(
-        default=AmazonBedrockEmbeddingFunction,
-        description="Amazon Bedrock embedding function class",
-    )
-    model_name: str = Field(
-        default="amazon.titan-embed-text-v1",
-        description="Model name to use for embeddings",
-        validation_alias="BEDROCK_MODEL_NAME",
-    )
-    session: Any = Field(
-        default_factory=create_aws_session, description="AWS session object"
-    )

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

@@ -1,19 +0,0 @@
-"""Type definitions for AWS embedding providers."""
-
-from typing import Annotated, Any, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class BedrockProviderConfig(TypedDict, total=False):
-    """Configuration for Bedrock provider."""
-
-    model_name: Annotated[str, "amazon.titan-embed-text-v1"]
-    session: Any
-
-
-class BedrockProviderSpec(TypedDict, total=False):
-    """Bedrock provider specification."""
-
-    provider: Required[Literal["amazon-bedrock"]]
-    config: BedrockProviderConfig

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

@@ -1,13 +0,0 @@
-"""Cohere embedding providers."""
-
-from crewai.rag.embeddings.providers.cohere.cohere_provider import CohereProvider
-from crewai.rag.embeddings.providers.cohere.types import (
-    CohereProviderConfig,
-    CohereProviderSpec,
-)
-
-__all__ = [
-    "CohereProvider",
-    "CohereProviderConfig",
-    "CohereProviderSpec",
-]

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

@@ -1,24 +0,0 @@
-"""Cohere embeddings provider."""
-
-from chromadb.utils.embedding_functions.cohere_embedding_function import (
-    CohereEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class CohereProvider(BaseEmbeddingsProvider[CohereEmbeddingFunction]):
-    """Cohere embeddings provider."""
-
-    embedding_callable: type[CohereEmbeddingFunction] = Field(
-        default=CohereEmbeddingFunction, description="Cohere embedding function class"
-    )
-    api_key: str = Field(
-        description="Cohere API key", validation_alias="COHERE_API_KEY"
-    )
-    model_name: str = Field(
-        default="large",
-        description="Model name to use for embeddings",
-        validation_alias="COHERE_MODEL_NAME",
-    )

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

@@ -1,19 +0,0 @@
-"""Type definitions for Cohere embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class CohereProviderConfig(TypedDict, total=False):
-    """Configuration for Cohere provider."""
-
-    api_key: str
-    model_name: Annotated[str, "large"]
-
-
-class CohereProviderSpec(TypedDict, total=False):
-    """Cohere provider specification."""
-
-    provider: Required[Literal["cohere"]]
-    config: CohereProviderConfig

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

@@ -1,13 +0,0 @@
-"""Custom embedding providers."""
-
-from crewai.rag.embeddings.providers.custom.custom_provider import CustomProvider
-from crewai.rag.embeddings.providers.custom.types import (
-    CustomProviderConfig,
-    CustomProviderSpec,
-)
-
-__all__ = [
-    "CustomProvider",
-    "CustomProviderConfig",
-    "CustomProviderSpec",
-]

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

@@ -1,19 +0,0 @@
-"""Custom embeddings provider for user-defined embedding functions."""
-
-from pydantic import Field
-from pydantic_settings import SettingsConfigDict
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.providers.custom.embedding_callable import (
-    CustomEmbeddingFunction,
-)
-
-
-class CustomProvider(BaseEmbeddingsProvider[CustomEmbeddingFunction]):
-    """Custom embeddings provider for user-defined embedding functions."""
-
-    embedding_callable: type[CustomEmbeddingFunction] = Field(
-        ..., description="Custom embedding function class"
-    )
-
-    model_config = SettingsConfigDict(extra="allow")

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

@@ -1,22 +0,0 @@
-"""Custom embedding function base implementation."""
-
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-from crewai.rag.core.types import Documents, Embeddings
-
-
-class CustomEmbeddingFunction(EmbeddingFunction[Documents]):
-    """Base class for custom embedding functions.
-
-    This provides a concrete implementation that can be subclassed for custom embeddings.
-    """
-
-    def __call__(self, input: Documents) -> Embeddings:
-        """Convert input documents to embeddings.
-
-        Args:
-            input: List of documents to embed.
-
-        Returns:
-            List of numpy arrays representing the embeddings.
-        """
-        raise NotImplementedError("Subclasses must implement __call__ method")

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

@@ -1,19 +0,0 @@
-"""Type definitions for custom embedding providers."""
-
-from typing import Literal
-
-from chromadb.api.types import EmbeddingFunction
-from typing_extensions import Required, TypedDict
-
-
-class CustomProviderConfig(TypedDict, total=False):
-    """Configuration for Custom provider."""
-
-    embedding_callable: type[EmbeddingFunction]
-
-
-class CustomProviderSpec(TypedDict, total=False):
-    """Custom provider specification."""
-
-    provider: Required[Literal["custom"]]
-    config: CustomProviderConfig

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

@@ -1,23 +0,0 @@
-"""Google embedding providers."""
-
-from crewai.rag.embeddings.providers.google.generative_ai import (
-    GenerativeAiProvider,
-)
-from crewai.rag.embeddings.providers.google.types import (
-    GenerativeAiProviderConfig,
-    GenerativeAiProviderSpec,
-    VertexAIProviderConfig,
-    VertexAIProviderSpec,
-)
-from crewai.rag.embeddings.providers.google.vertex import (
-    VertexAIProvider,
-)
-
-__all__ = [
-    "GenerativeAiProvider",
-    "GenerativeAiProviderConfig",
-    "GenerativeAiProviderSpec",
-    "VertexAIProvider",
-    "VertexAIProviderConfig",
-    "VertexAIProviderSpec",
-]

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

@@ -1,30 +0,0 @@
-"""Google Generative AI embeddings provider."""
-
-from chromadb.utils.embedding_functions.google_embedding_function import (
-    GoogleGenerativeAiEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class GenerativeAiProvider(BaseEmbeddingsProvider[GoogleGenerativeAiEmbeddingFunction]):
-    """Google Generative AI embeddings provider."""
-
-    embedding_callable: type[GoogleGenerativeAiEmbeddingFunction] = Field(
-        default=GoogleGenerativeAiEmbeddingFunction,
-        description="Google Generative AI embedding function class",
-    )
-    model_name: str = Field(
-        default="models/embedding-001",
-        description="Model name to use for embeddings",
-        validation_alias="GOOGLE_GENERATIVE_AI_MODEL_NAME",
-    )
-    api_key: str = Field(
-        description="Google API key", validation_alias="GOOGLE_API_KEY"
-    )
-    task_type: str = Field(
-        default="RETRIEVAL_DOCUMENT",
-        description="Task type for embeddings",
-        validation_alias="GOOGLE_GENERATIVE_AI_TASK_TYPE",
-    )

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

@@ -1,36 +0,0 @@
-"""Type definitions for Google embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class GenerativeAiProviderConfig(TypedDict, total=False):
-    """Configuration for Google Generative AI provider."""
-
-    api_key: str
-    model_name: Annotated[str, "models/embedding-001"]
-    task_type: Annotated[str, "RETRIEVAL_DOCUMENT"]
-
-
-class GenerativeAiProviderSpec(TypedDict):
-    """Google Generative AI provider specification."""
-
-    provider: Literal["google-generativeai"]
-    config: GenerativeAiProviderConfig
-
-
-class VertexAIProviderConfig(TypedDict, total=False):
-    """Configuration for Vertex AI provider."""
-
-    api_key: str
-    model_name: Annotated[str, "textembedding-gecko"]
-    project_id: Annotated[str, "cloud-large-language-models"]
-    region: Annotated[str, "us-central1"]
-
-
-class VertexAIProviderSpec(TypedDict, total=False):
-    """Vertex AI provider specification."""
-
-    provider: Required[Literal["google-vertex"]]
-    config: VertexAIProviderConfig

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

@@ -1,35 +0,0 @@
-"""Google Vertex AI embeddings provider."""
-
-from chromadb.utils.embedding_functions.google_embedding_function import (
-    GoogleVertexEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class VertexAIProvider(BaseEmbeddingsProvider[GoogleVertexEmbeddingFunction]):
-    """Google Vertex AI embeddings provider."""
-
-    embedding_callable: type[GoogleVertexEmbeddingFunction] = Field(
-        default=GoogleVertexEmbeddingFunction,
-        description="Vertex AI embedding function class",
-    )
-    model_name: str = Field(
-        default="textembedding-gecko",
-        description="Model name to use for embeddings",
-        validation_alias="GOOGLE_VERTEX_MODEL_NAME",
-    )
-    api_key: str = Field(
-        description="Google API key", validation_alias="GOOGLE_CLOUD_API_KEY"
-    )
-    project_id: str = Field(
-        default="cloud-large-language-models",
-        description="GCP project ID",
-        validation_alias="GOOGLE_CLOUD_PROJECT",
-    )
-    region: str = Field(
-        default="us-central1",
-        description="GCP region",
-        validation_alias="GOOGLE_CLOUD_REGION",
-    )

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

@@ -1,15 +0,0 @@
-"""HuggingFace embedding providers."""
-
-from crewai.rag.embeddings.providers.huggingface.huggingface_provider import (
-    HuggingFaceProvider,
-)
-from crewai.rag.embeddings.providers.huggingface.types import (
-    HuggingFaceProviderConfig,
-    HuggingFaceProviderSpec,
-)
-
-__all__ = [
-    "HuggingFaceProvider",
-    "HuggingFaceProviderConfig",
-    "HuggingFaceProviderSpec",
-]

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

@@ -1,20 +0,0 @@
-"""HuggingFace embeddings provider."""
-
-from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-    HuggingFaceEmbeddingServer,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class HuggingFaceProvider(BaseEmbeddingsProvider[HuggingFaceEmbeddingServer]):
-    """HuggingFace embeddings provider."""
-
-    embedding_callable: type[HuggingFaceEmbeddingServer] = Field(
-        default=HuggingFaceEmbeddingServer,
-        description="HuggingFace embedding function class",
-    )
-    url: str = Field(
-        description="HuggingFace API URL", validation_alias="HUGGINGFACE_URL"
-    )

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

@@ -1,18 +0,0 @@
-"""Type definitions for HuggingFace embedding providers."""
-
-from typing import Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class HuggingFaceProviderConfig(TypedDict, total=False):
-    """Configuration for HuggingFace provider."""
-
-    url: str
-
-
-class HuggingFaceProviderSpec(TypedDict, total=False):
-    """HuggingFace provider specification."""
-
-    provider: Required[Literal["huggingface"]]
-    config: HuggingFaceProviderConfig

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

@@ -1,15 +0,0 @@
-"""IBM embedding providers."""
-
-from crewai.rag.embeddings.providers.ibm.types import (
-    WatsonProviderConfig,
-    WatsonProviderSpec,
-)
-from crewai.rag.embeddings.providers.ibm.watson import (
-    WatsonProvider,
-)
-
-__all__ = [
-    "WatsonProvider",
-    "WatsonProviderConfig",
-    "WatsonProviderSpec",
-]

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

@@ -1,159 +0,0 @@
-"""IBM Watson embedding function implementation."""
-
-from typing import cast
-
-from typing_extensions import Unpack
-
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-from crewai.rag.core.types import Documents, Embeddings
-from crewai.rag.embeddings.providers.ibm.types import WatsonProviderConfig
-
-
-class WatsonEmbeddingFunction(EmbeddingFunction[Documents]):
-    """Embedding function for IBM Watson models."""
-
-    def __init__(self, **kwargs: Unpack[WatsonProviderConfig]) -> None:
-        """Initialize Watson embedding function.
-
-        Args:
-            **kwargs: Configuration parameters for Watson Embeddings and Credentials.
-        """
-        self._config = kwargs
-
-    def __call__(self, input: Documents) -> Embeddings:
-        """Generate embeddings for input documents.
-
-        Args:
-            input: List of documents to embed.
-
-        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]
-
-        embeddings_config: dict = {
-            "model_id": self._config["model_id"],
-        }
-        if "params" in self._config and self._config["params"] is not None:
-            embeddings_config["params"] = self._config["params"]
-        if "project_id" in self._config and self._config["project_id"] is not None:
-            embeddings_config["project_id"] = self._config["project_id"]
-        if "space_id" in self._config and self._config["space_id"] is not None:
-            embeddings_config["space_id"] = self._config["space_id"]
-        if "api_client" in self._config and self._config["api_client"] is not None:
-            embeddings_config["api_client"] = self._config["api_client"]
-        if "verify" in self._config and self._config["verify"] is not None:
-            embeddings_config["verify"] = self._config["verify"]
-        if "persistent_connection" in self._config:
-            embeddings_config["persistent_connection"] = self._config[
-                "persistent_connection"
-            ]
-        if "batch_size" in self._config:
-            embeddings_config["batch_size"] = self._config["batch_size"]
-        if "concurrency_limit" in self._config:
-            embeddings_config["concurrency_limit"] = self._config["concurrency_limit"]
-        if "max_retries" in self._config and self._config["max_retries"] is not None:
-            embeddings_config["max_retries"] = self._config["max_retries"]
-        if "delay_time" in self._config and self._config["delay_time"] is not None:
-            embeddings_config["delay_time"] = self._config["delay_time"]
-        if (
-            "retry_status_codes" in self._config
-            and self._config["retry_status_codes"] is not None
-        ):
-            embeddings_config["retry_status_codes"] = self._config["retry_status_codes"]
-
-        if "credentials" in self._config and self._config["credentials"] is not None:
-            embeddings_config["credentials"] = self._config["credentials"]
-        else:
-            cred_config: dict = {}
-            if "url" in self._config and self._config["url"] is not None:
-                cred_config["url"] = self._config["url"]
-            if "api_key" in self._config and self._config["api_key"] is not None:
-                cred_config["api_key"] = self._config["api_key"]
-            if "name" in self._config and self._config["name"] is not None:
-                cred_config["name"] = self._config["name"]
-            if (
-                "iam_serviceid_crn" in self._config
-                and self._config["iam_serviceid_crn"] is not None
-            ):
-                cred_config["iam_serviceid_crn"] = self._config["iam_serviceid_crn"]
-            if (
-                "trusted_profile_id" in self._config
-                and self._config["trusted_profile_id"] is not None
-            ):
-                cred_config["trusted_profile_id"] = self._config["trusted_profile_id"]
-            if "token" in self._config and self._config["token"] is not None:
-                cred_config["token"] = self._config["token"]
-            if (
-                "projects_token" in self._config
-                and self._config["projects_token"] is not None
-            ):
-                cred_config["projects_token"] = self._config["projects_token"]
-            if "username" in self._config and self._config["username"] is not None:
-                cred_config["username"] = self._config["username"]
-            if "password" in self._config and self._config["password"] is not None:
-                cred_config["password"] = self._config["password"]
-            if (
-                "instance_id" in self._config
-                and self._config["instance_id"] is not None
-            ):
-                cred_config["instance_id"] = self._config["instance_id"]
-            if "version" in self._config and self._config["version"] is not None:
-                cred_config["version"] = self._config["version"]
-            if (
-                "bedrock_url" in self._config
-                and self._config["bedrock_url"] is not None
-            ):
-                cred_config["bedrock_url"] = self._config["bedrock_url"]
-            if (
-                "platform_url" in self._config
-                and self._config["platform_url"] is not None
-            ):
-                cred_config["platform_url"] = self._config["platform_url"]
-            if "proxies" in self._config and self._config["proxies"] is not None:
-                cred_config["proxies"] = self._config["proxies"]
-            if (
-                "verify" not in embeddings_config
-                and "verify" in self._config
-                and self._config["verify"] is not None
-            ):
-                cred_config["verify"] = self._config["verify"]
-
-            if cred_config:
-                embeddings_config["credentials"] = Credentials(**cred_config)
-
-        if "params" not in embeddings_config:
-            embeddings_config["params"] = {
-                EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
-                EmbedParams.RETURN_OPTIONS: {"input_text": True},
-            }
-
-        embedding = watson_models.Embeddings(**embeddings_config)
-
-        try:
-            embeddings = embedding.embed_documents(input)
-            return cast(Embeddings, embeddings)
-        except Exception as e:
-            print(f"Error during Watson embedding: {e}")
-            raise
-
-    @staticmethod
-    def name() -> str:
-        """Return the name identifier for this embedding function."""
-        return "watson"

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

@@ -1,44 +0,0 @@
-"""Type definitions for IBM Watson embedding providers."""
-
-from typing import Annotated, Any, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class WatsonProviderConfig(TypedDict, total=False):
-    """Configuration for Watson provider."""
-
-    model_id: str
-    url: str
-    params: dict[str, str | dict[str, str]]
-    credentials: Any
-    project_id: str
-    space_id: str
-    api_client: Any
-    verify: bool | str
-    persistent_connection: Annotated[bool, True]
-    batch_size: Annotated[int, 100]
-    concurrency_limit: Annotated[int, 10]
-    max_retries: int
-    delay_time: float
-    retry_status_codes: list[int]
-    api_key: str
-    name: str
-    iam_serviceid_crn: str
-    trusted_profile_id: str
-    token: str
-    projects_token: str
-    username: str
-    password: str
-    instance_id: str
-    version: str
-    bedrock_url: str
-    platform_url: str
-    proxies: dict
-
-
-class WatsonProviderSpec(TypedDict, total=False):
-    """Watson provider specification."""
-
-    provider: Required[Literal["watson"]]
-    config: WatsonProviderConfig

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

@@ -1,122 +0,0 @@
-"""IBM Watson embeddings provider."""
-
-from typing import Any
-
-from pydantic import Field, model_validator
-from typing_extensions import Self
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.providers.ibm.embedding_callable import (
-    WatsonEmbeddingFunction,
-)
-
-
-class WatsonProvider(BaseEmbeddingsProvider[WatsonEmbeddingFunction]):
-    """IBM Watson embeddings provider.
-
-    Note: Requires custom implementation as Watson uses a different interface.
-    """
-
-    embedding_callable: type[WatsonEmbeddingFunction] = Field(
-        default=WatsonEmbeddingFunction, description="Watson embedding function class"
-    )
-    model_id: str = Field(
-        description="Watson model ID", validation_alias="WATSON_MODEL_ID"
-    )
-    params: dict[str, str | dict[str, str]] | None = Field(
-        default=None, description="Additional parameters"
-    )
-    credentials: Any | None = Field(default=None, description="Watson credentials")
-    project_id: str | None = Field(
-        default=None,
-        description="Watson project ID",
-        validation_alias="WATSON_PROJECT_ID",
-    )
-    space_id: str | None = Field(
-        default=None, description="Watson space ID", validation_alias="WATSON_SPACE_ID"
-    )
-    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"
-    )
-    persistent_connection: bool = Field(
-        default=True,
-        description="Use persistent connection",
-        validation_alias="WATSON_PERSISTENT_CONNECTION",
-    )
-    batch_size: int = Field(
-        default=100,
-        description="Batch size for processing",
-        validation_alias="WATSON_BATCH_SIZE",
-    )
-    concurrency_limit: int = Field(
-        default=10,
-        description="Concurrency limit",
-        validation_alias="WATSON_CONCURRENCY_LIMIT",
-    )
-    max_retries: int | None = Field(
-        default=None,
-        description="Maximum retries",
-        validation_alias="WATSON_MAX_RETRIES",
-    )
-    delay_time: float | None = Field(
-        default=None,
-        description="Delay time between retries",
-        validation_alias="WATSON_DELAY_TIME",
-    )
-    retry_status_codes: list[int] | None = Field(
-        default=None, description="HTTP status codes to retry on"
-    )
-    url: str = Field(description="Watson API URL", validation_alias="WATSON_URL")
-    api_key: str = Field(
-        description="Watson API key", validation_alias="WATSON_API_KEY"
-    )
-    name: str | None = Field(
-        default=None, description="Service name", validation_alias="WATSON_NAME"
-    )
-    iam_serviceid_crn: str | None = Field(
-        default=None,
-        description="IAM service ID CRN",
-        validation_alias="WATSON_IAM_SERVICEID_CRN",
-    )
-    trusted_profile_id: str | None = Field(
-        default=None,
-        description="Trusted profile ID",
-        validation_alias="WATSON_TRUSTED_PROFILE_ID",
-    )
-    token: str | None = Field(
-        default=None, description="Bearer token", validation_alias="WATSON_TOKEN"
-    )
-    projects_token: str | None = Field(
-        default=None,
-        description="Projects token",
-        validation_alias="WATSON_PROJECTS_TOKEN",
-    )
-    username: str | None = Field(
-        default=None, description="Username", validation_alias="WATSON_USERNAME"
-    )
-    password: str | None = Field(
-        default=None, description="Password", validation_alias="WATSON_PASSWORD"
-    )
-    instance_id: str | None = Field(
-        default=None,
-        description="Service instance ID",
-        validation_alias="WATSON_INSTANCE_ID",
-    )
-    version: str | None = Field(
-        default=None, description="API version", validation_alias="WATSON_VERSION"
-    )
-    bedrock_url: str | None = Field(
-        default=None, description="Bedrock URL", validation_alias="WATSON_BEDROCK_URL"
-    )
-    platform_url: str | None = Field(
-        default=None, description="Platform URL", validation_alias="WATSON_PLATFORM_URL"
-    )
-    proxies: dict | None = Field(default=None, description="Proxy configuration")
-
-    @model_validator(mode="after")
-    def validate_space_or_project(self) -> Self:
-        """Validate that either space_id or project_id is provided."""
-        if not self.space_id and not self.project_id:
-            raise ValueError("One of 'space_id' or 'project_id' must be provided")
-        return self

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

@@ -1,15 +0,0 @@
-"""Instructor embedding providers."""
-
-from crewai.rag.embeddings.providers.instructor.instructor_provider import (
-    InstructorProvider,
-)
-from crewai.rag.embeddings.providers.instructor.types import (
-    InstructorProviderConfig,
-    InstructorProviderSpec,
-)
-
-__all__ = [
-    "InstructorProvider",
-    "InstructorProviderConfig",
-    "InstructorProviderSpec",
-]

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

@@ -1,32 +0,0 @@
-"""Instructor embeddings provider."""
-
-from chromadb.utils.embedding_functions.instructor_embedding_function import (
-    InstructorEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class InstructorProvider(BaseEmbeddingsProvider[InstructorEmbeddingFunction]):
-    """Instructor embeddings provider."""
-
-    embedding_callable: type[InstructorEmbeddingFunction] = Field(
-        default=InstructorEmbeddingFunction,
-        description="Instructor embedding function class",
-    )
-    model_name: str = Field(
-        default="hkunlp/instructor-base",
-        description="Model name to use",
-        validation_alias="INSTRUCTOR_MODEL_NAME",
-    )
-    device: str = Field(
-        default="cpu",
-        description="Device to run model on (cpu or cuda)",
-        validation_alias="INSTRUCTOR_DEVICE",
-    )
-    instruction: str | None = Field(
-        default=None,
-        description="Instruction for embeddings",
-        validation_alias="INSTRUCTOR_INSTRUCTION",
-    )

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

@@ -1,20 +0,0 @@
-"""Type definitions for Instructor embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class InstructorProviderConfig(TypedDict, total=False):
-    """Configuration for Instructor provider."""
-
-    model_name: Annotated[str, "hkunlp/instructor-base"]
-    device: Annotated[str, "cpu"]
-    instruction: str
-
-
-class InstructorProviderSpec(TypedDict, total=False):
-    """Instructor provider specification."""
-
-    provider: Required[Literal["instructor"]]
-    config: InstructorProviderConfig

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

@@ -1,13 +0,0 @@
-"""Jina embedding providers."""
-
-from crewai.rag.embeddings.providers.jina.jina_provider import JinaProvider
-from crewai.rag.embeddings.providers.jina.types import (
-    JinaProviderConfig,
-    JinaProviderSpec,
-)
-
-__all__ = [
-    "JinaProvider",
-    "JinaProviderConfig",
-    "JinaProviderSpec",
-]

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

@@ -1,22 +0,0 @@
-"""Jina embeddings provider."""
-
-from chromadb.utils.embedding_functions.jina_embedding_function import (
-    JinaEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class JinaProvider(BaseEmbeddingsProvider[JinaEmbeddingFunction]):
-    """Jina embeddings provider."""
-
-    embedding_callable: type[JinaEmbeddingFunction] = Field(
-        default=JinaEmbeddingFunction, description="Jina embedding function class"
-    )
-    api_key: str = Field(description="Jina API key", validation_alias="JINA_API_KEY")
-    model_name: str = Field(
-        default="jina-embeddings-v2-base-en",
-        description="Model name to use for embeddings",
-        validation_alias="JINA_MODEL_NAME",
-    )

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

@@ -1,19 +0,0 @@
-"""Type definitions for Jina embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class JinaProviderConfig(TypedDict, total=False):
-    """Configuration for Jina provider."""
-
-    api_key: str
-    model_name: Annotated[str, "jina-embeddings-v2-base-en"]
-
-
-class JinaProviderSpec(TypedDict, total=False):
-    """Jina provider specification."""
-
-    provider: Required[Literal["jina"]]
-    config: JinaProviderConfig

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

@@ -1,15 +0,0 @@
-"""Microsoft embedding providers."""
-
-from crewai.rag.embeddings.providers.microsoft.azure import (
-    AzureProvider,
-)
-from crewai.rag.embeddings.providers.microsoft.types import (
-    AzureProviderConfig,
-    AzureProviderSpec,
-)
-
-__all__ = [
-    "AzureProvider",
-    "AzureProviderConfig",
-    "AzureProviderSpec",
-]

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

@@ -1,58 +0,0 @@
-"""Azure OpenAI embeddings provider."""
-
-from typing import Any
-
-from chromadb.utils.embedding_functions.openai_embedding_function import (
-    OpenAIEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class AzureProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
-    """Azure OpenAI embeddings provider."""
-
-    embedding_callable: type[OpenAIEmbeddingFunction] = Field(
-        default=OpenAIEmbeddingFunction,
-        description="Azure OpenAI embedding function class",
-    )
-    api_key: str = Field(description="Azure API key", validation_alias="OPENAI_API_KEY")
-    api_base: str | None = Field(
-        default=None,
-        description="Azure endpoint URL",
-        validation_alias="OPENAI_API_BASE",
-    )
-    api_type: str = Field(
-        default="azure",
-        description="API type for Azure",
-        validation_alias="OPENAI_API_TYPE",
-    )
-    api_version: str | None = Field(
-        default=None,
-        description="Azure API version",
-        validation_alias="OPENAI_API_VERSION",
-    )
-    model_name: str = Field(
-        default="text-embedding-ada-002",
-        description="Model name to use for embeddings",
-        validation_alias="OPENAI_MODEL_NAME",
-    )
-    default_headers: dict[str, Any] | None = Field(
-        default=None, description="Default headers for API requests"
-    )
-    dimensions: int | None = Field(
-        default=None,
-        description="Embedding dimensions",
-        validation_alias="OPENAI_DIMENSIONS",
-    )
-    deployment_id: str | None = Field(
-        default=None,
-        description="Azure deployment ID",
-        validation_alias="OPENAI_DEPLOYMENT_ID",
-    )
-    organization_id: str | None = Field(
-        default=None,
-        description="Organization ID",
-        validation_alias="OPENAI_ORGANIZATION_ID",
-    )

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

@@ -1,26 +0,0 @@
-"""Type definitions for Microsoft Azure embedding providers."""
-
-from typing import Annotated, Any, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class AzureProviderConfig(TypedDict, total=False):
-    """Configuration for Azure provider."""
-
-    api_key: str
-    api_base: str
-    api_type: Annotated[str, "azure"]
-    api_version: str
-    model_name: Annotated[str, "text-embedding-ada-002"]
-    default_headers: dict[str, Any]
-    dimensions: int
-    deployment_id: str
-    organization_id: str
-
-
-class AzureProviderSpec(TypedDict, total=False):
-    """Azure provider specification."""
-
-    provider: Required[Literal["azure"]]
-    config: AzureProviderConfig

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

@@ -1,15 +0,0 @@
-"""Ollama embedding providers."""
-
-from crewai.rag.embeddings.providers.ollama.ollama_provider import (
-    OllamaProvider,
-)
-from crewai.rag.embeddings.providers.ollama.types import (
-    OllamaProviderConfig,
-    OllamaProviderSpec,
-)
-
-__all__ = [
-    "OllamaProvider",
-    "OllamaProviderConfig",
-    "OllamaProviderSpec",
-]

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

@@ -1,25 +0,0 @@
-"""Ollama embeddings provider."""
-
-from chromadb.utils.embedding_functions.ollama_embedding_function import (
-    OllamaEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class OllamaProvider(BaseEmbeddingsProvider[OllamaEmbeddingFunction]):
-    """Ollama embeddings provider."""
-
-    embedding_callable: type[OllamaEmbeddingFunction] = Field(
-        default=OllamaEmbeddingFunction, description="Ollama embedding function class"
-    )
-    url: str = Field(
-        default="http://localhost:11434/api/embeddings",
-        description="Ollama API endpoint URL",
-        validation_alias="OLLAMA_URL",
-    )
-    model_name: str = Field(
-        description="Model name to use for embeddings",
-        validation_alias="OLLAMA_MODEL_NAME",
-    )

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

@@ -1,19 +0,0 @@
-"""Type definitions for Ollama embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class OllamaProviderConfig(TypedDict, total=False):
-    """Configuration for Ollama provider."""
-
-    url: Annotated[str, "http://localhost:11434/api/embeddings"]
-    model_name: str
-
-
-class OllamaProviderSpec(TypedDict, total=False):
-    """Ollama provider specification."""
-
-    provider: Required[Literal["ollama"]]
-    config: OllamaProviderConfig

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

@@ -1,13 +0,0 @@
-"""ONNX embedding providers."""
-
-from crewai.rag.embeddings.providers.onnx.onnx_provider import ONNXProvider
-from crewai.rag.embeddings.providers.onnx.types import (
-    ONNXProviderConfig,
-    ONNXProviderSpec,
-)
-
-__all__ = [
-    "ONNXProvider",
-    "ONNXProviderConfig",
-    "ONNXProviderSpec",
-]

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

@@ -1,19 +0,0 @@
-"""ONNX embeddings provider."""
-
-from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class ONNXProvider(BaseEmbeddingsProvider[ONNXMiniLM_L6_V2]):
-    """ONNX embeddings provider."""
-
-    embedding_callable: type[ONNXMiniLM_L6_V2] = Field(
-        default=ONNXMiniLM_L6_V2, description="ONNX MiniLM embedding function class"
-    )
-    preferred_providers: list[str] | None = Field(
-        default=None,
-        description="Preferred ONNX execution providers",
-        validation_alias="ONNX_PREFERRED_PROVIDERS",
-    )

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

@@ -1,18 +0,0 @@
-"""Type definitions for ONNX embedding providers."""
-
-from typing import Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class ONNXProviderConfig(TypedDict, total=False):
-    """Configuration for ONNX provider."""
-
-    preferred_providers: list[str]
-
-
-class ONNXProviderSpec(TypedDict, total=False):
-    """ONNX provider specification."""
-
-    provider: Required[Literal["onnx"]]
-    config: ONNXProviderConfig

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

@@ -1,15 +0,0 @@
-"""OpenAI embedding providers."""
-
-from crewai.rag.embeddings.providers.openai.openai_provider import (
-    OpenAIProvider,
-)
-from crewai.rag.embeddings.providers.openai.types import (
-    OpenAIProviderConfig,
-    OpenAIProviderSpec,
-)
-
-__all__ = [
-    "OpenAIProvider",
-    "OpenAIProviderConfig",
-    "OpenAIProviderSpec",
-]

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

@@ -1,58 +0,0 @@
-"""OpenAI embeddings provider."""
-
-from typing import Any
-
-from chromadb.utils.embedding_functions.openai_embedding_function import (
-    OpenAIEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class OpenAIProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
-    """OpenAI embeddings provider."""
-
-    embedding_callable: type[OpenAIEmbeddingFunction] = Field(
-        default=OpenAIEmbeddingFunction,
-        description="OpenAI embedding function class",
-    )
-    api_key: str | None = Field(
-        default=None, description="OpenAI API key", validation_alias="OPENAI_API_KEY"
-    )
-    model_name: str = Field(
-        default="text-embedding-ada-002",
-        description="Model name to use for embeddings",
-        validation_alias="OPENAI_MODEL_NAME",
-    )
-    api_base: str | None = Field(
-        default=None,
-        description="Base URL for API requests",
-        validation_alias="OPENAI_API_BASE",
-    )
-    api_type: str | None = Field(
-        default=None,
-        description="API type (e.g., 'azure')",
-        validation_alias="OPENAI_API_TYPE",
-    )
-    api_version: str | None = Field(
-        default=None, description="API version", validation_alias="OPENAI_API_VERSION"
-    )
-    default_headers: dict[str, Any] | None = Field(
-        default=None, description="Default headers for API requests"
-    )
-    dimensions: int | None = Field(
-        default=None,
-        description="Embedding dimensions",
-        validation_alias="OPENAI_DIMENSIONS",
-    )
-    deployment_id: str | None = Field(
-        default=None,
-        description="Azure deployment ID",
-        validation_alias="OPENAI_DEPLOYMENT_ID",
-    )
-    organization_id: str | None = Field(
-        default=None,
-        description="OpenAI organization ID",
-        validation_alias="OPENAI_ORGANIZATION_ID",
-    )

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

@@ -1,26 +0,0 @@
-"""Type definitions for OpenAI embedding providers."""
-
-from typing import Annotated, Any, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class OpenAIProviderConfig(TypedDict, total=False):
-    """Configuration for OpenAI provider."""
-
-    api_key: str
-    model_name: Annotated[str, "text-embedding-ada-002"]
-    api_base: str
-    api_type: str
-    api_version: str
-    default_headers: dict[str, Any]
-    dimensions: int
-    deployment_id: str
-    organization_id: str
-
-
-class OpenAIProviderSpec(TypedDict, total=False):
-    """OpenAI provider specification."""
-
-    provider: Required[Literal["openai"]]
-    config: OpenAIProviderConfig

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

@@ -1,15 +0,0 @@
-"""OpenCLIP embedding providers."""
-
-from crewai.rag.embeddings.providers.openclip.openclip_provider import (
-    OpenCLIPProvider,
-)
-from crewai.rag.embeddings.providers.openclip.types import (
-    OpenCLIPProviderConfig,
-    OpenCLIPProviderSpec,
-)
-
-__all__ = [
-    "OpenCLIPProvider",
-    "OpenCLIPProviderConfig",
-    "OpenCLIPProviderSpec",
-]

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

@@ -1,32 +0,0 @@
-"""OpenCLIP embeddings provider."""
-
-from chromadb.utils.embedding_functions.open_clip_embedding_function import (
-    OpenCLIPEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class OpenCLIPProvider(BaseEmbeddingsProvider[OpenCLIPEmbeddingFunction]):
-    """OpenCLIP embeddings provider."""
-
-    embedding_callable: type[OpenCLIPEmbeddingFunction] = Field(
-        default=OpenCLIPEmbeddingFunction,
-        description="OpenCLIP embedding function class",
-    )
-    model_name: str = Field(
-        default="ViT-B-32",
-        description="Model name to use",
-        validation_alias="OPENCLIP_MODEL_NAME",
-    )
-    checkpoint: str = Field(
-        default="laion2b_s34b_b79k",
-        description="Model checkpoint",
-        validation_alias="OPENCLIP_CHECKPOINT",
-    )
-    device: str | None = Field(
-        default="cpu",
-        description="Device to run model on",
-        validation_alias="OPENCLIP_DEVICE",
-    )

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

@@ -1,20 +0,0 @@
-"""Type definitions for OpenCLIP embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class OpenCLIPProviderConfig(TypedDict, total=False):
-    """Configuration for OpenCLIP provider."""
-
-    model_name: Annotated[str, "ViT-B-32"]
-    checkpoint: Annotated[str, "laion2b_s34b_b79k"]
-    device: Annotated[str, "cpu"]
-
-
-class OpenCLIPProviderSpec(TypedDict):
-    """OpenCLIP provider specification."""
-
-    provider: Required[Literal["openclip"]]
-    config: OpenCLIPProviderConfig

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

@@ -1,15 +0,0 @@
-"""Roboflow embedding providers."""
-
-from crewai.rag.embeddings.providers.roboflow.roboflow_provider import (
-    RoboflowProvider,
-)
-from crewai.rag.embeddings.providers.roboflow.types import (
-    RoboflowProviderConfig,
-    RoboflowProviderSpec,
-)
-
-__all__ = [
-    "RoboflowProvider",
-    "RoboflowProviderConfig",
-    "RoboflowProviderSpec",
-]

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

@@ -1,25 +0,0 @@
-"""Roboflow embeddings provider."""
-
-from chromadb.utils.embedding_functions.roboflow_embedding_function import (
-    RoboflowEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class RoboflowProvider(BaseEmbeddingsProvider[RoboflowEmbeddingFunction]):
-    """Roboflow embeddings provider."""
-
-    embedding_callable: type[RoboflowEmbeddingFunction] = Field(
-        default=RoboflowEmbeddingFunction,
-        description="Roboflow embedding function class",
-    )
-    api_key: str = Field(
-        default="", description="Roboflow API key", validation_alias="ROBOFLOW_API_KEY"
-    )
-    api_url: str = Field(
-        default="https://infer.roboflow.com",
-        description="Roboflow API URL",
-        validation_alias="ROBOFLOW_API_URL",
-    )

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

@@ -1,19 +0,0 @@
-"""Type definitions for Roboflow embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class RoboflowProviderConfig(TypedDict, total=False):
-    """Configuration for Roboflow provider."""
-
-    api_key: Annotated[str, ""]
-    api_url: Annotated[str, "https://infer.roboflow.com"]
-
-
-class RoboflowProviderSpec(TypedDict):
-    """Roboflow provider specification."""
-
-    provider: Required[Literal["roboflow"]]
-    config: RoboflowProviderConfig

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

@@ -1,15 +0,0 @@
-"""SentenceTransformer embedding providers."""
-
-from crewai.rag.embeddings.providers.sentence_transformer.sentence_transformer_provider import (
-    SentenceTransformerProvider,
-)
-from crewai.rag.embeddings.providers.sentence_transformer.types import (
-    SentenceTransformerProviderConfig,
-    SentenceTransformerProviderSpec,
-)
-
-__all__ = [
-    "SentenceTransformerProvider",
-    "SentenceTransformerProviderConfig",
-    "SentenceTransformerProviderSpec",
-]

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

@@ -1,34 +0,0 @@
-"""SentenceTransformer embeddings provider."""
-
-from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import (
-    SentenceTransformerEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class SentenceTransformerProvider(
-    BaseEmbeddingsProvider[SentenceTransformerEmbeddingFunction]
-):
-    """SentenceTransformer embeddings provider."""
-
-    embedding_callable: type[SentenceTransformerEmbeddingFunction] = Field(
-        default=SentenceTransformerEmbeddingFunction,
-        description="SentenceTransformer embedding function class",
-    )
-    model_name: str = Field(
-        default="all-MiniLM-L6-v2",
-        description="Model name to use",
-        validation_alias="SENTENCE_TRANSFORMER_MODEL_NAME",
-    )
-    device: str = Field(
-        default="cpu",
-        description="Device to run model on (cpu or cuda)",
-        validation_alias="SENTENCE_TRANSFORMER_DEVICE",
-    )
-    normalize_embeddings: bool = Field(
-        default=False,
-        description="Whether to normalize embeddings",
-        validation_alias="SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS",
-    )

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

@@ -1,20 +0,0 @@
-"""Type definitions for SentenceTransformer embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class SentenceTransformerProviderConfig(TypedDict, total=False):
-    """Configuration for SentenceTransformer provider."""
-
-    model_name: Annotated[str, "all-MiniLM-L6-v2"]
-    device: Annotated[str, "cpu"]
-    normalize_embeddings: Annotated[bool, False]
-
-
-class SentenceTransformerProviderSpec(TypedDict):
-    """SentenceTransformer provider specification."""
-
-    provider: Required[Literal["sentence-transformer"]]
-    config: SentenceTransformerProviderConfig

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

@@ -1,15 +0,0 @@
-"""Text2Vec embedding providers."""
-
-from crewai.rag.embeddings.providers.text2vec.text2vec_provider import (
-    Text2VecProvider,
-)
-from crewai.rag.embeddings.providers.text2vec.types import (
-    Text2VecProviderConfig,
-    Text2VecProviderSpec,
-)
-
-__all__ = [
-    "Text2VecProvider",
-    "Text2VecProviderConfig",
-    "Text2VecProviderSpec",
-]

src/crewai/rag/embeddings/providers/text2vec/text2vec_provider.py

@@ -1,22 +0,0 @@
-"""Text2Vec embeddings provider."""
-
-from chromadb.utils.embedding_functions.text2vec_embedding_function import (
-    Text2VecEmbeddingFunction,
-)
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-
-class Text2VecProvider(BaseEmbeddingsProvider[Text2VecEmbeddingFunction]):
-    """Text2Vec embeddings provider."""
-
-    embedding_callable: type[Text2VecEmbeddingFunction] = Field(
-        default=Text2VecEmbeddingFunction,
-        description="Text2Vec embedding function class",
-    )
-    model_name: str = Field(
-        default="shibing624/text2vec-base-chinese",
-        description="Model name to use",
-        validation_alias="TEXT2VEC_MODEL_NAME",
-    )

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

@@ -1,18 +0,0 @@
-"""Type definitions for Text2Vec embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class Text2VecProviderConfig(TypedDict, total=False):
-    """Configuration for Text2Vec provider."""
-
-    model_name: Annotated[str, "shibing624/text2vec-base-chinese"]
-
-
-class Text2VecProviderSpec(TypedDict):
-    """Text2Vec provider specification."""
-
-    provider: Required[Literal["text2vec"]]
-    config: Text2VecProviderConfig

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

@@ -1,15 +0,0 @@
-"""VoyageAI embedding providers."""
-
-from crewai.rag.embeddings.providers.voyageai.types import (
-    VoyageAIProviderConfig,
-    VoyageAIProviderSpec,
-)
-from crewai.rag.embeddings.providers.voyageai.voyageai_provider import (
-    VoyageAIProvider,
-)
-
-__all__ = [
-    "VoyageAIProvider",
-    "VoyageAIProviderConfig",
-    "VoyageAIProviderSpec",
-]

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

@@ -1,58 +0,0 @@
-"""VoyageAI embedding function implementation."""
-
-from typing import cast
-
-from typing_extensions import Unpack
-
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-from crewai.rag.core.types import Documents, Embeddings
-from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderConfig
-
-
-class VoyageAIEmbeddingFunction(EmbeddingFunction[Documents]):
-    """Embedding function for VoyageAI models."""
-
-    def __init__(self, **kwargs: Unpack[VoyageAIProviderConfig]) -> None:
-        """Initialize VoyageAI embedding function.
-
-        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"],
-            max_retries=kwargs.get("max_retries", 0),
-            timeout=kwargs.get("timeout"),
-        )
-
-    def __call__(self, input: Documents) -> Embeddings:
-        """Generate embeddings for input documents.
-
-        Args:
-            input: List of documents to embed.
-
-        Returns:
-            List of embedding vectors.
-        """
-
-        if isinstance(input, str):
-            input = [input]
-
-        result = self._client.embed(
-            texts=input,
-            model=self._config.get("model", "voyage-2"),
-            input_type=self._config.get("input_type"),
-            truncation=self._config.get("truncation", True),
-            output_dtype=self._config.get("output_dtype"),
-            output_dimension=self._config.get("output_dimension"),
-        )
-
-        return cast(Embeddings, result.embeddings)

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

@@ -1,25 +0,0 @@
-"""Type definitions for VoyageAI embedding providers."""
-
-from typing import Annotated, Literal
-
-from typing_extensions import Required, TypedDict
-
-
-class VoyageAIProviderConfig(TypedDict, total=False):
-    """Configuration for VoyageAI provider."""
-
-    api_key: str
-    model: Annotated[str, "voyage-2"]
-    input_type: str
-    truncation: Annotated[bool, True]
-    output_dtype: str
-    output_dimension: int
-    max_retries: Annotated[int, 0]
-    timeout: float
-
-
-class VoyageAIProviderSpec(TypedDict):
-    """VoyageAI provider specification."""
-
-    provider: Required[Literal["voyageai"]]
-    config: VoyageAIProviderConfig

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

@@ -1,55 +0,0 @@
-"""Voyage AI embeddings provider."""
-
-from pydantic import Field
-
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.providers.voyageai.embedding_callable import (
-    VoyageAIEmbeddingFunction,
-)
-
-
-class VoyageAIProvider(BaseEmbeddingsProvider[VoyageAIEmbeddingFunction]):
-    """Voyage AI embeddings provider."""
-
-    embedding_callable: type[VoyageAIEmbeddingFunction] = Field(
-        default=VoyageAIEmbeddingFunction,
-        description="Voyage AI embedding function class",
-    )
-    model: str = Field(
-        default="voyage-2",
-        description="Model to use for embeddings",
-        validation_alias="VOYAGEAI_MODEL",
-    )
-    api_key: str = Field(
-        description="Voyage AI API key", validation_alias="VOYAGEAI_API_KEY"
-    )
-    input_type: str | None = Field(
-        default=None,
-        description="Input type for embeddings",
-        validation_alias="VOYAGEAI_INPUT_TYPE",
-    )
-    truncation: bool = Field(
-        default=True,
-        description="Whether to truncate inputs",
-        validation_alias="VOYAGEAI_TRUNCATION",
-    )
-    output_dtype: str | None = Field(
-        default=None,
-        description="Output data type",
-        validation_alias="VOYAGEAI_OUTPUT_DTYPE",
-    )
-    output_dimension: int | None = Field(
-        default=None,
-        description="Output dimension",
-        validation_alias="VOYAGEAI_OUTPUT_DIMENSION",
-    )
-    max_retries: int = Field(
-        default=0,
-        description="Maximum retries for API calls",
-        validation_alias="VOYAGEAI_MAX_RETRIES",
-    )
-    timeout: float | None = Field(
-        default=None,
-        description="Timeout for API calls",
-        validation_alias="VOYAGEAI_TIMEOUT",
-    )

src/crewai/rag/embeddings/types.py

@@ -1,73 +1,63 @@
 """Type definitions for the embeddings module."""
 
-from typing import Literal, TypeAlias
+from typing import Literal
 
-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
-from crewai.rag.embeddings.providers.google.types import (
-    GenerativeAiProviderSpec,
-    VertexAIProviderSpec,
-)
-from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec
-from crewai.rag.embeddings.providers.ibm.types import WatsonProviderSpec
-from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
-from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
-from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
-from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec
-from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec
-from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
-from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec
-from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec
-from crewai.rag.embeddings.providers.sentence_transformer.types import (
-    SentenceTransformerProviderSpec,
-)
-from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
-from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
+from pydantic import BaseModel, Field, SecretStr
 
-ProviderSpec = (
-    AzureProviderSpec
-    | BedrockProviderSpec
-    | CohereProviderSpec
-    | CustomProviderSpec
-    | GenerativeAiProviderSpec
-    | HuggingFaceProviderSpec
-    | InstructorProviderSpec
-    | JinaProviderSpec
-    | OllamaProviderSpec
-    | ONNXProviderSpec
-    | OpenAIProviderSpec
-    | OpenCLIPProviderSpec
-    | RoboflowProviderSpec
-    | SentenceTransformerProviderSpec
-    | Text2VecProviderSpec
-    | VertexAIProviderSpec
-    | VoyageAIProviderSpec
-    | WatsonProviderSpec
-)
+from crewai.rag.types import EmbeddingFunction
 
-AllowedEmbeddingProviders = Literal[
-    "azure",
-    "amazon-bedrock",
+EmbeddingProvider = Literal[
+    "openai",
     "cohere",
-    "custom",
+    "ollama",
+    "huggingface",
+    "sentence-transformer",
+    "instructor",
+    "google-palm",
     "google-generativeai",
     "google-vertex",
-    "huggingface",
-    "instructor",
+    "amazon-bedrock",
     "jina",
-    "ollama",
-    "onnx",
-    "openai",
-    "openclip",
     "roboflow",
-    "sentence-transformer",
+    "openclip",
     "text2vec",
-    "voyageai",
+    "onnx",
     "watson",
 ]
+"""Supported embedding providers.
 
-EmbedderConfig: TypeAlias = (
-    ProviderSpec | BaseEmbeddingsProvider | type[BaseEmbeddingsProvider]
-)
+These correspond to the embedding functions available in ChromaDB's
+embedding_functions module. Each provider has specific requirements
+and configuration options.
+"""
+
+
+class EmbeddingOptions(BaseModel):
+    """Configuration options for embedding providers.
+
+    Generic attributes that can be passed to get_embedding_function
+    to configure various embedding providers.
+    """
+
+    provider: EmbeddingProvider = Field(
+        ..., description="Embedding provider name (e.g., 'openai', 'cohere', 'onnx')"
+    )
+    model_name: str | None = Field(
+        default=None, description="Model name for the embedding provider"
+    )
+    api_key: SecretStr | None = Field(
+        default=None, description="API key for the embedding provider"
+    )
+
+
+class EmbeddingConfig(BaseModel):
+    """Configuration wrapper for embedding functions.
+
+    Accepts either a pre-configured EmbeddingFunction or EmbeddingOptions
+    to create one. This provides flexibility in how embeddings are configured.
+
+    Attributes:
+        function: Either a callable EmbeddingFunction or EmbeddingOptions to create one
+    """
+
+    function: EmbeddingFunction | EmbeddingOptions

src/crewai/rag/qdrant/client.py

@@ -48,7 +48,6 @@ class QdrantClient(BaseClient):
         embedding_function: EmbeddingFunction | AsyncEmbeddingFunction,
         default_limit: int = 5,
         default_score_threshold: float = 0.6,
-        default_batch_size: int = 100,
     ) -> None:
         """Initialize QdrantClient with client and embedding function.
 
@@ -57,13 +56,11 @@ class QdrantClient(BaseClient):
             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.
-            default_batch_size: Default batch size for adding documents.
         """
         self.client = client
         self.embedding_function = embedding_function
         self.default_limit = default_limit
         self.default_score_threshold = default_score_threshold
-        self.default_batch_size = default_batch_size
 
     def create_collection(self, **kwargs: Unpack[QdrantCollectionCreateParams]) -> None:
         """Create a new collection in Qdrant.
@@ -237,7 +234,6 @@ class QdrantClient(BaseClient):
         Keyword Args:
             collection_name: The name of the collection to add documents to.
             documents: List of BaseRecord dicts containing document data.
-            batch_size: Optional batch size for processing documents (default: 100)
 
         Raises:
             ValueError: If collection doesn't exist or documents list is empty.
@@ -253,7 +249,6 @@ class QdrantClient(BaseClient):
 
         collection_name = kwargs["collection_name"]
         documents = kwargs["documents"]
-        batch_size = kwargs.get("batch_size", self.default_batch_size)
 
         if not documents:
             raise ValueError("Documents list cannot be empty")
@@ -261,20 +256,19 @@ class QdrantClient(BaseClient):
         if not self.client.collection_exists(collection_name):
             raise ValueError(f"Collection '{collection_name}' does not exist")
 
-        for i in range(0, len(documents), batch_size):
-            batch_docs = documents[i : min(i + batch_size, len(documents))]
-            points = []
-            for doc in batch_docs:
-                if _is_async_embedding_function(self.embedding_function):
-                    raise TypeError(
-                        "Async embedding function cannot be used with sync add_documents. "
-                        "Use aadd_documents instead."
-                    )
-                sync_fn = cast(EmbeddingFunction, self.embedding_function)
-                embedding = sync_fn(doc["content"])
-                point = _create_point_from_document(doc, embedding)
-                points.append(point)
-            self.client.upsert(collection_name=collection_name, points=points)
+        points = []
+        for doc in documents:
+            if _is_async_embedding_function(self.embedding_function):
+                raise TypeError(
+                    "Async embedding function cannot be used with sync add_documents. "
+                    "Use aadd_documents instead."
+                )
+            sync_fn = cast(EmbeddingFunction, self.embedding_function)
+            embedding = sync_fn(doc["content"])
+            point = _create_point_from_document(doc, embedding)
+            points.append(point)
+
+        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.
@@ -282,7 +276,6 @@ class QdrantClient(BaseClient):
         Keyword Args:
             collection_name: The name of the collection to add documents to.
             documents: List of BaseRecord dicts containing document data.
-            batch_size: Optional batch size for processing documents (default: 100)
 
         Raises:
             ValueError: If collection doesn't exist or documents list is empty.
@@ -298,7 +291,6 @@ class QdrantClient(BaseClient):
 
         collection_name = kwargs["collection_name"]
         documents = kwargs["documents"]
-        batch_size = kwargs.get("batch_size", self.default_batch_size)
 
         if not documents:
             raise ValueError("Documents list cannot be empty")
@@ -306,19 +298,18 @@ class QdrantClient(BaseClient):
         if not await self.client.collection_exists(collection_name):
             raise ValueError(f"Collection '{collection_name}' does not exist")
 
-        for i in range(0, len(documents), batch_size):
-            batch_docs = documents[i : min(i + batch_size, len(documents))]
-            points = []
-            for doc in batch_docs:
-                if _is_async_embedding_function(self.embedding_function):
-                    async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
-                    embedding = await async_fn(doc["content"])
-                else:
-                    sync_fn = cast(EmbeddingFunction, self.embedding_function)
-                    embedding = sync_fn(doc["content"])
-                point = _create_point_from_document(doc, embedding)
-                points.append(point)
-            await self.client.upsert(collection_name=collection_name, points=points)
+        points = []
+        for doc in documents:
+            if _is_async_embedding_function(self.embedding_function):
+                async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
+                embedding = await async_fn(doc["content"])
+            else:
+                sync_fn = cast(EmbeddingFunction, self.embedding_function)
+                embedding = sync_fn(doc["content"])
+            point = _create_point_from_document(doc, embedding)
+            points.append(point)
+
+        await self.client.upsert(collection_name=collection_name, points=points)
 
     def search(
         self, **kwargs: Unpack[BaseCollectionSearchParams]

src/crewai/rag/qdrant/factory.py

@@ -22,5 +22,4 @@ def create_client(config: QdrantConfig) -> QdrantClient:
         embedding_function=config.embedding_function,
         default_limit=config.limit,
         default_score_threshold=config.score_threshold,
-        default_batch_size=config.batch_size,
     )

src/crewai/rag/storage/base_rag_storage.py

@@ -1,9 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Any
 
-from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.types import ProviderSpec
-
 
 class BaseRAGStorage(ABC):
     """
@@ -16,7 +13,7 @@ class BaseRAGStorage(ABC):
         self,
         type: str,
         allow_reset: bool = True,
-        embedder_config: ProviderSpec | BaseEmbeddingsProvider | None = None,
+        embedder_config: dict[str, Any] | None = None,
         crew: Any = None,
     ):
         self.type = type

src/crewai/rag/types.py

@@ -24,7 +24,8 @@ class BaseRecord(TypedDict, total=False):
     )
 
 
-Embeddings: TypeAlias = list[list[float]]
+DenseVector: TypeAlias = list[float]
+IntVector: TypeAlias = list[int]
 
 EmbeddingFunction: TypeAlias = Callable[..., Any]
 

src/crewai/utilities/import_utils.py

@@ -2,94 +2,29 @@
 
 import importlib
 from types import ModuleType
-from typing import Annotated, Any, TypeAlias
-
-from pydantic import AfterValidator, TypeAdapter
-from typing_extensions import deprecated
 
 
-@deprecated(
-    "Not needed when using `crewai.utilities.import_utils.import_and_validate_definition`"
-)
 class OptionalDependencyError(ImportError):
     """Exception raised when an optional dependency is not installed."""
 
 
-@deprecated(
-    "Use `crewai.utilities.import_utils.import_and_validate_definition` instead."
-)
-def require(name: str, *, purpose: str, attr: str | None = None) -> ModuleType | Any:
-    """Import a module, optionally returning a specific attribute.
+def require(name: str, *, purpose: str) -> ModuleType:
+    """Import a module, raising a helpful error if it's not installed.
 
     Args:
         name: The module name to import.
         purpose: Description of what requires this dependency.
-        attr: Optional attribute name to get from the module.
 
     Returns:
-        The imported module or the specified attribute.
+        The imported module.
 
     Raises:
         OptionalDependencyError: If the module is not installed.
-        AttributeError: If the specified attribute doesn't exist.
     """
     try:
-        module = importlib.import_module(name)
-        if attr is not None:
-            return getattr(module, attr)
-        return module
+        return importlib.import_module(name)
     except ImportError as exc:
-        package_name = name.split(".")[0]
         raise OptionalDependencyError(
             f"{purpose} requires the optional dependency '{name}'.\n"
-            f"Install it with: uv add {package_name}"
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tests/knowledge/test_knowledge_storage_integration.py

@@ -11,7 +11,7 @@ from crewai.knowledge.storage.knowledge_storage import (  # type: ignore[import-
 
 @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client")
 @patch("crewai.knowledge.storage.knowledge_storage.create_client")
-@patch("crewai.knowledge.storage.knowledge_storage.build_embedder")
+@patch("crewai.knowledge.storage.knowledge_storage.get_embedding_function")
 def test_knowledge_storage_uses_rag_client(
     mock_get_embedding: MagicMock,
     mock_create_client: MagicMock,
@@ -122,7 +122,7 @@ def test_search_error_handling(mock_get_client: MagicMock) -> None:
 
 
 @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client")
-@patch("crewai.knowledge.storage.knowledge_storage.build_embedder")
+@patch("crewai.knowledge.storage.knowledge_storage.get_embedding_function")
 def test_embedding_configuration_flow(
     mock_get_embedding: MagicMock, mock_get_client: MagicMock
 ) -> None:

tests/rag/chromadb/test_client.py

@@ -34,30 +34,6 @@ def client(mock_chromadb_client) -> ChromaDBClient:
     return client
 
 
-@pytest.fixture
-def client_with_batch_size(mock_chromadb_client) -> ChromaDBClient:
-    """Create a ChromaDBClient instance with custom batch size for testing."""
-    mock_embedding = Mock()
-    client = ChromaDBClient(
-        client=mock_chromadb_client,
-        embedding_function=mock_embedding,
-        default_batch_size=2,
-    )
-    return client
-
-
-@pytest.fixture
-def async_client_with_batch_size(mock_async_chromadb_client) -> ChromaDBClient:
-    """Create a ChromaDBClient instance with async client and custom batch size for testing."""
-    mock_embedding = Mock()
-    client = ChromaDBClient(
-        client=mock_async_chromadb_client,
-        embedding_function=mock_embedding,
-        default_batch_size=2,
-    )
-    return client
-
-
 @pytest.fixture
 def async_client(mock_async_chromadb_client) -> ChromaDBClient:
     """Create a ChromaDBClient instance with async client for testing."""
@@ -636,139 +612,3 @@ class TestChromaDBClient:
         await async_client.areset()
 
         mock_async_chromadb_client.reset.assert_called_once_with()
-
-    def test_add_documents_with_batch_size(
-        self, client_with_batch_size, mock_chromadb_client
-    ) -> None:
-        """Test add_documents with batch size splits documents into batches."""
-        mock_collection = Mock()
-        mock_chromadb_client.get_or_create_collection.return_value = mock_collection
-
-        documents: list[BaseRecord] = [
-            {"doc_id": "id1", "content": "Document 1", "metadata": {"source": "test1"}},
-            {"doc_id": "id2", "content": "Document 2", "metadata": {"source": "test2"}},
-            {"doc_id": "id3", "content": "Document 3", "metadata": {"source": "test3"}},
-            {"doc_id": "id4", "content": "Document 4", "metadata": {"source": "test4"}},
-            {"doc_id": "id5", "content": "Document 5", "metadata": {"source": "test5"}},
-        ]
-
-        client_with_batch_size.add_documents(
-            collection_name="test_collection", documents=documents
-        )
-
-        assert mock_collection.upsert.call_count == 3
-
-        first_call = mock_collection.upsert.call_args_list[0]
-        assert first_call.kwargs["ids"] == ["id1", "id2"]
-        assert first_call.kwargs["documents"] == ["Document 1", "Document 2"]
-        assert first_call.kwargs["metadatas"] == [
-            {"source": "test1"},
-            {"source": "test2"},
-        ]
-
-        second_call = mock_collection.upsert.call_args_list[1]
-        assert second_call.kwargs["ids"] == ["id3", "id4"]
-        assert second_call.kwargs["documents"] == ["Document 3", "Document 4"]
-        assert second_call.kwargs["metadatas"] == [
-            {"source": "test3"},
-            {"source": "test4"},
-        ]
-
-        third_call = mock_collection.upsert.call_args_list[2]
-        assert third_call.kwargs["ids"] == ["id5"]
-        assert third_call.kwargs["documents"] == ["Document 5"]
-        assert third_call.kwargs["metadatas"] == [{"source": "test5"}]
-
-    def test_add_documents_with_explicit_batch_size(
-        self, client, mock_chromadb_client
-    ) -> None:
-        """Test add_documents with explicitly provided batch size."""
-        mock_collection = Mock()
-        mock_chromadb_client.get_or_create_collection.return_value = mock_collection
-
-        documents: list[BaseRecord] = [
-            {"doc_id": "id1", "content": "Document 1"},
-            {"doc_id": "id2", "content": "Document 2"},
-            {"doc_id": "id3", "content": "Document 3"},
-        ]
-
-        client.add_documents(
-            collection_name="test_collection", documents=documents, batch_size=1
-        )
-
-        assert mock_collection.upsert.call_count == 3
-        for i, call in enumerate(mock_collection.upsert.call_args_list):
-            assert len(call.kwargs["ids"]) == 1
-            assert call.kwargs["ids"] == [f"id{i + 1}"]
-
-    @pytest.mark.asyncio
-    async def test_aadd_documents_with_batch_size(
-        self, async_client_with_batch_size, mock_async_chromadb_client
-    ) -> None:
-        """Test aadd_documents with batch size splits documents into batches."""
-        mock_collection = AsyncMock()
-        mock_async_chromadb_client.get_or_create_collection = AsyncMock(
-            return_value=mock_collection
-        )
-
-        documents: list[BaseRecord] = [
-            {"doc_id": "id1", "content": "Document 1", "metadata": {"source": "test1"}},
-            {"doc_id": "id2", "content": "Document 2", "metadata": {"source": "test2"}},
-            {"doc_id": "id3", "content": "Document 3", "metadata": {"source": "test3"}},
-        ]
-
-        await async_client_with_batch_size.aadd_documents(
-            collection_name="test_collection", documents=documents
-        )
-
-        assert mock_collection.upsert.call_count == 2
-
-        first_call = mock_collection.upsert.call_args_list[0]
-        assert first_call.kwargs["ids"] == ["id1", "id2"]
-        assert first_call.kwargs["documents"] == ["Document 1", "Document 2"]
-
-        second_call = mock_collection.upsert.call_args_list[1]
-        assert second_call.kwargs["ids"] == ["id3"]
-        assert second_call.kwargs["documents"] == ["Document 3"]
-
-    @pytest.mark.asyncio
-    async def test_aadd_documents_with_explicit_batch_size(
-        self, async_client, mock_async_chromadb_client
-    ) -> None:
-        """Test aadd_documents with explicitly provided batch size."""
-        mock_collection = AsyncMock()
-        mock_async_chromadb_client.get_or_create_collection = AsyncMock(
-            return_value=mock_collection
-        )
-
-        documents: list[BaseRecord] = [
-            {"doc_id": "id1", "content": "Document 1"},
-            {"doc_id": "id2", "content": "Document 2"},
-            {"doc_id": "id3", "content": "Document 3"},
-            {"doc_id": "id4", "content": "Document 4"},
-        ]
-
-        await async_client.aadd_documents(
-            collection_name="test_collection", documents=documents, batch_size=3
-        )
-
-        assert mock_collection.upsert.call_count == 2
-
-        first_call = mock_collection.upsert.call_args_list[0]
-        assert len(first_call.kwargs["ids"]) == 3
-
-        second_call = mock_collection.upsert.call_args_list[1]
-        assert len(second_call.kwargs["ids"]) == 1
-
-    def test_client_default_batch_size_initialization(self) -> None:
-        """Test that client initializes with correct default batch size."""
-        mock_client = Mock()
-        mock_embedding = Mock()
-
-        client = ChromaDBClient(client=mock_client, embedding_function=mock_embedding)
-        assert client.default_batch_size == 100
-
-        custom_client = ChromaDBClient(
-            client=mock_client, embedding_function=mock_embedding, default_batch_size=50
-        )
-        assert custom_client.default_batch_size == 50

tests/rag/chromadb/test_utils.py

@@ -1,15 +1,11 @@
 """Tests for ChromaDB utility functions."""
 
-from crewai.rag.chromadb.types import PreparedDocuments
 from crewai.rag.chromadb.utils import (
     MAX_COLLECTION_LENGTH,
     MIN_COLLECTION_LENGTH,
-    _create_batch_slice,
     _is_ipv4_pattern,
-    _prepare_documents_for_chromadb,
     _sanitize_collection_name,
 )
-from crewai.rag.types import BaseRecord
 
 
 class TestChromaDBUtils:
@@ -97,206 +93,3 @@ class TestChromaDBUtils:
         assert len(sanitized) >= MIN_COLLECTION_LENGTH
         assert sanitized[0].isalnum()
         assert sanitized[-1].isalnum()
-
-
-class TestPrepareDocumentsForChromaDB:
-    """Test suite for _prepare_documents_for_chromadb function."""
-
-    def test_prepare_documents_with_doc_ids(self) -> None:
-        """Test preparing documents that already have doc_ids."""
-        documents: list[BaseRecord] = [
-            {
-                "doc_id": "id1",
-                "content": "First document",
-                "metadata": {"source": "test1"},
-            },
-            {
-                "doc_id": "id2",
-                "content": "Second document",
-                "metadata": {"source": "test2"},
-            },
-        ]
-
-        result = _prepare_documents_for_chromadb(documents)
-
-        assert result.ids == ["id1", "id2"]
-        assert result.texts == ["First document", "Second document"]
-        assert result.metadatas == [{"source": "test1"}, {"source": "test2"}]
-
-    def test_prepare_documents_generate_ids(self) -> None:
-        """Test preparing documents without doc_ids (should generate hashes)."""
-        documents: list[BaseRecord] = [
-            {"content": "Test content", "metadata": {"key": "value"}},
-            {"content": "Another test"},
-        ]
-
-        result = _prepare_documents_for_chromadb(documents)
-
-        assert len(result.ids) == 2
-        assert all(len(doc_id) == 64 for doc_id in result.ids)
-        assert result.texts == ["Test content", "Another test"]
-        assert result.metadatas == [{"key": "value"}, {}]
-
-    def test_prepare_documents_with_list_metadata(self) -> None:
-        """Test preparing documents with list metadata (should take first item)."""
-        documents: list[BaseRecord] = [
-            {"content": "Test", "metadata": [{"first": "item"}, {"second": "item"}]},
-            {"content": "Test2", "metadata": []},
-        ]
-
-        result = _prepare_documents_for_chromadb(documents)
-
-        assert result.metadatas == [{"first": "item"}, {}]
-
-    def test_prepare_documents_no_metadata(self) -> None:
-        """Test preparing documents without metadata."""
-        documents: list[BaseRecord] = [
-            {"content": "Document 1"},
-            {"content": "Document 2", "metadata": None},
-        ]
-
-        result = _prepare_documents_for_chromadb(documents)
-
-        assert result.metadatas == [{}, {}]
-
-    def test_prepare_documents_hash_consistency(self) -> None:
-        """Test that identical content produces identical hashes."""
-        documents1: list[BaseRecord] = [
-            {"content": "Same content", "metadata": {"key": "value"}}
-        ]
-        documents2: list[BaseRecord] = [
-            {"content": "Same content", "metadata": {"key": "value"}}
-        ]
-
-        result1 = _prepare_documents_for_chromadb(documents1)
-        result2 = _prepare_documents_for_chromadb(documents2)
-
-        assert result1.ids == result2.ids
-
-
-class TestCreateBatchSlice:
-    """Test suite for _create_batch_slice function."""
-
-    def test_create_batch_slice_normal(self) -> None:
-        """Test creating a normal batch slice."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3", "id4", "id5"],
-            texts=["doc1", "doc2", "doc3", "doc4", "doc5"],
-            metadatas=[{"a": 1}, {"b": 2}, {"c": 3}, {"d": 4}, {"e": 5}],
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=1, batch_size=3
-        )
-
-        assert batch_ids == ["id2", "id3", "id4"]
-        assert batch_texts == ["doc2", "doc3", "doc4"]
-        assert batch_metadatas == [{"b": 2}, {"c": 3}, {"d": 4}]
-
-    def test_create_batch_slice_at_end(self) -> None:
-        """Test creating a batch slice that goes beyond the end."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3"],
-            texts=["doc1", "doc2", "doc3"],
-            metadatas=[{"a": 1}, {"b": 2}, {"c": 3}],
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=2, batch_size=5
-        )
-
-        assert batch_ids == ["id3"]
-        assert batch_texts == ["doc3"]
-        assert batch_metadatas == [{"c": 3}]
-
-    def test_create_batch_slice_empty_batch(self) -> None:
-        """Test creating a batch slice starting beyond the data."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2"], texts=["doc1", "doc2"], metadatas=[{"a": 1}, {"b": 2}]
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=5, batch_size=3
-        )
-
-        assert batch_ids == []
-        assert batch_texts == []
-        assert batch_metadatas == []
-
-    def test_create_batch_slice_no_metadatas(self) -> None:
-        """Test creating a batch slice with no metadatas."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3"], texts=["doc1", "doc2", "doc3"], metadatas=[]
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=0, batch_size=2
-        )
-
-        assert batch_ids == ["id1", "id2"]
-        assert batch_texts == ["doc1", "doc2"]
-        assert batch_metadatas is None
-
-    def test_create_batch_slice_all_empty_metadatas(self) -> None:
-        """Test creating a batch slice where all metadatas are empty."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3"],
-            texts=["doc1", "doc2", "doc3"],
-            metadatas=[{}, {}, {}],
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=0, batch_size=3
-        )
-
-        assert batch_ids == ["id1", "id2", "id3"]
-        assert batch_texts == ["doc1", "doc2", "doc3"]
-        assert batch_metadatas is None
-
-    def test_create_batch_slice_some_empty_metadatas(self) -> None:
-        """Test creating a batch slice where some metadatas are empty."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3"],
-            texts=["doc1", "doc2", "doc3"],
-            metadatas=[{"a": 1}, {}, {"c": 3}],
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=0, batch_size=3
-        )
-
-        assert batch_ids == ["id1", "id2", "id3"]
-        assert batch_texts == ["doc1", "doc2", "doc3"]
-        assert batch_metadatas == [{"a": 1}, {}, {"c": 3}]
-
-    def test_create_batch_slice_zero_start_index(self) -> None:
-        """Test creating a batch slice starting from index 0."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3", "id4"],
-            texts=["doc1", "doc2", "doc3", "doc4"],
-            metadatas=[{"a": 1}, {"b": 2}, {"c": 3}, {"d": 4}],
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=0, batch_size=2
-        )
-
-        assert batch_ids == ["id1", "id2"]
-        assert batch_texts == ["doc1", "doc2"]
-        assert batch_metadatas == [{"a": 1}, {"b": 2}]
-
-    def test_create_batch_slice_single_item(self) -> None:
-        """Test creating a batch slice with batch size 1."""
-        prepared = PreparedDocuments(
-            ids=["id1", "id2", "id3"],
-            texts=["doc1", "doc2", "doc3"],
-            metadatas=[{"a": 1}, {"b": 2}, {"c": 3}],
-        )
-
-        batch_ids, batch_texts, batch_metadatas = _create_batch_slice(
-            prepared, start_index=1, batch_size=1
-        )
-
-        assert batch_ids == ["id2"]
-        assert batch_texts == ["doc2"]
-        assert batch_metadatas == [{"b": 2}]

tests/rag/embeddings/test_embedding_factory.py

@@ -1,244 +0,0 @@
-"""Tests for embedding function factory."""
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai.rag.embeddings.factory import build_embedder
-
-
-class TestEmbeddingFactory:
-    """Test embedding factory functions."""
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_openai(self, mock_import):
-        """Test building OpenAI embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "openai",
-            "config": {
-                "api_key": "test-key",
-                "model_name": "text-embedding-3-small",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.openai.openai_provider.OpenAIProvider"
-        )
-        mock_provider_class.assert_called_once()
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["api_key"] == "test-key"
-        assert call_kwargs["model_name"] == "text-embedding-3-small"
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_azure(self, mock_import):
-        """Test building Azure embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "azure",
-            "config": {
-                "api_key": "test-azure-key",
-                "api_base": "https://test.openai.azure.com/",
-                "api_type": "azure",
-                "api_version": "2023-05-15",
-                "model_name": "text-embedding-3-small",
-                "deployment_id": "test-deployment",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.microsoft.azure.AzureProvider"
-        )
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["api_key"] == "test-azure-key"
-        assert call_kwargs["api_base"] == "https://test.openai.azure.com/"
-        assert call_kwargs["api_type"] == "azure"
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_ollama(self, mock_import):
-        """Test building Ollama embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "ollama",
-            "config": {
-                "model_name": "nomic-embed-text",
-                "url": "http://localhost:11434",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.ollama.ollama_provider.OllamaProvider"
-        )
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_cohere(self, mock_import):
-        """Test building Cohere embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "cohere",
-            "config": {
-                "api_key": "cohere-key",
-                "model_name": "embed-english-v3.0",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.cohere.cohere_provider.CohereProvider"
-        )
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_voyageai(self, mock_import):
-        """Test building VoyageAI embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "voyageai",
-            "config": {
-                "api_key": "voyage-key",
-                "model": "voyage-2",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.voyageai.voyageai_provider.VoyageAIProvider"
-        )
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_watson(self, mock_import):
-        """Test building Watson embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "watson",
-            "config": {
-                "model_id": "ibm/slate-125m-english-rtrvr",
-                "api_key": "watson-key",
-                "url": "https://us-south.ml.cloud.ibm.com",
-                "project_id": "test-project",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.ibm.watson.WatsonProvider"
-        )
-
-    def test_build_embedder_unknown_provider(self):
-        """Test error handling for unknown provider."""
-        config = {"provider": "unknown-provider", "config": {}}
-
-        with pytest.raises(ValueError, match="Unknown provider: unknown-provider"):
-            build_embedder(config)
-
-    def test_build_embedder_missing_provider(self):
-        """Test error handling for missing provider key."""
-        config = {"config": {"api_key": "test-key"}}
-
-        with pytest.raises(KeyError):
-            build_embedder(config)
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_import_error(self, mock_import):
-        """Test error handling when provider import fails."""
-        mock_import.side_effect = ImportError("Module not found")
-
-        config = {"provider": "openai", "config": {"api_key": "test-key"}}
-
-        with pytest.raises(ImportError, match="Failed to import provider openai"):
-            build_embedder(config)
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_custom_provider(self, mock_import):
-        """Test building custom embedder."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_callable = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable = mock_embedding_callable
-
-        config = {
-            "provider": "custom",
-            "config": {"embedding_callable": mock_embedding_callable},
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.custom.custom_provider.CustomProvider"
-        )
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["embedding_callable"] == mock_embedding_callable
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    @patch("crewai.rag.embeddings.factory.build_embedder_from_provider")
-    def test_build_embedder_with_provider_instance(
-        self, mock_build_from_provider, mock_import
-    ):
-        """Test building embedder from provider instance."""
-        from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-
-        mock_provider = MagicMock(spec=BaseEmbeddingsProvider)
-        mock_embedding_function = MagicMock()
-        mock_build_from_provider.return_value = mock_embedding_function
-
-        result = build_embedder(mock_provider)
-
-        mock_build_from_provider.assert_called_once_with(mock_provider)
-        assert result == mock_embedding_function
-        mock_import.assert_not_called()

tests/rag/embeddings/test_factory_azure.py

@@ -1,122 +0,0 @@
-"""Test Azure embedder configuration with factory."""
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai.rag.embeddings.factory import build_embedder
-
-
-class TestAzureEmbedderFactory:
-    """Test Azure embedder configuration with factory function."""
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_azure_with_nested_config(self, mock_import):
-        """Test Azure configuration with nested config key."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        embedder_config = {
-            "provider": "azure",
-            "config": {
-                "api_key": "test-azure-key",
-                "api_base": "https://test.openai.azure.com/",
-                "api_type": "azure",
-                "api_version": "2023-05-15",
-                "model_name": "text-embedding-3-small",
-                "deployment_id": "test-deployment",
-            },
-        }
-
-        result = build_embedder(embedder_config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.microsoft.azure.AzureProvider"
-        )
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["api_key"] == "test-azure-key"
-        assert call_kwargs["api_base"] == "https://test.openai.azure.com/"
-        assert call_kwargs["api_type"] == "azure"
-        assert call_kwargs["api_version"] == "2023-05-15"
-        assert call_kwargs["model_name"] == "text-embedding-3-small"
-        assert call_kwargs["deployment_id"] == "test-deployment"
-
-        assert result == mock_embedding_function
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_regular_openai_with_nested_config(self, mock_import):
-        """Test regular OpenAI configuration with nested config."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        embedder_config = {
-            "provider": "openai",
-            "config": {"api_key": "test-openai-key", "model": "text-embedding-3-large"},
-        }
-
-        result = build_embedder(embedder_config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.openai.openai_provider.OpenAIProvider"
-        )
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["api_key"] == "test-openai-key"
-        assert call_kwargs["model"] == "text-embedding-3-large"
-
-        assert result == mock_embedding_function
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_azure_provider_with_minimal_config(self, mock_import):
-        """Test Azure provider with minimal required configuration."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        embedder_config = {
-            "provider": "azure",
-            "config": {
-                "api_key": "test-key",
-                "api_base": "https://test.openai.azure.com/",
-            },
-        }
-
-        build_embedder(embedder_config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.microsoft.azure.AzureProvider"
-        )
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["api_key"] == "test-key"
-        assert call_kwargs["api_base"] == "https://test.openai.azure.com/"
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_azure_import_error(self, mock_import):
-        """Test handling of import errors for Azure provider."""
-        mock_import.side_effect = ImportError("Failed to import Azure provider")
-
-        embedder_config = {
-            "provider": "azure",
-            "config": {"api_key": "test-key"},
-        }
-
-        with pytest.raises(ImportError) as exc_info:
-            build_embedder(embedder_config)
-
-        assert "Failed to import provider azure" in str(exc_info.value)

tests/rag/embeddings/test_factory_enhanced.py

@@ -0,0 +1,315 @@
+"""Enhanced tests for embedding function factory."""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai.rag.embeddings.factory import (  # type: ignore[import-untyped]
+    get_embedding_function,
+)
+from crewai.rag.embeddings.types import EmbeddingOptions  # type: ignore[import-untyped]
+
+
+def test_get_embedding_function_default() -> None:
+    """Test default embedding function when no config provided."""
+    with patch("crewai.rag.embeddings.factory.OpenAIEmbeddingFunction") as mock_openai:
+        mock_instance = MagicMock()
+        mock_openai.return_value = mock_instance
+
+        with patch(
+            "crewai.rag.embeddings.factory.os.getenv", return_value="test-api-key"
+        ):
+            result = get_embedding_function()
+
+        mock_openai.assert_called_once_with(
+            api_key="test-api-key", model_name="text-embedding-3-small"
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_with_embedding_options() -> None:
+    """Test embedding function creation with EmbeddingOptions object."""
+    with patch("crewai.rag.embeddings.factory.OpenAIEmbeddingFunction") as mock_openai:
+        mock_instance = MagicMock()
+        mock_openai.return_value = mock_instance
+
+        options = EmbeddingOptions(
+            provider="openai", api_key="test-key", model="text-embedding-3-large"
+        )
+
+        result = get_embedding_function(options)
+
+        call_kwargs = mock_openai.call_args.kwargs
+        assert "api_key" in call_kwargs
+        assert call_kwargs["api_key"].get_secret_value() == "test-key"
+        # OpenAI uses model_name parameter, not model
+        assert result == mock_instance
+
+
+def test_get_embedding_function_sentence_transformer() -> None:
+    """Test sentence transformer embedding function."""
+    with patch(
+        "crewai.rag.embeddings.factory.SentenceTransformerEmbeddingFunction"
+    ) as mock_st:
+        mock_instance = MagicMock()
+        mock_st.return_value = mock_instance
+
+        config = {"provider": "sentence-transformer", "model_name": "all-MiniLM-L6-v2"}
+
+        result = get_embedding_function(config)
+
+        mock_st.assert_called_once_with(model_name="all-MiniLM-L6-v2")
+        assert result == mock_instance
+
+
+def test_get_embedding_function_ollama() -> None:
+    """Test Ollama embedding function."""
+    with patch("crewai.rag.embeddings.factory.OllamaEmbeddingFunction") as mock_ollama:
+        mock_instance = MagicMock()
+        mock_ollama.return_value = mock_instance
+
+        config = {
+            "provider": "ollama",
+            "model_name": "nomic-embed-text",
+            "url": "http://localhost:11434",
+        }
+
+        result = get_embedding_function(config)
+
+        mock_ollama.assert_called_once_with(
+            model_name="nomic-embed-text", url="http://localhost:11434"
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_cohere() -> None:
+    """Test Cohere embedding function."""
+    with patch("crewai.rag.embeddings.factory.CohereEmbeddingFunction") as mock_cohere:
+        mock_instance = MagicMock()
+        mock_cohere.return_value = mock_instance
+
+        config = {
+            "provider": "cohere",
+            "api_key": "cohere-key",
+            "model_name": "embed-english-v3.0",
+        }
+
+        result = get_embedding_function(config)
+
+        mock_cohere.assert_called_once_with(
+            api_key="cohere-key", model_name="embed-english-v3.0"
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_huggingface() -> None:
+    """Test HuggingFace embedding function."""
+    with patch("crewai.rag.embeddings.factory.HuggingFaceEmbeddingFunction") as mock_hf:
+        mock_instance = MagicMock()
+        mock_hf.return_value = mock_instance
+
+        config = {
+            "provider": "huggingface",
+            "api_key": "hf-token",
+            "model_name": "sentence-transformers/all-MiniLM-L6-v2",
+        }
+
+        result = get_embedding_function(config)
+
+        mock_hf.assert_called_once_with(
+            api_key="hf-token", model_name="sentence-transformers/all-MiniLM-L6-v2"
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_onnx() -> None:
+    """Test ONNX embedding function."""
+    with patch("crewai.rag.embeddings.factory.ONNXMiniLM_L6_V2") as mock_onnx:
+        mock_instance = MagicMock()
+        mock_onnx.return_value = mock_instance
+
+        config = {"provider": "onnx"}
+
+        result = get_embedding_function(config)
+
+        mock_onnx.assert_called_once()
+        assert result == mock_instance
+
+
+def test_get_embedding_function_google_palm() -> None:
+    """Test Google PaLM embedding function."""
+    with patch(
+        "crewai.rag.embeddings.factory.GooglePalmEmbeddingFunction"
+    ) as mock_palm:
+        mock_instance = MagicMock()
+        mock_palm.return_value = mock_instance
+
+        config = {"provider": "google-palm", "api_key": "palm-key"}
+
+        result = get_embedding_function(config)
+
+        mock_palm.assert_called_once_with(api_key="palm-key")
+        assert result == mock_instance
+
+
+def test_get_embedding_function_amazon_bedrock() -> None:
+    """Test Amazon Bedrock embedding function."""
+    with patch(
+        "crewai.rag.embeddings.factory.AmazonBedrockEmbeddingFunction"
+    ) as mock_bedrock:
+        mock_instance = MagicMock()
+        mock_bedrock.return_value = mock_instance
+
+        config = {
+            "provider": "amazon-bedrock",
+            "region_name": "us-west-2",
+            "model_name": "amazon.titan-embed-text-v1",
+        }
+
+        result = get_embedding_function(config)
+
+        mock_bedrock.assert_called_once_with(
+            region_name="us-west-2", model_name="amazon.titan-embed-text-v1"
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_jina() -> None:
+    """Test Jina embedding function."""
+    with patch("crewai.rag.embeddings.factory.JinaEmbeddingFunction") as mock_jina:
+        mock_instance = MagicMock()
+        mock_jina.return_value = mock_instance
+
+        config = {
+            "provider": "jina",
+            "api_key": "jina-key",
+            "model_name": "jina-embeddings-v2-base-en",
+        }
+
+        result = get_embedding_function(config)
+
+        mock_jina.assert_called_once_with(
+            api_key="jina-key", model_name="jina-embeddings-v2-base-en"
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_unsupported_provider() -> None:
+    """Test handling of unsupported provider."""
+    config = {"provider": "unsupported-provider"}
+
+    with pytest.raises(ValueError, match="Unsupported provider: unsupported-provider"):
+        get_embedding_function(config)
+
+
+def test_get_embedding_function_config_modification() -> None:
+    """Test that original config dict is not modified."""
+    original_config = {
+        "provider": "openai",
+        "api_key": "test-key",
+        "model": "text-embedding-3-small",
+    }
+    config_copy = original_config.copy()
+
+    with patch("crewai.rag.embeddings.factory.OpenAIEmbeddingFunction"):
+        get_embedding_function(config_copy)
+
+    assert config_copy == original_config
+
+
+def test_get_embedding_function_exclude_none_values() -> None:
+    """Test that None values are excluded from embedding function calls."""
+    with patch("crewai.rag.embeddings.factory.OpenAIEmbeddingFunction") as mock_openai:
+        mock_instance = MagicMock()
+        mock_openai.return_value = mock_instance
+
+        options = EmbeddingOptions(provider="openai", api_key="test-key", model=None)
+
+        result = get_embedding_function(options)
+
+        call_kwargs = mock_openai.call_args.kwargs
+        assert "api_key" in call_kwargs
+        assert call_kwargs["api_key"].get_secret_value() == "test-key"
+        assert "model" not in call_kwargs
+        assert result == mock_instance
+
+
+def test_get_embedding_function_instructor() -> None:
+    """Test Instructor embedding function."""
+    with patch(
+        "crewai.rag.embeddings.factory.InstructorEmbeddingFunction"
+    ) as mock_instructor:
+        mock_instance = MagicMock()
+        mock_instructor.return_value = mock_instance
+
+        config = {"provider": "instructor", "model_name": "hkunlp/instructor-large"}
+
+        result = get_embedding_function(config)
+
+        mock_instructor.assert_called_once_with(model_name="hkunlp/instructor-large")
+        assert result == mock_instance
+
+
+def test_get_embedding_function_watson() -> None:
+    """Test Watson embedding function."""
+    with patch("crewai.rag.embeddings.factory._create_watson_embedding_function") as mock_watson:
+        mock_instance = MagicMock()
+        mock_watson.return_value = mock_instance
+
+        config = {
+            "provider": "watson",
+            "api_key": "watson-api-key",
+            "api_url": "https://watson-url.com",
+            "project_id": "watson-project-id",
+            "model_name": "ibm/slate-125m-english-rtrvr",
+        }
+
+        result = get_embedding_function(config)
+
+        mock_watson.assert_called_once_with(
+            api_key="watson-api-key",
+            api_url="https://watson-url.com",
+            project_id="watson-project-id",
+            model_name="ibm/slate-125m-english-rtrvr",
+        )
+        assert result == mock_instance
+
+
+def test_get_embedding_function_watson_missing_dependencies() -> None:
+    """Test Watson embedding function with missing dependencies."""
+    with patch("crewai.rag.embeddings.factory._create_watson_embedding_function") as mock_watson:
+        mock_watson.side_effect = ImportError(
+            "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+        )
+
+        config = {
+            "provider": "watson",
+            "api_key": "watson-api-key",
+            "api_url": "https://watson-url.com",
+            "project_id": "watson-project-id",
+            "model_name": "ibm/slate-125m-english-rtrvr",
+        }
+
+        with pytest.raises(ImportError, match="IBM Watson dependencies are not installed"):
+            get_embedding_function(config)
+
+
+def test_get_embedding_function_watson_with_embedding_options() -> None:
+    """Test Watson embedding function with EmbeddingOptions object."""
+    with patch("crewai.rag.embeddings.factory._create_watson_embedding_function") as mock_watson:
+        mock_instance = MagicMock()
+        mock_watson.return_value = mock_instance
+
+        options = EmbeddingOptions(
+            provider="watson",
+            api_key="watson-key",
+            model_name="ibm/slate-125m-english-rtrvr"
+        )
+
+        result = get_embedding_function(options)
+
+        call_kwargs = mock_watson.call_args.kwargs
+        assert "api_key" in call_kwargs
+        assert call_kwargs["api_key"].get_secret_value() == "watson-key"
+        assert call_kwargs["model_name"] == "ibm/slate-125m-english-rtrvr"
+        assert result == mock_instance

tests/rag/embeddings/test_watson_embedding_name.py

@@ -1,77 +0,0 @@
-"""Tests for Watson embedding function name method."""
-
-
-import pytest
-
-from crewai.rag.embeddings.providers.ibm.embedding_callable import (
-    WatsonEmbeddingFunction,
-)
-
-
-class TestWatsonEmbeddingName:
-    """Test Watson embedding function name method."""
-
-    def test_watson_embedding_function_has_name_method(self):
-        """Test that WatsonEmbeddingFunction has a name method."""
-        assert hasattr(WatsonEmbeddingFunction, 'name')
-        assert callable(WatsonEmbeddingFunction.name)
-
-    def test_watson_embedding_function_name_returns_watson(self):
-        """Test that the name method returns 'watson'."""
-        assert WatsonEmbeddingFunction.name() == "watson"
-
-    def test_watson_embedding_function_name_is_static(self):
-        """Test that the name method can be called without instantiation."""
-        name = WatsonEmbeddingFunction.name()
-        assert name == "watson"
-        assert isinstance(name, str)
-
-    def test_watson_embedding_function_name_with_chromadb_validation(self):
-        """Test that the name method works in ChromaDB validation scenario."""
-        config = {
-            "model_id": "test-model",
-            "api_key": "test-key",
-            "url": "https://test.com"
-        }
-
-        watson_func = WatsonEmbeddingFunction(**config)
-
-        try:
-            name = watson_func.name()
-            assert name == "watson"
-        except AttributeError as e:
-            pytest.fail(f"ChromaDB validation failed with AttributeError: {e}")
-
-    def test_watson_embedding_function_name_method_signature(self):
-        """Test that the name method has the correct signature."""
-        import inspect
-
-        name_method = WatsonEmbeddingFunction.name
-
-        assert isinstance(inspect.getattr_static(WatsonEmbeddingFunction, 'name'), staticmethod)
-
-        sig = inspect.signature(name_method)
-        if sig.return_annotation != inspect.Signature.empty:
-            assert sig.return_annotation is str
-
-    def test_watson_embedding_function_reproduces_original_issue(self):
-        """Test that reproduces the original issue scenario from #3597."""
-
-
-        config = {
-            "model_id": "ibm/slate-125m-english-rtrvr",
-            "api_key": "test-key",
-            "url": "https://us-south.ml.cloud.ibm.com",
-            "project_id": "test-project"
-        }
-
-        watson_func = WatsonEmbeddingFunction(**config)
-
-        name = watson_func.name()
-
-        assert name == "watson"
-        assert isinstance(name, str)
-
-        class_name = WatsonEmbeddingFunction.name()
-        assert class_name == "watson"
-        assert class_name == name