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ValueError(f"Could not parse LLM output: {llm_output}
")
#1477
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I am receiving a very similar response...
It seems that the ChatGPT api is handling the request, but something in the langchain parsing of the output is breaking. Any ideas? Thanks for these developments! |
It is possible that this is caused due to the nature of the current implementation, which puts all the prompts into the user role in ChatGPT. It is plausible to assume that if/when the code will be updated to support chat format with system/user/assistant roles, in these agents, it will work again, until then, we can still use the (really good) davinchi model. Thanks team! |
Super hacky, but I appended |
Sometimes the LLM ignores the instructions on the format which breaks the entire thing...like it clearly says the last line should be "Final Answer: " but if it comes back without that you are in trouble. I hope the new chat messages format will eliminate the need to parse the reply text |
I have the same problem, amplified by a SystemMessage in German. The model often forgets the correct tokens for Tool usage or the final answer, resulting in the parsing error. Has anyone an example where all the prompts (at least the descriptions, not necessarily the tokens) for an Agent and the Tools are translated, so the model can't get confused between multiple languages? |
can you show the code for that? |
This can happen if the model does not follow the given instructions. In most of the cases I have seen, it would proabably be better to simply return the output instead of throwing a |
It seem that because the LLM response with text start with "Thought", but the regex match code in langchain does not consider this. |
Is there any solution to this? Thanks. |
I tried to dig into a solution for this. It can be for a myriad for reasons it looks like. Basically, the model is not obeying the prompt. At the minimum, I think agent code should ask the model for a well-structure response that adhere to the prompt. |
but the model may do not adopt your request. @alexprice12 this is about standard |
When the model asks for a tool that doesn't exist, langchain tells the model that the tool they chose doesn't exist. I'm open to more than one solution. What are you thinking is the best approach here? |
I hope it's not too silly an idea, and looking at the code I'm not sure where such an intervention would take place, but: raise ValueError(f"Could not parse LLM output: `{llm_output}`") , the answer given by the llm was sent back to it complaining that it does not have the right format, and asking it to comply with it? |
Duplicate of #1358 |
any solution yet? |
This format_instructions prompt helped a bit to get the correct response format, open for suggestions on improvement: |
check this, worked for me: |
I encountered a similar issue while using Azure OpenAI with the Pandas agent and attempted the solution mentioned above, but unfortunately, it did not resolve the problem. To address this issue, I suggest creating a custom output parser specifically tailored to handle the responses from the Azure OpenAI Pandas Agent. With a custom output parser, we can ensure that the agent's output is handled appropriately, which may lead to resolving the problem more effectively. from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser class CustomOutputParser(AgentOutputParser):
from langchain.llms import AzureOpenAI |
Hi, @chun1617! I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale. From what I understand, the issue you raised titled "ValueError(f"Could not parse LLM output: Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself or it will be automatically closed in 7 days. Thank you for your contribution to the LangChain repository! |
Surrounding the tool's output with quotes worked for me: def my_calc(expr: str) -> str:
"""
Some custom tool
Evaluate the provided arithmetic expression in Python syntax.
Example: my_calc("2+2")
Result: 4
"""
expr = re.sub(r"[^0-9+\-*/().]", "", expr)
return f'"{eval(expr)}"' # surrounding the output |
@baskaryan Could you please help @chun1617 with the issue titled "ValueError(f"Could not parse LLM output: |
I'm still getting this error on version 0.1.4 using open-source LLM Qwen-14B-Chat model. Is it still a thing? |
🤖 Based on the traceback you've provided, it seems like the error is originating from the The error message "Could not parse LLM output" suggests that the method was unable to parse the output of the language model. This could be due to the structure or format of the output not being what the method expects. Here is the relevant code: class ConversationalAgent(Agent):
...
def _extract_tool_and_input(self, llm_output: str) -> Tuple[str, Dict[str, Any]]:
...
raise ValueError(f"Could not parse LLM output: `{llm_output}`") In this method, if the parsing fails for any reason, it raises a ValueError with the message "Could not parse LLM output". To resolve this issue, you might need to check the output of your language model and ensure it's in a format that the I hope this helps! If you have any more questions or need further clarification, feel free to ask. Sources: SourcesThis response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot. |
File "C:\Program Files\Python\Python310\lib\site-packages\langchain\chains\base.py", line 268, in run return self(kwargs)[self.output_keys[0]] File "C:\Program Files\Python\Python310\lib\site-packages\langchain\chains\base.py", line 168, in __call__ raise e File "C:\Program Files\Python\Python310\lib\site-packages\langchain\chains\base.py", line 165, in __call__ outputs = self._call(inputs) File "C:\Program Files\Python\Python310\lib\site-packages\langchain\agents\agent.py", line 503, in _call next_step_output = self._take_next_step( File "C:\Program Files\Python\Python310\lib\site-packages\langchain\agents\agent.py", line 406, in _take_next_step output = self.agent.plan(intermediate_steps, **inputs) File "C:\Program Files\Python\Python310\lib\site-packages\langchain\agents\agent.py", line 102, in plan action = self._get_next_action(full_inputs) File "C:\Program Files\Python\Python310\lib\site-packages\langchain\agents\agent.py", line 64, in _get_next_action parsed_output = self._extract_tool_and_input(full_output) File "C:\Program Files\Python\Python310\lib\site-packages\langchain\agents\conversational\base.py", line 84, in _extract_tool_and_input raise ValueError(f"Could not parse LLM output:
{llm_output}") ValueError: Could not parse LLM output:
Thought: Do I need to use a tool? YesAction: Use the requests library to write a Python code to do a post request
Action Input:
``
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