job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]print(synopsis_chain.apply(test_prompts))comet_callback.flush_tracker(synopsis_chain, finish=True)Scenario 3: Using An Agent with Tools‚Äãfrom langchain.agents import initialize_agent, load_toolsfrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIcomet_callback = CometCallbackHandler(    project_name="comet-example-langchain",    complexity_metrics=True,    stream_logs=True,    tags=["agent"],)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)agent = initialize_agent(    tools,    llm,    agent="zero-shot-react-description",    callbacks=callbacks,    verbose=True,)agent.run(    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")comet_callback.flush_tracker(agent, finish=True)Scenario 4: Using Custom Evaluation Metrics‚ÄãThe CometCallbackManager also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let's take a look at how this works. In the snippet below, we will use the ROUGE metric to evaluate the quality of a generated summary of an input prompt. %pip install rouge-scorefrom rouge_score import rouge_scorerfrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplateclass Rouge:    def __init__(self, reference):        self.reference = reference        self.scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True)    def compute_metric(self,
In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet.
In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet. ->: job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]print(synopsis_chain.apply(test_prompts))comet_callback.flush_tracker(synopsis_chain, finish=True)Scenario 3: Using An Agent with Tools‚Äãfrom langchain.agents import initialize_agent, load_toolsfrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIcomet_callback = CometCallbackHandler(    project_name="comet-example-langchain",    complexity_metrics=True,    stream_logs=True,    tags=["agent"],)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)agent = initialize_agent(    tools,    llm,    agent="zero-shot-react-description",    callbacks=callbacks,    verbose=True,)agent.run(    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")comet_callback.flush_tracker(agent, finish=True)Scenario 4: Using Custom Evaluation Metrics‚ÄãThe CometCallbackManager also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let's take a look at how this works. In the snippet below, we will use the ROUGE metric to evaluate the quality of a generated summary of an input prompt. %pip install rouge-scorefrom rouge_score import rouge_scorerfrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplateclass Rouge:    def __init__(self, reference):        self.reference = reference        self.scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True)    def compute_metric(self,