your agent (or any custom function) on each example.Apply evaluators to the resulting run traces and corresponding reference examples to generate automated feedback.The results will be visible in the LangSmith app.from langchain import hub# We will test this version of the promptprompt = hub.pull("wfh/langsmith-agent-prompt:798e7324")import functoolsfrom langchain.smith import (    arun_on_dataset,    run_on_dataset, )chain_results = run_on_dataset(    dataset_name=dataset_name,    llm_or_chain_factory=functools.partial(agent_factory, prompt=prompt),    evaluation=evaluation_config,    verbose=True,    client=client,    project_name=f"runnable-agent-test-5d466cbc-{unique_id}",    tags=["testing-notebook", "prompt:5d466cbc"],  # Optional, adds a tag to the resulting chain runs)# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.# These are logged as warnings here and captured as errors in the tracing UI.    View the evaluation results for project 'runnable-agent-test-5d466cbc-bf2162aa' at:    https://smith.langchain.com/o/ebbaf2eb-769b-4505-aca2-d11de10372a4/projects/p/0c3d22fa-f8b0-4608-b086-2187c18361a5    [>                                                 ] 0/5    Chain failed for example 54b4fce8-4492-409d-94af-708f51698b39 with inputs {'input': 'Who trained Llama-v2?'}    Error Type: TypeError, Message: DuckDuckGoSearchResults._run() got an unexpected keyword argument 'arg1'    [------------------------------------------------->] 5/5     Eval quantiles:                                   0.25       0.5      0.75      mean      mode    embedding_cosine_distance  0.086614  0.118841  0.183672  0.151444  0.050158    correctness                0.000000  0.500000  1.000000  0.500000  0.000000    score_string:accuracy      0.775000  1.000000  1.000000  0.775000  1.000000    helpfulness                0.750000  1.000000  1.000000  0.750000  1.000000Review the test results‚ÄãYou can review the test results tracing UI below by clicking
Open In Colab
Open In Colab ->: your agent (or any custom function) on each example.Apply evaluators to the resulting run traces and corresponding reference examples to generate automated feedback.The results will be visible in the LangSmith app.from langchain import hub# We will test this version of the promptprompt = hub.pull("wfh/langsmith-agent-prompt:798e7324")import functoolsfrom langchain.smith import (    arun_on_dataset,    run_on_dataset, )chain_results = run_on_dataset(    dataset_name=dataset_name,    llm_or_chain_factory=functools.partial(agent_factory, prompt=prompt),    evaluation=evaluation_config,    verbose=True,    client=client,    project_name=f"runnable-agent-test-5d466cbc-{unique_id}",    tags=["testing-notebook", "prompt:5d466cbc"],  # Optional, adds a tag to the resulting chain runs)# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.# These are logged as warnings here and captured as errors in the tracing UI.    View the evaluation results for project 'runnable-agent-test-5d466cbc-bf2162aa' at:    https://smith.langchain.com/o/ebbaf2eb-769b-4505-aca2-d11de10372a4/projects/p/0c3d22fa-f8b0-4608-b086-2187c18361a5    [>                                                 ] 0/5    Chain failed for example 54b4fce8-4492-409d-94af-708f51698b39 with inputs {'input': 'Who trained Llama-v2?'}    Error Type: TypeError, Message: DuckDuckGoSearchResults._run() got an unexpected keyword argument 'arg1'    [------------------------------------------------->] 5/5     Eval quantiles:                                   0.25       0.5      0.75      mean      mode    embedding_cosine_distance  0.086614  0.118841  0.183672  0.151444  0.050158    correctness                0.000000  0.500000  1.000000  0.500000  0.000000    score_string:accuracy      0.775000  1.000000  1.000000  0.775000  1.000000    helpfulness                0.750000  1.000000  1.000000  0.750000  1.000000Review the test results‚ÄãYou can review the test results tracing UI below by clicking