响应元数据
许多模型提供方会在其聊天生成响应中包含一些元数据。这些元数据可以通过 AIMessage.response_metadata: Dict 属性访问。取决于模型提供方和模型配置，它可能包含诸如 令牌计数、logprobs 等信息。
下面是几个不同提供方的响应元数据示例：
OpenAI
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4-turbo")
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'token_usage': {'completion_tokens': 164,
'prompt_tokens': 17,
'total_tokens': 181},
'model_name': 'gpt-4-turbo',
'system_fingerprint': 'fp_76f018034d',
'finish_reason': 'stop',
'logprobs': None}Anthropic
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'id': 'msg_01CzQyD7BX8nkhDNfT1QqvEp',
'model': 'claude-3-sonnet-20240229',
'stop_reason': 'end_turn',
'stop_sequence': None,
'usage': {'input_tokens': 17, 'output_tokens': 296}}Google VertexAI
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model="gemini-pro")
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'is_blocked': False,
'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH',
'probability_label': 'NEGLIGIBLE',
'blocked': False},
{'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',
'probability_label': 'NEGLIGIBLE',
'blocked': False},
{'category': 'HARM_CATEGORY_HARASSMENT',
'probability_label': 'NEGLIGIBLE',
'blocked': False},
{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',
'probability_label': 'NEGLIGIBLE',
'blocked': False}],
'citation_metadata': None,
'usage_metadata': {'prompt_token_count': 10,
'candidates_token_count': 30,
'total_token_count': 40}}Bedrock (Anthropic)
from langchain_aws import ChatBedrock
llm = ChatBedrock(model_id="anthropic.claude-v2")
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'model_id': 'anthropic.claude-v2',
'usage': {'prompt_tokens': 19, 'completion_tokens': 371, 'total_tokens': 390}}MistralAI
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI()
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'token_usage': {'prompt_tokens': 19,
'total_tokens': 141,
'completion_tokens': 122},
'model': 'mistral-small',
'finish_reason': 'stop'}Groq
from langchain_groq import ChatGroq
llm = ChatGroq()
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'token_usage': {'completion_time': 0.243,
'completion_tokens': 132,
'prompt_time': 0.022,
'prompt_tokens': 22,
'queue_time': None,
'total_time': 0.265,
'total_tokens': 154},
'model_name': 'mixtral-8x7b-32768',
'system_fingerprint': 'fp_7b44c65f25',
'finish_reason': 'stop',
'logprobs': None}TogetherAI
import os
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_API_KEY"],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'token_usage': {'completion_tokens': 208,
'prompt_tokens': 20,
'total_tokens': 228},
'model_name': 'mistralai/Mixtral-8x7B-Instruct-v0.1',
'system_fingerprint': None,
'finish_reason': 'eos',
'logprobs': None}FireworksAI
from langchain_fireworks import ChatFireworks
llm = ChatFireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct")
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
msg.response_metadata{'token_usage': {'prompt_tokens': 19,
'total_tokens': 219,
'completion_tokens': 200},
'model_name': 'accounts/fireworks/models/mixtral-8x7b-instruct',
'system_fingerprint': '',
'finish_reason': 'length',
'logprobs': None}