Support Exporting LangSmith Run Traces into Langfuse!

[p-barman]

Describe the feature or potential improvement

Loving the OSS project! Would love to see easy integration to export Langsmith run traces into Langfuse.

Eg: converting run traces -> Generations and logging to the Langfuse Cloud more seamlessly.

import json
from datetime import datetime
from uuid import UUID

class Trace:
    def __init__(self):
        self.generations = []

    def generation(self, **kwargs):
        generation = Generation(**kwargs)
        self.generations.append(generation)
        return generation

class Generation:
    def __init__(self, **kwargs):
        self.start_time = datetime.now()
        self.__dict__.update(kwargs)

    def end(self, output, usage):
        self.end_time = datetime.now()
        self.output = output
        self.usage = usage

def log_generation_from_run(run):
    trace = Trace()
    
    # Extract input messages )
    input_messages = []
    for msg in run.inputs['messages']: 
        message_type = msg["id"][-1]
        if message_type == "SystemMessage":
            role = "system"
        elif message_type == "HumanMessage":
            role = "user"
        elif message_type == "AIMessage":
            role = "assistant"
        else:
            role = message_type.lower().replace("message", "")
        
        input_messages.append({
            "role": role,
            "content": msg["kwargs"]["content"]
        })

    
    # Create generation
    generation = trace.generation(
        name=run.name,
        model=run.extra['invocation_params']['model'],
        model_parameters={
            k: v for k, v in run.extra['invocation_params'].items()
            if k not in ["model", "model_name"]
        },
        input=input_messages,
        metadata={
            "user_id": run.extra['metadata'].get('user_id'),
            "tags": run.tags,
            "session_id": str(run.session_id),
            "trace_id": str(run.trace_id)
        }
    )
    
    # Extract the output (last generation)
    output = run.outputs['generations'][0]['text']
    
    # Create usage information
    usage = {
        "prompt_tokens": run.prompt_tokens,
        "completion_tokens": run.completion_tokens,
        "total_tokens": run.total_tokens,
        "unit": "TOKENS"
    }
    
    # Update generation and set end time
    generation.end(
        output=output,
        usage=usage
    )
    
    return generation
    ```

### Additional information

_No response_

[marcklingen]

thanks for sharing! Would you feel comfortable adding an example notebook to our documentation on how to do this? Do you have any open questions that I can help you with?