mmbazel-labelbox

import labelbox as lb
import transformers
import torch
import torch.nn.functional as F
from PIL import Image
import requests
import numpy as np

# Add your API key
API_KEY = "your_api_key_here"

mmbazel-labelbox

import labelbox as lb

client = lb.Client(api_key="<YOUR_API_KEY>")

dataset = client.create_dataset(name='<dataset_name>')

dataset.create_data_rows("<data row payload>")

mmbazel-labelbox

from uuid import uuid4

def import_conversational (chain_results: dict[str:str], user_id_dict: dict[str:str], output_user_name: str) -> dict[str:str]:
    """Turn chain_result dictionary object from Langchain to conversation data for Labelbox

    Args:
        chain_results (dict[str:str]): LangChain evaluation results.
        user_id_dict (dict[str:str]): Dictionary matching chat type of LangChain(user) to Labelbox type with corresponding Labelbox UserID and Chat Alignment.
                                      {<Langchain user>: {id: <labelbox userid>, "alight", <Labelbox alignment (right or left)>}}
        output_user_name (str): LangChain output user type.

mmbazel-labelbox

chain_results = run_on_dataset(
    dataset_name=LS_DATASET_NAME,
    llm_or_chain_factory=functools.partial(
        create_agent, prompt=prompt, llm_with_tools=llm_with_tools
    ),
    evaluation=evaluation_config,
    verbose=True,
    client=client,
    project_name=f"tools-agent-test-5d466cbc-{unique_id}",
    # Project metadata communicates the experiment parameters,