Parsing error on langchain agent with gpt4all llm

Question

I am trying to integrate GPT4All with serpapi agent from langchain. But I am getting parsing error. Agent unable to parse llm output.

I am bit new to langchain framework. And I feel like I am missing something in the below snippet of code.

I beleive the agent is expecting an output Thought: .... but not getting one, may I get some help here?

from langchain.llms import GPT4All
from langchain.agents import load_tools
from dotenv import load_dotenv

load_dotenv()


llm = GPT4All(
    model="~/.cache/gpt4all/orca-mini-3b-gguf2-q4_0.gguf",
    verbose=True,
)

from langchain.agents import load_tools
from langchain.agents import initialize_agent

tools = load_tools(["serpapi"], llm=llm)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
agent.run("which club is Cristiano Ronaldo playing right now?")

Error

ValueError: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Could not parse LLM output: ```

Answer 1

This answer will probably be too late to help you, but maybe someone will stumble upon this and find it useful. I made good debugging progress with using the ConsoleCallbackHandler. It pretty much prints out all in and outputs between your chained elements. Here is a mini example:

from langchain.callbacks.tracers import ConsoleCallbackHandler

"which club is Cristiano Ronaldo playing right now?"
print(agent.invoke(prompt, config={"callbacks":[ConsoleCallbackHandler()]}))

Answer 2

Use handle_parsing_errors=True during agent initialization. The output parsing error occurs when the LLM's generated response doesn't match the expected format (because of failure to follow instructions or hallucination). This can happen no matter what callback you choose.

By setting handle_parsing_errors=True, you're telling the agent to keep regenerating the response until it receives one that can be parsed correctly

agent = initialize_agent(
 tools = tools,
 llm = llmm,
 agent="zero-shot-react-description",
 verbose=True, 
 handle_parsing_errors = True
)