ProvidersMoreCometOn this pageCometIn this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet.  Example Project: Comet with LangChainInstall Comet and Dependencies​import sys{sys.executable} -m spacy download en_core_web_smInitialize Comet and Set your Credentials​You can grab your Comet API Key here or click the link after initializing Cometimport comet_mlcomet_ml.init(project_name="comet-example-langchain")Set OpenAI and SerpAPI credentials​You will need an OpenAI API Key and a SerpAPI API Key to run the following examplesimport osos.environ["OPENAI_API_KEY"] = "..."# os.environ["OPENAI_ORGANIZATION"] = "..."os.environ["SERPAPI_API_KEY"] = "..."Scenario 1: Using just an LLM​from datetime import datetimefrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIcomet_callback = CometCallbackHandler(    project_name="comet-example-langchain",    complexity_metrics=True,    stream_logs=True,    tags=["llm"],    visualizations=["dep"],)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3)print("LLM result", llm_result)comet_callback.flush_tracker(llm, finish=True)Scenario 2: Using an LLM in a Chain​from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatecomet_callback = CometCallbackHandler(    complexity_metrics=True,    project_name="comet-example-langchain",    stream_logs=True,    tags=["synopsis-chain"],)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)template = """You are a playwright. Given the title of play, it is your 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​from 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​The 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, generation, prompt_idx, gen_idx):        prediction = generation.text        results = self.scorer.score(target=self.reference, prediction=prediction)        return {            "rougeLsum_score": results["rougeLsum"].fmeasure,            "reference": self.reference,        }reference = """The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.It was the first structure to reach a height of 300 metres.It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France ."""rouge_score = Rouge(reference=reference)template = """Given the following article, it is your job to write a summary.Article:{article}Summary: This is the summary for the above article:"""prompt_template = PromptTemplate(input_variables=["article"], template=template)comet_callback = CometCallbackHandler(    project_name="comet-example-langchain",    complexity_metrics=False,    stream_logs=True,    tags=["custom_metrics"],    custom_metrics=rouge_score.compute_metric,)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)test_prompts = [    {        "article": """                 The tower is 324 metres (1,063 ft) tall, about the same height as                 an 81-storey building, and the tallest structure in Paris. Its base is square,                 measuring 125 metres (410 ft) on each side.                 During its construction, the Eiffel Tower surpassed the                 Washington Monument to become the tallest man-made structure in the world,                 a title it held for 41 years until the Chrysler Building                 in New York City was finished in 1930.                 It was the first structure to reach a height of 300 metres.                 Due to the addition of a broadcasting aerial at the top of the tower in 1957,                 it is now taller than the Chrysler Building by 5.2 metres (17 ft).                 Excluding transmitters, the Eiffel Tower is the second tallest                 free-standing structure in France after the Millau Viaduct.                 """    }]print(synopsis_chain.apply(test_prompts, callbacks=callbacks))comet_callback.flush_tracker(synopsis_chain, finish=True)PreviousCollege ConfidentialNextConfident AIInstall Comet and DependenciesInitialize Comet and Set your CredentialsSet OpenAI and SerpAPI credentialsScenario 1: Using just an LLMScenario 2: Using an LLM in a ChainScenario 3: Using An Agent with ToolsScenario 4: Using Custom Evaluation Metrics