OutputParserException: Could not parse LLM output

[akashAD98]

System Info

Doing experiment on google collab

using llam2 7b quantized model

below is my code

agent with memory & i tried A gentExecutor

source_text_tool = Tool(
    name="The Prophet Source Text QA System",
    func=prophet_qa_chain.run,
    description="Useful when asked question related to philosophy or The Prophet."
)

analysis_text_tool = Tool(
    name="Other philosophy QA System",
    func=analysis_qa_chain.run,
    description="Useful when asked questions related to philosophy or Stoicism or Sikhs"
)


prefix = """
You're having a conversation with a human. You're helpful and answering
questions to your maximum ability.You have access to the following tools:
"""

suffix = """Let's Go!"
{chat_history}
Question: {input}

{agent_scratchpad}
"""


tools = [source_text_tool, analysis_text_tool]



prompt = ZeroShotAgent.create_prompt(tools,
                                     prefix=prefix,
                                     suffix=suffix,
                                     input_variables=["input", "chat_history","agent_scratchpad"])  #{chat_history}

memory = ConversationBufferMemory(memory_key="chat_history")


llm_chain = LLMChain(llm=load_llm(),
                     prompt=prompt)

agent = ZeroShotAgent(llm_chain=llm_chain,
                      tools=tools,
                      verbose=True)

agent_chain_memory = AgentExecutor.from_agent_and_tools(
    agent=agent,
    tools=tools,
    memory=memory, #its without memo
    verbose=True
)

error i im getting

> Entering new AgentExecutor chain...
---------------------------------------------------------------------------
OutputParserException                     Traceback (most recent call last)
[<ipython-input-31-a44cd2d0a1a6>](https://localhost:8080/#) in <cell line: 1>()
----> 1 agent_chain_memory.run(input="How should I handle my fear of death ?")

7 frames
[/usr/local/lib/python3.10/dist-packages/langchain/agents/mrkl/output_parser.py](https://localhost:8080/#) in parse(self, text)
     50 
     51         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
---> 52             raise OutputParserException(
     53                 f"Could not parse LLM output: `{text}`",
     54                 observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,

OutputParserException: Could not parse LLM output: `*Thought:* "I will take a deep breath and reflect on the nature of mortality, considering both the transience of life and the certainty of death."

*Action Input:* Choose one of the following options:
A) Seek guidance from The Prophet Muhammad's teachings on the afterlife.
B) Explore Stoicism or Sikh philosophy for coping mechanisms.
C) Utilize a QA system specifically designed to address questions related to philosophy and death.
*Observation:* The action taken will determine the final answer provided.`

Who can help?

@hwchase17 @agola11

Information

• The official example notebooks/scripts
• My own modified scripts

Related Components

• LLMs/Chat Models
• Embedding Models
• Prompts / Prompt Templates / Prompt Selectors
• Output Parsers
• Document Loaders
• Vector Stores / Retrievers
• Memory
• Agents / Agent Executors
• Tools / Toolkits
• Chains
• Callbacks/Tracing
• Async

Reproduction

# i used documents to store embedding in chromdadb & using below data

analysis_qa_chain = RetrievalQA.from_chain_type(
    llm=load_llm(),
    chain_type="map_reduce",
    retriever=web_db.as_retriever()
)

prophet_qa_chain = RetrievalQA.from_chain_type(
    llm=load_llm(),
    chain_type="map_reduce",
    retriever=prophet_db.as_retriever()
)

source_text_tool = Tool(
    name="The Prophet Source Text QA System",
    func=prophet_qa_chain.run,
    description="Useful when asked question related to philosophy or The Prophet."
)

analysis_text_tool = Tool(
    name="Other philosophy QA System",
    func=analysis_qa_chain.run,
    description="Useful when asked questions related to philosophy or Stoicism or Sikhs"
)


prefix = """
You're having a conversation with a human. You're helpful and answering
questions to your maximum ability.You have access to the following tools:
"""

suffix = """Let's Go!"
{chat_history}
Question: {input}

{agent_scratchpad}
"""
tools = [source_text_tool, analysis_text_tool]



prompt = ZeroShotAgent.create_prompt(tools,
                                     prefix=prefix,
                                     suffix=suffix,
                                     input_variables=["input", "chat_history","agent_scratchpad"])  #{chat_history}

memory = ConversationBufferMemory(memory_key="chat_history")


llm_chain = LLMChain(llm=load_llm(),
                     prompt=prompt)

agent = ZeroShotAgent(llm_chain=llm_chain,
                      tools=tools,
                      verbose=True)

agent_chain_memory = AgentExecutor.from_agent_and_tools(
    agent=agent,
    tools=tools,
    memory=memory, #its without memo
    verbose=True
)

agent_chain_memory.run(input="How should I handle my fear of death ?")

Expected behavior

im getting op but error also coming with this

> Entering new AgentExecutor chain...
---------------------------------------------------------------------------
OutputParserException                     Traceback (most recent call last)
[<ipython-input-31-a44cd2d0a1a6>](https://localhost:8080/#) in <cell line: 1>()
----> 1 agent_chain_memory.run(input="How should I handle my fear of death ?")

7 frames
[/usr/local/lib/python3.10/dist-packages/langchain/agents/mrkl/output_parser.py](https://localhost:8080/#) in parse(self, text)
     50 
     51         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
---> 52             raise OutputParserException(
     53                 f"Could not parse LLM output: `{text}`",
     54                 observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,

OutputParserException: Could not parse LLM output: `*Thought:* "I will take a deep breath and reflect on the nature of mortality, considering both the transience of life and the certainty of death."

*Action Input:* Choose one of the following options:
A) Seek guidance from The Prophet Muhammad's teachings on the afterlife.
B) Explore Stoicism or Sikh philosophy for coping mechanisms.
C) Utilize a QA system specifically designed to address questions related to philosophy and death.
*Observation:* The action taken will determine the final answer provided.`

[dosubot]

🤖

Hello,

The error you're encountering, OutputParserException, is raised when the parse method in the OutputParser class encounters an issue while trying to parse the output from the language model. This is evident from the try-except block in the parse method.

Here is the relevant code:

def parse(self, completion: str) -> T:
    try:
        parsed_completion = self.parser.parse(completion)
    except OutputParserException as e:
        new_completion = self.retry_chain.run(
            instructions=self.parser.get_format_instructions(),
            completion=completion,
            error=repr(e),
        )
        parsed_completion = self.parser.parse(new_completion)

    return parsed_completion

In this code, self.parser.parse(completion) is attempted. If this raises an OutputParserException, the exception is caught and the retry_chain is run to try and fix the error. If the error persists even after the retry, the OutputParserException will be raised again.

To resolve this issue, you need to ensure that the parse method of the parser object can correctly parse the completion. This might involve debugging the parse method or the data being passed to it.

There have been similar issues reported in the LangChain repository. For instance, Issue #1106 and Issue #2985 both deal with OutputParserException errors. The solution in these cases involved adjusting the regex string used for parsing the generation further.

I hope this helps! If you have any further questions or if the issue persists, please provide more details so I can assist you better.

Sources

• ValueError: Could not parse LLM output:
• Bug : could not parse LLM output: {llm_output}") when I run the same question several times
• ValueError: Could not parse LLM output:
• libs/langchain/langchain/output_parsers/fix.py
• libs/langchain/langchain/output_parsers/init.py
• libs/langchain/langchain/output_parsers/list.py
• libs/langchain/langchain/schema/output_parser.py

---

This 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.

[dosubot]

Hi, @akashAD98,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. It looks like you're encountering an OutputParserException while running an AgentExecutor chain in a Google Colab experiment using a LLM 7b quantized model. I have provided an explanation of the OutputParserException and suggested debugging the parse method or the data being passed to it. The response also references similar issues reported in the LangChain repository and provides links to relevant issues and code files.

Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, please let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or the issue will be automatically closed in 7 days.

Thank you for your understanding and cooperation. If you have any further questions or need assistance, feel free to reach out.