langchain.schema.OutputParserException: Could not parse LLM output: `Thought: Do I need to use a tool? No

[pradeepdev-1995]

System Info

langchain==0.0.219
python 3.9

Who can help?

No response

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

import os
from llama_index import LLMPredictor,ServiceContext,LangchainEmbedding
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.chat_models import AzureChatOpenAI

BASE_URL = "url"
API_KEY = "key"
DEPLOYMENT_NAME = "deployment_name"

model = AzureChatOpenAI(
    openai_api_base=BASE_URL,
    openai_api_version="version",
    deployment_name=DEPLOYMENT_NAME,
    openai_api_key=API_KEY,
    openai_api_type="azure",
)

from langchain.agents import initialize_agent

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("/Data").load_data()
llm_predictor = LLMPredictor(llm=model)
embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='huggingface model'))


service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor,embed_model=embed_model)

index = VectorStoreIndex.from_documents(documents=documents,service_context=service_context)

tools = [
    Tool(
        name="LlamaIndex",
        func=lambda q: str(index.as_query_engine().query(q)),
        description="useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.",
        return_direct=True,
    ),
]

memory = ConversationBufferMemory(memory_key="chat_history")
agent_executor = initialize_agent(
    tools, model, agent="conversational-react-description", memory=memory
)

while True:
    query = input("Enter query\n")
    print(agent_executor.run(input=query))

Trying the above code, but when i ask queries, it shows the error - 'langchain.schema.OutputParserException: Could not parse LLM output: `Thought: Do I need to use a tool? No'

Expected behavior

The error should not occur

[kashirin-dm]

The same!

[yousenwang]

#549

[Ecolow]

I think I found a pretty good fix:

class ConversationalAgent(Agent):
    """An agent that holds a conversation in addition to using tools."""

   #  ...   we don't care  ....

    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with."""
        return "New Thought Chain:\n"                       <--- THIS

Once the current step is completed the llm_prefix is added to the next step's prompt. By default, the prefix is Thought:, which the llm interprets as "Give me a thought and quit". Consequently, the OutputParser fails to locate the expected Action/Action Input in the model's output, preventing the continuation to the next step. By changing the prefix to New Thought Chain:\n you entice the model to create a whole new react chain containing Action/Action Input.

[dosubot]

Hi, @pradeepdev-1995,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, you are experiencing an error when using the langchain library to run a conversational agent. There have been comments from kashirin-dm and yousenwang, as well as a proposed fix from Ecolow involving a change to the llm_prefix property in the ConversationalAgent class.

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!

[PositivPy]

@dosubot You should probably update issue #1358 with my solution as well. I've been looking at the thread for months (I know) and they are still stuck at retrying rather than fixing the prefix like I did.

[dosubot]

🤖

@baskaryan, could you please assist with the issue mentioned in #1358? The user has provided an update and it seems they are still encountering the same problem with the conversational agent.

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[andrejsstepanovs]

I was struggling with same issue. This happens mostly only using weaker LLMs or models with small context. Solution that I found by debugging internals for langchain was to repeat actual format that we expect model to respond with. This works really reliable together with a $ tip than just telling model to 'remember what I told you about answer format'. Sadly I failed to find a good way how one can recover without calling LLM again.

Here is my handle error prompt:

def _handle_error(error: Exception) -> str:
    return """I could not parse your answer. 
It is really important and you will get 1000$ tip if you will use the following answer format:
```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of tools mentioned before.
Action Input: the input to the action
Observation: the result of the action
```

When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:

```
Thought: Do I need to use a tool? No
AI: [your response here]
```

Please answer again while complying to rules and format just mentioned!
"""

and of course pass it in as error handler response:

agent = initialize_agent(
    handle_parsing_errors=_handle_error,
    ...
)