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langchain-ai / langchain

🦜🔗 Build context-aware reasoning applications
https://python.langchain.com
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ValueError: An output parsing error occurred #16843

Closed shashi29 closed 4 months ago

shashi29 commented 7 months ago

Checked other resources

Example Code

from langchain_community.llms import Ollama llm = Ollama(model="codellama:70b-python") from langchain.agents.agent_types import AgentType from langchain_experimental.agents.agent_toolkits import create_csv_agent, create_pandas_dataframe_agent from langchain_openai import ChatOpenAI, OpenAI import pandas as pd import os import re

from datetime import datetime import pandas as pd import numpy as np from datetime import datetime, timedelta

df = pd.read_csv("sales_purchase_20Jan.csv")

agent = create_pandas_dataframe_agent( llm, df, verbose=True, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True, number_of_head_rows=5 )

instructions = """ Perform the following steps to address the given query: Step 1: Begin by verifying if the provided dataframe and instructions contain sufficient information for the required analysis. In case of insufficient details, respond with:

    {
      "table": {},
      "message": ["Please review and modify the prompt with more specifics."]
    }
Step 2: Should the query necessitate generating a table, structure your response using the following format:
```json
{
  "table": {
    "columns": ["column1", "column2", ...],
    "data": [[value1, value2, ...], [value1, value2, ...], ...]
  },
  "message": []
}
```
Step 3: For queries requiring solely a textual response, utilize the following format:
```json
{
  "table": {},
  "message": ["Your text response here"]
}
```
Step 4: Ensure consistent usage of standard decimal format without scientific notation. Replace any None/Null values with 0.0."
Query: """

prompt = instructions + '''Create a summary table that displays the cumulative sales for each item category ('Atta', 'Salt', 'Salt-C') across different months ('Month_Year'). The table should contain columns for 'Month_Year,' individual Item categories, and a 'Grand Total' column. The values in the table should represent the total sales value ('Sale_Value') for each Item category within the corresponding month.'''

agent.invoke(prompt)

Error Message and Stack Trace (if applicable)

OutputParserException Traceback (most recent call last) File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:1125, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1124 # Call the LLM to see what to do. -> 1125 output = self.agent.plan( 1126 intermediate_steps, 1127 callbacks=run_manager.get_child() if run_manager else None, 1128 **inputs, 1129 ) 1130 except OutputParserException as e:

File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:695, in Agent.plan(self, intermediate_steps, callbacks, kwargs) 694 full_output = self.llm_chain.predict(callbacks=callbacks, full_inputs) --> 695 return self.output_parser.parse(full_output)

File /usr/local/lib/python3.10/dist-packages/langchain/agents/mrkl/output_parser.py:63, in MRKLOutputParser.parse(self, text) 62 if not re.search(r"Action\s\d\s:[\s](.?)", text, re.DOTALL): ---> 63 raise OutputParserException( 64 f"Could not parse LLM output: {text}", 65 observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE, 66 llm_output=text, 67 send_to_llm=True, 68 ) 69 elif not re.search( 70 r"[\s]Action\s\d\sInput\s\d\s:[\s](.)", text, re.DOTALL 71 ):

OutputParserException: Could not parse LLM output: I need to perform a Pivot Table Calculation in order to get Grand Totals for each item and place it at bottom of the table. Action Input: 'pivot'

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) Cell In[23], line 1 ----> 1 agent.invoke(prompt)

File /usr/local/lib/python3.10/dist-packages/langchain/chains/base.py:162, in Chain.invoke(self, input, config, **kwargs) 160 except BaseException as e: 161 run_manager.on_chain_error(e) --> 162 raise e 163 run_manager.on_chain_end(outputs) 164 final_outputs: Dict[str, Any] = self.prep_outputs( 165 inputs, outputs, return_only_outputs 166 )

File /usr/local/lib/python3.10/dist-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs) 149 run_manager = callback_manager.on_chain_start( 150 dumpd(self), 151 inputs, 152 name=run_name, 153 ) 154 try: 155 outputs = ( --> 156 self._call(inputs, run_manager=run_manager) 157 if new_arg_supported 158 else self._call(inputs) 159 ) 160 except BaseException as e: 161 run_manager.on_chain_error(e)

File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:1391, in AgentExecutor._call(self, inputs, run_manager) 1389 # We now enter the agent loop (until it returns something). 1390 while self._should_continue(iterations, time_elapsed): -> 1391 next_step_output = self._take_next_step( 1392 name_to_tool_map, 1393 color_mapping, 1394 inputs, 1395 intermediate_steps, 1396 run_manager=run_manager, 1397 ) 1398 if isinstance(next_step_output, AgentFinish): 1399 return self._return( 1400 next_step_output, intermediate_steps, run_manager=run_manager 1401 )

File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:1097, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1088 def _take_next_step( 1089 self, 1090 name_to_tool_map: Dict[str, BaseTool], (...) 1094 run_manager: Optional[CallbackManagerForChainRun] = None, 1095 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1096 return self._consume_next_step( -> 1097 [ 1098 a 1099 for a in self._iter_next_step( 1100 name_to_tool_map, 1101 color_mapping, 1102 inputs, 1103 intermediate_steps, 1104 run_manager, 1105 ) 1106 ] 1107 )

File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:1097, in (.0) 1088 def _take_next_step( 1089 self, 1090 name_to_tool_map: Dict[str, BaseTool], (...) 1094 run_manager: Optional[CallbackManagerForChainRun] = None, 1095 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1096 return self._consume_next_step( -> 1097 [ 1098 a 1099 for a in self._iter_next_step( 1100 name_to_tool_map, 1101 color_mapping, 1102 inputs, 1103 intermediate_steps, 1104 run_manager, 1105 ) 1106 ] 1107 )

File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:1136, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1134 raise_error = False 1135 if raise_error: -> 1136 raise ValueError( 1137 "An output parsing error occurred. " 1138 "In order to pass this error back to the agent and have it try " 1139 "again, pass handle_parsing_errors=True to the AgentExecutor. " 1140 f"This is the error: {str(e)}" 1141 ) 1142 text = str(e) 1143 if isinstance(self.handle_parsing_errors, bool):

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: I need to perform a Pivot Table Calculation in order to get Grand Totals for each item and place it at bottom of the table. Action Input: 'pivot'

Description

File /usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py:1136, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1134 raise_error = False 1135 if raise_error: -> 1136 raise ValueError( 1137 "An output parsing error occurred. " 1138 "In order to pass this error back to the agent and have it try " 1139 "again, pass handle_parsing_errors=True to the AgentExecutor. " 1140 f"This is the error: {str(e)}" 1141 ) 1142 text = str(e) 1143 if isinstance(self.handle_parsing_errors, bool):

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: I need to perform a Pivot Table Calculation in order to get Grand Totals for each item and place it at bottom of the table. Action Input: 'pivot'

System Info

System Information

OS: Linux OS Version: #184-Ubuntu SMP Tue Oct 31 09:21:49 UTC 2023 Python Version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]

Package Information

langchain_core: 0.1.17 langchain: 0.1.4 langchain_community: 0.0.16 langchain_experimental: 0.0.49 langchain_openai: 0.0.5

Packages not installed (Not Necessarily a Problem)

The following packages were not found:

langgraph langserve

dosubot[bot] commented 7 months ago

🤖

Hey @shashi29, great to see you back! Hope you're doing well.

Thank you for providing the detailed error report. The error you're encountering is due to an issue with the output parsing in the LangChain framework. Specifically, the OutputParserException is being raised, which indicates that the output from the language model (LLM) could not be parsed correctly.

In your case, the error message is: Could not parse LLM output: ' I need to perform a Pivot Table Calculation in order to get Grand Totals for each item and place it at bottom of the table. Action Input: 'pivot'. This suggests that the LLM output did not match the expected format, causing the parsing to fail.

The OutputParserException is designed to handle such parsing errors. When this exception is raised, it can be caught and handled in ways to fix the parsing error, while other errors will be raised. The OutputParserException takes four arguments: error, observation, llm_output, and send_to_llm. The error is the error that's being re-raised or an error message. The observation is a string explanation of the error which can be passed to a model to try and remediate the issue. The llm_output is the string model output which is error-ing. The send_to_llm is a boolean indicating whether to send the observation and llm_output back to an Agent after an OutputParserException has been raised. This gives the underlying model driving the agent the context that the previous output was improperly structured, in the hopes that it will update the output to the correct format.

In your case, the error message suggests that the LLM output was not in the expected format. This could be due to a variety of reasons, such as an issue with the LLM itself, or a problem with the input data or instructions. To resolve this issue, you could try the following steps:

  1. Check the format of your input data and instructions to ensure they are correct and in the expected format.
  2. Check the version of your LLM and ensure it is compatible with the version of LangChain you are using.
  3. If the error persists, you may need to debug the LLM output to understand why it is not in the expected format. This could involve inspecting the raw LLM output, or adding additional logging to the output parsing code.

I hope this helps! If you have any further questions or if the issue persists, please don't hesitate to ask.

Sources

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.

shashi29 commented 7 months ago

@dosu-bot. I can still the same issues faced by other people and still, there is no resolution for this. Similar issue: https://github.com/langchain-ai/langchain/issues/7493

And I can see the issue is coming from this file libs/langchain/langchain/agents/mrkl/output_parser.py.

I can see there is a fix pattern id expecting for the output, This is working fine with Openai, but if we try with any other open source llm. This parts break

regex = ( r"Action\s\d\s:[\s](.?)[\s]Action\s\d\sInput\s\d\s:[\s](.)" ) action_match = re.search(regex, text, re.DOTALL)

And this is expecting a response in these formats:

Action 1: Do something Action 1 Input 1: User input here Action 2: Execute task Action 2 Input 2: Input data goes here Action 3: Perform action Action 3 Input 3: Input for action Action 4: Take action Action 4 Input 4: User-provided input Action 5: Complete task Action 5 Input 5: Input details

I think you should use some generic way for regex, or some similar way to find the more similar text and then update the instruction.

Can you suggest, where i can debug more to fix this error, what is the use of this output parser in the langchain .

dosubot[bot] commented 7 months ago

🤖

Based on the information you've provided, it seems like the error is being caused by the PydanticOutputFunctionsParser in LangChain. This parser is designed to parse the output of a Language Model (LLM) call into a Pydantic schema. However, it raises a ValueError if multiple Pydantic schemas are provided when args_only is set to True.

The error message you're seeing suggests that the output parser is expecting a specific format for the output, which is not being met. The expected format seems to be a single Pydantic schema. If multiple schemas are provided, and args_only is set to True, then the parser will raise a ValueError.

To debug this issue, you could start by checking the output of your LLM call. Make sure that it matches the expected format for the PydanticOutputFunctionsParser. If you're providing multiple Pydantic schemas, ensure that args_only is set to False.

If the issue persists, you might want to look into the implementation of the PydanticOutputFunctionsParser in the LangChain codebase. Specifically, you could check the validate_schema method in the PydanticOutputFunctionsParser class, which is where the ValueError is being raised.

Here is the relevant code for reference:

class PydanticOutputFunctionsParser(OutputFunctionsParser):
    """Parse an output as a pydantic object."""

    pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]]
    """The pydantic schema to parse the output with."""

    @root_validator(pre=True)
    def validate_schema(cls, values: Dict) -> Dict:
        schema = values["pydantic_schema"]
        if "args_only" not in values:
            values["args_only"] = isinstance(schema, type) and issubclass(
                schema, BaseModel
            )
        elif values["args_only"] and isinstance(schema, Dict):
            raise ValueError(
                "If multiple pydantic schemas are provided then args_only should be"
                " False."
            )
        return values

You can find this code in the file libs/community/langchain_community/output_parsers/ernie_functions.py.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

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.