Merge branch 'main' into Branch_1984

src/crewai/crew.py

@@ -293,7 +293,7 @@ class Crew(BaseModel):
                 ):
                     self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
-                        embedder_config=self.embedder,
+                        embedder=self.embedder,
                         collection_name="crew",
                     )
 

src/crewai/llm.py

@@ -5,15 +5,17 @@ import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, List, Optional, Union, cast
+from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
 
 from dotenv import load_dotenv
+from pydantic import BaseModel
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
     from litellm import Choices, get_supported_openai_params
     from litellm.types.utils import ModelResponse
+    from litellm.utils import supports_response_schema
 
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -128,7 +130,7 @@ class LLM:
         presence_penalty: Optional[float] = None,
         frequency_penalty: Optional[float] = None,
         logit_bias: Optional[Dict[int, float]] = None,
-        response_format: Optional[Dict[str, Any]] = None,
+        response_format: Optional[Type[BaseModel]] = None,
         seed: Optional[int] = None,
         logprobs: Optional[int] = None,
         top_logprobs: Optional[int] = None,
@@ -137,6 +139,7 @@ class LLM:
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
         **kwargs,
     ):
         self.model = model
@@ -159,6 +162,7 @@ class LLM:
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
+        self.reasoning_effort = reasoning_effort
         self.additional_params = kwargs
 
         litellm.drop_params = True
@@ -211,6 +215,9 @@ class LLM:
         response = llm.call(messages)
         print(response)
         """
+        # Validate parameters before proceeding with the call.
+        self._validate_call_params()
+
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
@@ -242,6 +249,7 @@ class LLM:
                     "api_key": self.api_key,
                     "stream": False,
                     "tools": tools,
+                    "reasoning_effort": self.reasoning_effort,
                     **self.additional_params,
                 }
 
@@ -309,6 +317,36 @@ class LLM:
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
+    def _get_custom_llm_provider(self) -> str:
+        """
+        Derives the custom_llm_provider from the model string.
+        - For example, if the model is "openrouter/deepseek/deepseek-chat", returns "openrouter".
+        - If the model is "gemini/gemini-1.5-pro", returns "gemini".
+        - If there is no '/', defaults to "openai".
+        """
+        if "/" in self.model:
+            return self.model.split("/")[0]
+        return "openai"
+
+    def _validate_call_params(self) -> None:
+        """
+        Validate parameters before making a call. Currently this only checks if
+        a response_format is provided and whether the model supports it.
+        The custom_llm_provider is dynamically determined from the model:
+          - E.g., "openrouter/deepseek/deepseek-chat" yields "openrouter"
+          - "gemini/gemini-1.5-pro" yields "gemini"
+          - If no slash is present, "openai" is assumed.
+        """
+        provider = self._get_custom_llm_provider()
+        if self.response_format is not None and not supports_response_schema(
+            model=self.model,
+            custom_llm_provider=provider,
+        ):
+            raise ValueError(
+                f"The model {self.model} does not support response_format for provider '{provider}'. "
+                "Please remove response_format or use a supported model."
+            )
+
     def supports_function_calling(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)

src/crewai/utilities/embedding_configurator.py

@@ -141,9 +141,11 @@ class EmbeddingConfigurator:
             AmazonBedrockEmbeddingFunction,
         )
 
-        return AmazonBedrockEmbeddingFunction(
-            session=config.get("session"),
-        )
+        # 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):