Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsAINetworkAirbyte Question AnsweringAmadeusAzure Cognitive ServicesClickUp Langchain ToolkitCSVDocument ComparisonGithubGitlabGmailGoogle Drive toolJiraJSONMultiOnOffice365OpenAPINatural Language APIsPandas DataframePlayWright BrowserPowerBI DatasetPythonSpark DataframeSpark SQLSQL DatabaseVectorstoreXorbitsMemoryCallbacksChat loadersComponentsAgents and toolkitsPowerBI DatasetOn this pagePowerBI DatasetThis notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.Note that, as this agent is in active development, all answers might not be correct. It runs against the executequery endpoint, which does not allow deletes.Notes:‚ÄãIt relies on authentication with the azure.identity package, which can be installed with pip install azure-identity. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.You can also supply a username to impersonate for use with datasets that have RLS enabled. The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.Testing was done mostly with a text-davinci-003 model, codex models did not seem to perform ver well.Initialization‚Äãfrom langchain.agents.agent_toolkits import create_pbi_agentfrom langchain.agents.agent_toolkits import PowerBIToolkitfrom langchain.utilities.powerbi import PowerBIDatasetfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import AgentExecutorfrom azure.identity import DefaultAzureCredentialfast_llm = ChatOpenAI(    temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True)smart_llm =
This notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.
This notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsAINetworkAirbyte Question AnsweringAmadeusAzure Cognitive ServicesClickUp Langchain ToolkitCSVDocument ComparisonGithubGitlabGmailGoogle Drive toolJiraJSONMultiOnOffice365OpenAPINatural Language APIsPandas DataframePlayWright BrowserPowerBI DatasetPythonSpark DataframeSpark SQLSQL DatabaseVectorstoreXorbitsMemoryCallbacksChat loadersComponentsAgents and toolkitsPowerBI DatasetOn this pagePowerBI DatasetThis notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.Note that, as this agent is in active development, all answers might not be correct. It runs against the executequery endpoint, which does not allow deletes.Notes:‚ÄãIt relies on authentication with the azure.identity package, which can be installed with pip install azure-identity. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.You can also supply a username to impersonate for use with datasets that have RLS enabled. The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.Testing was done mostly with a text-davinci-003 model, codex models did not seem to perform ver well.Initialization‚Äãfrom langchain.agents.agent_toolkits import create_pbi_agentfrom langchain.agents.agent_toolkits import PowerBIToolkitfrom langchain.utilities.powerbi import PowerBIDatasetfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import AgentExecutorfrom azure.identity import DefaultAzureCredentialfast_llm = ChatOpenAI(    temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True)smart_llm =