<td>1.0</td>      <td>{'input': 'What's LangSmith?'}</td>      <td>{'input': 'What's LangSmith?', 'output': 'Lang...</td>      <td>{'output': 'LangSmith is a unified platform fo...</td>    </tr>    <tr>      <th>762a616c-7aab-419c-9001-b43ab6200d26</th>      <td>0.098766</td>      <td>0.0</td>      <td>0.1</td>      <td>0.0</td>      <td>{'input': 'What is LangChain?'}</td>      <td>{'input': 'What is LangChain?', 'output': 'Lan...</td>      <td>{'output': 'LangChain is an open-source framew...</td>    </tr>  </tbody></table></div>(Optional) Compare to another prompt‚ÄãNow that we have our test run results, we can make changes to our agent and benchmark them. Let's try this again with a different prompt and see the results.candidate_prompt = hub.pull("wfh/langsmith-agent-prompt:39f3bbd0")chain_results = run_on_dataset(    dataset_name=dataset_name,    llm_or_chain_factory=functools.partial(agent_factory, prompt=candidate_prompt),    evaluation=evaluation_config,    verbose=True,    client=client,    project_name=f"runnable-agent-test-39f3bbd0-{unique_id}",    tags=["testing-notebook", "prompt:39f3bbd0"],  # Optional, adds a tag to the resulting chain runs)    View the evaluation results for project 'runnable-agent-test-39f3bbd0-bf2162aa' at:    https://smith.langchain.com/o/ebbaf2eb-769b-4505-aca2-d11de10372a4/projects/p/fa721ccc-dd0f-41c9-bf80-22215c44efd4    [------------------------------------------------->] 5/5     Eval quantiles:                                   0.25       0.5      0.75      mean      mode    embedding_cosine_distance  0.059506  0.155538  0.212864  0.157915  0.043119    correctness                0.000000  0.000000  1.000000  0.400000  0.000000    score_string:accuracy      0.700000  1.000000  1.000000  0.880000  1.000000    helpfulness                1.000000  1.000000  1.000000  0.800000  1.000000Exporting datasets and runs‚ÄãLangSmith lets you export data to common formats such as CSV or JSONL directly in the web app. You can also
Open In Colab
Open In Colab ->: <td>1.0</td>      <td>{'input': 'What's LangSmith?'}</td>      <td>{'input': 'What's LangSmith?', 'output': 'Lang...</td>      <td>{'output': 'LangSmith is a unified platform fo...</td>    </tr>    <tr>      <th>762a616c-7aab-419c-9001-b43ab6200d26</th>      <td>0.098766</td>      <td>0.0</td>      <td>0.1</td>      <td>0.0</td>      <td>{'input': 'What is LangChain?'}</td>      <td>{'input': 'What is LangChain?', 'output': 'Lan...</td>      <td>{'output': 'LangChain is an open-source framew...</td>    </tr>  </tbody></table></div>(Optional) Compare to another prompt‚ÄãNow that we have our test run results, we can make changes to our agent and benchmark them. Let's try this again with a different prompt and see the results.candidate_prompt = hub.pull("wfh/langsmith-agent-prompt:39f3bbd0")chain_results = run_on_dataset(    dataset_name=dataset_name,    llm_or_chain_factory=functools.partial(agent_factory, prompt=candidate_prompt),    evaluation=evaluation_config,    verbose=True,    client=client,    project_name=f"runnable-agent-test-39f3bbd0-{unique_id}",    tags=["testing-notebook", "prompt:39f3bbd0"],  # Optional, adds a tag to the resulting chain runs)    View the evaluation results for project 'runnable-agent-test-39f3bbd0-bf2162aa' at:    https://smith.langchain.com/o/ebbaf2eb-769b-4505-aca2-d11de10372a4/projects/p/fa721ccc-dd0f-41c9-bf80-22215c44efd4    [------------------------------------------------->] 5/5     Eval quantiles:                                   0.25       0.5      0.75      mean      mode    embedding_cosine_distance  0.059506  0.155538  0.212864  0.157915  0.043119    correctness                0.000000  0.000000  1.000000  0.400000  0.000000    score_string:accuracy      0.700000  1.000000  1.000000  0.880000  1.000000    helpfulness                1.000000  1.000000  1.000000  0.800000  1.000000Exporting datasets and runs‚ÄãLangSmith lets you export data to common formats such as CSV or JSONL directly in the web app. You can also