大型语言模型

MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...

# Be sure to set your API keys here too. See the Provider
# section below.

researcher:
    role: Research Specialist
    goal: Conduct comprehensive research and analysis
    backstory: A dedicated research professional with years of experience
    verbose: true
    llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
    # (see provider configuration examples below for more)

from crewai import LLM

# Basic configuration
llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...

# Advanced configuration with detailed parameters
llm = LLM(
    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
    temperature=0.7,        # Higher for more creative outputs
    timeout=120,            # Seconds to wait for response
    max_tokens=4000,        # Maximum length of response
    top_p=0.9,              # Nucleus sampling parameter
    frequency_penalty=0.1 , # Reduce repetition
    presence_penalty=0.1,   # Encourage topic diversity
    response_format={"type": "json"},  # For structured outputs
    seed=42                 # For reproducible results
)

from crewai import LLM

# Create an LLM with streaming enabled
llm = LLM(
    model="openai/gpt-4o",
    stream=True  # Enable streaming
)

from crewai.utilities.events import (
  LLMStreamChunkEvent
)
from crewai.utilities.events.base_event_listener import BaseEventListener

class MyCustomListener(BaseEventListener):
    def setup_listeners(self, crewai_event_bus):
        @crewai_event_bus.on(LLMStreamChunkEvent)
        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
          # Process each chunk as it arrives
          print(f"Received chunk: {event.chunk}")

my_listener = MyCustomListener()

# OpenAI
OPENAI_API_KEY=sk-...

# Anthropic
ANTHROPIC_API_KEY=sk-ant-...

# Correct
llm = LLM(model="openai/gpt-4")

# Incorrect
llm = LLM(model="gpt-4")

# Large context model
llm = LLM(model="openai/gpt-4o")  # 128K tokens