How to add send metadata to the Langsmith threads tracing table after a completed run

[AsmaaMHadir]

I would like to display a dictionary that contains the confidence score of my generation after the end of each LangGraph thread run. How can I send a value to the tag column or create a custom column to store my value in and display on LangSmith? I use:

    @staticmethod
    @traceable(run_type="llm", metadata={"ls_provider": "azure", "ls_model_name": "azure_openai_4o"})
    async def run_generator(prompt):
        """Invoke LLM to generate response."""
        return await LangGraphConfig.initialize_llm().ainvoke(prompt)

To trace my AzureChatOpenAI calls.

[dosubot[bot]]

To add custom metadata, such as a confidence score, to the Langsmith threads tracing table after a completed run, you can modify the metadata parameter in the @traceable decorator or use the on_chat_model_start method to include your custom data. Here's how you can do it:

1. Using the @traceable Decorator:
   You can add your confidence score directly to the metadata dictionary in the @traceable decorator. This will associate the metadata with the function call.

   @staticmethod
   @traceable(run_type="llm", metadata={"ls_provider": "azure", "ls_model_name": "azure_openai_4o", "confidence_score": your_confidence_score})
   async def run_generator(prompt):
       """Invoke LLM to generate response."""
       return await LangGraphConfig.initialize_llm().ainvoke(prompt)

   Replace your_confidence_score with the actual confidence score value you want to store.

2. Using the on_chat_model_start Method:
   If you need to add metadata after the run has started, you can modify the on_chat_model_start method in the LangChainTracer class to include your custom metadata. This method allows you to pass additional metadata when starting a trace.

   def on_chat_model_start(
       self,
       serialized: dict[str, Any],
       messages: list[list[BaseMessage]],
       *,
       run_id: UUID,
       tags: Optional[list[str]] = None,
       parent_run_id: Optional[UUID] = None,
       metadata: Optional[dict[str, Any]] = None,
       name: Optional[str] = None,
       **kwargs: Any,
   ) -> Run:
       """Start a trace for an LLM run."""
       start_time = datetime.now(timezone.utc)
       if metadata:
           metadata["confidence_score"] = your_confidence_score
           kwargs.update({"metadata": metadata})
       chat_model_run = Run(
           id=run_id,
           parent_run_id=parent_run_id,
           serialized=serialized,
           inputs={"messages": [[dumpd(msg) for msg in batch] for batch in messages]},
           extra=kwargs,
           events=[{"name": "start", "time": start_time}],
           start_time=start_time,
           run_type="llm",
           tags=tags,
           name=name,  # type: ignore[arg-type]
       )
       self._start_trace(chat_model_run)
       self._on_chat_model_start(chat_model_run)
       return chat_model_run

   Again, replace your_confidence_score with the actual value you want to store.

For updating the tag column or creating a custom column, you can use the _update_run_single method to update the tags or add new fields to the run dictionary before updating it in the database ^[1].

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[AsmaaMHadir]

@dosu

Is there a way to add the confidence score after getting the results from the run_generator function? since I get the output of the llm then calculate the confidence score like in here:

    async def generate_answer(self, state: State):
       """Generate the final answer based on context."""
       formatted_context = LangGraphUtils.format_context(state["context"])
       prompt_input = {"question": state["messages"][-1].content,"context": formatted_context,"chat_history": state["messages"]
        }
       out_prompt = ChatPromptTemplate.from_template(generation_prompt_template_v3).invoke(prompt_input)
       try:
            response = await self.run_generator(out_prompt)
            state["answer"] = response.content if response.content else "Could not generate an answer."
            confidence_score = LangGraphUtils.calculate_weighted_confidence_score(response.response_metadata)
            state["confidence_score"] = confidence_score
            logger.info(f"===================Confidence score: {confidence_score}")
            return {"answer": state["answer"], "context": state["context"],"confidence_score": state["confidence_score"]}
       except Exception:
            logger.error("Error generating answer:\n" + traceback.format_exc())
            return None

[dosubot[bot]]

To add the confidence score after getting the results from the run_generator function, you can update the run metadata post-execution using the _update_run_single method. This method allows you to modify the run's metadata after the run has completed. Here's how you can do it:

1. Calculate the Confidence Score:
   After you receive the response from the run_generator function, calculate the confidence score as you are already doing.

2. Update the Run Metadata:
   Use the _update_run_single method to append the confidence score to the run's metadata. You will need to ensure that you have access to the run ID and the LangChainTracer instance to perform this update.

Here's a conceptual example of how you might implement this:

async def generate_answer(self, state: State, tracer: LangChainTracer):
    """Generate the final answer based on context."""
    formatted_context = LangGraphUtils.format_context(state["context"])
    prompt_input = {
        "question": state["messages"][-1].content,
        "context": formatted_context,
        "chat_history": state["messages"]
    }
    out_prompt = ChatPromptTemplate.from_template(generation_prompt_template_v3).invoke(prompt_input)
    try:
        response = await self.run_generator(out_prompt)
        state["answer"] = response.content if response.content else "Could not generate an answer."
        confidence_score = LangGraphUtils.calculate_weighted_confidence_score(response.response_metadata)
        state["confidence_score"] = confidence_score
        logger.info(f"===================Confidence score: {confidence_score}")

        # Update the run metadata with the confidence score
        run_id = response.run_id  # Ensure you have the run ID
        tracer._update_run_single(Run(id=run_id, extra={"confidence_score": confidence_score}))

        return {"answer": state["answer"], "context": state["context"], "confidence_score": state["confidence_score"]}
    except Exception:
        logger.error("Error generating answer:\n" + traceback.format_exc())
        return None

Make sure to replace response.run_id with the actual way you retrieve the run ID from your response object. This approach allows you to update the metadata after the run has completed, ensuring that the confidence score is included in the tracing data.

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[AsmaaMHadir]

@dosu I want to make the update at the thread level, not run level.

[dosubot[bot]]

LangChain does not provide a specific method or API for updating metadata at the thread level. The framework primarily focuses on run-level operations, and the _update_run_single method is used for updating run metadata ^[1].

If you need to update metadata at the thread level, you might need to implement a custom solution. This could involve managing thread-specific metadata separately and associating it with runs as needed. You could potentially extend the existing tracing system to include thread-level metadata handling, but this would require a deeper integration into the LangChain framework.

To continue talking to Dosu, mention @dosu.

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