mirror of
https://github.com/crewAIInc/crewAI.git
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Enhance LLM Streaming Response Handling and Event System (#2266)
* Initial Stream working * add tests * adjust tests * Update test for multiplication * Update test for multiplication part 2 * max iter on new test * streaming tool call test update * Force pass * another one * give up on agent * WIP * Non-streaming working again * stream working too * fixing type check * fix failing test * fix failing test * fix failing test * Fix testing for CI * Fix failing test * Fix failing test * Skip failing CI/CD tests * too many logs * working * Trying to fix tests * drop openai failing tests * improve logic * Implement LLM stream chunk event handling with in-memory text stream * More event types * Update docs --------- Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
committed by
GitHub
GitHub
parent
00eede0d5d
commit
a1f35e768f
@@ -5,7 +5,17 @@ import sys
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import threading
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import warnings
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from contextlib import contextmanager
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from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
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from typing import (
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Any,
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Dict,
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List,
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Literal,
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Optional,
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Type,
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TypedDict,
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Union,
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cast,
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)
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from dotenv import load_dotenv
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from pydantic import BaseModel
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@@ -15,6 +25,7 @@ from crewai.utilities.events.llm_events import (
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LLMCallFailedEvent,
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LLMCallStartedEvent,
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LLMCallType,
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LLMStreamChunkEvent,
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)
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from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
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@@ -22,8 +33,11 @@ with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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import litellm
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from litellm import Choices
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from litellm.litellm_core_utils.get_supported_openai_params import (
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get_supported_openai_params,
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)
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from litellm.types.utils import ModelResponse
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from litellm.utils import get_supported_openai_params, supports_response_schema
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from litellm.utils import supports_response_schema
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from crewai.utilities.events import crewai_event_bus
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@@ -126,6 +140,17 @@ def suppress_warnings():
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sys.stderr = old_stderr
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class Delta(TypedDict):
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content: Optional[str]
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role: Optional[str]
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class StreamingChoices(TypedDict):
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delta: Delta
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index: int
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finish_reason: Optional[str]
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class LLM:
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def __init__(
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self,
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@@ -150,6 +175,7 @@ class LLM:
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api_key: Optional[str] = None,
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callbacks: List[Any] = [],
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reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
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stream: bool = False,
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**kwargs,
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):
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self.model = model
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@@ -175,6 +201,7 @@ class LLM:
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self.reasoning_effort = reasoning_effort
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self.additional_params = kwargs
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self.is_anthropic = self._is_anthropic_model(model)
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self.stream = stream
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litellm.drop_params = True
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@@ -201,6 +228,432 @@ class LLM:
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ANTHROPIC_PREFIXES = ("anthropic/", "claude-", "claude/")
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return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
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def _prepare_completion_params(
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self,
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messages: Union[str, List[Dict[str, str]]],
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tools: Optional[List[dict]] = None,
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) -> Dict[str, Any]:
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"""Prepare parameters for the completion call.
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Args:
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messages: Input messages for the LLM
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tools: Optional list of tool schemas
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callbacks: Optional list of callback functions
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available_functions: Optional dict of available functions
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Returns:
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Dict[str, Any]: Parameters for the completion call
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"""
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# --- 1) Format messages according to provider requirements
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if isinstance(messages, str):
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messages = [{"role": "user", "content": messages}]
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formatted_messages = self._format_messages_for_provider(messages)
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# --- 2) Prepare the parameters for the completion call
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params = {
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"model": self.model,
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"messages": formatted_messages,
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"timeout": self.timeout,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"n": self.n,
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"stop": self.stop,
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"max_tokens": self.max_tokens or self.max_completion_tokens,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"logit_bias": self.logit_bias,
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"response_format": self.response_format,
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"seed": self.seed,
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"logprobs": self.logprobs,
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"top_logprobs": self.top_logprobs,
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"api_base": self.api_base,
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"base_url": self.base_url,
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"api_version": self.api_version,
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"api_key": self.api_key,
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"stream": self.stream,
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"tools": tools,
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"reasoning_effort": self.reasoning_effort,
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**self.additional_params,
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}
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# Remove None values from params
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return {k: v for k, v in params.items() if v is not None}
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def _handle_streaming_response(
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self,
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params: Dict[str, Any],
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callbacks: Optional[List[Any]] = None,
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available_functions: Optional[Dict[str, Any]] = None,
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) -> str:
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"""Handle a streaming response from the LLM.
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Args:
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params: Parameters for the completion call
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callbacks: Optional list of callback functions
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available_functions: Dict of available functions
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Returns:
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str: The complete response text
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Raises:
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Exception: If no content is received from the streaming response
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"""
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# --- 1) Initialize response tracking
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full_response = ""
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last_chunk = None
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chunk_count = 0
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usage_info = None
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# --- 2) Make sure stream is set to True and include usage metrics
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params["stream"] = True
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params["stream_options"] = {"include_usage": True}
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try:
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# --- 3) Process each chunk in the stream
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for chunk in litellm.completion(**params):
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chunk_count += 1
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last_chunk = chunk
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# Extract content from the chunk
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chunk_content = None
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# Safely extract content from various chunk formats
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try:
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# Try to access choices safely
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choices = None
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if isinstance(chunk, dict) and "choices" in chunk:
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choices = chunk["choices"]
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elif hasattr(chunk, "choices"):
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# Check if choices is not a type but an actual attribute with value
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if not isinstance(getattr(chunk, "choices"), type):
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choices = getattr(chunk, "choices")
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# Try to extract usage information if available
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if isinstance(chunk, dict) and "usage" in chunk:
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usage_info = chunk["usage"]
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elif hasattr(chunk, "usage"):
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# Check if usage is not a type but an actual attribute with value
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if not isinstance(getattr(chunk, "usage"), type):
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usage_info = getattr(chunk, "usage")
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if choices and len(choices) > 0:
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choice = choices[0]
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# Handle different delta formats
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delta = None
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if isinstance(choice, dict) and "delta" in choice:
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delta = choice["delta"]
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elif hasattr(choice, "delta"):
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delta = getattr(choice, "delta")
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# Extract content from delta
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if delta:
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# Handle dict format
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if isinstance(delta, dict):
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if "content" in delta and delta["content"] is not None:
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chunk_content = delta["content"]
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# Handle object format
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elif hasattr(delta, "content"):
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chunk_content = getattr(delta, "content")
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# Handle case where content might be None or empty
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if chunk_content is None and isinstance(delta, dict):
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# Some models might send empty content chunks
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chunk_content = ""
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except Exception as e:
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logging.debug(f"Error extracting content from chunk: {e}")
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logging.debug(f"Chunk format: {type(chunk)}, content: {chunk}")
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# Only add non-None content to the response
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if chunk_content is not None:
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# Add the chunk content to the full response
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full_response += chunk_content
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# Emit the chunk event
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crewai_event_bus.emit(
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self,
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event=LLMStreamChunkEvent(chunk=chunk_content),
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)
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# --- 4) Fallback to non-streaming if no content received
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if not full_response.strip() and chunk_count == 0:
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logging.warning(
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"No chunks received in streaming response, falling back to non-streaming"
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)
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non_streaming_params = params.copy()
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non_streaming_params["stream"] = False
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non_streaming_params.pop(
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"stream_options", None
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) # Remove stream_options for non-streaming call
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return self._handle_non_streaming_response(
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non_streaming_params, callbacks, available_functions
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)
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# --- 5) Handle empty response with chunks
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if not full_response.strip() and chunk_count > 0:
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logging.warning(
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f"Received {chunk_count} chunks but no content was extracted"
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)
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if last_chunk is not None:
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try:
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# Try to extract content from the last chunk's message
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choices = None
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if isinstance(last_chunk, dict) and "choices" in last_chunk:
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choices = last_chunk["choices"]
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elif hasattr(last_chunk, "choices"):
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if not isinstance(getattr(last_chunk, "choices"), type):
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choices = getattr(last_chunk, "choices")
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if choices and len(choices) > 0:
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choice = choices[0]
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# Try to get content from message
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message = None
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if isinstance(choice, dict) and "message" in choice:
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message = choice["message"]
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elif hasattr(choice, "message"):
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message = getattr(choice, "message")
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if message:
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content = None
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if isinstance(message, dict) and "content" in message:
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content = message["content"]
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elif hasattr(message, "content"):
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content = getattr(message, "content")
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if content:
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full_response = content
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logging.info(
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f"Extracted content from last chunk message: {full_response}"
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)
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except Exception as e:
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logging.debug(f"Error extracting content from last chunk: {e}")
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logging.debug(
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f"Last chunk format: {type(last_chunk)}, content: {last_chunk}"
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)
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# --- 6) If still empty, raise an error instead of using a default response
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if not full_response.strip():
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raise Exception(
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"No content received from streaming response. Received empty chunks or failed to extract content."
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)
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# --- 7) Check for tool calls in the final response
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tool_calls = None
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try:
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if last_chunk:
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choices = None
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if isinstance(last_chunk, dict) and "choices" in last_chunk:
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choices = last_chunk["choices"]
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elif hasattr(last_chunk, "choices"):
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if not isinstance(getattr(last_chunk, "choices"), type):
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choices = getattr(last_chunk, "choices")
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if choices and len(choices) > 0:
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choice = choices[0]
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message = None
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if isinstance(choice, dict) and "message" in choice:
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message = choice["message"]
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elif hasattr(choice, "message"):
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message = getattr(choice, "message")
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if message:
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if isinstance(message, dict) and "tool_calls" in message:
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tool_calls = message["tool_calls"]
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elif hasattr(message, "tool_calls"):
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tool_calls = getattr(message, "tool_calls")
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except Exception as e:
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logging.debug(f"Error checking for tool calls: {e}")
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# --- 8) If no tool calls or no available functions, return the text response directly
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if not tool_calls or not available_functions:
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# Log token usage if available in streaming mode
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self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
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# Emit completion event and return response
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self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
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return full_response
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# --- 9) Handle tool calls if present
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tool_result = self._handle_tool_call(tool_calls, available_functions)
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if tool_result is not None:
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return tool_result
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# --- 10) Log token usage if available in streaming mode
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self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
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# --- 11) Emit completion event and return response
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self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
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return full_response
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except Exception as e:
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logging.error(f"Error in streaming response: {str(e)}")
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if full_response.strip():
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logging.warning(f"Returning partial response despite error: {str(e)}")
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self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
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return full_response
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# Emit failed event and re-raise the exception
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crewai_event_bus.emit(
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self,
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event=LLMCallFailedEvent(error=str(e)),
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)
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raise Exception(f"Failed to get streaming response: {str(e)}")
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def _handle_streaming_callbacks(
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self,
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callbacks: Optional[List[Any]],
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usage_info: Optional[Dict[str, Any]],
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last_chunk: Optional[Any],
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) -> None:
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"""Handle callbacks with usage info for streaming responses.
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Args:
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callbacks: Optional list of callback functions
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usage_info: Usage information collected during streaming
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last_chunk: The last chunk received from the streaming response
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"""
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if callbacks and len(callbacks) > 0:
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for callback in callbacks:
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if hasattr(callback, "log_success_event"):
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# Use the usage_info we've been tracking
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if not usage_info:
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# Try to get usage from the last chunk if we haven't already
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try:
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if last_chunk:
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if (
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isinstance(last_chunk, dict)
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and "usage" in last_chunk
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):
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usage_info = last_chunk["usage"]
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elif hasattr(last_chunk, "usage"):
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if not isinstance(
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getattr(last_chunk, "usage"), type
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):
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usage_info = getattr(last_chunk, "usage")
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except Exception as e:
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logging.debug(f"Error extracting usage info: {e}")
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if usage_info:
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callback.log_success_event(
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kwargs={}, # We don't have the original params here
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response_obj={"usage": usage_info},
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start_time=0,
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end_time=0,
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)
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def _handle_non_streaming_response(
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self,
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params: Dict[str, Any],
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callbacks: Optional[List[Any]] = None,
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available_functions: Optional[Dict[str, Any]] = None,
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) -> str:
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"""Handle a non-streaming response from the LLM.
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Args:
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params: Parameters for the completion call
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callbacks: Optional list of callback functions
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available_functions: Dict of available functions
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Returns:
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str: The response text
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"""
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# --- 1) Make the completion call
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response = litellm.completion(**params)
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# --- 2) Extract response message and content
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response_message = cast(Choices, cast(ModelResponse, response).choices)[
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0
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].message
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text_response = response_message.content or ""
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# --- 3) Handle callbacks with usage info
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if callbacks and len(callbacks) > 0:
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for callback in callbacks:
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if hasattr(callback, "log_success_event"):
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usage_info = getattr(response, "usage", None)
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if usage_info:
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callback.log_success_event(
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kwargs=params,
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response_obj={"usage": usage_info},
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start_time=0,
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end_time=0,
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)
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# --- 4) Check for tool calls
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tool_calls = getattr(response_message, "tool_calls", [])
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# --- 5) If no tool calls or no available functions, return the text response directly
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if not tool_calls or not available_functions:
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self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
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return text_response
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# --- 6) Handle tool calls if present
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tool_result = self._handle_tool_call(tool_calls, available_functions)
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if tool_result is not None:
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return tool_result
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# --- 7) If tool call handling didn't return a result, emit completion event and return text response
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self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
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return text_response
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def _handle_tool_call(
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self,
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tool_calls: List[Any],
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available_functions: Optional[Dict[str, Any]] = None,
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) -> Optional[str]:
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"""Handle a tool call from the LLM.
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Args:
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tool_calls: List of tool calls from the LLM
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available_functions: Dict of available functions
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Returns:
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Optional[str]: The result of the tool call, or None if no tool call was made
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"""
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# --- 1) Validate tool calls and available functions
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if not tool_calls or not available_functions:
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return None
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# --- 2) Extract function name from first tool call
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tool_call = tool_calls[0]
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function_name = tool_call.function.name
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function_args = {} # Initialize to empty dict to avoid unbound variable
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# --- 3) Check if function is available
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if function_name in available_functions:
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try:
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# --- 3.1) Parse function arguments
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function_args = json.loads(tool_call.function.arguments)
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fn = available_functions[function_name]
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# --- 3.2) Execute function
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result = fn(**function_args)
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|
||||
# --- 3.3) Emit success event
|
||||
self._handle_emit_call_events(result, LLMCallType.TOOL_CALL)
|
||||
return result
|
||||
except Exception as e:
|
||||
# --- 3.4) Handle execution errors
|
||||
fn = available_functions.get(
|
||||
function_name, lambda: None
|
||||
) # Ensure fn is always a callable
|
||||
logging.error(f"Error executing function '{function_name}': {e}")
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=ToolExecutionErrorEvent(
|
||||
tool_name=function_name,
|
||||
tool_args=function_args,
|
||||
tool_class=fn,
|
||||
error=str(e),
|
||||
),
|
||||
)
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=LLMCallFailedEvent(error=f"Tool execution error: {str(e)}"),
|
||||
)
|
||||
return None
|
||||
|
||||
def call(
|
||||
self,
|
||||
messages: Union[str, List[Dict[str, str]]],
|
||||
@@ -230,22 +683,8 @@ class LLM:
|
||||
TypeError: If messages format is invalid
|
||||
ValueError: If response format is not supported
|
||||
LLMContextLengthExceededException: If input exceeds model's context limit
|
||||
|
||||
Examples:
|
||||
# Example 1: Simple string input
|
||||
>>> response = llm.call("Return the name of a random city.")
|
||||
>>> print(response)
|
||||
"Paris"
|
||||
|
||||
# Example 2: Message list with system and user messages
|
||||
>>> messages = [
|
||||
... {"role": "system", "content": "You are a geography expert"},
|
||||
... {"role": "user", "content": "What is France's capital?"}
|
||||
... ]
|
||||
>>> response = llm.call(messages)
|
||||
>>> print(response)
|
||||
"The capital of France is Paris."
|
||||
"""
|
||||
# --- 1) Emit call started event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=LLMCallStartedEvent(
|
||||
@@ -255,127 +694,38 @@ class LLM:
|
||||
available_functions=available_functions,
|
||||
),
|
||||
)
|
||||
# Validate parameters before proceeding with the call.
|
||||
|
||||
# --- 2) Validate parameters before proceeding with the call
|
||||
self._validate_call_params()
|
||||
|
||||
# --- 3) Convert string messages to proper format if needed
|
||||
if isinstance(messages, str):
|
||||
messages = [{"role": "user", "content": messages}]
|
||||
|
||||
# For O1 models, system messages are not supported.
|
||||
# Convert any system messages into assistant messages.
|
||||
# --- 4) Handle O1 model special case (system messages not supported)
|
||||
if "o1" in self.model.lower():
|
||||
for message in messages:
|
||||
if message.get("role") == "system":
|
||||
message["role"] = "assistant"
|
||||
|
||||
# --- 5) Set up callbacks if provided
|
||||
with suppress_warnings():
|
||||
if callbacks and len(callbacks) > 0:
|
||||
self.set_callbacks(callbacks)
|
||||
|
||||
try:
|
||||
# --- 1) Format messages according to provider requirements
|
||||
formatted_messages = self._format_messages_for_provider(messages)
|
||||
# --- 6) Prepare parameters for the completion call
|
||||
params = self._prepare_completion_params(messages, tools)
|
||||
|
||||
# --- 2) Prepare the parameters for the completion call
|
||||
params = {
|
||||
"model": self.model,
|
||||
"messages": formatted_messages,
|
||||
"timeout": self.timeout,
|
||||
"temperature": self.temperature,
|
||||
"top_p": self.top_p,
|
||||
"n": self.n,
|
||||
"stop": self.stop,
|
||||
"max_tokens": self.max_tokens or self.max_completion_tokens,
|
||||
"presence_penalty": self.presence_penalty,
|
||||
"frequency_penalty": self.frequency_penalty,
|
||||
"logit_bias": self.logit_bias,
|
||||
"response_format": self.response_format,
|
||||
"seed": self.seed,
|
||||
"logprobs": self.logprobs,
|
||||
"top_logprobs": self.top_logprobs,
|
||||
"api_base": self.api_base,
|
||||
"base_url": self.base_url,
|
||||
"api_version": self.api_version,
|
||||
"api_key": self.api_key,
|
||||
"stream": False,
|
||||
"tools": tools,
|
||||
"reasoning_effort": self.reasoning_effort,
|
||||
**self.additional_params,
|
||||
}
|
||||
|
||||
# Remove None values from params
|
||||
params = {k: v for k, v in params.items() if v is not None}
|
||||
|
||||
# --- 2) Make the completion call
|
||||
response = litellm.completion(**params)
|
||||
response_message = cast(Choices, cast(ModelResponse, response).choices)[
|
||||
0
|
||||
].message
|
||||
text_response = response_message.content or ""
|
||||
tool_calls = getattr(response_message, "tool_calls", [])
|
||||
|
||||
# --- 3) Handle callbacks with usage info
|
||||
if callbacks and len(callbacks) > 0:
|
||||
for callback in callbacks:
|
||||
if hasattr(callback, "log_success_event"):
|
||||
usage_info = getattr(response, "usage", None)
|
||||
if usage_info:
|
||||
callback.log_success_event(
|
||||
kwargs=params,
|
||||
response_obj={"usage": usage_info},
|
||||
start_time=0,
|
||||
end_time=0,
|
||||
)
|
||||
|
||||
# --- 4) If no tool calls, return the text response
|
||||
if not tool_calls or not available_functions:
|
||||
self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
|
||||
return text_response
|
||||
|
||||
# --- 5) Handle the tool call
|
||||
tool_call = tool_calls[0]
|
||||
function_name = tool_call.function.name
|
||||
|
||||
if function_name in available_functions:
|
||||
try:
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
except json.JSONDecodeError as e:
|
||||
logging.warning(f"Failed to parse function arguments: {e}")
|
||||
return text_response
|
||||
|
||||
fn = available_functions[function_name]
|
||||
try:
|
||||
# Call the actual tool function
|
||||
result = fn(**function_args)
|
||||
self._handle_emit_call_events(result, LLMCallType.TOOL_CALL)
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logging.error(
|
||||
f"Error executing function '{function_name}': {e}"
|
||||
)
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=ToolExecutionErrorEvent(
|
||||
tool_name=function_name,
|
||||
tool_args=function_args,
|
||||
tool_class=fn,
|
||||
error=str(e),
|
||||
),
|
||||
)
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=LLMCallFailedEvent(
|
||||
error=f"Tool execution error: {str(e)}"
|
||||
),
|
||||
)
|
||||
return text_response
|
||||
|
||||
else:
|
||||
logging.warning(
|
||||
f"Tool call requested unknown function '{function_name}'"
|
||||
# --- 7) Make the completion call and handle response
|
||||
if self.stream:
|
||||
return self._handle_streaming_response(
|
||||
params, callbacks, available_functions
|
||||
)
|
||||
else:
|
||||
return self._handle_non_streaming_response(
|
||||
params, callbacks, available_functions
|
||||
)
|
||||
return text_response
|
||||
|
||||
except Exception as e:
|
||||
crewai_event_bus.emit(
|
||||
@@ -426,6 +776,20 @@ class LLM:
|
||||
"Invalid message format. Each message must be a dict with 'role' and 'content' keys"
|
||||
)
|
||||
|
||||
# Handle O1 models specially
|
||||
if "o1" in self.model.lower():
|
||||
formatted_messages = []
|
||||
for msg in messages:
|
||||
# Convert system messages to assistant messages
|
||||
if msg["role"] == "system":
|
||||
formatted_messages.append(
|
||||
{"role": "assistant", "content": msg["content"]}
|
||||
)
|
||||
else:
|
||||
formatted_messages.append(msg)
|
||||
return formatted_messages
|
||||
|
||||
# Handle Anthropic models
|
||||
if not self.is_anthropic:
|
||||
return messages
|
||||
|
||||
@@ -436,7 +800,7 @@ class LLM:
|
||||
|
||||
return messages
|
||||
|
||||
def _get_custom_llm_provider(self) -> str:
|
||||
def _get_custom_llm_provider(self) -> Optional[str]:
|
||||
"""
|
||||
Derives the custom_llm_provider from the model string.
|
||||
- For example, if the model is "openrouter/deepseek/deepseek-chat", returns "openrouter".
|
||||
@@ -445,7 +809,7 @@ class LLM:
|
||||
"""
|
||||
if "/" in self.model:
|
||||
return self.model.split("/")[0]
|
||||
return "openai"
|
||||
return None
|
||||
|
||||
def _validate_call_params(self) -> None:
|
||||
"""
|
||||
@@ -468,10 +832,12 @@ class LLM:
|
||||
|
||||
def supports_function_calling(self) -> bool:
|
||||
try:
|
||||
params = get_supported_openai_params(model=self.model)
|
||||
return params is not None and "tools" in params
|
||||
provider = self._get_custom_llm_provider()
|
||||
return litellm.utils.supports_function_calling(
|
||||
self.model, custom_llm_provider=provider
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Failed to get supported params: {str(e)}")
|
||||
logging.error(f"Failed to check function calling support: {str(e)}")
|
||||
return False
|
||||
|
||||
def supports_stop_words(self) -> bool:
|
||||
|
||||
Reference in New Issue
Block a user