I met the probolem langchain_core.exceptions.OutputParserException: Invalid json output when i want to use the langchain to generate qa list from a input txt by using a llm

[LZS1991]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

nltk.data.path.append('./data/nltk_data')

chat_model_path = "E:/02-PythonProject/09_LLM/LLM-Model/Qwen1.5-1.8B-Chat"

QA_PAIRS_SYSTEM_PROMPT = """
<Context></Context> The tag contains a piece of text, learn and analyze it, and organize the learning outcomes:
-Ask questions and provide answers for each question.
-The answer should be detailed and complete, and the original description should be retained as much as possible.
-The answer can include Markdown elements such as regular text, links, code, tables, public notices, media links, etc.
-Up to 30 questions can be asked.
"""
QA_PAIRS_HUMAN_PROMPT = """  
Please organize the learning outcomes in the following format: 
<Context>  
txt  
</Context>    
[  
{{"question": "Q1","answer":"A1"}},  
{{"question": "Q2","answer":"A2"}},  
]  
------  

Let's start. 

<Context>  
{text}  
</Context>  
"""

class QaPair(BaseModel):
    question: str = Field(description='question')
    answer: str = Field(description='answer')


class QaPairs(BaseModel):
    qas: List[QaPair] = Field(description='QAList')


parser = JsonOutputParser(pydantic_object=QaPairs)


def split_document(filepath):
    loader = UnstructuredLoader(filepath)
    text_spliter = RecursiveCharacterTextSplitter(
        chunk_size=2048,
        chunk_overlap=128
    )
    documents = loader.load_and_split(text_spliter)
    return documents


def create_chain():
    prompt = ChatPromptTemplate.from_messages([
        ("system", QA_PAIRS_SYSTEM_PROMPT),
        ("human", QA_PAIRS_HUMAN_PROMPT)
    ])

    device = torch.device("cuda:0")
    tokenizer = AutoTokenizer.from_pretrained(chat_model_path, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(chat_model_path, trust_remote_code=True, device_map="auto")
    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        device_map='auto',
        # max_length=1024,
        max_new_tokens=1024,  # the bigest token
        top_p=1,
        repetition_penalty=1.15
    )
    llama_model = HuggingFacePipeline(pipeline=pipe)

    parser = JsonOutputParser(pydantic_object=QaPairs)
    chain = prompt | llama_model | parser
    return chain


def main():
    chain = create_chain()
    documents = split_document('test.txt')
    with open(f'dataset.json', 'w', encoding='utf-8') as f:
        datas = []
    bar = tqdm(total=len(documents))
    for idx, doc in enumerate(documents):
        bar.update(idx + 1)
        print(doc.page_content)
        out = chain.invoke({'text': doc.page_content})
        print(out)
        datas += out
        f.write(json.dumps(datas, ensure_ascii=False))


if __name__ == '__main__':
    main()

Description

I am trying to using langchain to generate dataset in alpaca format from an input txt by using a llm (Qwen1.5-1.8B-Chat), i want to get a json file contains the result,but the code met a probolem:
Traceback (most recent call last):
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\output_parsers\json.py", line 84, in parse_result
return parse_json_markdown(text)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\utils\json.py", line 147, in parse_json_markdown
return _parse_json(json_str, parser=parser)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\utils\json.py", line 163, in parse_json
return parser(json_str)
^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\utils\json.py", line 118, in parse_partial_json
return json.loads(s, strict=strict)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\json_init.py", line 359, in loads
return cls(**kw).decode(s)
^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "E:\02-PythonProject\97_AITools\LLMTools\llm_for_training_dataset_generation.py", line 111, in
main()
File "E:\02-PythonProject\97_AITools\LLMTools\llm_for_training_dataset_generation.py", line 104, in main
out = chain.invoke({'text': doc.page_content})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\runnables\base.py", line 2878, in invoke
input = context.run(step.invoke, input, config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\output_parsers\base.py", line 192, in invoke
return self._call_with_config(
^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\runnables\base.py", line 1785, in _call_with_config
context.run(
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\runnables\config.py", line 397, in call_func_with_variable_args
return func(input, **kwargs) # type: ignore[call-arg]
^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\output_parsers\base.py", line 193, in
lambda inner_input: self.parse_result([Generation(text=inner_input)]),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AnacondaREPO\envs\OpenLLM\Lib\site-packages\langchain_core\output_parsers\json.py", line 87, in parse_result
raise OutputParserException(msg, llm_output=text) from e
langchain_core.exceptions.OutputParserException: Invalid json output: System:

System Info

System Information

OS: Windows
OS Version: 10.0.22631
Python Version: 3.11.9 | packaged by Anaconda, Inc. | (main, Apr 19 2024, 16:40:41) [MSC v.1916 64 bit (AMD64)]

Package Information

langchain_core: 0.2.34
langchain: 0.2.14
langchain_community: 0.2.12
langsmith: 0.1.104
langchain_huggingface: 0.0.3
langchain_text_splitters: 0.2.2
langchain_unstructured: 0.1.2

Optional packages not installed

langgraph
langserve

Other Dependencies

aiohttp: 3.10.1
async-timeout: Installed. No version info available.
dataclasses-json: 0.6.7
httpx: 0.27.0
huggingface-hub: 0.24.5
jsonpatch: 1.33
numpy: 1.26.4
orjson: 3.10.6
packaging: 24.1
pydantic: 2.8.2
PyYAML: 6.0.2
requests: 2.32.3
sentence-transformers: 3.0.1
SQLAlchemy: 2.0.32
tenacity: 8.5.0
tokenizers: 0.19.1
transformers: 4.43.4
typing-extensions: 4.12.2
unstructured-client: 0.24.1
unstructured[all-docs]: Installed. No version info available.

[dosubot[bot]]

Hey, @LZS1991! I'm here to help you out with any bugs, questions, or contributions you might have while waiting for a human maintainer. Let's squash those bugs together! 🤖

I found a similar issue that might help you resolve the langchain_core.exceptions.OutputParserException: Invalid json output error. The issue is often related to the language model's output not being valid JSON. Here are some steps you can take to handle this:

1. Validate the JSON Output: Before attempting to parse the output as JSON, add a validation step to check if the output is valid JSON.

2. Handle Parsing Errors: Implement error handling to manage cases where the output is not valid JSON.

Here is an example of how you can modify your code to include these steps:

import json
from langchain_core.exceptions import OutputParserException

def _call(
    self,
    inputs: Dict[str, Any],
    run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, List]:
    docs = self.text_splitter.create_documents([inputs[self.input_key]])
    results = self.llm_chain.generate(
        [{"text": d.page_content} for d in docs], run_manager=run_manager
    )
    
    qa = []
    for res in results.generations:
        try:
            qa.append(json.loads(res[0].text))
        except json.JSONDecodeError:
            # Handle the invalid JSON output case
            print(f"Invalid JSON output: {res[0].text}")
            # You can choose to skip, log, or handle the invalid output as needed
            continue
    
    return {self.output_key: qa}

Additionally, you can use the RegexParser class from LangChain, which is designed to parse specific patterns from the LLM output. This parser uses a regex pattern to extract the required information and handles cases where the output does not match the expected format. Here is an example of how you can use this parser:

from langchain.output_parsers.regex import RegexParser

_QA_OUTPUT_PARSER = RegexParser(
    regex=r"QUESTION: (.*?)\n+ANSWER: (.*)", output_keys=["query", "answer"]
)

def _call(
    self,
    inputs: Dict[str, Any],
    run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, List]:
    docs = self.text_splitter.create_documents([inputs[self.input_key]])
    results = self.llm_chain.generate(
        [{"text": d.page_content} for d in docs], run_manager=run_manager
    )
    
    qa = []
    for res in results.generations:
        try:
            parsed_output = _QA_OUTPUT_PARSER.parse(res[0].text)
            qa.append(parsed_output)
        except OutputParserException as e:
            # Handle the invalid output case
            print(f"Invalid output: {res[0].text}")
            # You can choose to skip, log, or handle the invalid output as needed
            continue
    
    return {self.output_key: qa}

This approach ensures that your application can handle cases where the language model's output does not match the expected format, thereby preventing the OutputParserException from occurring ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[LZS1991]

This answer can not help me