and toolkitsMemoryCallbacksChat loadersProvidersMoreWeights & BiasesWeights & BiasesThis 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.View Report Note: the WandbCallbackHandler is being deprecated in favour of the WandbTracer . In future please use the WandbTracer as it is more flexible and allows for more granular logging. To know more about the WandbTracer refer to the agent_with_wandb_tracing.html notebook or use the following colab notebook. To know more about Weights & Biases Prompts refer to the following prompts documentation.pip install wandbpip install pandaspip install textstatpip install spacypython -m spacy download en_core_web_smimport osos.environ["WANDB_API_KEY"] = ""# os.environ["OPENAI_API_KEY"] = ""# os.environ["SERPAPI_API_KEY"] = ""from datetime import datetimefrom langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAICallback Handler that logs to Weights and Biases.Parameters:    job_type (str): The type of job.    project (str): The project to log to.    entity (str): The entity to log to.    tags (list): The tags to log.    group (str): The group to log to.    name (str): The name of the run.    notes (str): The notes to log.    visualize (bool): Whether to visualize the run.    complexity_metrics (bool): Whether to log complexity metrics.    stream_logs (bool): Whether to stream callback actions to W&BDefault values for WandbCallbackHandler(...)visualize: bool = False,complexity_metrics: bool = False,stream_logs: bool = False,NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"""Main function.This function is used to try the callback handler.Scenarios:1. OpenAI LLM2. Chain with multiple SubChains on multiple
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. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreWeights & BiasesWeights & BiasesThis 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.View Report Note: the WandbCallbackHandler is being deprecated in favour of the WandbTracer . In future please use the WandbTracer as it is more flexible and allows for more granular logging. To know more about the WandbTracer refer to the agent_with_wandb_tracing.html notebook or use the following colab notebook. To know more about Weights & Biases Prompts refer to the following prompts documentation.pip install wandbpip install pandaspip install textstatpip install spacypython -m spacy download en_core_web_smimport osos.environ["WANDB_API_KEY"] = ""# os.environ["OPENAI_API_KEY"] = ""# os.environ["SERPAPI_API_KEY"] = ""from datetime import datetimefrom langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAICallback Handler that logs to Weights and Biases.Parameters:    job_type (str): The type of job.    project (str): The project to log to.    entity (str): The entity to log to.    tags (list): The tags to log.    group (str): The group to log to.    name (str): The name of the run.    notes (str): The notes to log.    visualize (bool): Whether to visualize the run.    complexity_metrics (bool): Whether to log complexity metrics.    stream_logs (bool): Whether to stream callback actions to W&BDefault values for WandbCallbackHandler(...)visualize: bool = False,complexity_metrics: bool = False,stream_logs: bool = False,NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"""Main function.This function is used to try the callback handler.Scenarios:1. OpenAI LLM2. Chain with multiple SubChains on multiple