How to get response_metadata from a result with StructuredOutputParser? #25604
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Example Codellm = ChatOpenAI(
model="gpt-4o",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=api_info['api_key'],
organization=api_info['organization']
# base_url="...",
# other params...
)
class InMemoryHistory(BaseChatMessageHistory):
def __init__(self):
self.messages = []
def add_messages(self, messages: List[BaseMessage]) -> None:
self.messages.extend(messages)
def clear(self) -> None:
self.messages = []
def __getitem__(self, index):
return self.messages[index]
def __setitem__(self, index, value):
self.messages[index] = value
def __delitem__(self, index):
del self.messages[index]
def __len__(self):
return len(self.messages)
# Store for chat histories
store = {}
def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:
if (user_id, conversation_id) not in store:
store[(user_id, conversation_id)] = InMemoryHistory()
return store[(user_id, conversation_id)]
# JSON 출력을 위한 응답 스키마 정의
response_schemas = [
ResponseSchema(name="name", description="A good name for the company"),
ResponseSchema(name="industry", description="The industry the company is in")
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
# 첫 번째 체인: 회사 이름과 산업 생성
prompt_template_1 = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template(
"What is a good name for a company that makes {product}? "
"Also, specify the industry.\n{format_instructions}"
)
],
input_variables=["product"],
partial_variables={"format_instructions": output_parser.get_format_instructions()}
)
chain_1 = (
prompt_template_1
| llm
| output_parser
)
# 체인 실행 및 결과 출력
product = "eco-friendly water bottles"
result_1 = chain_1.invoke({"product": product})
print("Chain 1 output:", result_1)DescriptionIf I don't set output parser, the result's format is {'content', 'response_metadata'}. I need 'response_metadata' information to check tokens. System Infolangchain==0.2.9 linux(ubuntu) |
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Replies: 1 comment 7 replies
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To get from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4-turbo")
msg = llm.invoke([("human", "What's the oldest known example of cuneiform")])
response_metadata = msg.response_metadataThis code snippet demonstrates how to retrieve the {
"token_usage": {
"completion_tokens": 5,
"prompt_tokens": 28,
"total_tokens": 33,
},
"model_name": "gpt-4o",
"system_fingerprint": "fp_319be4768e",
"finish_reason": "stop",
"logprobs": None,
}In your specific case, you can modify your code to access the # Chain execution and result output
product = "eco-friendly water bottles"
result_1 = chain_1.invoke({"product": product})
# Accessing response_metadata
response_metadata = result_1.response_metadata
print("Chain 1 output:", result_1)
print("Response Metadata:", response_metadata)This will allow you to retrieve and print the |
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@dosu and get response_metadata either |
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To use Here is an example of how you can achieve this: from langchain.output_parsers.structured import StructuredOutputParser, ResponseSchema
from langchain_openai import ChatOpenAI
# Define your response schemas
response_schemas = [
ResponseSchema(name="name", description="A good name for the company"),
ResponseSchema(name="industry", description="The industry the company is in")
]
# Create the parser from the response schemas
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
# Initialize the language model
llm = ChatOpenAI(model="gpt-4-turbo")
# Define the prompt template
prompt_template_1 = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template(
"What is a good name for a company that makes {product}? "
"Also, specify the industry.\n{format_instructions}"
)
],
input_variables=["product"],
partial_variables={"format_instructions": output_parser.get_format_instructions()}
)
# Create the chain
chain_1 = (
prompt_template_1
| llm
| output_parser
)
# Execute the chain and get the result
product = "eco-friendly water bottles"
result_1 = chain_1.invoke({"product": product})
# Access the parsed output and response metadata
parsed_output = output_parser.parse(result_1.content)
response_metadata = result_1.response_metadata
print("Chain 1 output:", parsed_output)
print("Response Metadata:", response_metadata)In this example, |
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AttributeError Traceback (most recent call last) AttributeError: 'dict' object has no attribute 'content' |
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To access from langchain.output_parsers.structured import StructuredOutputParser, ResponseSchema
from langchain_openai import ChatOpenAI
# Define your response schemas
response_schemas = [
ResponseSchema(name="name", description="A good name for the company"),
ResponseSchema(name="industry", description="The industry the company is in")
]
# Create the parser from the response schemas
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
# Initialize the language model
llm = ChatOpenAI(model="gpt-4-turbo")
# Define the prompt template
prompt_template_1 = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template(
"What is a good name for a company that makes {product}? "
"Also, specify the industry.\n{format_instructions}"
)
],
input_variables=["product"],
partial_variables={"format_instructions": output_parser.get_format_instructions()}
)
# Create the chain
chain_1 = (
prompt_template_1
| llm
| output_parser
)
# Execute the chain and get the result
product = "eco-friendly water bottles"
result_1 = chain_1.invoke({"product": product})
# Access the parsed output and response metadata
parsed_output = result_1.get("content")
response_metadata = result_1.get("response_metadata")
print("Chain 1 output:", parsed_output)
print("Response Metadata:", response_metadata)In this example, |
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I just removed 'output_parser' from 'chain_1' then it worked. |
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wasn't that the point of your question? |
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Hi, Yes I removed it from the chain. But I processed the StructuredOutput parser after 'invoke'. My original code is like But I removed output_parser like And then processed output_parser Therefore, I just separated the process to 2 steps. |
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To access
response_metadatawhen usingStructuredOutputParserwithresponse_schemas, you can directly access the dictionary keys since the result is a dictionary. Here is an example of how you can do it: