mirror of
https://github.com/crewAIInc/crewAI.git
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Lorenze/bedrock llm (#3693)
* feat: add AWS Bedrock support and update dependencies - Introduced BedrockCompletion class for AWS Bedrock integration in LLM. - Added boto3 as a new dependency in both pyproject.toml and uv.lock. - Updated LLM class to support Bedrock provider. - Created new files for Bedrock provider implementation. * using converse api * converse * linted * refactor: update BedrockCompletion class to improve parameter handling - Changed max_tokens from a fixed integer to an optional integer. - Simplified model ID assignment by removing the inference profile mapping method. - Cleaned up comments and unnecessary code related to tool specifications and model-specific parameters.
This commit is contained in:
Lorenze Jay
@@ -85,6 +85,9 @@ voyageai = [
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litellm = [
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"litellm>=1.74.9",
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]
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boto3 = [
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"boto3>=1.40.45",
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]
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[project.scripts]
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@@ -367,6 +367,14 @@ class LLM(BaseLLM):
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except ImportError:
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return None
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elif provider == "bedrock":
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try:
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from crewai.llms.providers.bedrock.completion import BedrockCompletion
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return BedrockCompletion
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except ImportError:
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return None
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return None
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def __init__(
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553
lib/crewai/src/crewai/llms/providers/bedrock/completion.py
Normal file
553
lib/crewai/src/crewai/llms/providers/bedrock/completion.py
Normal file
@@ -0,0 +1,553 @@
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from collections.abc import Mapping, Sequence
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import logging
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import os
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from typing import Any
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from crewai.events.types.llm_events import LLMCallType
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from crewai.llms.base_llm import BaseLLM
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from crewai.utilities.agent_utils import is_context_length_exceeded
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from crewai.utilities.exceptions.context_window_exceeding_exception import (
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LLMContextLengthExceededError,
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)
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try:
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from boto3.session import Session
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from botocore.config import Config
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from botocore.exceptions import BotoCoreError, ClientError
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except ImportError:
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raise ImportError(
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"AWS Bedrock native provider not available, to install: `uv add boto3`"
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) from None
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class BedrockCompletion(BaseLLM):
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"""AWS Bedrock native completion implementation using the Converse API.
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This class provides direct integration with AWS Bedrock using the modern
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Converse API, which provides a unified interface across all Bedrock models.
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"""
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def __init__(
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self,
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model: str = "anthropic.claude-3-5-sonnet-20241022-v2:0",
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aws_access_key_id: str | None = None,
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aws_secret_access_key: str | None = None,
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aws_session_token: str | None = None,
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region_name: str = "us-east-1",
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temperature: float | None = None,
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max_tokens: int | None = None,
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top_p: float | None = None,
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top_k: int | None = None,
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stop_sequences: Sequence[str] | None = None,
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stream: bool = False,
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**kwargs,
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):
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"""Initialize AWS Bedrock completion client."""
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# Extract provider from kwargs to avoid duplicate argument
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kwargs.pop("provider", None)
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super().__init__(
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model=model,
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temperature=temperature,
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stop=stop_sequences or [],
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provider="bedrock",
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**kwargs,
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)
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# Initialize Bedrock client with proper configuration
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session = Session(
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aws_access_key_id=aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID"),
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aws_secret_access_key=aws_secret_access_key
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or os.getenv("AWS_SECRET_ACCESS_KEY"),
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aws_session_token=aws_session_token or os.getenv("AWS_SESSION_TOKEN"),
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region_name=region_name,
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)
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# Configure client with timeouts and retries following AWS best practices
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config = Config(
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connect_timeout=60,
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read_timeout=300,
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retries={
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"max_attempts": 3,
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"mode": "adaptive",
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},
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tcp_keepalive=True,
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)
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self.client = session.client("bedrock-runtime", config=config)
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self.region_name = region_name
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# Store completion parameters
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self.max_tokens = max_tokens
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self.top_p = top_p
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self.top_k = top_k
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self.stream = stream
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self.stop_sequences = stop_sequences or []
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# Model-specific settings
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self.is_claude_model = "claude" in model.lower()
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self.supports_tools = True # Converse API supports tools for most models
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self.supports_streaming = True
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# Handle inference profiles for newer models
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self.model_id = model
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def call(
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self,
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messages: str | list[dict[str, str]],
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tools: Sequence[Mapping[str, Any]] | None = None,
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callbacks: list[Any] | None = None,
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available_functions: dict[str, Any] | None = None,
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from_task: Any | None = None,
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from_agent: Any | None = None,
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) -> str | Any:
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"""Call AWS Bedrock Converse API."""
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try:
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# Emit call started event
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self._emit_call_started_event(
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messages=messages,
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tools=tools,
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callbacks=callbacks,
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available_functions=available_functions,
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from_task=from_task,
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from_agent=from_agent,
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)
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# Format messages for Converse API
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formatted_messages, system_message = self._format_messages_for_converse(
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messages
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)
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# Prepare tool configuration
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tool_config = None
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if tools:
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tool_config = {"tools": self._format_tools_for_converse(tools)}
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# Prepare request body
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body = {
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"inferenceConfig": self._get_inference_config(),
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}
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# Add system message if present
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if system_message:
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body["system"] = [{"text": system_message}]
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# Add tool config if present
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if tool_config:
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body["toolConfig"] = tool_config
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if self.stream:
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return self._handle_streaming_converse(
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formatted_messages, body, available_functions, from_task, from_agent
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)
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return self._handle_converse(
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formatted_messages, body, available_functions, from_task, from_agent
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)
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except Exception as e:
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if is_context_length_exceeded(e):
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logging.error(f"Context window exceeded: {e}")
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raise LLMContextLengthExceededError(str(e)) from e
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error_msg = f"AWS Bedrock API call failed: {e!s}"
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logging.error(error_msg)
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self._emit_call_failed_event(
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error=error_msg, from_task=from_task, from_agent=from_agent
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)
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raise
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def _handle_converse(
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self,
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messages: list[dict[str, Any]],
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body: dict[str, Any],
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available_functions: Mapping[str, Any] | None = None,
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from_task: Any | None = None,
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from_agent: Any | None = None,
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) -> str:
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"""Handle non-streaming converse API call following AWS best practices."""
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try:
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# Validate messages format before API call
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if not messages:
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raise ValueError("Messages cannot be empty")
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# Ensure we have valid message structure
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for i, msg in enumerate(messages):
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if (
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not isinstance(msg, dict)
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or "role" not in msg
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or "content" not in msg
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):
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raise ValueError(f"Invalid message format at index {i}")
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# Call Bedrock Converse API with proper error handling
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response = self.client.converse(
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modelId=self.model_id, messages=messages, **body
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)
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# Track token usage according to AWS response format
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if "usage" in response:
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self._track_token_usage_internal(response["usage"])
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# Extract content following AWS response structure
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output = response.get("output", {})
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message = output.get("message", {})
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content = message.get("content", [])
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if not content:
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logging.warning("No content in Bedrock response")
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return (
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"I apologize, but I received an empty response. Please try again."
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)
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# Extract text content from response
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text_content = ""
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for content_block in content:
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# Handle different content block types as per AWS documentation
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if "text" in content_block:
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text_content += content_block["text"]
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elif content_block.get("type") == "toolUse" and available_functions:
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# Handle tool use according to AWS format
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tool_use = content_block["toolUse"]
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function_name = tool_use.get("name")
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function_args = tool_use.get("input", {})
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result = self._handle_tool_execution(
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function_name=function_name,
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function_args=function_args,
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available_functions=available_functions,
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from_task=from_task,
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from_agent=from_agent,
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)
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if result is not None:
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return result
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# Apply stop sequences if configured
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text_content = self._apply_stop_words(text_content)
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# Validate final response
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if not text_content or text_content.strip() == "":
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logging.warning("Extracted empty text content from Bedrock response")
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text_content = "I apologize, but I couldn't generate a proper response. Please try again."
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self._emit_call_completed_event(
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response=text_content,
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call_type=LLMCallType.LLM_CALL,
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from_task=from_task,
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from_agent=from_agent,
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messages=messages,
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)
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return text_content
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except ClientError as e:
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# Handle all AWS ClientError exceptions as per documentation
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error_code = e.response.get("Error", {}).get("Code", "Unknown")
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error_msg = e.response.get("Error", {}).get("Message", str(e))
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# Log the specific error for debugging
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logging.error(f"AWS Bedrock ClientError ({error_code}): {error_msg}")
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# Handle specific error codes as documented
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if error_code == "ValidationException":
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# This is the error we're seeing with Cohere
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if "last turn" in error_msg and "user message" in error_msg:
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raise ValueError(
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f"Conversation format error: {error_msg}. Check message alternation."
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) from e
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raise ValueError(f"Request validation failed: {error_msg}") from e
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if error_code == "AccessDeniedException":
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raise PermissionError(
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f"Access denied to model {self.model_id}: {error_msg}"
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) from e
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if error_code == "ResourceNotFoundException":
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raise ValueError(f"Model {self.model_id} not found: {error_msg}") from e
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if error_code == "ThrottlingException":
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raise RuntimeError(
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f"API throttled, please retry later: {error_msg}"
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) from e
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if error_code == "ModelTimeoutException":
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raise TimeoutError(f"Model request timed out: {error_msg}") from e
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if error_code == "ServiceQuotaExceededException":
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raise RuntimeError(f"Service quota exceeded: {error_msg}") from e
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if error_code == "ModelNotReadyException":
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raise RuntimeError(
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f"Model {self.model_id} not ready: {error_msg}"
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) from e
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if error_code == "ModelErrorException":
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raise RuntimeError(f"Model error: {error_msg}") from e
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if error_code == "InternalServerException":
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raise RuntimeError(f"Internal server error: {error_msg}") from e
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if error_code == "ServiceUnavailableException":
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raise RuntimeError(f"Service unavailable: {error_msg}") from e
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raise RuntimeError(f"Bedrock API error ({error_code}): {error_msg}") from e
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except BotoCoreError as e:
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error_msg = f"Bedrock connection error: {e}"
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logging.error(error_msg)
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raise ConnectionError(error_msg) from e
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except Exception as e:
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# Catch any other unexpected errors
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error_msg = f"Unexpected error in Bedrock converse call: {e}"
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logging.error(error_msg)
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raise RuntimeError(error_msg) from e
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def _handle_streaming_converse(
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self,
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messages: list[dict[str, Any]],
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body: dict[str, Any],
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available_functions: dict[str, Any] | None = None,
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from_task: Any | None = None,
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from_agent: Any | None = None,
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) -> str:
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"""Handle streaming converse API call."""
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full_response = ""
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try:
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response = self.client.converse_stream(
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modelId=self.model_id, messages=messages, **body
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)
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stream = response.get("stream")
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if stream:
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for event in stream:
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if "contentBlockDelta" in event:
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delta = event["contentBlockDelta"]["delta"]
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if "text" in delta:
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text_chunk = delta["text"]
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logging.debug(f"Streaming text chunk: {text_chunk[:50]}...")
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full_response += text_chunk
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self._emit_stream_chunk_event(
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chunk=text_chunk,
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from_task=from_task,
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from_agent=from_agent,
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)
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elif "messageStop" in event:
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# Handle end of message
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break
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except ClientError as e:
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error_msg = self._handle_client_error(e)
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raise RuntimeError(error_msg) from e
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except BotoCoreError as e:
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error_msg = f"Bedrock streaming connection error: {e}"
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logging.error(error_msg)
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raise ConnectionError(error_msg) from e
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# Apply stop words to full response
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full_response = self._apply_stop_words(full_response)
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# Ensure we don't return empty content
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if not full_response or full_response.strip() == "":
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logging.warning("Bedrock streaming returned empty content, using fallback")
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full_response = (
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"I apologize, but I couldn't generate a response. Please try again."
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)
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# Emit completion event
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self._emit_call_completed_event(
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response=full_response,
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call_type=LLMCallType.LLM_CALL,
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from_task=from_task,
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from_agent=from_agent,
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messages=messages,
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)
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return full_response
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def _format_messages_for_converse(
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self, messages: str | list[dict[str, str]]
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) -> tuple[list[dict[str, Any]], str | None]:
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"""Format messages for Converse API following AWS documentation."""
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# Use base class formatting first
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formatted_messages = self._format_messages(messages)
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converse_messages = []
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system_message = None
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for message in formatted_messages:
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role = message.get("role")
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content = message.get("content", "")
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|
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if role == "system":
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# Extract system message - Converse API handles it separately
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if system_message:
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system_message += f"\n\n{content}"
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else:
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system_message = content
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else:
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# Convert to Converse API format with proper content structure
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converse_messages.append({"role": role, "content": [{"text": content}]})
|
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|
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# CRITICAL: Handle model-specific conversation requirements
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# Cohere and some other models require conversation to end with user message
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if converse_messages:
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last_message = converse_messages[-1]
|
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if last_message["role"] == "assistant":
|
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# For Cohere models, add a continuation user message
|
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if "cohere" in self.model.lower():
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converse_messages.append(
|
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{
|
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"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"text": "Please continue and provide your final answer."
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
# For other models that might have similar requirements
|
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elif any(
|
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model_family in self.model.lower()
|
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for model_family in ["command", "coral"]
|
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):
|
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converse_messages.append(
|
||||
{
|
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"role": "user",
|
||||
"content": [{"text": "Continue your response."}],
|
||||
}
|
||||
)
|
||||
|
||||
# Ensure first message is from user (required by Converse API)
|
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if not converse_messages:
|
||||
converse_messages.append(
|
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{
|
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"role": "user",
|
||||
"content": [{"text": "Hello, please help me with my request."}],
|
||||
}
|
||||
)
|
||||
elif converse_messages[0]["role"] != "user":
|
||||
converse_messages.insert(
|
||||
0,
|
||||
{
|
||||
"role": "user",
|
||||
"content": [{"text": "Hello, please help me with my request."}],
|
||||
},
|
||||
)
|
||||
|
||||
return converse_messages, system_message
|
||||
|
||||
def _format_tools_for_converse(self, tools: list[dict]) -> list[dict]:
|
||||
"""Convert CrewAI tools to Converse API format following AWS specification."""
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
|
||||
converse_tools = []
|
||||
|
||||
for tool in tools:
|
||||
try:
|
||||
name, description, parameters = safe_tool_conversion(tool, "Bedrock")
|
||||
|
||||
converse_tool = {
|
||||
"toolSpec": {
|
||||
"name": name,
|
||||
"description": description,
|
||||
}
|
||||
}
|
||||
|
||||
if parameters and isinstance(parameters, dict):
|
||||
converse_tool["toolSpec"]["inputSchema"] = {"json": parameters}
|
||||
|
||||
converse_tools.append(converse_tool)
|
||||
|
||||
except Exception as e: # noqa: PERF203
|
||||
logging.warning(
|
||||
f"Failed to convert tool {tool.get('name', 'unknown')}: {e}"
|
||||
)
|
||||
continue
|
||||
|
||||
return converse_tools
|
||||
|
||||
def _get_inference_config(self) -> dict[str, Any]:
|
||||
"""Get inference configuration following AWS Converse API specification."""
|
||||
config = {}
|
||||
|
||||
if self.max_tokens:
|
||||
config["maxTokens"] = self.max_tokens
|
||||
|
||||
if self.temperature is not None:
|
||||
config["temperature"] = float(self.temperature)
|
||||
if self.top_p is not None:
|
||||
config["topP"] = float(self.top_p)
|
||||
if self.stop_sequences:
|
||||
config["stopSequences"] = self.stop_sequences
|
||||
|
||||
if self.is_claude_model and self.top_k is not None:
|
||||
# top_k is supported by Claude models
|
||||
config["topK"] = int(self.top_k)
|
||||
|
||||
return config
|
||||
|
||||
def _handle_client_error(self, e: ClientError) -> str:
|
||||
"""Handle AWS ClientError with specific error codes and return error message."""
|
||||
error_code = e.response.get("Error", {}).get("Code", "Unknown")
|
||||
error_msg = e.response.get("Error", {}).get("Message", str(e))
|
||||
|
||||
error_mapping = {
|
||||
"AccessDeniedException": f"Access denied to model {self.model_id}: {error_msg}",
|
||||
"ResourceNotFoundException": f"Model {self.model_id} not found: {error_msg}",
|
||||
"ThrottlingException": f"API throttled, please retry later: {error_msg}",
|
||||
"ValidationException": f"Invalid request: {error_msg}",
|
||||
"ModelTimeoutException": f"Model request timed out: {error_msg}",
|
||||
"ServiceQuotaExceededException": f"Service quota exceeded: {error_msg}",
|
||||
"ModelNotReadyException": f"Model {self.model_id} not ready: {error_msg}",
|
||||
"ModelErrorException": f"Model error: {error_msg}",
|
||||
}
|
||||
|
||||
full_error_msg = error_mapping.get(
|
||||
error_code, f"Bedrock API error: {error_msg}"
|
||||
)
|
||||
logging.error(f"Bedrock client error ({error_code}): {full_error_msg}")
|
||||
|
||||
return full_error_msg
|
||||
|
||||
def _track_token_usage_internal(self, usage: dict[str, Any]) -> None:
|
||||
"""Track token usage from Bedrock response."""
|
||||
input_tokens = usage.get("inputTokens", 0)
|
||||
output_tokens = usage.get("outputTokens", 0)
|
||||
total_tokens = usage.get("totalTokens", input_tokens + output_tokens)
|
||||
|
||||
self._token_usage["prompt_tokens"] += input_tokens
|
||||
self._token_usage["completion_tokens"] += output_tokens
|
||||
self._token_usage["total_tokens"] += total_tokens
|
||||
self._token_usage["successful_requests"] += 1
|
||||
|
||||
def supports_function_calling(self) -> bool:
|
||||
"""Check if the model supports function calling."""
|
||||
return self.supports_tools
|
||||
|
||||
def supports_stop_words(self) -> bool:
|
||||
"""Check if the model supports stop words."""
|
||||
return True
|
||||
|
||||
def get_context_window_size(self) -> int:
|
||||
"""Get the context window size for the model."""
|
||||
from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
|
||||
|
||||
# Context window sizes for common Bedrock models
|
||||
context_windows = {
|
||||
"anthropic.claude-3-5-sonnet": 200000,
|
||||
"anthropic.claude-3-5-haiku": 200000,
|
||||
"anthropic.claude-3-opus": 200000,
|
||||
"anthropic.claude-3-sonnet": 200000,
|
||||
"anthropic.claude-3-haiku": 200000,
|
||||
"anthropic.claude-3-7-sonnet": 200000,
|
||||
"anthropic.claude-v2": 100000,
|
||||
"amazon.titan-text-express": 8000,
|
||||
"ai21.j2-ultra": 8192,
|
||||
"cohere.command-text": 4096,
|
||||
"meta.llama2-13b-chat": 4096,
|
||||
"meta.llama2-70b-chat": 4096,
|
||||
"meta.llama3-70b-instruct": 128000,
|
||||
"deepseek.r1": 32768,
|
||||
}
|
||||
|
||||
# Find the best match for the model name
|
||||
for model_prefix, size in context_windows.items():
|
||||
if self.model.startswith(model_prefix):
|
||||
return int(size * CONTEXT_WINDOW_USAGE_RATIO)
|
||||
|
||||
# Default context window size
|
||||
return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
|
||||
6
uv.lock
generated
6
uv.lock
generated
@@ -1020,6 +1020,9 @@ aisuite = [
|
||||
aws = [
|
||||
{ name = "boto3" },
|
||||
]
|
||||
boto3 = [
|
||||
{ name = "boto3" },
|
||||
]
|
||||
docling = [
|
||||
{ name = "docling" },
|
||||
]
|
||||
@@ -1060,6 +1063,7 @@ requires-dist = [
|
||||
{ name = "appdirs", specifier = ">=1.4.4" },
|
||||
{ name = "blinker", specifier = ">=1.9.0" },
|
||||
{ name = "boto3", marker = "extra == 'aws'", specifier = ">=1.40.38" },
|
||||
{ name = "boto3", marker = "extra == 'boto3'", specifier = ">=1.40.45" },
|
||||
{ name = "chromadb", specifier = "~=1.1.0" },
|
||||
{ name = "click", specifier = ">=8.1.7" },
|
||||
{ name = "crewai-tools", marker = "extra == 'tools'", editable = "lib/crewai-tools" },
|
||||
@@ -1095,7 +1099,7 @@ requires-dist = [
|
||||
{ name = "uv", specifier = ">=0.4.25" },
|
||||
{ name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.3.5" },
|
||||
]
|
||||
provides-extras = ["aisuite", "aws", "docling", "embeddings", "litellm", "mem0", "openpyxl", "pandas", "pdfplumber", "qdrant", "tools", "voyageai", "watson"]
|
||||
provides-extras = ["aisuite", "aws", "boto3", "docling", "embeddings", "litellm", "mem0", "openpyxl", "pandas", "pdfplumber", "qdrant", "tools", "voyageai", "watson"]
|
||||
|
||||
[[package]]
|
||||
name = "crewai-devtools"
|
||||
|
||||
Reference in New Issue
Block a user