Merge branch 'main' into brandon/gemini-google-docs

docs/concepts/memory.mdx

@@ -447,38 +447,6 @@ my_crew = Crew(
 )
 ```
 
-### Using Amazon Bedrock embeddings
-
-```python Code
-# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
-
-import os
-import boto3
-from crewai import Crew, Agent, Task, Process
-
-boto3_session = boto3.Session(
-    region_name=os.environ.get("AWS_REGION_NAME"),
-    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
-    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
-)
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    embedder={
-    "provider": "bedrock",
-        "config":{
-            "session": boto3_session,
-            "model": "amazon.titan-embed-text-v2:0",
-            "vector_dimension": 1024
-        }
-    }
-    verbose=True
-)
-```
-
 ### Adding Custom Embedding Function
 
 ```python Code

src/crewai/llm.py

@@ -117,39 +117,6 @@ def suppress_warnings():
 
 
 class LLM:
-    """LLM class for handling model interactions.
-
-    Args:
-        model: The model identifier; should not start with 'models/'.
-              Examples: 'gemini/gemini-1.5-pro', 'anthropic/claude-3'
-        timeout: Optional timeout for model calls
-        temperature: Optional temperature parameter
-        max_tokens: Optional maximum tokens for completion
-        max_completion_tokens: Optional maximum completion tokens
-        logprobs: Optional log probabilities
-        top_p: Optional nucleus sampling parameter
-        n: Optional number of completions
-        stop: Optional stop sequences
-        presence_penalty: Optional presence penalty
-        frequency_penalty: Optional frequency penalty
-        logit_bias: Optional token biasing
-        user: Optional user identifier
-        response_format: Optional response format configuration
-        seed: Optional random seed
-        tools: Optional list of tools
-        tool_choice: Optional tool choice configuration
-        api_base: Optional API base URL
-        api_key: Optional API key
-        api_version: Optional API version
-        base_url: Optional base URL
-        top_logprobs: Optional top log probabilities
-        callbacks: Optional list of callbacks
-        reasoning_effort: Optional reasoning effort level
-
-    Raises:
-        ValueError: If the model name starts with 'models/' or is empty
-        TypeError: If model is not a string
-    """
     def __init__(
         self,
         model: str,
@@ -175,20 +142,6 @@ class LLM:
         reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
         **kwargs,
     ):
-        # Constants for model name validation
-        INVALID_MODEL_PREFIX = "models/"
-        
-        # Validate model name
-        if not isinstance(model, str):
-            raise TypeError("Model name must be a string")
-        if not model:
-            raise ValueError("Model name cannot be empty")
-        if model.startswith(INVALID_MODEL_PREFIX):
-            raise ValueError(
-                f'Invalid model name "{model}": Model names should not start with "{INVALID_MODEL_PREFIX}". '
-                'Use the provider prefix instead (e.g., "gemini/model-name").'
-            )
-        
         self.model = model
         self.timeout = timeout
         self.temperature = temperature

tests/llm_test.py

@@ -252,29 +252,6 @@ def test_validate_call_params_no_response_format():
     llm._validate_call_params()
 
 
-class TestModelNameValidation:
-    """Tests for model name validation in LLM class."""
-    
-    def test_models_prefix_rejection(self):
-        """Test that model names with 'models/' prefix are rejected."""
-        with pytest.raises(ValueError, match="should not start with \"models/\""):
-            LLM(model="models/gemini/gemini-1.5-pro")
-
-    def test_valid_model_names(self):
-        """Test that valid model names are accepted."""
-        LLM(model="gemini/gemini-1.5-pro")
-        LLM(model="anthropic/claude-3-opus-20240229-v1:0")
-        LLM(model="openai/gpt-4")
-        LLM(model="openai/gpt-4 turbo")  # Space in model name should work
-        
-    def test_edge_cases(self):
-        """Test edge cases for model name validation."""
-        with pytest.raises(ValueError, match="cannot be empty"):
-            LLM(model="")  # Empty string
-        with pytest.raises(TypeError, match="must be a string"):
-            LLM(model=None)  # None value
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_o3_mini_reasoning_effort_high():
     llm = LLM(
@@ -347,16 +324,13 @@ def test_anthropic_model_detection():
         ("claude-instant", True),
         ("claude/v1", True),
         ("gpt-4", False),
+        ("", False),
         ("anthropomorphic", False),  # Should not match partial words
     ]
     
     for model, expected in models:
         llm = LLM(model=model)
-        assert llm._is_anthropic_model(model) == expected, f"Failed for model: {model}"
-    
-    # Test empty model name separately since it raises ValueError
-    with pytest.raises(ValueError, match="cannot be empty"):
-        LLM(model="")
+        assert llm.is_anthropic == expected, f"Failed for model: {model}"
 
 def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
     """Test Anthropic message formatting with fixtures."""