text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerMLflowMLflowThis notebook goes over how to track your LangChain experiments into your MLflow Serverpip install azureml-mlflowpip install pandaspip install textstatpip install spacypip install openaipip install google-search-resultspython -m spacy download en_core_web_smimport osos.environ["MLFLOW_TRACKING_URI"] = ""os.environ["OPENAI_API_KEY"] = ""os.environ["SERPAPI_API_KEY"] = ""from langchain.callbacks import MlflowCallbackHandlerfrom langchain.llms import OpenAI"""Main function.This function is used to try the callback handler.Scenarios:1. OpenAI LLM2. Chain with multiple SubChains on multiple generations3. Agent with Tools"""mlflow_callback = MlflowCallbackHandler()llm = OpenAI(    model_name="gpt-3.5-turbo", temperature=0, callbacks=[mlflow_callback], verbose=True)# SCENARIO 1 - LLMllm_result = llm.generate(["Tell me a joke"])mlflow_callback.flush_tracker(llm)from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChain# SCENARIO 2 - Chaintemplate = """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"],