= LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)test_prompts = [    {        "title": "documentary about good video games that push the boundary of game design"    },    {"title": "cocaine bear vs heroin wolf"},    {"title": "the best in class mlops tooling"},]synopsis_chain.apply(test_prompts)wandb_callback.flush_tracker(synopsis_chain, name="agent")Waiting for W&B process to finish... <strong style="color:green">(success).</strong>View run <strong style="color:#cdcd00">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>VBox(children=(Label(value='Waiting for wandb.init()...\r'), FloatProgress(value=0.016736786816666675, max=1.0‚Ä¶Tracking run with wandb version 0.14.0Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/>View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>from langchain.agents import initialize_agent, load_toolsfrom langchain.agents import AgentType# SCENARIO 3 - Agent with Toolstools = load_tools(["serpapi", "llm-math"], llm=llm)agent = initialize_agent(    tools,    llm,
This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.
This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see. ->: = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)test_prompts = [    {        "title": "documentary about good video games that push the boundary of game design"    },    {"title": "cocaine bear vs heroin wolf"},    {"title": "the best in class mlops tooling"},]synopsis_chain.apply(test_prompts)wandb_callback.flush_tracker(synopsis_chain, name="agent")Waiting for W&B process to finish... <strong style="color:green">(success).</strong>View run <strong style="color:#cdcd00">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>VBox(children=(Label(value='Waiting for wandb.init()...\r'), FloatProgress(value=0.016736786816666675, max=1.0‚Ä¶Tracking run with wandb version 0.14.0Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/>View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>from langchain.agents import initialize_agent, load_toolsfrom langchain.agents import AgentType# SCENARIO 3 - Agent with Toolstools = load_tools(["serpapi", "llm-math"], llm=llm)agent = initialize_agent(    tools,    llm,