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2,700	AINetwork \| ü¶úÔ∏èüîó Langchain	AI Network is a layer 1 blockchain designed to accommodate	AI Network is a layer 1 blockchain designed to accommodate ->: AINetwork \| ü¶úÔ∏èüîó Langchain
2,701	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat	AI Network is a layer 1 blockchain designed to accommodate	AI Network is a layer 1 blockchain designed to accommodate ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat
2,702	and toolkitsMemoryCallbacksChat loadersProvidersMoreAINetworkOn this pageAINetworkAI Network is a layer 1 blockchain designed to accommodate	AI Network is a layer 1 blockchain designed to accommodate	AI Network is a layer 1 blockchain designed to accommodate ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreAINetworkOn this pageAINetworkAI Network is a layer 1 blockchain designed to accommodate
2,703	large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).Installation and Setup‚ÄãYou need to install ain-py python package.pip install ain-pyYou need to set the AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY environmental variable to your AIN Blockchain Account Private Key.Toolkit‚ÄãSee a usage example.from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkitPreviousAimNextAirbyteInstallation and SetupToolkitCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	AI Network is a layer 1 blockchain designed to accommodate	AI Network is a layer 1 blockchain designed to accommodate ->: large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).Installation and Setup‚ÄãYou need to install ain-py python package.pip install ain-pyYou need to set the AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY environmental variable to your AIN Blockchain Account Private Key.Toolkit‚ÄãSee a usage example.from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkitPreviousAimNextAirbyteInstallation and SetupToolkitCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,704	AINetwork \| ü¶úÔ∏èüîó Langchain	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: AINetwork \| ü¶úÔ∏èüîó Langchain
2,705	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 toolkitsAINetworkOn this pageAINetworkAI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).The AINetwork Toolkit is a set of tools for interacting with the AINetwork Blockchain. These tools allow you to transfer AIN, read and write values, create apps, and set permissions for specific paths within the blockchain database.Installing dependencies‚ÄãBefore using the AINetwork Toolkit, you need to install the ain-py package. You can install it with pip:pip install ain-pySet environmental variables‚ÄãYou need to set the AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY environmental variable to your AIN Blockchain Account Private Key.import osos.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = ""Get AIN Blockchain private key‚Äãimport osfrom ain.account import Accountif os.environ.get("AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY", None): account = Account(os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"])else: account = Account.create() os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = account.private_key print( f"""address: {account.address}private_key: {account.private_key}""" )# IMPORTANT: If you plan to use this account in the future, make sure to save the# private key in a secure place. Losing access	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: 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 toolkitsAINetworkOn this pageAINetworkAI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).The AINetwork Toolkit is a set of tools for interacting with the AINetwork Blockchain. These tools allow you to transfer AIN, read and write values, create apps, and set permissions for specific paths within the blockchain database.Installing dependencies‚ÄãBefore using the AINetwork Toolkit, you need to install the ain-py package. You can install it with pip:pip install ain-pySet environmental variables‚ÄãYou need to set the AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY environmental variable to your AIN Blockchain Account Private Key.import osos.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = ""Get AIN Blockchain private key‚Äãimport osfrom ain.account import Accountif os.environ.get("AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY", None): account = Account(os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"])else: account = Account.create() os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = account.private_key print( f"""address: {account.address}private_key: {account.private_key}""" )# IMPORTANT: If you plan to use this account in the future, make sure to save the# private key in a secure place. Losing access
2,706	private key in a secure place. Losing access to your private key means losing# access to your account. address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac private_key: f5e2f359bb6b7836a2ac70815473d1a290c517f847d096f5effe818de8c2cf14 Initialize the AINetwork Toolkit‚ÄãYou can initialize the AINetwork Toolkit like this:from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkittoolkit = AINetworkToolkit()tools = toolkit.get_tools()address = tools[0].interface.wallet.defaultAccount.addressInitialize the Agent with the AINetwork Toolkit‚ÄãYou can initialize the agent with the AINetwork Toolkit like this:from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agent, AgentTypellm = ChatOpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, verbose=True, agent=AgentType.OPENAI_FUNCTIONS,)Example Usage‚ÄãHere are some examples of how you can use the agent with the AINetwork Toolkit:Define App name to test‚ÄãappName = f"langchain_demo_{address.lower()}"Create an app in the AINetwork Blockchain database‚Äãprint( agent.run( f"Create an app in the AINetwork Blockchain database with the name {appName}" )) > Entering new AgentExecutor chain... Invoking: `AINappOps` with `{'type': 'SET_ADMIN', 'appName': 'langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}` {"tx_hash": "0x018846d6a9fc111edb1a2246ae2484ef05573bd2c584f3d0da155fa4b4936a9e", "result": {"gas_amount_total": {"bandwidth": {"service": 4002, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 2}}, "state": {"service": 1640}}, "gas_cost_total": 0, "func_results": {"_createApp": {"op_results": {"0": {"path": "/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "1": {"path": "/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "2": {"path":	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: private key in a secure place. Losing access to your private key means losing# access to your account. address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac private_key: f5e2f359bb6b7836a2ac70815473d1a290c517f847d096f5effe818de8c2cf14 Initialize the AINetwork Toolkit‚ÄãYou can initialize the AINetwork Toolkit like this:from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkittoolkit = AINetworkToolkit()tools = toolkit.get_tools()address = tools[0].interface.wallet.defaultAccount.addressInitialize the Agent with the AINetwork Toolkit‚ÄãYou can initialize the agent with the AINetwork Toolkit like this:from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agent, AgentTypellm = ChatOpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, verbose=True, agent=AgentType.OPENAI_FUNCTIONS,)Example Usage‚ÄãHere are some examples of how you can use the agent with the AINetwork Toolkit:Define App name to test‚ÄãappName = f"langchain_demo_{address.lower()}"Create an app in the AINetwork Blockchain database‚Äãprint( agent.run( f"Create an app in the AINetwork Blockchain database with the name {appName}" )) > Entering new AgentExecutor chain... Invoking: `AINappOps` with `{'type': 'SET_ADMIN', 'appName': 'langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}` {"tx_hash": "0x018846d6a9fc111edb1a2246ae2484ef05573bd2c584f3d0da155fa4b4936a9e", "result": {"gas_amount_total": {"bandwidth": {"service": 4002, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 2}}, "state": {"service": 1640}}, "gas_cost_total": 0, "func_results": {"_createApp": {"op_results": {"0": {"path": "/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "1": {"path": "/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "2": {"path":
2,707	0, "bandwidth_gas_amount": 1}}, "2": {"path": "/manage_app/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/config/admin", "result": {"code": 0, "bandwidth_gas_amount": 1}}}, "code": 0, "bandwidth_gas_amount": 2000}}, "code": 0, "bandwidth_gas_amount": 2001, "gas_amount_charged": 5642}}The app with the name "langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac" has been created in the AINetwork Blockchain database. > Finished chain. The app with the name "langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac" has been created in the AINetwork Blockchain database.Set a value at a given path in the AINetwork Blockchain database‚Äãprint( agent.run(f"Set the value {{1: 2, '34': 56}} at the path /apps/{appName}/object .")) > Entering new AgentExecutor chain... Invoking: `AINvalueOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object', 'value': {'1': 2, '34': 56}}` {"tx_hash": "0x3d1a16d9808830088cdf4d37f90f4b1fa1242e2d5f6f983829064f45107b5279", "result": {"gas_amount_total": {"bandwidth": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 1}}, "state": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 674}}}, "gas_cost_total": 0, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 0}}The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object. > Finished chain. The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object.Set permissions for a path in the AINetwork Blockchain database‚Äãprint( agent.run( f"Set the write permissions for the path /apps/{appName}/user/$from with the" " eval string auth.addr===$from ." )) > Entering new AgentExecutor chain... Invoking: `AINruleOps` with `{'type': 'SET', 'path':	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: 0, "bandwidth_gas_amount": 1}}, "2": {"path": "/manage_app/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/config/admin", "result": {"code": 0, "bandwidth_gas_amount": 1}}}, "code": 0, "bandwidth_gas_amount": 2000}}, "code": 0, "bandwidth_gas_amount": 2001, "gas_amount_charged": 5642}}The app with the name "langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac" has been created in the AINetwork Blockchain database. > Finished chain. The app with the name "langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac" has been created in the AINetwork Blockchain database.Set a value at a given path in the AINetwork Blockchain database‚Äãprint( agent.run(f"Set the value {{1: 2, '34': 56}} at the path /apps/{appName}/object .")) > Entering new AgentExecutor chain... Invoking: `AINvalueOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object', 'value': {'1': 2, '34': 56}}` {"tx_hash": "0x3d1a16d9808830088cdf4d37f90f4b1fa1242e2d5f6f983829064f45107b5279", "result": {"gas_amount_total": {"bandwidth": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 1}}, "state": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 674}}}, "gas_cost_total": 0, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 0}}The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object. > Finished chain. The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object.Set permissions for a path in the AINetwork Blockchain database‚Äãprint( agent.run( f"Set the write permissions for the path /apps/{appName}/user/$from with the" " eval string auth.addr===$from ." )) > Entering new AgentExecutor chain... Invoking: `AINruleOps` with `{'type': 'SET', 'path':
2,708	`AINruleOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from', 'eval': 'auth.addr===$from'}` {"tx_hash": "0x37d5264e580f6a217a347059a735bfa9eb5aad85ff28a95531c6dc09252664d2", "result": {"gas_amount_total": {"bandwidth": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 1}}, "state": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 712}}}, "gas_cost_total": 0, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 0}}The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`. > Finished chain. The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`.Retrieve the permissions for a path in the AINetwork Blockchain database‚Äãprint(agent.run(f"Retrieve the permissions for the path /apps/{appName}.")) > Entering new AgentExecutor chain... Invoking: `AINownerOps` with `{'type': 'GET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}` {".owner": {"owners": {"0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac": {"branch_owner": true, "write_function": true, "write_owner": true, "write_rule": true}}}}The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows: - Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac - branch_owner: true - write_function: true - write_owner: true - write_rule: true > Finished chain. The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows: - Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac - branch_owner: true - write_function: true - write_owner: true - write_rule:	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: `AINruleOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from', 'eval': 'auth.addr===$from'}` {"tx_hash": "0x37d5264e580f6a217a347059a735bfa9eb5aad85ff28a95531c6dc09252664d2", "result": {"gas_amount_total": {"bandwidth": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 1}}, "state": {"service": 0, "app": {"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac": 712}}}, "gas_cost_total": 0, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 0}}The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`. > Finished chain. The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`.Retrieve the permissions for a path in the AINetwork Blockchain database‚Äãprint(agent.run(f"Retrieve the permissions for the path /apps/{appName}.")) > Entering new AgentExecutor chain... Invoking: `AINownerOps` with `{'type': 'GET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}` {".owner": {"owners": {"0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac": {"branch_owner": true, "write_function": true, "write_owner": true, "write_rule": true}}}}The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows: - Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac - branch_owner: true - write_function: true - write_owner: true - write_rule: true > Finished chain. The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows: - Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac - branch_owner: true - write_function: true - write_owner: true - write_rule:
2,709	true - write_owner: true - write_rule: trueGet AIN from faucet‚Äãcurl http://faucet.ainetwork.ai/api/test/{address}/ {"result":"0x0eb07b67b7d0a702cb60e865d3deafff3070d8508077ef793d69d6819fd92ea3","time":1692348112376}Get AIN Balance‚Äãprint(agent.run(f"Check AIN balance of {address}")) > Entering new AgentExecutor chain... Invoking: `AINvalueOps` with `{'type': 'GET', 'path': '/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance'}` 100The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN. > Finished chain. The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN.Transfer AIN‚Äãprint( agent.run( "Transfer 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b" )) > Entering new AgentExecutor chain... Invoking: `AINtransfer` with `{'address': '0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b', 'amount': 100}` {"tx_hash": "0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e", "result": {"gas_amount_total": {"bandwidth": {"service": 3}, "state": {"service": 866}}, "gas_cost_total": 0, "func_results": {"_transfer": {"op_results": {"0": {"path": "/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "1": {"path": "/accounts/0x19937B227b1b13f29e7AB18676a89EA3BDEA9C5b/balance", "result": {"code": 0, "bandwidth_gas_amount": 1}}}, "code": 0, "bandwidth_gas_amount": 0}}, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 869}}The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e. > Finished chain. The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: true - write_owner: true - write_rule: trueGet AIN from faucet‚Äãcurl http://faucet.ainetwork.ai/api/test/{address}/ {"result":"0x0eb07b67b7d0a702cb60e865d3deafff3070d8508077ef793d69d6819fd92ea3","time":1692348112376}Get AIN Balance‚Äãprint(agent.run(f"Check AIN balance of {address}")) > Entering new AgentExecutor chain... Invoking: `AINvalueOps` with `{'type': 'GET', 'path': '/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance'}` 100The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN. > Finished chain. The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN.Transfer AIN‚Äãprint( agent.run( "Transfer 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b" )) > Entering new AgentExecutor chain... Invoking: `AINtransfer` with `{'address': '0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b', 'amount': 100}` {"tx_hash": "0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e", "result": {"gas_amount_total": {"bandwidth": {"service": 3}, "state": {"service": 866}}, "gas_cost_total": 0, "func_results": {"_transfer": {"op_results": {"0": {"path": "/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance", "result": {"code": 0, "bandwidth_gas_amount": 1}}, "1": {"path": "/accounts/0x19937B227b1b13f29e7AB18676a89EA3BDEA9C5b/balance", "result": {"code": 0, "bandwidth_gas_amount": 1}}}, "code": 0, "bandwidth_gas_amount": 0}}, "code": 0, "bandwidth_gas_amount": 1, "gas_amount_charged": 869}}The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e. > Finished chain. The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is
2,710	was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e.PreviousAgents and toolkitsNextAirbyte Question AnsweringInstalling dependenciesSet environmental variablesGet AIN Blockchain private keyInitialize the AINetwork ToolkitInitialize the Agent with the AINetwork ToolkitExample UsageDefine App name to testCreate an app in the AINetwork Blockchain databaseSet a value at a given path in the AINetwork Blockchain databaseSet permissions for a path in the AINetwork Blockchain databaseRetrieve the permissions for a path in the AINetwork Blockchain databaseGet AIN from faucetGet AIN BalanceTransfer AINCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).	AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs). ->: was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e.PreviousAgents and toolkitsNextAirbyte Question AnsweringInstalling dependenciesSet environmental variablesGet AIN Blockchain private keyInitialize the AINetwork ToolkitInitialize the Agent with the AINetwork ToolkitExample UsageDefine App name to testCreate an app in the AINetwork Blockchain databaseSet a value at a given path in the AINetwork Blockchain databaseSet permissions for a path in the AINetwork Blockchain databaseRetrieve the permissions for a path in the AINetwork Blockchain databaseGet AIN from faucetGet AIN BalanceTransfer AINCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,711	Gmail \| ü¶úÔ∏èüîó Langchain	This notebook walks through connecting a LangChain email to the Gmail API.	This notebook walks through connecting a LangChain email to the Gmail API. ->: Gmail \| ü¶úÔ∏èüîó Langchain
2,712	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 toolkitsGmailOn this pageGmailThis notebook walks through connecting a LangChain email to the Gmail API.To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you've downloaded the credentials.json file, you can start using the Gmail API. Once this is done, we'll install the required libraries.pip install --upgrade google-api-python-client > /dev/nullpip install --upgrade google-auth-oauthlib > /dev/nullpip install --upgrade google-auth-httplib2 > /dev/nullpip install beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messagesCreate the Toolkit‚ÄãBy default the toolkit reads the local credentials.json file. You can also manually provide a Credentials object.from langchain.agents.agent_toolkits import GmailToolkittoolkit = GmailToolkit()Customizing Authentication‚ÄãBehind the scenes, a googleapi resource is created using the following methods.	This notebook walks through connecting a LangChain email to the Gmail API.	This notebook walks through connecting a LangChain email to the Gmail API. ->: 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 toolkitsGmailOn this pageGmailThis notebook walks through connecting a LangChain email to the Gmail API.To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you've downloaded the credentials.json file, you can start using the Gmail API. Once this is done, we'll install the required libraries.pip install --upgrade google-api-python-client > /dev/nullpip install --upgrade google-auth-oauthlib > /dev/nullpip install --upgrade google-auth-httplib2 > /dev/nullpip install beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messagesCreate the Toolkit‚ÄãBy default the toolkit reads the local credentials.json file. You can also manually provide a Credentials object.from langchain.agents.agent_toolkits import GmailToolkittoolkit = GmailToolkit()Customizing Authentication‚ÄãBehind the scenes, a googleapi resource is created using the following methods.
2,713	you can manually build a googleapi resource for more auth control. from langchain.tools.gmail.utils import build_resource_service, get_gmail_credentials# Can review scopes here https://developers.google.com/gmail/api/auth/scopes# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'credentials = get_gmail_credentials( token_file="token.json", scopes=["https://mail.google.com/"], client_secrets_file="credentials.json",)api_resource = build_resource_service(credentials=credentials)toolkit = GmailToolkit(api_resource=api_resource)tools = toolkit.get_tools()tools [GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailSendMessage(name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailSearch(name='search_gmail', description=('Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.',), args_schema=<class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailGetMessage(name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None,	This notebook walks through connecting a LangChain email to the Gmail API.	This notebook walks through connecting a LangChain email to the Gmail API. ->: you can manually build a googleapi resource for more auth control. from langchain.tools.gmail.utils import build_resource_service, get_gmail_credentials# Can review scopes here https://developers.google.com/gmail/api/auth/scopes# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'credentials = get_gmail_credentials( token_file="token.json", scopes=["https://mail.google.com/"], client_secrets_file="credentials.json",)api_resource = build_resource_service(credentials=credentials)toolkit = GmailToolkit(api_resource=api_resource)tools = toolkit.get_tools()tools [GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailSendMessage(name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailSearch(name='search_gmail', description=('Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.',), args_schema=<class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailGetMessage(name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None,
2,714	verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>)]Use within an Agent‚Äãfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot" " who is looking to collaborate on some research with her" " estranged friend, a cat. Under no circumstances may you send the message, however.") WARNING:root:Failed to load default session, using empty session: 0 WARNING:root:Failed to persist run: {"detail":"Not Found"} 'I have created a draft email for you to edit. The draft Id is r5681294731961864018.'agent.run("Could you search in my drafts for the latest email?") WARNING:root:Failed to load default session, using empty session: 0 WARNING:root:Failed to persist run: {"detail":"Not Found"} "The latest email in your drafts is from [email protected] with the subject 'Collaboration Opportunity'. The body of the email reads: 'Dear [Friend], I hope this letter finds you well. I am writing to you in the hopes of rekindling our friendship and to discuss the possibility of collaborating on some research together. I know that we have had our differences in the past, but I believe that we can put them aside and work together for the greater good. I look forward to hearing from you. Sincerely,	This notebook walks through connecting a LangChain email to the Gmail API.	This notebook walks through connecting a LangChain email to the Gmail API. ->: verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>), GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>)]Use within an Agent‚Äãfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot" " who is looking to collaborate on some research with her" " estranged friend, a cat. Under no circumstances may you send the message, however.") WARNING:root:Failed to load default session, using empty session: 0 WARNING:root:Failed to persist run: {"detail":"Not Found"} 'I have created a draft email for you to edit. The draft Id is r5681294731961864018.'agent.run("Could you search in my drafts for the latest email?") WARNING:root:Failed to load default session, using empty session: 0 WARNING:root:Failed to persist run: {"detail":"Not Found"} "The latest email in your drafts is from [email protected] with the subject 'Collaboration Opportunity'. The body of the email reads: 'Dear [Friend], I hope this letter finds you well. I am writing to you in the hopes of rekindling our friendship and to discuss the possibility of collaborating on some research together. I know that we have had our differences in the past, but I believe that we can put them aside and work together for the greater good. I look forward to hearing from you. Sincerely,
2,715	I look forward to hearing from you. Sincerely, [Parrot]'"PreviousGitlabNextGoogle Drive toolCreate the ToolkitCustomizing AuthenticationUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook walks through connecting a LangChain email to the Gmail API.	This notebook walks through connecting a LangChain email to the Gmail API. ->: I look forward to hearing from you. Sincerely, [Parrot]'"PreviousGitlabNextGoogle Drive toolCreate the ToolkitCustomizing AuthenticationUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,716	Xorbits \| ü¶úÔ∏èüîó Langchain	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering. ->: Xorbits \| ü¶úÔ∏èüîó Langchain
2,717	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 toolkitsXorbitsOn this pageXorbitsThis notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.Pandas examples‚Äãimport xorbits.pandas as pdfrom langchain.agents import create_xorbits_agentfrom langchain.llms import OpenAIdata = pd.read_csv("titanic.csv")agent = create_xorbits_agent(OpenAI(temperature=0), data, verbose=True) 0%\| \| 0.00/100 [00:00<?, ?it/s]agent.run("How many rows and columns are there?") > Entering new chain... Thought: I need to count the number of rows and columns Action: python_repl_ast Action Input: data.shape Observation: (891, 12) Thought: I now know the final answer Final Answer: There are 891 rows and 12 columns. > Finished chain. 'There are 891 rows and 12 columns.'agent.run("How many people are in pclass 1?") > Entering new chain... 0%\| \| 0.00/100 [00:00<?, ?it/s] Thought: I need to count the number of people in pclass 1 Action: python_repl_ast Action Input: data[data['Pclass'] == 1].shape[0] Observation: 216 Thought: I now know the final	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering. ->: 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 toolkitsXorbitsOn this pageXorbitsThis notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.Pandas examples‚Äãimport xorbits.pandas as pdfrom langchain.agents import create_xorbits_agentfrom langchain.llms import OpenAIdata = pd.read_csv("titanic.csv")agent = create_xorbits_agent(OpenAI(temperature=0), data, verbose=True) 0%\| \| 0.00/100 [00:00<?, ?it/s]agent.run("How many rows and columns are there?") > Entering new chain... Thought: I need to count the number of rows and columns Action: python_repl_ast Action Input: data.shape Observation: (891, 12) Thought: I now know the final answer Final Answer: There are 891 rows and 12 columns. > Finished chain. 'There are 891 rows and 12 columns.'agent.run("How many people are in pclass 1?") > Entering new chain... 0%\| \| 0.00/100 [00:00<?, ?it/s] Thought: I need to count the number of people in pclass 1 Action: python_repl_ast Action Input: data[data['Pclass'] == 1].shape[0] Observation: 216 Thought: I now know the final
2,718	Observation: 216 Thought: I now know the final answer Final Answer: There are 216 people in pclass 1. > Finished chain. 'There are 216 people in pclass 1.'agent.run("whats the mean age?") > Entering new chain... Thought: I need to calculate the mean age Action: python_repl_ast Action Input: data['Age'].mean() 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: 29.69911764705882 Thought: I now know the final answer Final Answer: The mean age is 29.69911764705882. > Finished chain. 'The mean age is 29.69911764705882.'agent.run("Group the data by sex and find the average age for each group") > Entering new chain... Thought: I need to group the data by sex and then find the average age for each group Action: python_repl_ast Action Input: data.groupby('Sex')['Age'].mean() 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: Sex female 27.915709 male 30.726645 Name: Age, dtype: float64 Thought: I now know the average age for each group Final Answer: The average age for female passengers is 27.92 and the average age for male passengers is 30.73. > Finished chain. 'The average age for female passengers is 27.92 and the average age for male passengers is 30.73.'agent.run( "Show the number of people whose age is greater than 30 and fare is between 30 and 50 , and pclass is either 1 or 2") > Entering new chain... 0%\| \| 0.00/100 [00:00<?, ?it/s] Thought: I need to filter the dataframe to get the desired result Action: python_repl_ast Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) \| (data['Pclass'] == 2))].shape[0] Observation: 20 Thought: I now know the final answer Final Answer: 20 > Finished chain. '20'Numpy examples‚Äãimport xorbits.numpy as npfrom langchain.agents import create_xorbits_agentfrom langchain.llms import	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering. ->: Observation: 216 Thought: I now know the final answer Final Answer: There are 216 people in pclass 1. > Finished chain. 'There are 216 people in pclass 1.'agent.run("whats the mean age?") > Entering new chain... Thought: I need to calculate the mean age Action: python_repl_ast Action Input: data['Age'].mean() 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: 29.69911764705882 Thought: I now know the final answer Final Answer: The mean age is 29.69911764705882. > Finished chain. 'The mean age is 29.69911764705882.'agent.run("Group the data by sex and find the average age for each group") > Entering new chain... Thought: I need to group the data by sex and then find the average age for each group Action: python_repl_ast Action Input: data.groupby('Sex')['Age'].mean() 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: Sex female 27.915709 male 30.726645 Name: Age, dtype: float64 Thought: I now know the average age for each group Final Answer: The average age for female passengers is 27.92 and the average age for male passengers is 30.73. > Finished chain. 'The average age for female passengers is 27.92 and the average age for male passengers is 30.73.'agent.run( "Show the number of people whose age is greater than 30 and fare is between 30 and 50 , and pclass is either 1 or 2") > Entering new chain... 0%\| \| 0.00/100 [00:00<?, ?it/s] Thought: I need to filter the dataframe to get the desired result Action: python_repl_ast Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) \| (data['Pclass'] == 2))].shape[0] Observation: 20 Thought: I now know the final answer Final Answer: 20 > Finished chain. '20'Numpy examples‚Äãimport xorbits.numpy as npfrom langchain.agents import create_xorbits_agentfrom langchain.llms import
2,719	create_xorbits_agentfrom langchain.llms import OpenAIarr = np.array([1, 2, 3, 4, 5, 6])agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True) 0%\| \| 0.00/100 [00:00<?, ?it/s]agent.run("Give the shape of the array ") > Entering new chain... Thought: I need to find out the shape of the array Action: python_repl_ast Action Input: data.shape Observation: (6,) Thought: I now know the final answer Final Answer: The shape of the array is (6,). > Finished chain. 'The shape of the array is (6,).'agent.run("Give the 2nd element of the array ") > Entering new chain... Thought: I need to access the 2nd element of the array Action: python_repl_ast Action Input: data[1] 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: 2 Thought: I now know the final answer Final Answer: 2 > Finished chain. '2'agent.run( "Reshape the array into a 2-dimensional array with 2 rows and 3 columns, and then transpose it") > Entering new chain... Thought: I need to reshape the array and then transpose it Action: python_repl_ast Action Input: np.reshape(data, (2,3)).T 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: [[1 4] [2 5] [3 6]] Thought: I now know the final answer Final Answer: The reshaped and transposed array is [[1 4], [2 5], [3 6]]. > Finished chain. 'The reshaped and transposed array is [[1 4], [2 5], [3 6]].'agent.run( "Reshape the array into a 2-dimensional array with 3 rows and 2 columns and sum the array along the first axis") > Entering new chain... Thought: I need to reshape the array and then sum it Action: python_repl_ast Action Input: np.sum(np.reshape(data, (3,2)), axis=0) 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: [ 9 12] Thought: I now know the final answer Final Answer: The sum of the array along the first axis is [9, 12]. >	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering. ->: create_xorbits_agentfrom langchain.llms import OpenAIarr = np.array([1, 2, 3, 4, 5, 6])agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True) 0%\| \| 0.00/100 [00:00<?, ?it/s]agent.run("Give the shape of the array ") > Entering new chain... Thought: I need to find out the shape of the array Action: python_repl_ast Action Input: data.shape Observation: (6,) Thought: I now know the final answer Final Answer: The shape of the array is (6,). > Finished chain. 'The shape of the array is (6,).'agent.run("Give the 2nd element of the array ") > Entering new chain... Thought: I need to access the 2nd element of the array Action: python_repl_ast Action Input: data[1] 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: 2 Thought: I now know the final answer Final Answer: 2 > Finished chain. '2'agent.run( "Reshape the array into a 2-dimensional array with 2 rows and 3 columns, and then transpose it") > Entering new chain... Thought: I need to reshape the array and then transpose it Action: python_repl_ast Action Input: np.reshape(data, (2,3)).T 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: [[1 4] [2 5] [3 6]] Thought: I now know the final answer Final Answer: The reshaped and transposed array is [[1 4], [2 5], [3 6]]. > Finished chain. 'The reshaped and transposed array is [[1 4], [2 5], [3 6]].'agent.run( "Reshape the array into a 2-dimensional array with 3 rows and 2 columns and sum the array along the first axis") > Entering new chain... Thought: I need to reshape the array and then sum it Action: python_repl_ast Action Input: np.sum(np.reshape(data, (3,2)), axis=0) 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: [ 9 12] Thought: I now know the final answer Final Answer: The sum of the array along the first axis is [9, 12]. >
2,720	array along the first axis is [9, 12]. > Finished chain. 'The sum of the array along the first axis is [9, 12].'arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True) 0%\| \| 0.00/100 [00:00<?, ?it/s]agent.run("calculate the covariance matrix") > Entering new chain... Thought: I need to use the numpy covariance function Action: python_repl_ast Action Input: np.cov(data) 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: [[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]] Thought: I now know the final answer Final Answer: The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]]. > Finished chain. 'The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].'agent.run("compute the U of Singular Value Decomposition of the matrix") > Entering new chain... Thought: I need to use the SVD function Action: python_repl_ast Action Input: U, S, V = np.linalg.svd(data) Observation: Thought: I now have the U matrix Final Answer: U = [[-0.70710678 -0.70710678] [-0.70710678 0.70710678]] > Finished chain. 'U = [[-0.70710678 -0.70710678]\n [-0.70710678 0.70710678]]'PreviousVectorstoreNextMemoryPandas examplesNumpy examplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering. ->: array along the first axis is [9, 12]. > Finished chain. 'The sum of the array along the first axis is [9, 12].'arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True) 0%\| \| 0.00/100 [00:00<?, ?it/s]agent.run("calculate the covariance matrix") > Entering new chain... Thought: I need to use the numpy covariance function Action: python_repl_ast Action Input: np.cov(data) 0%\| \| 0.00/100 [00:00<?, ?it/s] Observation: [[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]] Thought: I now know the final answer Final Answer: The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]]. > Finished chain. 'The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].'agent.run("compute the U of Singular Value Decomposition of the matrix") > Entering new chain... Thought: I need to use the SVD function Action: python_repl_ast Action Input: U, S, V = np.linalg.svd(data) Observation: Thought: I now have the U matrix Final Answer: U = [[-0.70710678 -0.70710678] [-0.70710678 0.70710678]] > Finished chain. 'U = [[-0.70710678 -0.70710678]\n [-0.70710678 0.70710678]]'PreviousVectorstoreNextMemoryPandas examplesNumpy examplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,721	Spark Dataframe \| ü¶úÔ∏èüîó Langchain	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: Spark Dataframe \| ü¶úÔ∏èüîó Langchain
2,722	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 toolkitsSpark DataframeOn this pageSpark DataframeThis notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.import osos.environ["OPENAI_API_KEY"] = "...input your openai api key here..."Spark DataFrame example‚Äãfrom langchain.llms import OpenAIfrom pyspark.sql import SparkSessionfrom langchain.agents import create_spark_dataframe_agentspark = SparkSession.builder.getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show() 23/05/15 20:33:10 WARN Utils: Your hostname, Mikes-Mac-mini.local resolves to a loopback address: 127.0.0.1; using 192.168.68.115 instead (on interface en1) 23/05/15 20:33:10 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 23/05/15 20:33:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: 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 toolkitsSpark DataframeOn this pageSpark DataframeThis notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.import osos.environ["OPENAI_API_KEY"] = "...input your openai api key here..."Spark DataFrame example‚Äãfrom langchain.llms import OpenAIfrom pyspark.sql import SparkSessionfrom langchain.agents import create_spark_dataframe_agentspark = SparkSession.builder.getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show() 23/05/15 20:33:10 WARN Utils: Your hostname, Mikes-Mac-mini.local resolves to a loopback address: 127.0.0.1; using 192.168.68.115 instead (on interface en1) 23/05/15 20:33:10 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 23/05/15 20:33:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2,723	using builtin-java classes where applicable +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \|PassengerId\|Survived\|Pclass\| Name\| Sex\| Age\|SibSp\|Parch\| Ticket\| Fare\|Cabin\|Embarked\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \| 1\| 0\| 3\|Braund, Mr. Owen ...\| male\|22.0\| 1\| 0\| A/5 21171\| 7.25\| null\| S\| \| 2\| 1\| 1\|Cumings, Mrs. Joh...\|female\|38.0\| 1\| 0\| PC 17599\|71.2833\| C85\| C\| \| 3\| 1\| 3\|Heikkinen, Miss. ...\|female\|26.0\| 0\| 0\|STON/O2. 3101282\| 7.925\| null\| S\| \| 4\| 1\| 1\|Futrelle, Mrs. Ja...\|female\|35.0\| 1\| 0\| 113803\| 53.1\| C123\| S\| \| 5\| 0\| 3\|Allen, Mr. Willia...\| male\|35.0\| 0\| 0\| 373450\| 8.05\| null\| S\| \| 6\| 0\| 3\| Moran, Mr. James\| male\|null\| 0\| 0\| 330877\| 8.4583\| null\| Q\| \| 7\| 0\| 1\|McCarthy, Mr. Tim...\| male\|54.0\| 0\| 0\| 17463\|51.8625\| E46\| S\| \| 8\| 0\| 3\|Palsson, Master. ...\| male\| 2.0\| 3\| 1\| 349909\| 21.075\| null\| S\| \| 9\| 1\| 3\|Johnson, Mrs. Osc...\|female\|27.0\| 0\| 2\| 347742\|11.1333\| null\| S\| \| 10\| 1\| 2\|Nasser, Mrs. Nich...\|female\|14.0\| 1\| 0\| 237736\|30.0708\| null\| C\| \| 11\| 1\| 3\|Sandstrom, Miss. ...\|female\| 4.0\| 1\| 1\| PP 9549\| 16.7\| G6\| S\| \| 12\| 1\| 1\|Bonnell, Miss. El...\|female\|58.0\| 0\| 0\| 113783\| 26.55\| C103\| S\| \| 13\| 0\| 3\|Saundercock, Mr. ...\| male\|20.0\| 0\| 0\| A/5. 2151\| 8.05\| null\| S\| \| 14\| 0\| 3\|Andersson, Mr. An...\| male\|39.0\|	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: using builtin-java classes where applicable +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \|PassengerId\|Survived\|Pclass\| Name\| Sex\| Age\|SibSp\|Parch\| Ticket\| Fare\|Cabin\|Embarked\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \| 1\| 0\| 3\|Braund, Mr. Owen ...\| male\|22.0\| 1\| 0\| A/5 21171\| 7.25\| null\| S\| \| 2\| 1\| 1\|Cumings, Mrs. Joh...\|female\|38.0\| 1\| 0\| PC 17599\|71.2833\| C85\| C\| \| 3\| 1\| 3\|Heikkinen, Miss. ...\|female\|26.0\| 0\| 0\|STON/O2. 3101282\| 7.925\| null\| S\| \| 4\| 1\| 1\|Futrelle, Mrs. Ja...\|female\|35.0\| 1\| 0\| 113803\| 53.1\| C123\| S\| \| 5\| 0\| 3\|Allen, Mr. Willia...\| male\|35.0\| 0\| 0\| 373450\| 8.05\| null\| S\| \| 6\| 0\| 3\| Moran, Mr. James\| male\|null\| 0\| 0\| 330877\| 8.4583\| null\| Q\| \| 7\| 0\| 1\|McCarthy, Mr. Tim...\| male\|54.0\| 0\| 0\| 17463\|51.8625\| E46\| S\| \| 8\| 0\| 3\|Palsson, Master. ...\| male\| 2.0\| 3\| 1\| 349909\| 21.075\| null\| S\| \| 9\| 1\| 3\|Johnson, Mrs. Osc...\|female\|27.0\| 0\| 2\| 347742\|11.1333\| null\| S\| \| 10\| 1\| 2\|Nasser, Mrs. Nich...\|female\|14.0\| 1\| 0\| 237736\|30.0708\| null\| C\| \| 11\| 1\| 3\|Sandstrom, Miss. ...\|female\| 4.0\| 1\| 1\| PP 9549\| 16.7\| G6\| S\| \| 12\| 1\| 1\|Bonnell, Miss. El...\|female\|58.0\| 0\| 0\| 113783\| 26.55\| C103\| S\| \| 13\| 0\| 3\|Saundercock, Mr. ...\| male\|20.0\| 0\| 0\| A/5. 2151\| 8.05\| null\| S\| \| 14\| 0\| 3\|Andersson, Mr. An...\| male\|39.0\|
2,724	0\| 3\|Andersson, Mr. An...\| male\|39.0\| 1\| 5\| 347082\| 31.275\| null\| S\| \| 15\| 0\| 3\|Vestrom, Miss. Hu...\|female\|14.0\| 0\| 0\| 350406\| 7.8542\| null\| S\| \| 16\| 1\| 2\|Hewlett, Mrs. (Ma...\|female\|55.0\| 0\| 0\| 248706\| 16.0\| null\| S\| \| 17\| 0\| 3\|Rice, Master. Eugene\| male\| 2.0\| 4\| 1\| 382652\| 29.125\| null\| Q\| \| 18\| 1\| 2\|Williams, Mr. Cha...\| male\|null\| 0\| 0\| 244373\| 13.0\| null\| S\| \| 19\| 0\| 3\|Vander Planke, Mr...\|female\|31.0\| 1\| 0\| 345763\| 18.0\| null\| S\| \| 20\| 1\| 3\|Masselmani, Mrs. ...\|female\|null\| 0\| 0\| 2649\| 7.225\| null\| C\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)agent.run("how many rows are there?") > Entering new AgentExecutor chain... Thought: I need to find out how many rows are in the dataframe Action: python_repl_ast Action Input: df.count() Observation: 891 Thought: I now know the final answer Final Answer: There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") > Entering new AgentExecutor chain... Thought: I need to find out how many people have more than 3 siblings Action: python_repl_ast Action Input: df.filter(df.SibSp > 3).count() Observation: 30 Thought: I now know the final answer Final Answer: 30 people have more than 3 siblings. > Finished chain. '30 people have more than 3 siblings.'agent.run("whats the square root of the average age?") > Entering new AgentExecutor chain... Thought: I need to get the	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: 0\| 3\|Andersson, Mr. An...\| male\|39.0\| 1\| 5\| 347082\| 31.275\| null\| S\| \| 15\| 0\| 3\|Vestrom, Miss. Hu...\|female\|14.0\| 0\| 0\| 350406\| 7.8542\| null\| S\| \| 16\| 1\| 2\|Hewlett, Mrs. (Ma...\|female\|55.0\| 0\| 0\| 248706\| 16.0\| null\| S\| \| 17\| 0\| 3\|Rice, Master. Eugene\| male\| 2.0\| 4\| 1\| 382652\| 29.125\| null\| Q\| \| 18\| 1\| 2\|Williams, Mr. Cha...\| male\|null\| 0\| 0\| 244373\| 13.0\| null\| S\| \| 19\| 0\| 3\|Vander Planke, Mr...\|female\|31.0\| 1\| 0\| 345763\| 18.0\| null\| S\| \| 20\| 1\| 3\|Masselmani, Mrs. ...\|female\|null\| 0\| 0\| 2649\| 7.225\| null\| C\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)agent.run("how many rows are there?") > Entering new AgentExecutor chain... Thought: I need to find out how many rows are in the dataframe Action: python_repl_ast Action Input: df.count() Observation: 891 Thought: I now know the final answer Final Answer: There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") > Entering new AgentExecutor chain... Thought: I need to find out how many people have more than 3 siblings Action: python_repl_ast Action Input: df.filter(df.SibSp > 3).count() Observation: 30 Thought: I now know the final answer Final Answer: 30 people have more than 3 siblings. > Finished chain. '30 people have more than 3 siblings.'agent.run("whats the square root of the average age?") > Entering new AgentExecutor chain... Thought: I need to get the
2,725	chain... Thought: I need to get the average age first Action: python_repl_ast Action Input: df.agg({"Age": "mean"}).collect()[0][0] Observation: 29.69911764705882 Thought: I now have the average age, I need to get the square root Action: python_repl_ast Action Input: math.sqrt(29.69911764705882) Observation: name 'math' is not defined Thought: I need to import math first Action: python_repl_ast Action Input: import math Observation: Thought: I now have the math library imported, I can get the square root Action: python_repl_ast Action Input: math.sqrt(29.69911764705882) Observation: 5.449689683556195 Thought: I now know the final answer Final Answer: 5.449689683556195 > Finished chain. '5.449689683556195'spark.stop()Spark Connect example‚Äã# in apache-spark root directory. (tested here with "spark-3.4.0-bin-hadoop3 and later")# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script../sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0from pyspark.sql import SparkSession# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by# creating a remote Spark session on the client where our application runs. Before we can do that, we need# to make sure to stop the existing regular Spark session because it cannot coexist with the remote# Spark Connect session we are about to create.SparkSession.builder.master("local[*]").getOrCreate().stop() 23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.# The command we used above to launch the server configured Spark to run as localhost:15002.# So now we can create a remote Spark session on the client using the following command.spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show()	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: chain... Thought: I need to get the average age first Action: python_repl_ast Action Input: df.agg({"Age": "mean"}).collect()[0][0] Observation: 29.69911764705882 Thought: I now have the average age, I need to get the square root Action: python_repl_ast Action Input: math.sqrt(29.69911764705882) Observation: name 'math' is not defined Thought: I need to import math first Action: python_repl_ast Action Input: import math Observation: Thought: I now have the math library imported, I can get the square root Action: python_repl_ast Action Input: math.sqrt(29.69911764705882) Observation: 5.449689683556195 Thought: I now know the final answer Final Answer: 5.449689683556195 > Finished chain. '5.449689683556195'spark.stop()Spark Connect example‚Äã# in apache-spark root directory. (tested here with "spark-3.4.0-bin-hadoop3 and later")# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script../sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0from pyspark.sql import SparkSession# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by# creating a remote Spark session on the client where our application runs. Before we can do that, we need# to make sure to stop the existing regular Spark session because it cannot coexist with the remote# Spark Connect session we are about to create.SparkSession.builder.master("local[*]").getOrCreate().stop() 23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.# The command we used above to launch the server configured Spark to run as localhost:15002.# So now we can create a remote Spark session on the client using the following command.spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show()
2,726	header=True, inferSchema=True)df.show() +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \|PassengerId\|Survived\|Pclass\| Name\| Sex\| Age\|SibSp\|Parch\| Ticket\| Fare\|Cabin\|Embarked\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \| 1\| 0\| 3\|Braund, Mr. Owen ...\| male\|22.0\| 1\| 0\| A/5 21171\| 7.25\| null\| S\| \| 2\| 1\| 1\|Cumings, Mrs. Joh...\|female\|38.0\| 1\| 0\| PC 17599\|71.2833\| C85\| C\| \| 3\| 1\| 3\|Heikkinen, Miss. ...\|female\|26.0\| 0\| 0\|STON/O2. 3101282\| 7.925\| null\| S\| \| 4\| 1\| 1\|Futrelle, Mrs. Ja...\|female\|35.0\| 1\| 0\| 113803\| 53.1\| C123\| S\| \| 5\| 0\| 3\|Allen, Mr. Willia...\| male\|35.0\| 0\| 0\| 373450\| 8.05\| null\| S\| \| 6\| 0\| 3\| Moran, Mr. James\| male\|null\| 0\| 0\| 330877\| 8.4583\| null\| Q\| \| 7\| 0\| 1\|McCarthy, Mr. Tim...\| male\|54.0\| 0\| 0\| 17463\|51.8625\| E46\| S\| \| 8\| 0\| 3\|Palsson, Master. ...\| male\| 2.0\| 3\| 1\| 349909\| 21.075\| null\| S\| \| 9\| 1\| 3\|Johnson, Mrs. Osc...\|female\|27.0\| 0\| 2\| 347742\|11.1333\| null\| S\| \| 10\| 1\| 2\|Nasser, Mrs. Nich...\|female\|14.0\| 1\| 0\| 237736\|30.0708\| null\| C\| \| 11\| 1\| 3\|Sandstrom, Miss. ...\|female\| 4.0\| 1\| 1\| PP 9549\| 16.7\| G6\| S\| \| 12\| 1\| 1\|Bonnell, Miss. El...\|female\|58.0\| 0\| 0\| 113783\| 26.55\| C103\| S\| \| 13\| 0\| 3\|Saundercock, Mr. ...\| male\|20.0\| 0\| 0\| A/5. 2151\| 8.05\| null\| S\| \| 14\| 0\| 3\|Andersson, Mr. An...\| male\|39.0\| 1\|	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: header=True, inferSchema=True)df.show() +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \|PassengerId\|Survived\|Pclass\| Name\| Sex\| Age\|SibSp\|Parch\| Ticket\| Fare\|Cabin\|Embarked\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ \| 1\| 0\| 3\|Braund, Mr. Owen ...\| male\|22.0\| 1\| 0\| A/5 21171\| 7.25\| null\| S\| \| 2\| 1\| 1\|Cumings, Mrs. Joh...\|female\|38.0\| 1\| 0\| PC 17599\|71.2833\| C85\| C\| \| 3\| 1\| 3\|Heikkinen, Miss. ...\|female\|26.0\| 0\| 0\|STON/O2. 3101282\| 7.925\| null\| S\| \| 4\| 1\| 1\|Futrelle, Mrs. Ja...\|female\|35.0\| 1\| 0\| 113803\| 53.1\| C123\| S\| \| 5\| 0\| 3\|Allen, Mr. Willia...\| male\|35.0\| 0\| 0\| 373450\| 8.05\| null\| S\| \| 6\| 0\| 3\| Moran, Mr. James\| male\|null\| 0\| 0\| 330877\| 8.4583\| null\| Q\| \| 7\| 0\| 1\|McCarthy, Mr. Tim...\| male\|54.0\| 0\| 0\| 17463\|51.8625\| E46\| S\| \| 8\| 0\| 3\|Palsson, Master. ...\| male\| 2.0\| 3\| 1\| 349909\| 21.075\| null\| S\| \| 9\| 1\| 3\|Johnson, Mrs. Osc...\|female\|27.0\| 0\| 2\| 347742\|11.1333\| null\| S\| \| 10\| 1\| 2\|Nasser, Mrs. Nich...\|female\|14.0\| 1\| 0\| 237736\|30.0708\| null\| C\| \| 11\| 1\| 3\|Sandstrom, Miss. ...\|female\| 4.0\| 1\| 1\| PP 9549\| 16.7\| G6\| S\| \| 12\| 1\| 1\|Bonnell, Miss. El...\|female\|58.0\| 0\| 0\| 113783\| 26.55\| C103\| S\| \| 13\| 0\| 3\|Saundercock, Mr. ...\| male\|20.0\| 0\| 0\| A/5. 2151\| 8.05\| null\| S\| \| 14\| 0\| 3\|Andersson, Mr. An...\| male\|39.0\| 1\|
2,727	0\| 3\|Andersson, Mr. An...\| male\|39.0\| 1\| 5\| 347082\| 31.275\| null\| S\| \| 15\| 0\| 3\|Vestrom, Miss. Hu...\|female\|14.0\| 0\| 0\| 350406\| 7.8542\| null\| S\| \| 16\| 1\| 2\|Hewlett, Mrs. (Ma...\|female\|55.0\| 0\| 0\| 248706\| 16.0\| null\| S\| \| 17\| 0\| 3\|Rice, Master. Eugene\| male\| 2.0\| 4\| 1\| 382652\| 29.125\| null\| Q\| \| 18\| 1\| 2\|Williams, Mr. Cha...\| male\|null\| 0\| 0\| 244373\| 13.0\| null\| S\| \| 19\| 0\| 3\|Vander Planke, Mr...\|female\|31.0\| 1\| 0\| 345763\| 18.0\| null\| S\| \| 20\| 1\| 3\|Masselmani, Mrs. ...\|female\|null\| 0\| 0\| 2649\| 7.225\| null\| C\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows from langchain.agents import create_spark_dataframe_agentfrom langchain.llms import OpenAIimport osos.environ["OPENAI_API_KEY"] = "...input your openai api key here..."agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)agent.run( """who bought the most expensive ticket?You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html""") > Entering new AgentExecutor chain... Thought: I need to find the row with the highest fare Action: python_repl_ast Action Input: df.sort(df.Fare.desc()).first() Observation: Row(PassengerId=259, Survived=1, Pclass=1, Name='Ward, Miss. Anna', Sex='female', Age=35.0, SibSp=0, Parch=0, Ticket='PC 17755', Fare=512.3292, Cabin=None, Embarked='C') Thought: I now know the name of the person who bought the most expensive ticket Final Answer: Miss. Anna Ward > Finished chain. 'Miss. Anna Ward'spark.stop()PreviousPythonNextSpark SQLSpark DataFrame exampleSpark	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: 0\| 3\|Andersson, Mr. An...\| male\|39.0\| 1\| 5\| 347082\| 31.275\| null\| S\| \| 15\| 0\| 3\|Vestrom, Miss. Hu...\|female\|14.0\| 0\| 0\| 350406\| 7.8542\| null\| S\| \| 16\| 1\| 2\|Hewlett, Mrs. (Ma...\|female\|55.0\| 0\| 0\| 248706\| 16.0\| null\| S\| \| 17\| 0\| 3\|Rice, Master. Eugene\| male\| 2.0\| 4\| 1\| 382652\| 29.125\| null\| Q\| \| 18\| 1\| 2\|Williams, Mr. Cha...\| male\|null\| 0\| 0\| 244373\| 13.0\| null\| S\| \| 19\| 0\| 3\|Vander Planke, Mr...\|female\|31.0\| 1\| 0\| 345763\| 18.0\| null\| S\| \| 20\| 1\| 3\|Masselmani, Mrs. ...\|female\|null\| 0\| 0\| 2649\| 7.225\| null\| C\| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows from langchain.agents import create_spark_dataframe_agentfrom langchain.llms import OpenAIimport osos.environ["OPENAI_API_KEY"] = "...input your openai api key here..."agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)agent.run( """who bought the most expensive ticket?You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html""") > Entering new AgentExecutor chain... Thought: I need to find the row with the highest fare Action: python_repl_ast Action Input: df.sort(df.Fare.desc()).first() Observation: Row(PassengerId=259, Survived=1, Pclass=1, Name='Ward, Miss. Anna', Sex='female', Age=35.0, SibSp=0, Parch=0, Ticket='PC 17755', Fare=512.3292, Cabin=None, Embarked='C') Thought: I now know the name of the person who bought the most expensive ticket Final Answer: Miss. Anna Ward > Finished chain. 'Miss. Anna Ward'spark.stop()PreviousPythonNextSpark SQLSpark DataFrame exampleSpark
2,728	SQLSpark DataFrame exampleSpark Connect exampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: SQLSpark DataFrame exampleSpark Connect exampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,729	Amadeus \| ü¶úÔ∏èüîó Langchain	This notebook walks you through connecting LangChain to the Amadeus travel information API	This notebook walks you through connecting LangChain to the Amadeus travel information API ->: Amadeus \| ü¶úÔ∏èüîó Langchain
2,730	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 toolkitsAmadeusOn this pageAmadeusThis notebook walks you through connecting LangChain to the Amadeus travel information APITo use this toolkit, you will need to set up your credentials explained in the Amadeus for developers getting started overview. Once you've received a AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET, you can input them as environmental variables below.pip install --upgrade amadeus > /dev/nullAssign Environmental Variables‚ÄãThe toolkit will read the AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET environmental variables to authenticate the user so you need to set them here. You will also need to set your OPENAI_API_KEY to use the agent later.# Set environmental variables hereimport osos.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"os.environ["OPENAI_API_KEY"] = "API_KEY"Create the Amadeus Toolkit and Get Tools‚ÄãTo start, you need to create the toolkit, so you can access its tools later.from langchain.agents.agent_toolkits.amadeus.toolkit import AmadeusToolkittoolkit = AmadeusToolkit()tools = toolkit.get_tools()Use Amadeus Toolkit within an Agent‚Äãfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, verbose=False,	This notebook walks you through connecting LangChain to the Amadeus travel information API	This notebook walks you through connecting LangChain to the Amadeus travel information API ->: 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 toolkitsAmadeusOn this pageAmadeusThis notebook walks you through connecting LangChain to the Amadeus travel information APITo use this toolkit, you will need to set up your credentials explained in the Amadeus for developers getting started overview. Once you've received a AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET, you can input them as environmental variables below.pip install --upgrade amadeus > /dev/nullAssign Environmental Variables‚ÄãThe toolkit will read the AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET environmental variables to authenticate the user so you need to set them here. You will also need to set your OPENAI_API_KEY to use the agent later.# Set environmental variables hereimport osos.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"os.environ["OPENAI_API_KEY"] = "API_KEY"Create the Amadeus Toolkit and Get Tools‚ÄãTo start, you need to create the toolkit, so you can access its tools later.from langchain.agents.agent_toolkits.amadeus.toolkit import AmadeusToolkittoolkit = AmadeusToolkit()tools = toolkit.get_tools()Use Amadeus Toolkit within an Agent‚Äãfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, verbose=False,
2,731	tools=tools, llm=llm, verbose=False, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run("What is the name of the airport in Cali, Colombia?") 'The closest airport to Cali, Colombia is Alfonso Bonilla Arag√≥n International Airport (CLO).'agent.run( "What is the departure time of the cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska?") 'The cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska has a departure time of 16:42 and a total price of 276.08 EURO.'agent.run( "At what time does earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska land in Nebraska?") 'The earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska lands in Lincoln, Nebraska at 16:07.'agent.run( "What is the full travel time for the cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023?") 'The cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023 is a Spirit Airlines flight with a total price of 84.02 EURO and a total travel time of 8 hours and 43 minutes.'agent.run( "Please draft a concise email from Santiago to Paul, Santiago's travel agent, asking him to book the earliest flight from DFW to DCA on Aug 28, 2023. Include all flight details in the email.") 'Dear Paul,\n\nI am writing to request that you book the earliest flight from DFW to DCA on Aug 28, 2023. The flight details are as follows:\n\nFlight 1: DFW to ATL, departing at 7:15 AM, arriving at 10:25 AM, flight number 983, carrier Delta Air Lines\nFlight 2: ATL to DCA, departing at 12:15 PM, arriving at 2:02 PM, flight number 759, carrier Delta Air Lines\n\nThank you for your help.\n\nSincerely,\nSantiago'PreviousAirbyte Question AnsweringNextAzure Cognitive ServicesAssign Environmental VariablesCreate the Amadeus Toolkit and Get ToolsUse Amadeus Toolkit within an	This notebook walks you through connecting LangChain to the Amadeus travel information API	This notebook walks you through connecting LangChain to the Amadeus travel information API ->: tools=tools, llm=llm, verbose=False, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run("What is the name of the airport in Cali, Colombia?") 'The closest airport to Cali, Colombia is Alfonso Bonilla Arag√≥n International Airport (CLO).'agent.run( "What is the departure time of the cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska?") 'The cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska has a departure time of 16:42 and a total price of 276.08 EURO.'agent.run( "At what time does earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska land in Nebraska?") 'The earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska lands in Lincoln, Nebraska at 16:07.'agent.run( "What is the full travel time for the cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023?") 'The cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023 is a Spirit Airlines flight with a total price of 84.02 EURO and a total travel time of 8 hours and 43 minutes.'agent.run( "Please draft a concise email from Santiago to Paul, Santiago's travel agent, asking him to book the earliest flight from DFW to DCA on Aug 28, 2023. Include all flight details in the email.") 'Dear Paul,\n\nI am writing to request that you book the earliest flight from DFW to DCA on Aug 28, 2023. The flight details are as follows:\n\nFlight 1: DFW to ATL, departing at 7:15 AM, arriving at 10:25 AM, flight number 983, carrier Delta Air Lines\nFlight 2: ATL to DCA, departing at 12:15 PM, arriving at 2:02 PM, flight number 759, carrier Delta Air Lines\n\nThank you for your help.\n\nSincerely,\nSantiago'PreviousAirbyte Question AnsweringNextAzure Cognitive ServicesAssign Environmental VariablesCreate the Amadeus Toolkit and Get ToolsUse Amadeus Toolkit within an
2,732	and Get ToolsUse Amadeus Toolkit within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook walks you through connecting LangChain to the Amadeus travel information API	This notebook walks you through connecting LangChain to the Amadeus travel information API ->: and Get ToolsUse Amadeus Toolkit within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,733	Jira \| ü¶úÔ∏èüîó Langchain	This notebook goes over how to use the Jira toolkit.	This notebook goes over how to use the Jira toolkit. ->: Jira \| ü¶úÔ∏èüîó Langchain
2,734	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 toolkitsJiraJiraThis notebook goes over how to use the Jira toolkit.The Jira toolkit allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.htmlTo use this tool, you must first set as environment variables: JIRA_API_TOKEN JIRA_USERNAME	This notebook goes over how to use the Jira toolkit.	This notebook goes over how to use the Jira toolkit. ->: 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 toolkitsJiraJiraThis notebook goes over how to use the Jira toolkit.The Jira toolkit allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.htmlTo use this tool, you must first set as environment variables: JIRA_API_TOKEN JIRA_USERNAME
2,735	JIRA_API_TOKEN JIRA_USERNAME JIRA_INSTANCE_URL%pip install atlassian-python-apiimport osfrom langchain.agents import AgentTypefrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits.jira.toolkit import JiraToolkitfrom langchain.llms import OpenAIfrom langchain.utilities.jira import JiraAPIWrapperos.environ["JIRA_API_TOKEN"] = "abc"os.environ["JIRA_USERNAME"] = "123"os.environ["JIRA_INSTANCE_URL"] = "https://jira.atlassian.com"os.environ["OPENAI_API_KEY"] = "xyz"llm = OpenAI(temperature=0)jira = JiraAPIWrapper()toolkit = JiraToolkit.from_jira_api_wrapper(jira)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("make a new issue in project PW to remind me to make more fried rice") > Entering new AgentExecutor chain... I need to create an issue in project PW Action: Create Issue Action Input: {"summary": "Make more fried rice", "description": "Reminder to make more fried rice", "issuetype": {"name": "Task"}, "priority": {"name": "Low"}, "project": {"key": "PW"}} Observation: None Thought: I now know the final answer Final Answer: A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice". > Finished chain. 'A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice".'PreviousGoogle Drive toolNextJSONCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook goes over how to use the Jira toolkit.	This notebook goes over how to use the Jira toolkit. ->: JIRA_API_TOKEN JIRA_USERNAME JIRA_INSTANCE_URL%pip install atlassian-python-apiimport osfrom langchain.agents import AgentTypefrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits.jira.toolkit import JiraToolkitfrom langchain.llms import OpenAIfrom langchain.utilities.jira import JiraAPIWrapperos.environ["JIRA_API_TOKEN"] = "abc"os.environ["JIRA_USERNAME"] = "123"os.environ["JIRA_INSTANCE_URL"] = "https://jira.atlassian.com"os.environ["OPENAI_API_KEY"] = "xyz"llm = OpenAI(temperature=0)jira = JiraAPIWrapper()toolkit = JiraToolkit.from_jira_api_wrapper(jira)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("make a new issue in project PW to remind me to make more fried rice") > Entering new AgentExecutor chain... I need to create an issue in project PW Action: Create Issue Action Input: {"summary": "Make more fried rice", "description": "Reminder to make more fried rice", "issuetype": {"name": "Task"}, "priority": {"name": "Low"}, "project": {"key": "PW"}} Observation: None Thought: I now know the final answer Final Answer: A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice". > Finished chain. 'A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice".'PreviousGoogle Drive toolNextJSONCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,736	CSV \| ü¶úÔ∏èüîó Langchain	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering. ->: CSV \| ü¶úÔ∏èüîó Langchain
2,737	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 toolkitsCSVOn this pageCSVThis notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.from langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.agents.agent_types import AgentTypefrom langchain.agents import create_csv_agentUsing ZERO_SHOT_REACT_DESCRIPTION‚ÄãThis shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above.agent = create_csv_agent( OpenAI(temperature=0), "titanic.csv", verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)Using OpenAI Functions‚ÄãThis shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above.agent = create_csv_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), "titanic.csv", verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows are there?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df.shape[0]` 891There are 891 rows in the	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering. ->: 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 toolkitsCSVOn this pageCSVThis notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.from langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.agents.agent_types import AgentTypefrom langchain.agents import create_csv_agentUsing ZERO_SHOT_REACT_DESCRIPTION‚ÄãThis shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above.agent = create_csv_agent( OpenAI(temperature=0), "titanic.csv", verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)Using OpenAI Functions‚ÄãThis shows how to initialize the agent using the OPENAI_FUNCTIONS agent type. Note that this is an alternative to the above.agent = create_csv_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), "titanic.csv", verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows are there?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df.shape[0]` 891There are 891 rows in the
2,738	891There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df[df['SibSp'] > 3]['PassengerId'].count()` 30There are 30 people in the dataframe who have more than 3 siblings. > Finished chain. 'There are 30 people in the dataframe who have more than 3 siblings.'agent.run("whats the square root of the average age?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `import pandas as pd import math # Create a dataframe data = {'Age': [22, 38, 26, 35, 35]} df = pd.DataFrame(data) # Calculate the average age average_age = df['Age'].mean() # Calculate the square root of the average age square_root = math.sqrt(average_age) square_root` 5.585696017507576The square root of the average age is approximately 5.59. > Finished chain. 'The square root of the average age is approximately 5.59.'Multi CSV Example‚ÄãThis next part shows how the agent can interact with multiple csv files passed in as a list.agent = create_csv_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), ["titanic.csv", "titanic_age_fillna.csv"], verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows in the age column are different between the two dfs?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df1['Age'].nunique() - df2['Age'].nunique()` -1There is 1 row in the age column that is different between the two dataframes. > Finished chain. 'There is 1 row in the age column that is different between the two dataframes.'PreviousClickUp Langchain ToolkitNextDocument ComparisonUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsMulti CSV	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering. ->: 891There are 891 rows in the dataframe. > Finished chain. 'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df[df['SibSp'] > 3]['PassengerId'].count()` 30There are 30 people in the dataframe who have more than 3 siblings. > Finished chain. 'There are 30 people in the dataframe who have more than 3 siblings.'agent.run("whats the square root of the average age?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `import pandas as pd import math # Create a dataframe data = {'Age': [22, 38, 26, 35, 35]} df = pd.DataFrame(data) # Calculate the average age average_age = df['Age'].mean() # Calculate the square root of the average age square_root = math.sqrt(average_age) square_root` 5.585696017507576The square root of the average age is approximately 5.59. > Finished chain. 'The square root of the average age is approximately 5.59.'Multi CSV Example‚ÄãThis next part shows how the agent can interact with multiple csv files passed in as a list.agent = create_csv_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"), ["titanic.csv", "titanic_age_fillna.csv"], verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows in the age column are different between the two dfs?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df1['Age'].nunique() - df2['Age'].nunique()` -1There is 1 row in the age column that is different between the two dataframes. > Finished chain. 'There is 1 row in the age column that is different between the two dataframes.'PreviousClickUp Langchain ToolkitNextDocument ComparisonUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsMulti CSV
2,739	OpenAI FunctionsMulti CSV ExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.	This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering. ->: OpenAI FunctionsMulti CSV ExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.