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` ValueError: Could not parse output` when using `QAGenerateChain`'s `.apply_and_parse()` method #7559

Closed AlxndrMlk closed 10 months ago

AlxndrMlk commented 1 year ago

System Info

python==3.9.17 langchain==0.0.190

Win 11 64 bit

Who can help?

@hwchase17 @agol

Information

Related Components

Reproduction

# Instantiate the chain
example_gen_chain = QAGenerateChain.from_llm(ChatOpenAI())

example_gen_chain.apply_and_parse([{'doc': data[2]}])

[Out]:

ValueError                                Traceback (most recent call last)
Cell In[36], line 1
----> 1 example_gen_chain.apply_and_parse([{'doc': data[2]}])

File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\chains\llm.py:257, in LLMChain.apply_and_parse(self, input_list, callbacks)
    255 """Call apply and then parse the results."""
    256 result = self.apply(input_list, callbacks=callbacks)
--> 257 return self._parse_result(result)

File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\chains\llm.py:263, in LLMChain._parse_result(self, result)
    259 def _parse_result(
    260     self, result: List[Dict[str, str]]
    261 ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
    262     if self.prompt.output_parser is not None:
--> 263         return [
    264             self.prompt.output_parser.parse(res[self.output_key]) for res in result
    265         ]
    266     else:
    267         return result

File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\chains\llm.py:264, in <listcomp>(.0)
    259 def _parse_result(
    260     self, result: List[Dict[str, str]]
    261 ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
    262     if self.prompt.output_parser is not None:
    263         return [
--> 264             self.prompt.output_parser.parse(res[self.output_key]) for res in result
    265         ]
    266     else:
    267         return result

File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\output_parsers\regex.py:28, in RegexParser.parse(self, text)
     26 else:
     27     if self.default_output_key is None:
---> 28         raise ValueError(f"Could not parse output: {text}")
     29     else:
     30         return {
     31             key: text if key == self.default_output_key else ""
     32             for key in self.output_keys
     33         }

ValueError: Could not parse output: QUESTION: What is the fabric composition of the Maine Expedition Shirt with PrimaLoft®?

ANSWER: The fabric composition of the Maine Expedition Shirt with PrimaLoft® is 85% premium wool and 15% nylon.

Expected behavior

Returns parsed output.

AlxndrMlk commented 1 year ago

Note that this only happens on some documents and works on others.

--One hypothesis I have is that special characters like "®" or quotation marks " might be the issue, but I am not entirely sure that this is the case.--

[EDIT]: It seems this hypothesis is not true as the problem also occurred in the following case:

ValueError: Could not parse output: QUESTION: What are the features of the Heritage Rugby, Traditional Fit Long-Sleeve Shoulder Stripe shirt?

ANSWER: The Heritage Rugby, Traditional Fit Long-Sleeve Shoulder Stripe shirt features rubber buttons, a rugged all-cotton fabric, a woven collar, herringbone-taped double placket, ribbed cuffs, and a droptail hem. It is lighter than the original rugby shirt but still has the same familiar feel. The traditional fit of the shirt is relaxed through the chest, sleeve, and waist. Additionally, the shirt is washed for the appearance of being worn for years and is finished with chambray trim at the collar, label, and side vents.
dosubot[bot] commented 1 year ago

Answer generated by a 🤖

Answer

It appears that you're encountering a ValueError because the RegexParser is unable to parse the output from the language model. This could be due to the output format not aligning with the regular expression pattern that the RegexParser is using.

When you create the QAGenerateChain, it's configured with a specific output parser based on the output_parser argument. If you set output_parser to "pydantic", it uses a PydanticOutputFunctionsParser. If it's set to "base", an OutputFunctionsParser is used.

To address this issue, you need to ensure that the output_parser argument is compatible with the format of the output from the language model. If your output has a structured format, you might want to use the "pydantic" option and provide a Pydantic model as the schema argument. Here's an example of how you can do this:

from langchain.chains.openai_functions.qa_with_structure import QAGenerateChain
from langchain.agents import ChatOpenAI
from pydantic import BaseModel

# Define your Pydantic model
class MyModel(BaseModel):
    question: str
    answer: str

# Instantiate the chain
example_gen_chain = QAGenerateChain.from_llm(ChatOpenAI(), output_parser="pydantic", schema=MyModel)

# Now the apply_and_parse method should work as expected
example_gen_chain.apply_and_parse([{'doc': data[2]}])

In this example, MyModel is a Pydantic model that matches the structure of the output. If the output doesn't match this structure, a ValueError will be raised.

Please note that this is one potential solution. If you believe this is a bug that could impact other users, feel free to make a pull request with a proposed fix. If this solution doesn't align with your experience, I would recommend upgrading to the latest version of LangChain to ensure you have the most recent updates and bug fixes.

Sources

This response is meant to be useful, save you time, and share context. 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.

AlxndrMlk commented 1 year ago

That's fine, but the parser works on some other very similar examples.

It seems that there's some bug in the parser that does not allow it to parse the answers in some particular situations (I haven't figured out what is the common denominator of these scenarios yet)

97k commented 1 year ago

Facing the same issue (langchain v0.0.179)!

I believe the issue is QAGenerateChain.apply_and_parse method doesn't process langchain.schema.Document type. I passed doc.page_content which is str and it worked!

image

But the typehint shoes it accepts Any It should mention str @hwchase17 @AlxndrMlk image

97k commented 1 year ago

@AlxndrMlk This behaves in a weird way, I re-ran the code right now and it wasn't working. It was giving consistent error, then I fed a different data point, on which it worked. I again tried on the one it was giving error earlier but after previous successful run, this also ran successfully!

I'll turn on the debug mode and will check what is actually going on under the hood.

Thanks for posting your comment, it helped!

dosubot[bot] commented 11 months ago

Hi, @AlxndrMlk! I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.

Based on the information provided, it seems that you encountered a ValueError when using the QAGenerateChain's .apply_and_parse() method. Dosubot suggested that the issue may be due to the output format not aligning with the regular expression pattern used by the RegexParser. They provided a potential solution by ensuring that the output_parser argument is compatible with the output format. You mentioned that the parser works on some similar examples, but there seems to be a bug that prevents parsing in certain situations. Another user, 97k, also faced the same issue and found that passing a str instead of doc.page_content to the method resolved the issue. However, it is mentioned that the issue behaves inconsistently and further investigation is needed with debug mode.

Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself or it will be automatically closed in 7 days.

Thank you for your contribution to the LangChain repository!