TypeError: Object of type FieldInfo is not JSON serializable

[Drexx56]

I am building a simple RAG usin ChatNvidia as my llm but I keep getting this error:
TypeError: Object of type FieldInfo is not JSON serializable
each time I try running this code:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

rag_chain = (
    {"context": retriever | format_docs, "input": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

for chunk in rag_chain.stream("What price will NNPC sell Dangote fuel?"):
        print(json.dumps(chunk, cls=CustomEncoder), end="", flush=True)

can someone please help me out.

[dosubot[bot]]

Hey there, @Drexx56! I'm a bot here to help you with bug fixes, questions, and contributions. Let's tackle this issue together while we wait for a human maintainer.

To resolve the "TypeError: Object of type FieldInfo is not JSON serializable" error when running your RAG code using ChatNvidia as the LLM, you need to ensure that all objects being serialized to JSON are properly converted to serializable types. In the provided code, the _convert_message_to_dict function is used to convert BaseMessage objects to dictionaries, which are JSON serializable.

Here is the relevant part of the code that handles the conversion:

def _convert_message_to_dict(message: BaseMessage) -> dict:
    message_dict: Dict[str, Any]
    if isinstance(message, ChatMessage):
        message_dict = {"role": message.role, "content": message.content}
    elif isinstance(message, SystemMessage):
        message_dict = {"role": "system", "content": message.content}
    elif isinstance(message, HumanMessage):
        message_dict = {"role": "user", "content": message.content}
    elif isinstance(message, AIMessage):
        message_dict = {"role": "assistant", "content": message.content}
    else:
        raise TypeError(f"Got unknown type {message}")

    return message_dict

Ensure that this function is correctly converting all message types to dictionaries before they are included in the payload. The _get_payload method uses this function to prepare the payload:

def _get_payload(self, prompt: str) -> dict:
    params = self._invocation_params
    messages = self.prefix_messages + [HumanMessage(content=prompt)]
    params.update(
        {
            "messages": [_convert_message_to_dict(m) for m in messages],
        }
    )
    return params

By ensuring that all BaseMessage objects are converted to dictionaries using _convert_message_to_dict, you can avoid the "TypeError: Object of type FieldInfo is not JSON serializable" error ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[Drexx56]

@dosu it doesn't seem to solve the problem

[dosubot[bot]]

To further diagnose and resolve the issue, let's ensure that all components in your rag_chain pipeline are properly handled and that any custom objects are correctly serialized. Here are a few steps to help you debug and fix the issue:

1. Check the Structure of the Streamed Data:
   Ensure that the data being streamed by rag_chain.stream is in a format that can be serialized by json.dumps. You can print the chunks directly to see their structure:

   for chunk in rag_chain.stream("What price will NNPC sell Dangote fuel?"):
       print(chunk)

2. Update the Custom Encoder:
   Extend your CustomEncoder to handle any other custom objects that might be part of the streamed data. Here’s an example that includes handling for FieldInfo and any other potential custom objects:

   import json
   from pydantic import FieldInfo
   
   class CustomEncoder(json.JSONEncoder):
       def default(self, obj):
           if isinstance(obj, FieldInfo):
               return obj.__dict__  # Convert FieldInfo to a serializable format
           # Add more custom handling if needed
           return super().default(obj)
   
   # Your existing code
   for chunk in rag_chain.stream("What price will NNPC sell Dangote fuel?"):
       print(json.dumps(chunk, cls=CustomEncoder), end="", flush=True)

3. Ensure All Components are Serializable:
   Verify that all components in your rag_chain pipeline are returning serializable data. For example, ensure that the retriever, prompt, and llm objects are correctly configured and returning data in expected formats.

4. Debugging Specific Components:
   If the issue persists, isolate and debug specific components of the pipeline. For instance, check the output of the retriever and prompt separately to ensure they are returning serializable data.

   # Check retriever output
   context = retriever.retrieve("What price will NNPC sell Dangote fuel?")
   print(context)
   
   # Check prompt output
   formatted_prompt = prompt.format(context=context, question="What price will NNPC sell Dangote fuel?")
   print(formatted_prompt)

By following these steps, you should be able to identify and resolve the serialization issue. If you encounter specific errors or need further assistance, please provide additional details or error messages.

To continue talking to Dosu, mention @dosu.

[Drexx56]

here is my new code block but i still keep getting this error

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

for chunk in rag_chain.stream("What is Task Decomposition?"):
    print(chunk, end="", flush=True)

[Drexx56]

@dosu

here is my new code block but i still keep getting this error

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

for chunk in rag_chain.stream("What is Task Decomposition?"):
    print(chunk, end="", flush=True)

[dosubot[bot]]

The error you're encountering might be due to the way the rag_chain is constructed or how the components are interacting. Let's break down the code and ensure each part is correctly set up.

1. Ensure retriever and prompt are correctly defined:
   Make sure that retriever and prompt are properly initialized and returning the expected formats. The retriever should return documents in the expected format, and the prompt should be correctly formatted.

2. Check the format_docs function:
   Ensure that the format_docs function is correctly formatting the documents returned by the retriever.

3. Verify the llm component:
   Ensure that the llm component is correctly set up and returning a string output, as expected by the StrOutputParser.

Here is a revised version of your code with additional checks:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

# Ensure retriever, prompt, and llm are correctly defined
# Example definitions (replace with your actual definitions)
retriever = ...  # Your retriever instance
prompt = ...  # Your prompt instance
llm = ...  # Your LLM instance

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Debugging: Print each chunk to see its structure
for chunk in rag_chain.stream("What is Task Decomposition?"):
    print(chunk, end="", flush=True)

Additional Debugging Steps

1. Print Intermediate Outputs:
   Print the outputs of each component in the chain to ensure they are returning the expected formats.

   # Check retriever output
   context = retriever.retrieve("What is Task Decomposition?")
   formatted_context = format_docs(context)
   print("Formatted Context:", formatted_context)
   
   # Check prompt output
   formatted_prompt = prompt.format(context=formatted_context, question="What is Task Decomposition?")
   print("Formatted Prompt:", formatted_prompt)
   
   # Check LLM output
   llm_output = llm(formatted_prompt)
   print("LLM Output:", llm_output)

2. Ensure StrOutputParser is Correctly Handling the Output:
   The StrOutputParser should be able to handle the output from the llm component. Ensure that the llm component is returning a string.

By following these steps, you should be able to identify where the serialization issue is occurring and resolve it. If you encounter specific errors or need further assistance, please provide additional details or error messages.

To continue talking to Dosu, mention @dosu.