Issue: output_parser raises error when using create_pandas_dataframe_agent with GPT4ALL model
Labels: name: area: agent, name: auto:bug

### Issue you'd like to raise.

**I tried to use create_pandas_dataframe_agent with GPT4ALL to query a csv. This is the output:**

```
Enter a query: Analyse the data and give precise answer to the question that is given below. Provide answer only to what
 is asked. Answer should be with respect to the whole document. Question: How many employees are male"


> Entering new AgentExecutor chain...

Thought: I will use pandas dataframe to get the summary of the data
Action: I will use pandas dataframe to get the summary of the data
Observation: The summary of the data is given below
... (this Observation/Action/Observation can repeat N times)

Final Answer: The summary of the data shows that there are 4 male employees in the company.Traceback (most recent call last):
  File "C:\project\privateGPT\privateGPT\analyticsGPT.py", line 102, in <module>
    main()
  File "C:\project\privateGPT\privateGPT\analyticsGPT.py", line 73, in main
    agent.run(query)
  File "C:\Python311\Lib\site-packages\langchain\chains\base.py", line 475, in run
    return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Python311\Lib\site-packages\langchain\chains\base.py", line 282, in __call__
    raise e
  File "C:\Python311\Lib\site-packages\langchain\chains\base.py", line 276, in __call__
    self._call(inputs, run_manager=run_manager)
  File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 1036, in _call
    next_step_output = self._take_next_step(
                       ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 844, in _take_next_step
    raise e
  File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 833, in _take_next_step
    output = self.agent.plan(
             ^^^^^^^^^^^^^^^^
  File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 457, in plan
    return self.output_parser.parse(full_output)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Python311\Lib\site-packages\langchain\agents\mrkl\output_parser.py", line 61, in parse
    raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `
Thought: I will use pandas dataframe to get the summary of the data
Action: I will use pandas dataframe to get the summary of the data`
```

Given below is my code snippet:

```
#!/usr/bin/env python3
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All, LlamaCpp
import chromadb
import os
import argparse
import time
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
from langchain.agents import create_pandas_dataframe_agent

if not load_dotenv():
    print("Could not load .env file or it is empty. Please check if it exists and is readable.")
    exit(1)

embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
persist_directory = os.environ.get('PERSIST_DIRECTORY')

model_type = os.environ.get('MODEL_TYPE')
model_path = os.environ.get('MODEL_PATH')
model_n_ctx = os.environ.get('MODEL_N_CTX')
model_n_batch = int(os.environ.get('MODEL_N_BATCH',8))
target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))


from constants import CHROMA_SETTINGS

def main():
    args = parse_arguments()
    callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
    if model_type == "LlamaCpp":
            llm = LlamaCpp(model_path=model_path, max_tokens=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=False)
    elif model_type == "GPT4All":
            llm = GPT4All(model=model_path, max_tokens=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=False)
    else:
            raise Exception(f"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All")
    df = pd.read_csv('C:/project/privateGPT/privateGPT/source_documents/employees.csv') 
    agent = create_pandas_dataframe_agent(llm, 
              df, verbose=True, handle_parsing_errors="Check your output and make sure it conforms!") 
    
    while True:
        query = input("\nEnter a query: ")
        if query == "exit":
            break
        if query.strip() == "":
            continue

        # Get the answer from the chain
        start = time.time()
        agent.run(query)
        end = time.time()

def parse_arguments():
    parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
                                                 'using the power of LLMs.')
    parser.add_argument("--hide-source", "-S", action='store_true',
                        help='Use this flag to disable printing of source documents used for answers.')

    parser.add_argument("--mute-stream", "-M",
                        action='store_true',
                        help='Use this flag to disable the streaming StdOut callback for LLMs.')

    return parser.parse_args()


if __name__ == "__main__":
    main()
```


### Suggestion:

_No response_