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2,900 | Figma | ü¶úÔ∏èüîó Langchain | Figma is a collaborative web application for interface design. | Figma is a collaborative web application for interface design. ->: Figma | ü¶úÔ∏èüîó Langchain |
2,901 | Skip to main contentü¶úÔ∏èüîó 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 | Figma is a collaborative web application for interface design. | Figma is a collaborative web application for interface design. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,902 | and toolkitsMemoryCallbacksChat loadersProvidersMoreFigmaOn this pageFigmaFigma is a collaborative web application for interface design.Installation and Setup​The Figma API requires an access token, node_ids, and a file key.The file key can be pulled from the URL. https://www.figma.com/file/{filekey}/sampleFilenameNode IDs are also available in the URL. Click on anything and look for the '?node-id={node_id}' param.Access token instructions.Document Loader​See a usage example.from langchain.document_loaders import FigmaFileLoaderPreviousFacebook FaissNextFireworksInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Figma is a collaborative web application for interface design. | Figma is a collaborative web application for interface design. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreFigmaOn this pageFigmaFigma is a collaborative web application for interface design.Installation and Setup​The Figma API requires an access token, node_ids, and a file key.The file key can be pulled from the URL. https://www.figma.com/file/{filekey}/sampleFilenameNode IDs are also available in the URL. Click on anything and look for the '?node-id={node_id}' param.Access token instructions.Document Loader​See a usage example.from langchain.document_loaders import FigmaFileLoaderPreviousFacebook FaissNextFireworksInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,903 | Postgres Embedding | ü¶úÔ∏èüîó Langchain | pgembedding is an open-source package for | pgembedding is an open-source package for ->: Postgres Embedding | ü¶úÔ∏èüîó Langchain |
2,904 | Skip to main contentü¶úÔ∏èüîó 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 | pgembedding is an open-source package for | pgembedding is an open-source package for ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,905 | and toolkitsMemoryCallbacksChat loadersProvidersMorePostgres EmbeddingOn this pagePostgres Embeddingpg_embedding is an open-source package for | pgembedding is an open-source package for | pgembedding is an open-source package for ->: and toolkitsMemoryCallbacksChat loadersProvidersMorePostgres EmbeddingOn this pagePostgres Embeddingpg_embedding is an open-source package for |
2,906 | vector similarity search using Postgres and the Hierarchical Navigable Small Worlds
algorithm for approximate nearest neighbor search.Installation and Setup​We need to install several python packages.pip install openaipip install psycopg2-binarypip install tiktokenVector Store​See a usage example.from langchain.vectorstores import PGEmbeddingPreviousPetalsNextPGVectorInstallation and SetupVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | pgembedding is an open-source package for | pgembedding is an open-source package for ->: vector similarity search using Postgres and the Hierarchical Navigable Small Worlds
algorithm for approximate nearest neighbor search.Installation and Setup​We need to install several python packages.pip install openaipip install psycopg2-binarypip install tiktokenVector Store​See a usage example.from langchain.vectorstores import PGEmbeddingPreviousPetalsNextPGVectorInstallation and SetupVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,907 | 2Markdown | ü¶úÔ∏èüîó Langchain | 2markdown service transforms website content into structured markdown files. | 2markdown service transforms website content into structured markdown files. ->: 2Markdown | ü¶úÔ∏èüîó Langchain |
2,908 | Skip to main contentü¶úÔ∏èüîó 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 | 2markdown service transforms website content into structured markdown files. | 2markdown service transforms website content into structured markdown files. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,909 | and toolkitsMemoryCallbacksChat loadersProvidersMore2MarkdownOn this page2Markdown2markdown service transforms website content into structured markdown files.Installation and Setup​We need the API key. See instructions how to get it.Document Loader​See a usage example.from langchain.document_loaders import ToMarkdownLoaderPreviousTigrisNextTrelloInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | 2markdown service transforms website content into structured markdown files. | 2markdown service transforms website content into structured markdown files. ->: and toolkitsMemoryCallbacksChat loadersProvidersMore2MarkdownOn this page2Markdown2markdown service transforms website content into structured markdown files.Installation and Setup​We need the API key. See instructions how to get it.Document Loader​See a usage example.from langchain.document_loaders import ToMarkdownLoaderPreviousTigrisNextTrelloInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,910 | Konko | ü¶úÔ∏èüîó Langchain | This page covers how to run models on Konko within LangChain. | This page covers how to run models on Konko within LangChain. ->: Konko | ü¶úÔ∏èüîó Langchain |
2,911 | Skip to main contentü¶úÔ∏èüîó 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 | This page covers how to run models on Konko within LangChain. | This page covers how to run models on Konko within LangChain. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,912 | and toolkitsMemoryCallbacksChat loadersProvidersMoreKonkoOn this pageKonkoThis page covers how to run models on Konko within LangChain.Konko API is a fully managed API designed to help application developers:Select the right LLM(s) for their application | This page covers how to run models on Konko within LangChain. | This page covers how to run models on Konko within LangChain. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreKonkoOn this pageKonkoThis page covers how to run models on Konko within LangChain.Konko API is a fully managed API designed to help application developers:Select the right LLM(s) for their application |
2,913 | Prototype with various open-source and proprietary LLMs | This page covers how to run models on Konko within LangChain. | This page covers how to run models on Konko within LangChain. ->: Prototype with various open-source and proprietary LLMs |
2,914 | Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructureInstallation and Setup​First you'll need an API key​You can request it by messaging [email protected] Install Konko AI's Python SDK​1. Enable a Python3.8+ environment​2. Set API Keys​Option 1: Set Environment Variables​You can set environment variables for KONKO_API_KEY (Required)OPENAI_API_KEY (Optional)In your current shell session, use the export command:export KONKO_API_KEY={your_KONKO_API_KEY_here}export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #OptionalAlternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.Option 2: Set API Keys Programmatically​If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:konko.set_api_key('your_KONKO_API_KEY_here') konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional3. Install the SDK​pip install konko4. Verify Installation & Authentication​#Confirm konko has installed successfullyimport konko#Confirm API keys from Konko and OpenAI are set properlykonko.Model.list()Calling a model​Find a model on the Konko Introduction pageFor example, for this LLama 2 model. The model id would be: "meta-llama/Llama-2-13b-chat-hf"Another way to find the list of models running on the Konko instance is through this endpoint.From here, we can initialize our model:chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf')And run it:msg = HumanMessage(content="Hi")chat_response = chat_instance([msg])PreviousJinaNextLanceDBInstallation and SetupFirst you'll need an API keyInstall Konko AI's Python SDKCalling a modelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 | This page covers how to run models on Konko within LangChain. | This page covers how to run models on Konko within LangChain. ->: Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructureInstallation and Setup​First you'll need an API key​You can request it by messaging [email protected] Install Konko AI's Python SDK​1. Enable a Python3.8+ environment​2. Set API Keys​Option 1: Set Environment Variables​You can set environment variables for KONKO_API_KEY (Required)OPENAI_API_KEY (Optional)In your current shell session, use the export command:export KONKO_API_KEY={your_KONKO_API_KEY_here}export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #OptionalAlternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.Option 2: Set API Keys Programmatically​If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:konko.set_api_key('your_KONKO_API_KEY_here') konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional3. Install the SDK​pip install konko4. Verify Installation & Authentication​#Confirm konko has installed successfullyimport konko#Confirm API keys from Konko and OpenAI are set properlykonko.Model.list()Calling a model​Find a model on the Konko Introduction pageFor example, for this LLama 2 model. The model id would be: "meta-llama/Llama-2-13b-chat-hf"Another way to find the list of models running on the Konko instance is through this endpoint.From here, we can initialize our model:chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf')And run it:msg = HumanMessage(content="Hi")chat_response = chat_instance([msg])PreviousJinaNextLanceDBInstallation and SetupFirst you'll need an API keyInstall Konko AI's Python SDKCalling a modelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 |
2,915 | © 2023 LangChain, Inc. | This page covers how to run models on Konko within LangChain. | This page covers how to run models on Konko within LangChain. ->: © 2023 LangChain, Inc. |
2,916 | Roam | ü¶úÔ∏èüîó Langchain | ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base. | ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base. ->: Roam | ü¶úÔ∏èüîó Langchain |
2,917 | Skip to main contentü¶úÔ∏èüîó 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 | ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base. | ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,918 | and toolkitsMemoryCallbacksChat loadersProvidersMoreRoamOn this pageRoamROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.Installation and Setup​There isn't any special setup for it.Document Loader​See a usage example.from langchain.document_loaders import RoamLoaderPreviousReplicateNextRocksetInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base. | ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreRoamOn this pageRoamROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.Installation and Setup​There isn't any special setup for it.Document Loader​See a usage example.from langchain.document_loaders import RoamLoaderPreviousReplicateNextRocksetInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,919 | Spreedly | ü¶úÔ∏èüîó Langchain | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements. | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements. ->: Spreedly | ü¶úÔ∏èüîó Langchain |
2,920 | Skip to main contentü¶úÔ∏èüîó 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 | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements. | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,921 | and toolkitsMemoryCallbacksChat loadersProvidersMoreSpreedlyOn this pageSpreedlySpreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements.Installation and Setup​See setup instructions.Document Loader​See a usage example.from langchain.document_loaders import SpreedlyLoaderPreviousspaCyNextStarRocksInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements. | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreSpreedlyOn this pageSpreedlySpreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements.Installation and Setup​See setup instructions.Document Loader​See a usage example.from langchain.document_loaders import SpreedlyLoaderPreviousspaCyNextStarRocksInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,922 | Beautiful Soup | ü¶úÔ∏èüîó Langchain | Beautiful Soup is a Python package for parsing | Beautiful Soup is a Python package for parsing ->: Beautiful Soup | ü¶úÔ∏èüîó Langchain |
2,923 | Skip to main contentü¶úÔ∏èüîó 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 | Beautiful Soup is a Python package for parsing | Beautiful Soup is a Python package for parsing ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,924 | and toolkitsMemoryCallbacksChat loadersProvidersMoreBeautiful SoupOn this pageBeautiful SoupBeautiful Soup is a Python package for parsing | Beautiful Soup is a Python package for parsing | Beautiful Soup is a Python package for parsing ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreBeautiful SoupOn this pageBeautiful SoupBeautiful Soup is a Python package for parsing |
2,925 | HTML and XML documents (including having malformed markup, i.e. non-closed tags, so named after tag soup).
It creates a parse tree for parsed pages that can be used to extract data from HTML,[3] which
is useful for web scraping.Installation and Setup​pip install beautifulsoup4Document Transformer​See a usage example.from langchain.document_loaders import BeautifulSoupTransformerPreviousBeamNextBiliBiliInstallation and SetupDocument TransformerCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Beautiful Soup is a Python package for parsing | Beautiful Soup is a Python package for parsing ->: HTML and XML documents (including having malformed markup, i.e. non-closed tags, so named after tag soup).
It creates a parse tree for parsed pages that can be used to extract data from HTML,[3] which
is useful for web scraping.Installation and Setup​pip install beautifulsoup4Document Transformer​See a usage example.from langchain.document_loaders import BeautifulSoupTransformerPreviousBeamNextBiliBiliInstallation and SetupDocument TransformerCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,926 | Hologres | ü¶úÔ∏èüîó Langchain | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. ->: Hologres | ü¶úÔ∏èüîó Langchain |
2,927 | Skip to main contentü¶úÔ∏èüîó 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 | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,928 | and toolkitsMemoryCallbacksChat loadersProvidersMoreHologresOn this pageHologresHologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreHologresOn this pageHologresHologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. |
2,929 | Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services. Hologres provides vector database functionality by adopting Proxima.
Proxima is a high-performance software library developed by Alibaba DAMO Academy. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open-source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.Installation and Setup​Click here to fast deploy a Hologres cloud instance.pip install psycopg2Vector Store​See a usage example.from langchain.vectorstores import HologresPreviousHeliconeNextHTML to textInstallation and SetupVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. ->: Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services. Hologres provides vector database functionality by adopting Proxima.
Proxima is a high-performance software library developed by Alibaba DAMO Academy. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open-source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.Installation and Setup​Click here to fast deploy a Hologres cloud instance.pip install psycopg2Vector Store​See a usage example.from langchain.vectorstores import HologresPreviousHeliconeNextHTML to textInstallation and SetupVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,930 | Brave Search | ü¶úÔ∏èüîó Langchain | Brave Search is a search engine developed by Brave Software. | Brave Search is a search engine developed by Brave Software. ->: Brave Search | ü¶úÔ∏èüîó Langchain |
2,931 | Skip to main contentü¶úÔ∏èüîó 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 | Brave Search is a search engine developed by Brave Software. | Brave Search is a search engine developed by Brave Software. ->: Skip to main contentü¶úÔ∏èüîó 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 |
2,932 | and toolkitsMemoryCallbacksChat loadersProvidersMoreBrave SearchOn this pageBrave SearchBrave Search is a search engine developed by Brave Software.Brave Search uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% | Brave Search is a search engine developed by Brave Software. | Brave Search is a search engine developed by Brave Software. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreBrave SearchOn this pageBrave SearchBrave Search is a search engine developed by Brave Software.Brave Search uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% |
2,933 | of search results without relying on any third-parties, with the remainder being retrieved
server-side from the Bing API or (on an opt-in basis) client-side from Google. According
to Brave, the index was kept "intentionally smaller than that of Google or Bing" in order to
help avoid spam and other low-quality content, with the disadvantage that "Brave Search is
not yet as good as Google in recovering long-tail queries."Brave Search Premium: As of April 2023 Brave Search is an ad-free website, but it will
eventually switch to a new model that will include ads and premium users will get an ad-free experience.
User data including IP addresses won't be collected from its users by default. A premium account
will be required for opt-in data-collection.Installation and Setup​To get access to the Brave Search API, you need to create an account and get an API key.Document Loader​See a usage example.from langchain.document_loaders import BraveSearchLoaderTool​See a usage example.from langchain.tools import BraveSearchPreviousBlackboardNextCassandraInstallation and SetupDocument LoaderToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Brave Search is a search engine developed by Brave Software. | Brave Search is a search engine developed by Brave Software. ->: of search results without relying on any third-parties, with the remainder being retrieved
server-side from the Bing API or (on an opt-in basis) client-side from Google. According
to Brave, the index was kept "intentionally smaller than that of Google or Bing" in order to
help avoid spam and other low-quality content, with the disadvantage that "Brave Search is
not yet as good as Google in recovering long-tail queries."Brave Search Premium: As of April 2023 Brave Search is an ad-free website, but it will
eventually switch to a new model that will include ads and premium users will get an ad-free experience.
User data including IP addresses won't be collected from its users by default. A premium account
will be required for opt-in data-collection.Installation and Setup​To get access to the Brave Search API, you need to create an account and get an API key.Document Loader​See a usage example.from langchain.document_loaders import BraveSearchLoaderTool​See a usage example.from langchain.tools import BraveSearchPreviousBlackboardNextCassandraInstallation and SetupDocument LoaderToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,934 | Brave Search | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the Brave Search tool. | This notebook goes over how to use the Brave Search tool. ->: Brave Search | ü¶úÔ∏èüîó Langchain |
2,935 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsBrave SearchBrave SearchThis notebook goes over how to use the Brave Search tool.from langchain.tools import BraveSearchapi_key = "API KEY"tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})tool.run("obama middle name") '[{"title": "Obama\'s Middle Name -- My Last Name -- is \'Hussein.\' So?", "link": "https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/", "snippet": "I wasn\\u2019t sure whether to laugh or cry a few days back listening to radio talk show host Bill Cunningham repeatedly scream Barack <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong> \\u2014 my last <strong>name</strong> \\u2014 as if he had anti-Muslim Tourette\\u2019s. \\u201cHussein,\\u201d Cunningham hissed like he was beckoning Satan when shouting the ..."}, {"title": "What\'s up with Obama\'s middle name? - Quora", "link": "https://www.quora.com/Whats-up-with-Obamas-middle-name", "snippet": "Answer (1 of 15): A better question would be, \\u201cWhat\\u2019s up with <strong>Obama</strong>\\u2019s first <strong>name</strong>?\\u201d | This notebook goes over how to use the Brave Search tool. | This notebook goes over how to use the Brave Search tool. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsBrave SearchBrave SearchThis notebook goes over how to use the Brave Search tool.from langchain.tools import BraveSearchapi_key = "API KEY"tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})tool.run("obama middle name") '[{"title": "Obama\'s Middle Name -- My Last Name -- is \'Hussein.\' So?", "link": "https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/", "snippet": "I wasn\\u2019t sure whether to laugh or cry a few days back listening to radio talk show host Bill Cunningham repeatedly scream Barack <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong> \\u2014 my last <strong>name</strong> \\u2014 as if he had anti-Muslim Tourette\\u2019s. \\u201cHussein,\\u201d Cunningham hissed like he was beckoning Satan when shouting the ..."}, {"title": "What\'s up with Obama\'s middle name? - Quora", "link": "https://www.quora.com/Whats-up-with-Obamas-middle-name", "snippet": "Answer (1 of 15): A better question would be, \\u201cWhat\\u2019s up with <strong>Obama</strong>\\u2019s first <strong>name</strong>?\\u201d |
2,936 | first <strong>name</strong>?\\u201d President Barack Hussein <strong>Obama</strong>\\u2019s father\\u2019s <strong>name</strong> was Barack Hussein <strong>Obama</strong>. He was <strong>named</strong> after his father. Hussein, <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong>, is a very common Arabic <strong>name</strong>, meaning "good," "handsome," or ..."}, {"title": "Barack Obama | Biography, Parents, Education, Presidency, Books, ...", "link": "https://www.britannica.com/biography/Barack-Obama", "snippet": "Barack <strong>Obama</strong>, in full Barack Hussein <strong>Obama</strong> II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009\\u201317) and the first African American to hold the office. Before winning the presidency, <strong>Obama</strong> represented Illinois in the U.S."}]'PreviousBing SearchNextChatGPT PluginsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the Brave Search tool. | This notebook goes over how to use the Brave Search tool. ->: first <strong>name</strong>?\\u201d President Barack Hussein <strong>Obama</strong>\\u2019s father\\u2019s <strong>name</strong> was Barack Hussein <strong>Obama</strong>. He was <strong>named</strong> after his father. Hussein, <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong>, is a very common Arabic <strong>name</strong>, meaning "good," "handsome," or ..."}, {"title": "Barack Obama | Biography, Parents, Education, Presidency, Books, ...", "link": "https://www.britannica.com/biography/Barack-Obama", "snippet": "Barack <strong>Obama</strong>, in full Barack Hussein <strong>Obama</strong> II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009\\u201317) and the first African American to hold the office. Before winning the presidency, <strong>Obama</strong> represented Illinois in the U.S."}]'PreviousBing SearchNextChatGPT PluginsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,937 | Wikipedia | ü¶úÔ∏èüîó Langchain | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: Wikipedia | ü¶úÔ∏èüîó Langchain |
2,938 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsWikipediaWikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.First, you need to install wikipedia python package.pip install wikipediafrom langchain.tools import WikipediaQueryRunfrom langchain.utilities import WikipediaAPIWrapperwikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())wikipedia.run("HUNTER X HUNTER") 'Page: Hunter √ó Hunter\nSummary: Hunter √ó Hunter (stylized as HUNTER√óHUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s sh≈çnen manga magazine Weekly Sh≈çnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tank≈çbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsWikipediaWikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.First, you need to install wikipedia python package.pip install wikipediafrom langchain.tools import WikipediaQueryRunfrom langchain.utilities import WikipediaAPIWrapperwikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())wikipedia.run("HUNTER X HUNTER") 'Page: Hunter √ó Hunter\nSummary: Hunter √ó Hunter (stylized as HUNTER√óHUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s sh≈çnen manga magazine Weekly Sh≈çnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tank≈çbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a |
2,939 | who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter √ó Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter √ó Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter √ó Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\nPage: Hunter √ó Hunter (2011 TV series)\nSummary: Hunter √ó Hunter is an anime television series that aired from 2011 to 2014 based on Yoshihiro Togashi\'s manga series Hunter √ó Hunter. The story begins with a young boy named Gon Freecss, who one day discovers that the father who he thought was dead, is in fact alive and well. He learns that his father, Ging, is a legendary "Hunter", an | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter √ó Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter √ó Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter √ó Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\nPage: Hunter √ó Hunter (2011 TV series)\nSummary: Hunter √ó Hunter is an anime television series that aired from 2011 to 2014 based on Yoshihiro Togashi\'s manga series Hunter √ó Hunter. The story begins with a young boy named Gon Freecss, who one day discovers that the father who he thought was dead, is in fact alive and well. He learns that his father, Ging, is a legendary "Hunter", an |
2,940 | his father, Ging, is a legendary "Hunter", an individual who has proven themselves an elite member of humanity. Despite the fact that Ging left his son with his relatives in order to pursue his own dreams, Gon becomes determined to follow in his father\'s footsteps, pass the rigorous "Hunter Examination", and eventually find his father to become a Hunter in his own right.\nThis new Hunter × Hunter anime was announced on July 24, 2011. It is a complete reboot starting from the beginning of the original manga, with no connection to the first anime television series from 1999. Produced by Nippon TV, VAP, Shueisha and Madhouse, the series is directed by Hiroshi Kōjina, with Atsushi Maekawa and Tsutomu Kamishiro handling series composition, Takahiro Yoshimatsu designing the characters and Yoshihisa Hirano composing the music. Instead of having the old cast reprise their roles for the new adaptation, the series features an entirely new cast to voice the characters. The new series premiered airing weekly on Nippon TV and the nationwide Nippon News Network from October 2, 2011. The series started to be collected in both DVD and Blu-ray format on January 25, 2012. Viz Media has licensed the anime for a DVD/Blu-ray release in North America with an English dub. On television, the series began airing on Adult Swim\'s Toonami programming block on April 17, 2016, and ended on June 23, 2019.The anime series\' opening theme is alternated between the song "Departure!" and an alternate version titled "Departure! -Second Version-" both sung by Galneryus\' voc'PreviousTwilioNextWolfram AlphaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. | Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: his father, Ging, is a legendary "Hunter", an individual who has proven themselves an elite member of humanity. Despite the fact that Ging left his son with his relatives in order to pursue his own dreams, Gon becomes determined to follow in his father\'s footsteps, pass the rigorous "Hunter Examination", and eventually find his father to become a Hunter in his own right.\nThis new Hunter × Hunter anime was announced on July 24, 2011. It is a complete reboot starting from the beginning of the original manga, with no connection to the first anime television series from 1999. Produced by Nippon TV, VAP, Shueisha and Madhouse, the series is directed by Hiroshi Kōjina, with Atsushi Maekawa and Tsutomu Kamishiro handling series composition, Takahiro Yoshimatsu designing the characters and Yoshihisa Hirano composing the music. Instead of having the old cast reprise their roles for the new adaptation, the series features an entirely new cast to voice the characters. The new series premiered airing weekly on Nippon TV and the nationwide Nippon News Network from October 2, 2011. The series started to be collected in both DVD and Blu-ray format on January 25, 2012. Viz Media has licensed the anime for a DVD/Blu-ray release in North America with an English dub. On television, the series began airing on Adult Swim\'s Toonami programming block on April 17, 2016, and ended on June 23, 2019.The anime series\' opening theme is alternated between the song "Departure!" and an alternate version titled "Departure! -Second Version-" both sung by Galneryus\' voc'PreviousTwilioNextWolfram AlphaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,941 | Human as a tool | ü¶úÔ∏èüîó Langchain | Human are AGI so they can certainly be used as a tool to help out AI agent | Human are AGI so they can certainly be used as a tool to help out AI agent ->: Human as a tool | ü¶úÔ∏èüîó Langchain |
2,942 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsHuman as a toolOn this pageHuman as a toolHuman are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.from langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0.0)math_llm = OpenAI(temperature=0.0)tools = load_tools( ["human", "llm-math"], llm=math_llm,)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)In the above code you can see the tool takes input directly from command line. | Human are AGI so they can certainly be used as a tool to help out AI agent | Human are AGI so they can certainly be used as a tool to help out AI agent ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsHuman as a toolOn this pageHuman as a toolHuman are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.from langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0.0)math_llm = OpenAI(temperature=0.0)tools = load_tools( ["human", "llm-math"], llm=math_llm,)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)In the above code you can see the tool takes input directly from command line. |
2,943 | You can customize prompt_func and input_func according to your need (as shown below).agent_chain.run("What's my friend Eric's surname?")# Answer with 'Zhu' > Entering new AgentExecutor chain... I don't know Eric's surname, so I should ask a human for guidance. Action: Human Action Input: "What is Eric's surname?" What is Eric's surname? Zhu Observation: Zhu Thought:I now know Eric's surname is Zhu. Final Answer: Eric's surname is Zhu. > Finished chain. "Eric's surname is Zhu."Configuring the Input Function‚ÄãBy default, the HumanInputRun tool uses the python input function to get input from the user.
You can customize the input_func to be anything you'd like. | Human are AGI so they can certainly be used as a tool to help out AI agent | Human are AGI so they can certainly be used as a tool to help out AI agent ->: You can customize prompt_func and input_func according to your need (as shown below).agent_chain.run("What's my friend Eric's surname?")# Answer with 'Zhu' > Entering new AgentExecutor chain... I don't know Eric's surname, so I should ask a human for guidance. Action: Human Action Input: "What is Eric's surname?" What is Eric's surname? Zhu Observation: Zhu Thought:I now know Eric's surname is Zhu. Final Answer: Eric's surname is Zhu. > Finished chain. "Eric's surname is Zhu."Configuring the Input Function‚ÄãBy default, the HumanInputRun tool uses the python input function to get input from the user.
You can customize the input_func to be anything you'd like. |
2,944 | For instance, if you want to accept multi-line input, you could do the following:def get_input() -> str: print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.") contents = [] while True: try: line = input() except EOFError: break if line == "q": break contents.append(line) return "\n".join(contents)# You can modify the tool when loadingtools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)# Or you can directly instantiate the toolfrom langchain.tools import HumanInputRuntool = HumanInputRun(input_func=get_input)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run("I need help attributing a quote") > Entering new AgentExecutor chain... I should ask a human for guidance Action: Human Action Input: "Can you help me attribute a quote?" Can you help me attribute a quote? Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. vini vidi vici q Observation: vini vidi vici Thought:I need to provide more context about the quote Action: Human Action Input: "The quote is 'Veni, vidi, vici'" The quote is 'Veni, vidi, vici' Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. oh who said it q Observation: oh who said it Thought:I can use DuckDuckGo Search to find out who said the quote Action: DuckDuckGo Search Action Input: "Who said 'Veni, vidi, vici'?" Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical | Human are AGI so they can certainly be used as a tool to help out AI agent | Human are AGI so they can certainly be used as a tool to help out AI agent ->: For instance, if you want to accept multi-line input, you could do the following:def get_input() -> str: print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.") contents = [] while True: try: line = input() except EOFError: break if line == "q": break contents.append(line) return "\n".join(contents)# You can modify the tool when loadingtools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)# Or you can directly instantiate the toolfrom langchain.tools import HumanInputRuntool = HumanInputRun(input_func=get_input)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run("I need help attributing a quote") > Entering new AgentExecutor chain... I should ask a human for guidance Action: Human Action Input: "Can you help me attribute a quote?" Can you help me attribute a quote? Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. vini vidi vici q Observation: vini vidi vici Thought:I need to provide more context about the quote Action: Human Action Input: "The quote is 'Veni, vidi, vici'" The quote is 'Veni, vidi, vici' Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. oh who said it q Observation: oh who said it Thought:I can use DuckDuckGo Search to find out who said the quote Action: DuckDuckGo Search Action Input: "Who said 'Veni, vidi, vici'?" Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical |
2,945 | Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weÀêniÀê wiÀêdiÀê wiÀêkiÀê], Ecclesiastical Latin: [Ààveni Ààvidi Ààvit Éi]; "I came; I saw; I conquered") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve¬∑ ni, vi¬∑ di, vi¬∑ ci ÀåwƒÅ-nƒì Àåwƒì-dƒì Ààwƒì-kƒì ÀåvƒÅ-nƒì Àåvƒì-dƒì Ààvƒì-chƒì : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Veniz√©los See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph. Thought:I now know the final answer Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered". > Finished chain. 'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".'PreviousHuggingFace Hub ToolsNextIFTTT WebHooksConfiguring the Input FunctionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. | Human are AGI so they can certainly be used as a tool to help out AI agent | Human are AGI so they can certainly be used as a tool to help out AI agent ->: Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weÀêniÀê wiÀêdiÀê wiÀêkiÀê], Ecclesiastical Latin: [Ààveni Ààvidi Ààvit Éi]; "I came; I saw; I conquered") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve¬∑ ni, vi¬∑ di, vi¬∑ ci ÀåwƒÅ-nƒì Àåwƒì-dƒì Ààwƒì-kƒì ÀåvƒÅ-nƒì Àåvƒì-dƒì Ààvƒì-chƒì : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Veniz√©los See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph. Thought:I now know the final answer Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered". > Finished chain. 'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".'PreviousHuggingFace Hub ToolsNextIFTTT WebHooksConfiguring the Input FunctionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. |
2,946 | Yahoo Finance News | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the yahoofinancenews tool with an agent. | This notebook goes over how to use the yahoofinancenews tool with an agent. ->: Yahoo Finance News | ü¶úÔ∏èüîó Langchain |
2,947 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsYahoo Finance NewsOn this pageYahoo Finance NewsThis notebook goes over how to use the yahoo_finance_news tool with an agent. Setting up‚ÄãFirst, you need to install yfinance python package.pip install yfinanceExample with Chain‚Äãimport osos.environ["OPENAI_API_KEY"] = "..."from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agent, AgentTypefrom langchain.tools.yahoo_finance_news import YahooFinanceNewsTool llm = ChatOpenAI(temperature=0.0)tools = [YahooFinanceNewsTool()]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "What happens today with Microsoft stocks?",) > Entering new AgentExecutor chain... I should check the latest financial news about Microsoft stocks. Action: yahoo_finance_news Action Input: MSFT Observation: Microsoft (MSFT) Gains But Lags Market: What You Should Know In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day. Thought:I have the latest information on Microsoft stocks. Final | This notebook goes over how to use the yahoofinancenews tool with an agent. | This notebook goes over how to use the yahoofinancenews tool with an agent. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsYahoo Finance NewsOn this pageYahoo Finance NewsThis notebook goes over how to use the yahoo_finance_news tool with an agent. Setting up‚ÄãFirst, you need to install yfinance python package.pip install yfinanceExample with Chain‚Äãimport osos.environ["OPENAI_API_KEY"] = "..."from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agent, AgentTypefrom langchain.tools.yahoo_finance_news import YahooFinanceNewsTool llm = ChatOpenAI(temperature=0.0)tools = [YahooFinanceNewsTool()]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "What happens today with Microsoft stocks?",) > Entering new AgentExecutor chain... I should check the latest financial news about Microsoft stocks. Action: yahoo_finance_news Action Input: MSFT Observation: Microsoft (MSFT) Gains But Lags Market: What You Should Know In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day. Thought:I have the latest information on Microsoft stocks. Final |
2,948 | latest information on Microsoft stocks. Final Answer: Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day. > Finished chain. 'Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.'agent_chain.run( "How does Microsoft feels today comparing with Nvidia?",) > Entering new AgentExecutor chain... I should compare the current sentiment of Microsoft and Nvidia. Action: yahoo_finance_news Action Input: MSFT Observation: Microsoft (MSFT) Gains But Lags Market: What You Should Know In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day. Thought:I need to find the current sentiment of Nvidia as well. Action: yahoo_finance_news Action Input: NVDA Observation: Thought:I now know the current sentiment of both Microsoft and Nvidia. Final Answer: I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft. > Finished chain. 'I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.'How YahooFinanceNewsTool works?tool = YahooFinanceNewsTool()tool.run("NVDA") 'No news found for company that searched with NVDA ticker.'res = tool.run("AAPL")print(res) Top Research Reports for Apple, Broadcom & Caterpillar Today's Research Daily features new research reports on 16 major stocks, including Apple Inc. (AAPL), Broadcom Inc. (AVGO) and Caterpillar Inc. (CAT). Apple Stock on Pace for Worst Month of the Year Apple (AAPL) shares are on pace for their worst month of the year, according to Dow Jones Market Data. The stock is down 4.8% so far in August, putting it on pace for its worst month since December 2022, when it fell 12%.PreviousWolfram AlphaNextYouTubeSetting upExample with ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the yahoofinancenews tool with an agent. | This notebook goes over how to use the yahoofinancenews tool with an agent. ->: latest information on Microsoft stocks. Final Answer: Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day. > Finished chain. 'Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.'agent_chain.run( "How does Microsoft feels today comparing with Nvidia?",) > Entering new AgentExecutor chain... I should compare the current sentiment of Microsoft and Nvidia. Action: yahoo_finance_news Action Input: MSFT Observation: Microsoft (MSFT) Gains But Lags Market: What You Should Know In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day. Thought:I need to find the current sentiment of Nvidia as well. Action: yahoo_finance_news Action Input: NVDA Observation: Thought:I now know the current sentiment of both Microsoft and Nvidia. Final Answer: I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft. > Finished chain. 'I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.'How YahooFinanceNewsTool works?tool = YahooFinanceNewsTool()tool.run("NVDA") 'No news found for company that searched with NVDA ticker.'res = tool.run("AAPL")print(res) Top Research Reports for Apple, Broadcom & Caterpillar Today's Research Daily features new research reports on 16 major stocks, including Apple Inc. (AAPL), Broadcom Inc. (AVGO) and Caterpillar Inc. (CAT). Apple Stock on Pace for Worst Month of the Year Apple (AAPL) shares are on pace for their worst month of the year, according to Dow Jones Market Data. The stock is down 4.8% so far in August, putting it on pace for its worst month since December 2022, when it fell 12%.PreviousWolfram AlphaNextYouTubeSetting upExample with ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,949 | Gradio | ü¶úÔ∏èüîó Langchain | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ ->: Gradio | ü¶úÔ∏èüîó Langchain |
2,950 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGradioOn this pageGradioThere are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æSpecifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a Gradio tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different Gradio tool to apply OCR to a document on your Google Drive and then answer questions about it.It's very easy to create you own tool if you want to use a space that's not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!# !pip install gradio_toolsUsing a tool‚Äãfrom gradio_tools.tools import StableDiffusionToollocal_file_path = StableDiffusionTool().langchain.run( "Please create a photo of a dog riding a skateboard")local_file_path Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ‚úî Job Status: Status.STARTING eta: | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGradioOn this pageGradioThere are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æSpecifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a Gradio tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different Gradio tool to apply OCR to a document on your Google Drive and then answer questions about it.It's very easy to create you own tool if you want to use a space that's not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!# !pip install gradio_toolsUsing a tool‚Äãfrom gradio_tools.tools import StableDiffusionToollocal_file_path = StableDiffusionTool().langchain.run( "Please create a photo of a dog riding a skateboard")local_file_path Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ‚úî Job Status: Status.STARTING eta: |
2,951 | ‚úî Job Status: Status.STARTING eta: None '/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg'from PIL import Imageim = Image.open(local_file_path)display(im)Using within an agent‚Äãfrom langchain.agents import initialize_agentfrom langchain.llms import OpenAIfrom gradio_tools.tools import ( StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool, TextToVideoTool,)from langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")tools = [ StableDiffusionTool().langchain, ImageCaptioningTool().langchain, StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain,]agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( "Please create a photo of a dog riding a skateboard " "but improve my prompt prior to using an image generator." "Please caption the generated image and create a video for it using the improved prompt." )) Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ‚úî Loaded as API: https://taesiri-blip-2.hf.space ‚úî Loaded as API: https://microsoft-promptist.hf.space ‚úî Loaded as API: https://damo-vilab-modelscope-text-to-video-synthesis.hf.space ‚úî > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: StableDiffusionPromptGenerator Action Input: A dog riding a skateboard Job Status: Status.STARTING eta: None Observation: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha Thought: Do I need to use a tool? Yes Action: StableDiffusion Action Input: A dog riding a skateboard, digital painting, artstation, concept art, | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ ->: ‚úî Job Status: Status.STARTING eta: None '/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg'from PIL import Imageim = Image.open(local_file_path)display(im)Using within an agent‚Äãfrom langchain.agents import initialize_agentfrom langchain.llms import OpenAIfrom gradio_tools.tools import ( StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool, TextToVideoTool,)from langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")tools = [ StableDiffusionTool().langchain, ImageCaptioningTool().langchain, StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain,]agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( "Please create a photo of a dog riding a skateboard " "but improve my prompt prior to using an image generator." "Please caption the generated image and create a video for it using the improved prompt." )) Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ‚úî Loaded as API: https://taesiri-blip-2.hf.space ‚úî Loaded as API: https://microsoft-promptist.hf.space ‚úî Loaded as API: https://damo-vilab-modelscope-text-to-video-synthesis.hf.space ‚úî > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: StableDiffusionPromptGenerator Action Input: A dog riding a skateboard Job Status: Status.STARTING eta: None Observation: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha Thought: Do I need to use a tool? Yes Action: StableDiffusion Action Input: A dog riding a skateboard, digital painting, artstation, concept art, |
2,952 | digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha Job Status: Status.STARTING eta: None Job Status: Status.PROCESSING eta: None Observation: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg Thought: Do I need to use a tool? Yes Action: ImageCaptioner Action Input: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg Job Status: Status.STARTING eta: None Observation: a painting of a dog sitting on a skateboard Thought: Do I need to use a tool? Yes Action: TextToVideo Action Input: a painting of a dog sitting on a skateboard Job Status: Status.STARTING eta: None Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis) Job Status: Status.IN_QUEUE eta: 73.89824726581574 Due to heavy traffic on this app, the prediction will take approximately 42 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis) Job Status: Status.IN_QUEUE eta: 42.49370198879602 Job Status: Status.IN_QUEUE eta: 21.314297944849187 Observation: /var/folders/bm/ylzhm36n075cslb9fvvbgq640000gn/T/tmp5snj_nmzf20_cb3m.mp4 Thought: Do I need to use a tool? No AI: Here is a video of a painting of a dog sitting on a skateboard. > Finished chain.PreviousGoogle SerperNextGraphQLUsing a toolUsing within an agentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ | There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers ü¶æ ->: digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha Job Status: Status.STARTING eta: None Job Status: Status.PROCESSING eta: None Observation: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg Thought: Do I need to use a tool? Yes Action: ImageCaptioner Action Input: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg Job Status: Status.STARTING eta: None Observation: a painting of a dog sitting on a skateboard Thought: Do I need to use a tool? Yes Action: TextToVideo Action Input: a painting of a dog sitting on a skateboard Job Status: Status.STARTING eta: None Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis) Job Status: Status.IN_QUEUE eta: 73.89824726581574 Due to heavy traffic on this app, the prediction will take approximately 42 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis) Job Status: Status.IN_QUEUE eta: 42.49370198879602 Job Status: Status.IN_QUEUE eta: 21.314297944849187 Observation: /var/folders/bm/ylzhm36n075cslb9fvvbgq640000gn/T/tmp5snj_nmzf20_cb3m.mp4 Thought: Do I need to use a tool? No AI: Here is a video of a painting of a dog sitting on a skateboard. > Finished chain.PreviousGoogle SerperNextGraphQLUsing a toolUsing within an agentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. |
2,953 | GraphQL | ü¶úÔ∏èüîó Langchain | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. ->: GraphQL | ü¶úÔ∏èüîó Langchain |
2,954 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGraphQLGraphQLGraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.By including a BaseGraphQLTool in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.This Jupyter Notebook demonstrates how to use the GraphQLAPIWrapper component with an Agent.In this example, we'll be using the public Star Wars GraphQL API available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.First, you need to install httpx and gql Python packages.pip install httpx gql > /dev/nullNow, let's create a BaseGraphQLTool instance with the specified Star Wars API endpoint and initialize an Agent with the tool.from langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypefrom langchain.utilities import | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGraphQLGraphQLGraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.By including a BaseGraphQLTool in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.This Jupyter Notebook demonstrates how to use the GraphQLAPIWrapper component with an Agent.In this example, we'll be using the public Star Wars GraphQL API available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.First, you need to install httpx and gql Python packages.pip install httpx gql > /dev/nullNow, let's create a BaseGraphQLTool instance with the specified Star Wars API endpoint and initialize an Agent with the tool.from langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypefrom langchain.utilities import |
2,955 | AgentTypefrom langchain.utilities import GraphQLAPIWrapperllm = OpenAI(temperature=0)tools = load_tools( ["graphql"], graphql_endpoint="https://swapi-graphql.netlify.app/.netlify/functions/index",)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)Now, we can use the Agent to run queries against the Star Wars GraphQL API. Let's ask the Agent to list all the Star Wars films and their release dates.graphql_fields = """allFilms { films { title director releaseDate speciesConnection { species { name classification homeworld { name } } } } }"""suffix = "Search for the titles of all the stawars films stored in the graphql database that has this schema "agent.run(suffix + graphql_fields) > Entering new AgentExecutor chain... I need to query the graphql database to get the titles of all the star wars films Action: query_graphql Action Input: query { allFilms { films { title } } } Observation: "{\n \"allFilms\": {\n \"films\": [\n {\n \"title\": \"A New Hope\"\n },\n {\n \"title\": \"The Empire Strikes Back\"\n },\n {\n \"title\": \"Return of the Jedi\"\n },\n {\n \"title\": \"The Phantom Menace\"\n },\n {\n \"title\": \"Attack of the Clones\"\n },\n {\n \"title\": \"Revenge of the Sith\"\n }\n ]\n }\n}" Thought: I now know the titles of all the star wars films Final Answer: The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith. > Finished chain. 'The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.'PreviousGradioNextHuggingFace Hub | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. ->: AgentTypefrom langchain.utilities import GraphQLAPIWrapperllm = OpenAI(temperature=0)tools = load_tools( ["graphql"], graphql_endpoint="https://swapi-graphql.netlify.app/.netlify/functions/index",)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)Now, we can use the Agent to run queries against the Star Wars GraphQL API. Let's ask the Agent to list all the Star Wars films and their release dates.graphql_fields = """allFilms { films { title director releaseDate speciesConnection { species { name classification homeworld { name } } } } }"""suffix = "Search for the titles of all the stawars films stored in the graphql database that has this schema "agent.run(suffix + graphql_fields) > Entering new AgentExecutor chain... I need to query the graphql database to get the titles of all the star wars films Action: query_graphql Action Input: query { allFilms { films { title } } } Observation: "{\n \"allFilms\": {\n \"films\": [\n {\n \"title\": \"A New Hope\"\n },\n {\n \"title\": \"The Empire Strikes Back\"\n },\n {\n \"title\": \"Return of the Jedi\"\n },\n {\n \"title\": \"The Phantom Menace\"\n },\n {\n \"title\": \"Attack of the Clones\"\n },\n {\n \"title\": \"Revenge of the Sith\"\n }\n ]\n }\n}" Thought: I now know the titles of all the star wars films Final Answer: The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith. > Finished chain. 'The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.'PreviousGradioNextHuggingFace Hub |
2,956 | of the Sith.'PreviousGradioNextHuggingFace Hub ToolsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. | GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. ->: of the Sith.'PreviousGradioNextHuggingFace Hub ToolsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,957 | YouTube | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsYouTubeYouTubeYouTube Search package searches YouTube videos avoiding using their heavily rate-limited API.It uses the form on the YouTube homepage and scrapes the resulting page.This notebook shows how to use a tool to search YouTube.Adapted from https://github.com/venuv/langchain_yt_tools#! pip install youtube_searchfrom langchain.tools import YouTubeSearchTooltool = YouTubeSearchTool()tool.run("lex friedman") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu']"You can also specify the number of results that are returnedtool.run("lex friedman,5") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=YVJ8gTnDC4Y&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=Udh22kuLebg&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=L_Guz73e6fw&pp=ygUMbGV4IGZyaWVkbWFu']"PreviousYahoo Finance NewsNextZapier Natural Language ActionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. | YouTube Search package searches YouTube videos avoiding using their heavily rate-limited API. | YouTube Search package searches YouTube videos avoiding using their heavily rate-limited API. ->: YouTube | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsYouTubeYouTubeYouTube Search package searches YouTube videos avoiding using their heavily rate-limited API.It uses the form on the YouTube homepage and scrapes the resulting page.This notebook shows how to use a tool to search YouTube.Adapted from https://github.com/venuv/langchain_yt_tools#! pip install youtube_searchfrom langchain.tools import YouTubeSearchTooltool = YouTubeSearchTool()tool.run("lex friedman") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu']"You can also specify the number of results that are returnedtool.run("lex friedman,5") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=YVJ8gTnDC4Y&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=Udh22kuLebg&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=L_Guz73e6fw&pp=ygUMbGV4IGZyaWVkbWFu']"PreviousYahoo Finance NewsNextZapier Natural Language ActionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. |
2,958 | Twilio | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels. | This notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels. ->: Twilio | ü¶úÔ∏èüîó Langchain |
2,959 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsTwilioOn this pageTwilioThis notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels.Twilio Messaging Channels facilitates integrations with 3rd party messaging apps and lets you send messages through WhatsApp Business Platform (GA), Facebook Messenger (Public Beta) and Google Business Messages (Private Beta).Setup‚ÄãTo use this tool you need to install the Python Twilio package twilio# !pip install twilioYou'll also need to set up a Twilio account and get your credentials. You'll need your Account String Identifier (SID) and your Auth Token. You'll also need a number to send messages from.You can either pass these in to the TwilioAPIWrapper as named parameters account_sid, auth_token, from_number, or you can set the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_FROM_NUMBER.Sending an SMS‚Äãfrom langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="baz,")twilio.run("hello world", "+16162904619")Sending a WhatsApp Message‚ÄãYou'll need | This notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels. | This notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsTwilioOn this pageTwilioThis notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels.Twilio Messaging Channels facilitates integrations with 3rd party messaging apps and lets you send messages through WhatsApp Business Platform (GA), Facebook Messenger (Public Beta) and Google Business Messages (Private Beta).Setup‚ÄãTo use this tool you need to install the Python Twilio package twilio# !pip install twilioYou'll also need to set up a Twilio account and get your credentials. You'll need your Account String Identifier (SID) and your Auth Token. You'll also need a number to send messages from.You can either pass these in to the TwilioAPIWrapper as named parameters account_sid, auth_token, from_number, or you can set the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_FROM_NUMBER.Sending an SMS‚Äãfrom langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="baz,")twilio.run("hello world", "+16162904619")Sending a WhatsApp Message‚ÄãYou'll need |
2,960 | a WhatsApp Message​You'll need to link your WhatsApp Business Account with Twilio. You'll also need to make sure that the number to send messages from is configured as a WhatsApp Enabled Sender on Twilio and registered with WhatsApp.from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="whatsapp: baz,")twilio.run("hello world", "whatsapp: +16162904619")PreviousSerpAPINextWikipediaSetupSending an SMSSending a WhatsApp MessageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels. | This notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels. ->: a WhatsApp Message​You'll need to link your WhatsApp Business Account with Twilio. You'll also need to make sure that the number to send messages from is configured as a WhatsApp Enabled Sender on Twilio and registered with WhatsApp.from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="whatsapp: baz,")twilio.run("hello world", "whatsapp: +16162904619")PreviousSerpAPINextWikipediaSetupSending an SMSSending a WhatsApp MessageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,961 | Bing Search | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the bing search component. | This notebook goes over how to use the bing search component. ->: Bing Search | ü¶úÔ∏èüîó Langchain |
2,962 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsBing SearchOn this pageBing SearchThis notebook goes over how to use the bing search component.First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found here.Then we will need to set some environment variables.import osos.environ["BING_SUBSCRIPTION_KEY"] = "<key>"os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"from langchain.utilities import BingSearchAPIWrappersearch = BingSearchAPIWrapper()search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor. <b>Python</b> releases by version number: Release version Release date Click for more. <b>Python</b> 3.11.1 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.10.9 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.9.16 Dec. 6, 2022 Download Release Notes. | This notebook goes over how to use the bing search component. | This notebook goes over how to use the bing search component. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsBing SearchOn this pageBing SearchThis notebook goes over how to use the bing search component.First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found here.Then we will need to set some environment variables.import osos.environ["BING_SUBSCRIPTION_KEY"] = "<key>"os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"from langchain.utilities import BingSearchAPIWrappersearch = BingSearchAPIWrapper()search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor. <b>Python</b> releases by version number: Release version Release date Click for more. <b>Python</b> 3.11.1 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.10.9 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.9.16 Dec. 6, 2022 Download Release Notes. |
2,963 | 3.9.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.8.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in <b>Python</b> and see how it works with several simple examples.. The operator ‘+=’ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, <b>Python</b>, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the <b>Python</b> language and system. It helps to have a <b>Python</b> interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It's a great first language because <b>Python</b> code is concise and easy to read. Whatever you want to do, <b>python</b> can do it. From web development to machine learning to data science, <b>Python</b> is the language for you. To install <b>Python</b> using the Microsoft Store: Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store. Once the store is open, select Search from the upper-right menu and enter "<b>Python</b>". Select which version of <b>Python</b> you would like to use from the results under Apps. Under the “<b>Python</b> Releases for Mac OS X” heading, click the link for the Latest <b>Python</b> 3 Release - <b>Python</b> 3.x.x. As of this writing, the latest version was <b>Python</b> 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move | This notebook goes over how to use the bing search component. | This notebook goes over how to use the bing search component. ->: 3.9.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.8.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in <b>Python</b> and see how it works with several simple examples.. The operator ‘+=’ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, <b>Python</b>, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the <b>Python</b> language and system. It helps to have a <b>Python</b> interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It's a great first language because <b>Python</b> code is concise and easy to read. Whatever you want to do, <b>python</b> can do it. From web development to machine learning to data science, <b>Python</b> is the language for you. To install <b>Python</b> using the Microsoft Store: Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store. Once the store is open, select Search from the upper-right menu and enter "<b>Python</b>". Select which version of <b>Python</b> you would like to use from the results under Apps. Under the “<b>Python</b> Releases for Mac OS X” heading, click the link for the Latest <b>Python</b> 3 Release - <b>Python</b> 3.x.x. As of this writing, the latest version was <b>Python</b> 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move |
2,964 | When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'Number of results​You can use the k parameter to set the number of resultssearch = BingSearchAPIWrapper(k=1)search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor.'Metadata Results​Run query through BingSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search = BingSearchAPIWrapper()search.results("apples", 5) [{'snippet': 'Lady Alice. Pink Lady <b>apples</b> aren’t the only lady in the apple family. Lady Alice <b>apples</b> were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.', 'title': '25 Types of Apples - Jessica Gavin', 'link': 'https://www.jessicagavin.com/types-of-apples/'}, {'snippet': '<b>Apples</b> can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the apple’s skin before eating it, you won ...', 'title': 'Apples: Nutrition & Health Benefits - WebMD', 'link': 'https://www.webmd.com/food-recipes/benefits-apples'}, {'snippet': '<b>Apples</b> boast many vitamins and minerals, though not in high amounts. However, <b>apples</b> are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...', 'title': 'Apples 101: Nutrition Facts and Health Benefits', 'link': | This notebook goes over how to use the bing search component. | This notebook goes over how to use the bing search component. ->: When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'Number of results​You can use the k parameter to set the number of resultssearch = BingSearchAPIWrapper(k=1)search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor.'Metadata Results​Run query through BingSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search = BingSearchAPIWrapper()search.results("apples", 5) [{'snippet': 'Lady Alice. Pink Lady <b>apples</b> aren’t the only lady in the apple family. Lady Alice <b>apples</b> were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.', 'title': '25 Types of Apples - Jessica Gavin', 'link': 'https://www.jessicagavin.com/types-of-apples/'}, {'snippet': '<b>Apples</b> can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the apple’s skin before eating it, you won ...', 'title': 'Apples: Nutrition & Health Benefits - WebMD', 'link': 'https://www.webmd.com/food-recipes/benefits-apples'}, {'snippet': '<b>Apples</b> boast many vitamins and minerals, though not in high amounts. However, <b>apples</b> are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...', 'title': 'Apples 101: Nutrition Facts and Health Benefits', 'link': |
2,965 | Facts and Health Benefits', 'link': 'https://www.healthline.com/nutrition/foods/apples'}, {'snippet': 'Weight management. The fibers in <b>apples</b> can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly <b>apples</b> and pears, were associated with the least amount of weight gain over time.', 'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health', 'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]PreviousBearly Code InterpreterNextBrave SearchNumber of resultsMetadata ResultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the bing search component. | This notebook goes over how to use the bing search component. ->: Facts and Health Benefits', 'link': 'https://www.healthline.com/nutrition/foods/apples'}, {'snippet': 'Weight management. The fibers in <b>apples</b> can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly <b>apples</b> and pears, were associated with the least amount of weight gain over time.', 'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health', 'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]PreviousBearly Code InterpreterNextBrave SearchNumber of resultsMetadata ResultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,966 | ArXiv | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the arxiv tool with an agent. | This notebook goes over how to use the arxiv tool with an agent. ->: ArXiv | ü¶úÔ∏èüîó Langchain |
2,967 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsArXivOn this pageArXivThis notebook goes over how to use the arxiv tool with an agent. First, you need to install arxiv python package.pip install arxivfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypellm = ChatOpenAI(temperature=0.0)tools = load_tools( ["arxiv"],)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "What's the paper 1605.08386 about?",) > Entering new AgentExecutor chain... I need to use Arxiv to search for the paper. Action: Arxiv Action Input: "1605.08386" Observation: Published: 2016-05-26 Title: Heat-bath random walks with Markov bases Authors: Caprice Stanley, Tobias Windisch Summary: Graphs on lattice points are studied whose edges come from a finite set of allowed moves of arbitrary length. We show that the diameter of these graphs on fibers of a fixed integer matrix can be bounded from above by a constant. We then study the mixing behaviour of heat-bath random walks on these graphs. We also state | This notebook goes over how to use the arxiv tool with an agent. | This notebook goes over how to use the arxiv tool with an agent. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsArXivOn this pageArXivThis notebook goes over how to use the arxiv tool with an agent. First, you need to install arxiv python package.pip install arxivfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypellm = ChatOpenAI(temperature=0.0)tools = load_tools( ["arxiv"],)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "What's the paper 1605.08386 about?",) > Entering new AgentExecutor chain... I need to use Arxiv to search for the paper. Action: Arxiv Action Input: "1605.08386" Observation: Published: 2016-05-26 Title: Heat-bath random walks with Markov bases Authors: Caprice Stanley, Tobias Windisch Summary: Graphs on lattice points are studied whose edges come from a finite set of allowed moves of arbitrary length. We show that the diameter of these graphs on fibers of a fixed integer matrix can be bounded from above by a constant. We then study the mixing behaviour of heat-bath random walks on these graphs. We also state |
2,968 | random walks on these graphs. We also state explicit conditions on the set of moves so that the heat-bath random walk, a generalization of the Glauber dynamics, is an expander in fixed dimension. Thought:The paper is about heat-bath random walks with Markov bases on graphs of lattice points. Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points. > Finished chain. 'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'The ArXiv API Wrapper‚ÄãThe tool uses the API Wrapper. Below, we explore some of the features it provides.from langchain.utilities import ArxivAPIWrapperRun a query to get information about some scientific article/articles. The query text is limited to 300 characters.It returns these article fields:Publishing dateTitleAuthorsSummaryNext query returns information about one article with arxiv Id equal "1605.08386". arxiv = ArxivAPIWrapper()docs = arxiv.run("1605.08386")docs 'Published: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'Now, we want to get information about one author, Caprice Stanley.This query returns information about three articles. By default, the query returns information only about three top articles.docs = arxiv.run("Caprice Stanley")docs 'Published: 2017-10-10\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\nAuthors: Caprice | This notebook goes over how to use the arxiv tool with an agent. | This notebook goes over how to use the arxiv tool with an agent. ->: random walks on these graphs. We also state explicit conditions on the set of moves so that the heat-bath random walk, a generalization of the Glauber dynamics, is an expander in fixed dimension. Thought:The paper is about heat-bath random walks with Markov bases on graphs of lattice points. Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points. > Finished chain. 'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'The ArXiv API Wrapper‚ÄãThe tool uses the API Wrapper. Below, we explore some of the features it provides.from langchain.utilities import ArxivAPIWrapperRun a query to get information about some scientific article/articles. The query text is limited to 300 characters.It returns these article fields:Publishing dateTitleAuthorsSummaryNext query returns information about one article with arxiv Id equal "1605.08386". arxiv = ArxivAPIWrapper()docs = arxiv.run("1605.08386")docs 'Published: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'Now, we want to get information about one author, Caprice Stanley.This query returns information about three articles. By default, the query returns information only about three top articles.docs = arxiv.run("Caprice Stanley")docs 'Published: 2017-10-10\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\nAuthors: Caprice |
2,969 | by a Linear Recurrence\nAuthors: Caprice Stanley, Seth Sullivant\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\ninteger sequence $\\{ G_n \\}_{n \\geq 1}$ generated by a linear recurrence\nrelation. Fourier analysis provides explicit formulas to compute the\neigenvalues of the transition matrices and we use this to bound the mixing time\nof the random walks.\n\nPublished: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.\n\nPublished: 2003-03-18\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\nAuthors: V. Plyaskin\nSummary: The results on the fluxes of charged particles and neutrinos from a\n3-dimensional (3D) simulation of atmospheric showers are presented. An\nagreement of calculated fluxes with data on charged particles from the AMS and\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\nexperimental sites are compared with results from other calculations.'Now, we are trying to find information about non-existing article. In this case, the response is "No good Arxiv Result was found"docs = arxiv.run("1605.08386WWW")docs 'No good Arxiv Result was found'PreviousApifyNextAWS LambdaThe ArXiv API WrapperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the arxiv tool with an agent. | This notebook goes over how to use the arxiv tool with an agent. ->: by a Linear Recurrence\nAuthors: Caprice Stanley, Seth Sullivant\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\ninteger sequence $\\{ G_n \\}_{n \\geq 1}$ generated by a linear recurrence\nrelation. Fourier analysis provides explicit formulas to compute the\neigenvalues of the transition matrices and we use this to bound the mixing time\nof the random walks.\n\nPublished: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.\n\nPublished: 2003-03-18\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\nAuthors: V. Plyaskin\nSummary: The results on the fluxes of charged particles and neutrinos from a\n3-dimensional (3D) simulation of atmospheric showers are presented. An\nagreement of calculated fluxes with data on charged particles from the AMS and\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\nexperimental sites are compared with results from other calculations.'Now, we are trying to find information about non-existing article. In this case, the response is "No good Arxiv Result was found"docs = arxiv.run("1605.08386WWW")docs 'No good Arxiv Result was found'PreviousApifyNextAWS LambdaThe ArXiv API WrapperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,970 | Eleven Labs Text2Speech | ü¶úÔ∏èüîó Langchain | This notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities. | This notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities. ->: Eleven Labs Text2Speech | ü¶úÔ∏èüîó Langchain |
2,971 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsEleven Labs Text2SpeechOn this pageEleven Labs Text2SpeechThis notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities.First, you need to set up an ElevenLabs account. You can follow the instructions here.# !pip install elevenlabsimport osos.environ["ELEVEN_API_KEY"] = ""Usage‚Äãfrom langchain.tools import ElevenLabsText2SpeechTooltext_to_speak = "Hello world! I am the real slim shady"tts = ElevenLabsText2SpeechTool()tts.name 'eleven_labs_text2speech'We can generate audio, save it to the temporary file and then play it.speech_file = tts.run(text_to_speak)tts.play(speech_file)Or stream audio directly.tts.stream_speech(text_to_speak)Use within an Agent‚Äãfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentType, load_toolsllm = OpenAI(temperature=0)tools = load_tools(["eleven_labs_text2speech"])agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)audio_file = agent.run("Tell me a joke and read it out for me.") > Entering new AgentExecutor chain... | This notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities. | This notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsEleven Labs Text2SpeechOn this pageEleven Labs Text2SpeechThis notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities.First, you need to set up an ElevenLabs account. You can follow the instructions here.# !pip install elevenlabsimport osos.environ["ELEVEN_API_KEY"] = ""Usage‚Äãfrom langchain.tools import ElevenLabsText2SpeechTooltext_to_speak = "Hello world! I am the real slim shady"tts = ElevenLabsText2SpeechTool()tts.name 'eleven_labs_text2speech'We can generate audio, save it to the temporary file and then play it.speech_file = tts.run(text_to_speak)tts.play(speech_file)Or stream audio directly.tts.stream_speech(text_to_speak)Use within an Agent‚Äãfrom langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentType, load_toolsllm = OpenAI(temperature=0)tools = load_tools(["eleven_labs_text2speech"])agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)audio_file = agent.run("Tell me a joke and read it out for me.") > Entering new AgentExecutor chain... |
2,972 | > Entering new AgentExecutor chain... Action: ``` { "action": "eleven_labs_text2speech", "action_input": { "query": "Why did the chicken cross the playground? To get to the other slide!" } } ``` Observation: /tmp/tmpsfg783f1.wav Thought: I have the audio file ready to be sent to the human Action: ``` { "action": "Final Answer", "action_input": "/tmp/tmpsfg783f1.wav" } ``` > Finished chain.tts.play(audio_file)PreviousEden AINextFile SystemUsageUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities. | This notebook shows how to interact with the ElevenLabs API to achieve text-to-speech capabilities. ->: > Entering new AgentExecutor chain... Action: ``` { "action": "eleven_labs_text2speech", "action_input": { "query": "Why did the chicken cross the playground? To get to the other slide!" } } ``` Observation: /tmp/tmpsfg783f1.wav Thought: I have the audio file ready to be sent to the human Action: ``` { "action": "Final Answer", "action_input": "/tmp/tmpsfg783f1.wav" } ``` > Finished chain.tts.play(audio_file)PreviousEden AINextFile SystemUsageUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,973 | Eden AI | ü¶úÔ∏èüîó Langchain | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: Eden AI | ü¶úÔ∏èüîó Langchain |
2,974 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsEden AIOn this pageEden AIThis Jupyter Notebook demonstrates how to use Eden AI tools with an Agent.Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/ )By including an Edenai tool in the list of tools provided to an Agent, you can grant your Agent the ability to do multiple tasks, such as:speech to texttext to speechtext explicit content detection image explicit content detectionobject detectionOCR invoice parsingOCR ID parsingIn this example, we will go through the process of utilizing the Edenai tools to create an Agent that can perform some of the tasks listed above.Accessing the EDENAI's API requires an API key, which you can get by creating an account https://app.edenai.run/user/register and heading here | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsEden AIOn this pageEden AIThis Jupyter Notebook demonstrates how to use Eden AI tools with an Agent.Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/ )By including an Edenai tool in the list of tools provided to an Agent, you can grant your Agent the ability to do multiple tasks, such as:speech to texttext to speechtext explicit content detection image explicit content detectionobject detectionOCR invoice parsingOCR ID parsingIn this example, we will go through the process of utilizing the Edenai tools to create an Agent that can perform some of the tasks listed above.Accessing the EDENAI's API requires an API key, which you can get by creating an account https://app.edenai.run/user/register and heading here |
2,975 | and heading here https://app.edenai.run/admin/account/settingsOnce we have a key we'll want to set it as the environment variable EDENAI_API_KEY or you can pass the key in directly via the edenai_api_key named parameter when initiating the EdenAI tools, e.g. EdenAiTextModerationTool(edenai_api_key="...")from langchain.tools.edenai import ( EdenAiSpeechToTextTool, EdenAiTextToSpeechTool, EdenAiExplicitImageTool, EdenAiObjectDetectionTool, EdenAiParsingIDTool, EdenAiParsingInvoiceTool, EdenAiTextModerationTool,)from langchain.llms import EdenAIfrom langchain.agents import initialize_agent, AgentTypellm=EdenAI(feature="text",provider="openai", params={"temperature" : 0.2,"max_tokens" : 250})tools = [ EdenAiTextModerationTool(providers=["openai"],language="en"), EdenAiObjectDetectionTool(providers=["google","api4ai"]), EdenAiTextToSpeechTool(providers=["amazon"],language="en",voice="MALE"), EdenAiExplicitImageTool(providers=["amazon","google"]), EdenAiSpeechToTextTool(providers=["amazon"]), EdenAiParsingIDTool(providers=["amazon","klippa"],language="en"), EdenAiParsingInvoiceTool(providers=["amazon","google"],language="en"),]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True,)Example with text‚Äãinput_ = """i have this text : 'i want to slap you' first : i want to know if this text contains explicit content or not .second : if it does contain explicit content i want to know what is the explicit content in this text, third : i want to make the text into speech .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to scan the text for explicit content and then convert it to speech Action: edenai_explicit_content_detection_text Action Input: 'i want to slap you' Observation: nsfw_likelihood: 3 | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: and heading here https://app.edenai.run/admin/account/settingsOnce we have a key we'll want to set it as the environment variable EDENAI_API_KEY or you can pass the key in directly via the edenai_api_key named parameter when initiating the EdenAI tools, e.g. EdenAiTextModerationTool(edenai_api_key="...")from langchain.tools.edenai import ( EdenAiSpeechToTextTool, EdenAiTextToSpeechTool, EdenAiExplicitImageTool, EdenAiObjectDetectionTool, EdenAiParsingIDTool, EdenAiParsingInvoiceTool, EdenAiTextModerationTool,)from langchain.llms import EdenAIfrom langchain.agents import initialize_agent, AgentTypellm=EdenAI(feature="text",provider="openai", params={"temperature" : 0.2,"max_tokens" : 250})tools = [ EdenAiTextModerationTool(providers=["openai"],language="en"), EdenAiObjectDetectionTool(providers=["google","api4ai"]), EdenAiTextToSpeechTool(providers=["amazon"],language="en",voice="MALE"), EdenAiExplicitImageTool(providers=["amazon","google"]), EdenAiSpeechToTextTool(providers=["amazon"]), EdenAiParsingIDTool(providers=["amazon","klippa"],language="en"), EdenAiParsingInvoiceTool(providers=["amazon","google"],language="en"),]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True,)Example with text‚Äãinput_ = """i have this text : 'i want to slap you' first : i want to know if this text contains explicit content or not .second : if it does contain explicit content i want to know what is the explicit content in this text, third : i want to make the text into speech .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to scan the text for explicit content and then convert it to speech Action: edenai_explicit_content_detection_text Action Input: 'i want to slap you' Observation: nsfw_likelihood: 3 |
2,976 | slap you' Observation: nsfw_likelihood: 3 "sexual": 1 "hate": 1 "harassment": 1 "self-harm": 1 "sexual/minors": 1 "hate/threatening": 1 "violence/graphic": 1 "self-harm/intent": 1 "self-harm/instructions": 1 "harassment/threatening": 1 "violence": 3 Thought: I now need to convert the text to speech Action: edenai_text_to_speech Action Input: 'i want to slap you' Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer Final Answer: The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn > Finished chain.you can have more details of the execution by printing the result result['output'] 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn'result {'input': " i have this text : 'i want to slap you' | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: slap you' Observation: nsfw_likelihood: 3 "sexual": 1 "hate": 1 "harassment": 1 "self-harm": 1 "sexual/minors": 1 "hate/threatening": 1 "violence/graphic": 1 "self-harm/intent": 1 "self-harm/instructions": 1 "harassment/threatening": 1 "violence": 3 Thought: I now need to convert the text to speech Action: edenai_text_to_speech Action Input: 'i want to slap you' Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer Final Answer: The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn > Finished chain.you can have more details of the execution by printing the result result['output'] 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn'result {'input': " i have this text : 'i want to slap you' |
2,977 | " i have this text : 'i want to slap you' \n first : i want to know if this text contains explicit content or not .\n second : if it does contain explicit content i want to know what is the explicit content in this text, \n third : i want to make the text into speech .\n if there is URL in the observations , you will always put it in the output (final answer) .\n\n ", 'output': 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn', 'intermediate_steps': [(AgentAction(tool='edenai_explicit_content_detection_text', tool_input="'i want to slap you'", log=" I need to scan the text for explicit content and then convert it to speech\nAction: edenai_explicit_content_detection_text\nAction Input: 'i want to slap you'"), 'nsfw_likelihood: 3\n"sexual": 1\n"hate": 1\n"harassment": 1\n"self-harm": 1\n"sexual/minors": 1\n"hate/threatening": 1\n"violence/graphic": 1\n"self-harm/intent": 1\n"self-harm/instructions": 1\n"harassment/threatening": 1\n"violence": 3'), (AgentAction(tool='edenai_text_to_speech', tool_input="'i want to slap you'", log=" I now need to convert the text to speech\nAction: edenai_text_to_speech\nAction Input: 'i want to slap you'"), | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: " i have this text : 'i want to slap you' \n first : i want to know if this text contains explicit content or not .\n second : if it does contain explicit content i want to know what is the explicit content in this text, \n third : i want to make the text into speech .\n if there is URL in the observations , you will always put it in the output (final answer) .\n\n ", 'output': 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn', 'intermediate_steps': [(AgentAction(tool='edenai_explicit_content_detection_text', tool_input="'i want to slap you'", log=" I need to scan the text for explicit content and then convert it to speech\nAction: edenai_explicit_content_detection_text\nAction Input: 'i want to slap you'"), 'nsfw_likelihood: 3\n"sexual": 1\n"hate": 1\n"harassment": 1\n"self-harm": 1\n"sexual/minors": 1\n"hate/threatening": 1\n"violence/graphic": 1\n"self-harm/intent": 1\n"self-harm/instructions": 1\n"harassment/threatening": 1\n"violence": 3'), (AgentAction(tool='edenai_text_to_speech', tool_input="'i want to slap you'", log=" I now need to convert the text to speech\nAction: edenai_text_to_speech\nAction Input: 'i want to slap you'"), |
2,978 | Input: 'i want to slap you'"), 'https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK')]}Example with images‚Äãinput_ = """i have this url of an image : "https://static.javatpoint.com/images/objects.jpg"first : i want to know if the image contain objects .second : if it does contain objects , i want to know if any of them is harmful, third : if none of them is harmfull , make this text into a speech : 'this item is safe' .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech. Action: edenai_object_detection Action Input: https://static.javatpoint.com/images/objects.jpg Observation: Apple - Confidence 0.94003654 Apple - Confidence 0.94003654 Apple - Confidence 0.94003654 Backpack - Confidence 0.7481894 Backpack - Confidence 0.7481894 Backpack - Confidence 0.7481894 Luggage & bags - Confidence 0.70691586 Luggage & bags - Confidence 0.70691586 Luggage & bags - Confidence 0.70691586 Container - Confidence 0.654727 Container - Confidence 0.654727 Container - Confidence 0.654727 Luggage & bags - Confidence 0.5871518 Luggage & bags - Confidence 0.5871518 Luggage & bags - Confidence 0.5871518 Thought: I need to check if any of the objects are harmful. Action: edenai_explicit_content_detection_text Action Input: Apple, Backpack, Luggage & bags, Container | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: Input: 'i want to slap you'"), 'https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK')]}Example with images‚Äãinput_ = """i have this url of an image : "https://static.javatpoint.com/images/objects.jpg"first : i want to know if the image contain objects .second : if it does contain objects , i want to know if any of them is harmful, third : if none of them is harmfull , make this text into a speech : 'this item is safe' .if there is URL in the observations , you will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech. Action: edenai_object_detection Action Input: https://static.javatpoint.com/images/objects.jpg Observation: Apple - Confidence 0.94003654 Apple - Confidence 0.94003654 Apple - Confidence 0.94003654 Backpack - Confidence 0.7481894 Backpack - Confidence 0.7481894 Backpack - Confidence 0.7481894 Luggage & bags - Confidence 0.70691586 Luggage & bags - Confidence 0.70691586 Luggage & bags - Confidence 0.70691586 Container - Confidence 0.654727 Container - Confidence 0.654727 Container - Confidence 0.654727 Luggage & bags - Confidence 0.5871518 Luggage & bags - Confidence 0.5871518 Luggage & bags - Confidence 0.5871518 Thought: I need to check if any of the objects are harmful. Action: edenai_explicit_content_detection_text Action Input: Apple, Backpack, Luggage & bags, Container |
2,979 | Apple, Backpack, Luggage & bags, Container Observation: nsfw_likelihood: 2 "sexually explicit": 1 "sexually suggestive": 2 "offensive": 1 nsfw_likelihood: 1 "sexual": 1 "hate": 1 "harassment": 1 "self-harm": 1 "sexual/minors": 1 "hate/threatening": 1 "violence/graphic": 1 "self-harm/intent": 1 "self-harm/instructions": 1 "harassment/threatening": 1 "violence": 1 Thought: None of the objects are harmful. Action: edenai_text_to_speech Action Input: 'this item is safe' Observation: https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer. Final Answer: The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt > Finished chain.result['output'] "The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: Apple, Backpack, Luggage & bags, Container Observation: nsfw_likelihood: 2 "sexually explicit": 1 "sexually suggestive": 2 "offensive": 1 nsfw_likelihood: 1 "sexual": 1 "hate": 1 "harassment": 1 "self-harm": 1 "sexual/minors": 1 "hate/threatening": 1 "violence/graphic": 1 "self-harm/intent": 1 "self-harm/instructions": 1 "harassment/threatening": 1 "violence": 1 Thought: None of the objects are harmful. Action: edenai_text_to_speech Action Input: 'this item is safe' Observation: https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer. Final Answer: The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt > Finished chain.result['output'] "The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at |
2,980 | item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt"you can have more details of the execution by printing the result result {'input': ' i have this url of an image : "https://static.javatpoint.com/images/objects.jpg"\n first : i want to know if the image contain objects .\n second : if it does contain objects , i want to know if any of them is harmful, \n third : if none of them is harmfull , make this text into a speech : \'this item is safe\' .\n if there is URL in the observations , you will always put it in the output (final answer) .\n ', 'output': "The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt", 'intermediate_steps': [(AgentAction(tool='edenai_object_detection', tool_input='https://static.javatpoint.com/images/objects.jpg', log=' I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\nAction: edenai_object_detection\nAction Input: https://static.javatpoint.com/images/objects.jpg'), 'Apple - Confidence 0.94003654\nApple - Confidence 0.94003654\nApple - Confidence 0.94003654\nBackpack - Confidence 0.7481894\nBackpack - Confidence 0.7481894\nBackpack - Confidence 0.7481894\nLuggage & bags - Confidence | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt"you can have more details of the execution by printing the result result {'input': ' i have this url of an image : "https://static.javatpoint.com/images/objects.jpg"\n first : i want to know if the image contain objects .\n second : if it does contain objects , i want to know if any of them is harmful, \n third : if none of them is harmfull , make this text into a speech : \'this item is safe\' .\n if there is URL in the observations , you will always put it in the output (final answer) .\n ', 'output': "The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt", 'intermediate_steps': [(AgentAction(tool='edenai_object_detection', tool_input='https://static.javatpoint.com/images/objects.jpg', log=' I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\nAction: edenai_object_detection\nAction Input: https://static.javatpoint.com/images/objects.jpg'), 'Apple - Confidence 0.94003654\nApple - Confidence 0.94003654\nApple - Confidence 0.94003654\nBackpack - Confidence 0.7481894\nBackpack - Confidence 0.7481894\nBackpack - Confidence 0.7481894\nLuggage & bags - Confidence |
2,981 | Confidence 0.7481894\nLuggage & bags - Confidence 0.70691586\nLuggage & bags - Confidence 0.70691586\nLuggage & bags - Confidence 0.70691586\nContainer - Confidence 0.654727\nContainer - Confidence 0.654727\nContainer - Confidence 0.654727\nLuggage & bags - Confidence 0.5871518\nLuggage & bags - Confidence 0.5871518\nLuggage & bags - Confidence 0.5871518'), (AgentAction(tool='edenai_explicit_content_detection_text', tool_input='Apple, Backpack, Luggage & bags, Container', log=' I need to check if any of the objects are harmful.\nAction: edenai_explicit_content_detection_text\nAction Input: Apple, Backpack, Luggage & bags, Container'), 'nsfw_likelihood: 2\n"sexually explicit": 1\n"sexually suggestive": 2\n"offensive": 1\nnsfw_likelihood: 1\n"sexual": 1\n"hate": 1\n"harassment": 1\n"self-harm": 1\n"sexual/minors": 1\n"hate/threatening": 1\n"violence/graphic": 1\n"self-harm/intent": 1\n"self-harm/instructions": 1\n"harassment/threatening": 1\n"violence": 1'), (AgentAction(tool='edenai_text_to_speech', tool_input="'this item is safe'", log=" None of the objects are harmful.\nAction: edenai_text_to_speech\nAction Input: 'this item is safe'"), 'https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK')]}Example with OCR images‚Äãinput_ = """i have this url of an id: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg"i want to extract the information in it.create a text welcoming the person by his name and make it into speech .if there is URL in the observations , you will always put it in the output (final answer) | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: Confidence 0.7481894\nLuggage & bags - Confidence 0.70691586\nLuggage & bags - Confidence 0.70691586\nLuggage & bags - Confidence 0.70691586\nContainer - Confidence 0.654727\nContainer - Confidence 0.654727\nContainer - Confidence 0.654727\nLuggage & bags - Confidence 0.5871518\nLuggage & bags - Confidence 0.5871518\nLuggage & bags - Confidence 0.5871518'), (AgentAction(tool='edenai_explicit_content_detection_text', tool_input='Apple, Backpack, Luggage & bags, Container', log=' I need to check if any of the objects are harmful.\nAction: edenai_explicit_content_detection_text\nAction Input: Apple, Backpack, Luggage & bags, Container'), 'nsfw_likelihood: 2\n"sexually explicit": 1\n"sexually suggestive": 2\n"offensive": 1\nnsfw_likelihood: 1\n"sexual": 1\n"hate": 1\n"harassment": 1\n"self-harm": 1\n"sexual/minors": 1\n"hate/threatening": 1\n"violence/graphic": 1\n"self-harm/intent": 1\n"self-harm/instructions": 1\n"harassment/threatening": 1\n"violence": 1'), (AgentAction(tool='edenai_text_to_speech', tool_input="'this item is safe'", log=" None of the objects are harmful.\nAction: edenai_text_to_speech\nAction Input: 'this item is safe'"), 'https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK')]}Example with OCR images‚Äãinput_ = """i have this url of an id: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg"i want to extract the information in it.create a text welcoming the person by his name and make it into speech .if there is URL in the observations , you will always put it in the output (final answer) |
2,982 | will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to extract the information from the ID and then convert it to text and then to speech Action: edenai_identity_parsing Action Input: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg" Observation: last_name : value : ANGELA given_names : value : GREENE birth_place : birth_date : value : 2000-11-09 issuance_date : expire_date : document_id : issuing_state : address : age : country : document_type : value : DRIVER LICENSE FRONT gender : image_id : image_signature : mrz : nationality : Thought: I now need to convert the information to text and then to speech Action: edenai_text_to_speech Action Input: "Welcome Angela Greene!" Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer Final Answer: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y > Finished chain.result['output'] | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: will always put it in the output (final answer) ."""result = agent_chain(input_) > Entering new AgentExecutor chain... I need to extract the information from the ID and then convert it to text and then to speech Action: edenai_identity_parsing Action Input: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg" Observation: last_name : value : ANGELA given_names : value : GREENE birth_place : birth_date : value : 2000-11-09 issuance_date : expire_date : document_id : issuing_state : address : age : country : document_type : value : DRIVER LICENSE FRONT gender : image_id : image_signature : mrz : nationality : Thought: I now need to convert the information to text and then to speech Action: edenai_text_to_speech Action Input: "Welcome Angela Greene!" Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK Thought: I now know the final answer Final Answer: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y > Finished chain.result['output'] |
2,983 | > Finished chain.result['output'] 'https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y'input_ = """i have this url of an invoice document: "https://app.edenai.run/assets/img/data_1.72e3bdcc.png"i want to extract the information in it.and answer these questions :who is the customer ?what is the company name ? """result=agent_chain() > Entering new AgentExecutor chain... I need to extract information from the invoice document Action: edenai_invoice_parsing Action Input: "https://app.edenai.run/assets/img/data_1.72e3bdcc.png" Observation: customer_information : customer_name : Damita J Goldsmith customer_address : 201 Stan Fey Dr,Upper Marlboro, MD 20774 customer_shipping_address : 201 Stan Fey Drive,Upper Marlboro merchant_information : merchant_name : SNG Engineering Inc merchant_address : 344 Main St #200 Gaithersburg, MD 20878 USA merchant_phone : +1 301 548 0055 invoice_number : 014-03 taxes : payment_term : on receipt of service date : 2003-01-20 po_number : locale : bank_informations : item_lines : description : Field inspection of construction on 1/19/2003 deficiencies in house,construction, Garage drive way & legal support to Attorney to Thought: I now know the answer to the questions Final Answer: The customer is Damita J Goldsmith and the company name is SNG Engineering Inc. > Finished chain.result['output'] 'The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.'PreviousDuckDuckGo SearchNextEleven Labs Text2SpeechExample with textExample with imagesExample with OCR | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: > Finished chain.result['output'] 'https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y'input_ = """i have this url of an invoice document: "https://app.edenai.run/assets/img/data_1.72e3bdcc.png"i want to extract the information in it.and answer these questions :who is the customer ?what is the company name ? """result=agent_chain() > Entering new AgentExecutor chain... I need to extract information from the invoice document Action: edenai_invoice_parsing Action Input: "https://app.edenai.run/assets/img/data_1.72e3bdcc.png" Observation: customer_information : customer_name : Damita J Goldsmith customer_address : 201 Stan Fey Dr,Upper Marlboro, MD 20774 customer_shipping_address : 201 Stan Fey Drive,Upper Marlboro merchant_information : merchant_name : SNG Engineering Inc merchant_address : 344 Main St #200 Gaithersburg, MD 20878 USA merchant_phone : +1 301 548 0055 invoice_number : 014-03 taxes : payment_term : on receipt of service date : 2003-01-20 po_number : locale : bank_informations : item_lines : description : Field inspection of construction on 1/19/2003 deficiencies in house,construction, Garage drive way & legal support to Attorney to Thought: I now know the answer to the questions Final Answer: The customer is Damita J Goldsmith and the company name is SNG Engineering Inc. > Finished chain.result['output'] 'The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.'PreviousDuckDuckGo SearchNextEleven Labs Text2SpeechExample with textExample with imagesExample with OCR |
2,984 | with textExample with imagesExample with OCR imagesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. | This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent. ->: with textExample with imagesExample with OCR imagesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,985 | SceneXplain | ü¶úÔ∏èüîó Langchain | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. ->: SceneXplain | ü¶úÔ∏èüîó Langchain |
2,986 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSceneXplainOn this pageSceneXplainSceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.To use this tool, you'll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.import osos.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"from langchain.agents import load_toolstools = load_tools(["sceneXplain"])Or directly instantiate the tool.from langchain.tools import SceneXplainTooltool = SceneXplainTool()Usage in an Agent‚ÄãThe tool can be used in any LangChain agent as follows:from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( "What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. " "Is it movie or a game? If it is a movie, what is the name of the movie?" | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSceneXplainOn this pageSceneXplainSceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.To use this tool, you'll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.import osos.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"from langchain.agents import load_toolstools = load_tools(["sceneXplain"])Or directly instantiate the tool.from langchain.tools import SceneXplainTooltool = SceneXplainTool()Usage in an Agent‚ÄãThe tool can be used in any LangChain agent as follows:from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( "What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. " "Is it movie or a game? If it is a movie, what is the name of the movie?" |
2,987 | it is a movie, what is the name of the movie?" ))print(output) > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: Image Explainer Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight. Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm. In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image. Thought: Do I need to use a tool? No AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro. > Finished chain. This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.PreviousRequestsNextSearch ToolsUsage in an | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. ->: it is a movie, what is the name of the movie?" ))print(output) > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: Image Explainer Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight. Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm. In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image. Thought: Do I need to use a tool? No AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro. > Finished chain. This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.PreviousRequestsNextSearch ToolsUsage in an |
2,988 | ToolsUsage in an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. | SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool. ->: ToolsUsage in an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,989 | HuggingFace Hub Tools | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsHuggingFace Hub ToolsHuggingFace Hub ToolsHuggingface Tools that supporting text I/O can be
loaded directly using the load_huggingface_tool function.# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1pip install --upgrade transformers huggingface_hub > /dev/nullfrom langchain.agents import load_huggingface_tooltool = load_huggingface_tool("lysandre/hf-model-downloads")print(f"{tool.name}: {tool.description}") model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpointtool.run("text-classification") 'facebook/bart-large-mnli'PreviousGraphQLNextHuman as a toolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. | Huggingface Tools that supporting text I/O can be | Huggingface Tools that supporting text I/O can be ->: HuggingFace Hub Tools | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsHuggingFace Hub ToolsHuggingFace Hub ToolsHuggingface Tools that supporting text I/O can be
loaded directly using the load_huggingface_tool function.# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1pip install --upgrade transformers huggingface_hub > /dev/nullfrom langchain.agents import load_huggingface_tooltool = load_huggingface_tool("lysandre/hf-model-downloads")print(f"{tool.name}: {tool.description}") model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpointtool.run("text-classification") 'facebook/bart-large-mnli'PreviousGraphQLNextHuman as a toolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,990 | Nuclia Understanding | ü¶úÔ∏èüîó Langchain | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. ->: Nuclia Understanding | ü¶úÔ∏èüîó Langchain |
2,991 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsNuclia UnderstandingOn this pageNuclia UnderstandingNuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever it is (using speech-to-text or OCR when needed), it identifies entities, it aslo extracts metadata, embedded files (like images in a PDF), and web links. It also provides a summary of the content.To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at https://nuclia.cloud, and then create a NUA key.#!pip install --upgrade protobuf#!pip install nucliadb-protosimport osos.environ["NUCLIA_ZONE"] = "<YOUR_ZONE>" # e.g. europe-1os.environ["NUCLIA_NUA_KEY"] = "<YOUR_API_KEY>"from langchain.tools.nuclia import NucliaUnderstandingAPInua = NucliaUnderstandingAPI(enable_ml=False)You can push files to the Nuclia | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsNuclia UnderstandingOn this pageNuclia UnderstandingNuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever it is (using speech-to-text or OCR when needed), it identifies entities, it aslo extracts metadata, embedded files (like images in a PDF), and web links. It also provides a summary of the content.To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at https://nuclia.cloud, and then create a NUA key.#!pip install --upgrade protobuf#!pip install nucliadb-protosimport osos.environ["NUCLIA_ZONE"] = "<YOUR_ZONE>" # e.g. europe-1os.environ["NUCLIA_NUA_KEY"] = "<YOUR_API_KEY>"from langchain.tools.nuclia import NucliaUnderstandingAPInua = NucliaUnderstandingAPI(enable_ml=False)You can push files to the Nuclia |
2,992 | can push files to the Nuclia Understanding API using the push action. As the processing is done asynchronously, the results might be returned in a different order than the files were pushed. That is why you need to provide an id to match the results with the corresponding file.nua.run({"action": "push", "id": "1", "path": "./report.docx"})nua.run({"action": "push", "id": "2", "path": "./interview.mp4"})You can now call the pull action in a loop until you get the JSON-formatted result.import timepending = Truedata = Nonewhile pending: time.sleep(15) data = nua.run({"action": "pull", "id": "1", "path": None}) if data: print(data) pending = False else: print("waiting...")You can also do it in one step in async mode, you only need to do a push, and it will wait until the results are pulled:import asyncioasync def process(): data = await nua.arun( {"action": "push", "id": "1", "path": "./talk.mp4", "text": None} ) print(data)asyncio.run(process())Retrieved information‚ÄãNuclia returns the following information:file metadataextracted textnested text (like text in an embedded image)a summary (only when enable_ml is set to True)paragraphs and sentences splitting (defined by the position of their first and last characters, plus start time and end time for a video or audio file)named entities: people, dates, places, organizations, etc. (only when enable_ml is set to True)linksa thumbnailembedded filesthe vector representations of the text (only when enable_ml is set to True)Note: Generated files (thumbnail, extracted embedded files, etc.) are provided as a token. You can download them with the /processing/download endpoint. Also at any level, if an attribute exceeds a certain size, it will be put in a downloadable file and will be replaced in the document by a file pointer. This will consist of {"file": {"uri": "JWT_TOKEN"}}. The rule is that if the size of the message is greater than 1000000 characters, the biggest parts | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. ->: can push files to the Nuclia Understanding API using the push action. As the processing is done asynchronously, the results might be returned in a different order than the files were pushed. That is why you need to provide an id to match the results with the corresponding file.nua.run({"action": "push", "id": "1", "path": "./report.docx"})nua.run({"action": "push", "id": "2", "path": "./interview.mp4"})You can now call the pull action in a loop until you get the JSON-formatted result.import timepending = Truedata = Nonewhile pending: time.sleep(15) data = nua.run({"action": "pull", "id": "1", "path": None}) if data: print(data) pending = False else: print("waiting...")You can also do it in one step in async mode, you only need to do a push, and it will wait until the results are pulled:import asyncioasync def process(): data = await nua.arun( {"action": "push", "id": "1", "path": "./talk.mp4", "text": None} ) print(data)asyncio.run(process())Retrieved information‚ÄãNuclia returns the following information:file metadataextracted textnested text (like text in an embedded image)a summary (only when enable_ml is set to True)paragraphs and sentences splitting (defined by the position of their first and last characters, plus start time and end time for a video or audio file)named entities: people, dates, places, organizations, etc. (only when enable_ml is set to True)linksa thumbnailembedded filesthe vector representations of the text (only when enable_ml is set to True)Note: Generated files (thumbnail, extracted embedded files, etc.) are provided as a token. You can download them with the /processing/download endpoint. Also at any level, if an attribute exceeds a certain size, it will be put in a downloadable file and will be replaced in the document by a file pointer. This will consist of {"file": {"uri": "JWT_TOKEN"}}. The rule is that if the size of the message is greater than 1000000 characters, the biggest parts |
2,993 | than 1000000 characters, the biggest parts will be moved to downloadable files. First, the compression process will target vectors. If that is not enough, it will target large field metadata, and finally it will target extracted text.PreviousMetaphor SearchNextOpenWeatherMapRetrieved informationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing. ->: than 1000000 characters, the biggest parts will be moved to downloadable files. First, the compression process will target vectors. If that is not enough, it will target large field metadata, and finally it will target extracted text.PreviousMetaphor SearchNextOpenWeatherMapRetrieved informationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
2,994 | Google Serper | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: Google Serper | ü¶úÔ∏èüîó Langchain |
2,995 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGoogle SerperOn this pageGoogle SerperThis notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key.import osimport pprintos.environ["SERPER_API_KEY"] = ""from langchain.utilities import GoogleSerperAPIWrappersearch = GoogleSerperAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'As part of a Self Ask With Search Chain‚Äãos.environ["OPENAI_API_KEY"] = ""from langchain.utilities import GoogleSerperAPIWrapperfrom langchain.llms.openai import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = GoogleSerperAPIWrapper()tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search", )]self_ask_with_search = initialize_agent( tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run( "What is the hometown of the reigning men's U.S. Open champion?") > Entering new AgentExecutor chain... | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGoogle SerperOn this pageGoogle SerperThis notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key.import osimport pprintos.environ["SERPER_API_KEY"] = ""from langchain.utilities import GoogleSerperAPIWrappersearch = GoogleSerperAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'As part of a Self Ask With Search Chain‚Äãos.environ["OPENAI_API_KEY"] = ""from langchain.utilities import GoogleSerperAPIWrapperfrom langchain.llms.openai import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = GoogleSerperAPIWrapper()tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search", )]self_ask_with_search = initialize_agent( tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run( "What is the hometown of the reigning men's U.S. Open champion?") > Entering new AgentExecutor chain... |
2,996 | > Entering new AgentExecutor chain... Yes. Follow up: Who is the reigning men's U.S. Open champion? Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion. Follow up: Where is Carlos Alcaraz from? Intermediate answer: El Palmar, Spain So the final answer is: El Palmar, Spain > Finished chain. 'El Palmar, Spain'Obtaining results with metadata​If you would also like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = GoogleSerperAPIWrapper()results = search.results("Apple Inc.")pprint.pp(results) {'searchParameters': {'q': 'Apple Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'search'}, 'knowledgeGraph': {'title': 'Apple', 'type': 'Technology company', 'website': 'http://www.apple.com/', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQwGQRv5TjjkycpctY66mOg_e2-npacrmjAb6_jAWhzlzkFE3OTjxyzbA&s=0', 'description': 'Apple Inc. is an American multinational ' 'technology company headquartered in ' 'Cupertino, California. Apple is the ' "world's largest technology company by " 'revenue, with US$394.3 billion in 2022 ' 'revenue. As of March 2023, Apple is the ' "world's biggest...", 'descriptionSource': 'Wikipedia', 'descriptionLink': 'https://en.wikipedia.org/wiki/Apple_Inc.', 'attributes': {'Customer service': '1 (800) 275-2273', 'CEO': 'Tim Cook (Aug 24, 2011–)', | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: > Entering new AgentExecutor chain... Yes. Follow up: Who is the reigning men's U.S. Open champion? Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion. Follow up: Where is Carlos Alcaraz from? Intermediate answer: El Palmar, Spain So the final answer is: El Palmar, Spain > Finished chain. 'El Palmar, Spain'Obtaining results with metadata​If you would also like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = GoogleSerperAPIWrapper()results = search.results("Apple Inc.")pprint.pp(results) {'searchParameters': {'q': 'Apple Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'search'}, 'knowledgeGraph': {'title': 'Apple', 'type': 'Technology company', 'website': 'http://www.apple.com/', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQwGQRv5TjjkycpctY66mOg_e2-npacrmjAb6_jAWhzlzkFE3OTjxyzbA&s=0', 'description': 'Apple Inc. is an American multinational ' 'technology company headquartered in ' 'Cupertino, California. Apple is the ' "world's largest technology company by " 'revenue, with US$394.3 billion in 2022 ' 'revenue. As of March 2023, Apple is the ' "world's biggest...", 'descriptionSource': 'Wikipedia', 'descriptionLink': 'https://en.wikipedia.org/wiki/Apple_Inc.', 'attributes': {'Customer service': '1 (800) 275-2273', 'CEO': 'Tim Cook (Aug 24, 2011–)', |
2,997 | 'Tim Cook (Aug 24, 2011–)', 'Headquarters': 'Cupertino, CA', 'Founded': 'April 1, 1976, Los Altos, CA', 'Founders': 'Steve Jobs, Steve Wozniak, ' 'Ronald Wayne, and more', 'Products': 'iPhone, iPad, Apple TV, and ' 'more'}}, 'organic': [{'title': 'Apple', 'link': 'https://www.apple.com/', 'snippet': 'Discover the innovative world of Apple and shop ' 'everything iPhone, iPad, Apple Watch, Mac, and Apple ' 'TV, plus explore accessories, entertainment, ...', 'sitelinks': [{'title': 'Support', 'link': 'https://support.apple.com/'}, {'title': 'iPhone', 'link': 'https://www.apple.com/iphone/'}, {'title': 'Site Map', 'link': 'https://www.apple.com/sitemap/'}, {'title': 'Business', 'link': 'https://www.apple.com/business/'}, {'title': 'Mac', 'link': 'https://www.apple.com/mac/'}, {'title': 'Watch', 'link': 'https://www.apple.com/watch/'}], 'position': 1}, {'title': 'Apple Inc. - Wikipedia', 'link': 'https://en.wikipedia.org/wiki/Apple_Inc.', 'snippet': 'Apple Inc. is an American multinational technology ' 'company headquartered in Cupertino, California. ' "Apple is the world's largest technology company by " 'revenue, ...', | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'Tim Cook (Aug 24, 2011–)', 'Headquarters': 'Cupertino, CA', 'Founded': 'April 1, 1976, Los Altos, CA', 'Founders': 'Steve Jobs, Steve Wozniak, ' 'Ronald Wayne, and more', 'Products': 'iPhone, iPad, Apple TV, and ' 'more'}}, 'organic': [{'title': 'Apple', 'link': 'https://www.apple.com/', 'snippet': 'Discover the innovative world of Apple and shop ' 'everything iPhone, iPad, Apple Watch, Mac, and Apple ' 'TV, plus explore accessories, entertainment, ...', 'sitelinks': [{'title': 'Support', 'link': 'https://support.apple.com/'}, {'title': 'iPhone', 'link': 'https://www.apple.com/iphone/'}, {'title': 'Site Map', 'link': 'https://www.apple.com/sitemap/'}, {'title': 'Business', 'link': 'https://www.apple.com/business/'}, {'title': 'Mac', 'link': 'https://www.apple.com/mac/'}, {'title': 'Watch', 'link': 'https://www.apple.com/watch/'}], 'position': 1}, {'title': 'Apple Inc. - Wikipedia', 'link': 'https://en.wikipedia.org/wiki/Apple_Inc.', 'snippet': 'Apple Inc. is an American multinational technology ' 'company headquartered in Cupertino, California. ' "Apple is the world's largest technology company by " 'revenue, ...', |
2,998 | " 'revenue, ...', 'attributes': {'Products': 'AirPods; Apple Watch; iPad; iPhone; ' 'Mac; Full list', 'Founders': 'Steve Jobs; Steve Wozniak; Ronald ' 'Wayne; Mike Markkula'}, 'sitelinks': [{'title': 'History', 'link': 'https://en.wikipedia.org/wiki/History_of_Apple_Inc.'}, {'title': 'Timeline of Apple Inc. products', 'link': 'https://en.wikipedia.org/wiki/Timeline_of_Apple_Inc._products'}, {'title': 'Litigation involving Apple Inc.', 'link': 'https://en.wikipedia.org/wiki/Litigation_involving_Apple_Inc.'}, {'title': 'Apple Store', 'link': 'https://en.wikipedia.org/wiki/Apple_Store'}], 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvmB5fT1LjqpZx02UM7IJq0Buoqt0DZs_y0dqwxwSWyP4PIN9FaxuTea0&s', 'position': 2}, {'title': 'Apple Inc. | History, Products, Headquarters, & Facts ' '| Britannica', 'link': 'https://www.britannica.com/topic/Apple-Inc', 'snippet': 'Apple Inc., formerly Apple Computer, Inc., American ' 'manufacturer of personal computers, smartphones, ' 'tablet computers, computer peripherals, and computer ' '...', 'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony ' 'Ive Tim Cook Angela Ahrendts', 'Date': '1976 - present'}, 'imageUrl': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: " 'revenue, ...', 'attributes': {'Products': 'AirPods; Apple Watch; iPad; iPhone; ' 'Mac; Full list', 'Founders': 'Steve Jobs; Steve Wozniak; Ronald ' 'Wayne; Mike Markkula'}, 'sitelinks': [{'title': 'History', 'link': 'https://en.wikipedia.org/wiki/History_of_Apple_Inc.'}, {'title': 'Timeline of Apple Inc. products', 'link': 'https://en.wikipedia.org/wiki/Timeline_of_Apple_Inc._products'}, {'title': 'Litigation involving Apple Inc.', 'link': 'https://en.wikipedia.org/wiki/Litigation_involving_Apple_Inc.'}, {'title': 'Apple Store', 'link': 'https://en.wikipedia.org/wiki/Apple_Store'}], 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvmB5fT1LjqpZx02UM7IJq0Buoqt0DZs_y0dqwxwSWyP4PIN9FaxuTea0&s', 'position': 2}, {'title': 'Apple Inc. | History, Products, Headquarters, & Facts ' '| Britannica', 'link': 'https://www.britannica.com/topic/Apple-Inc', 'snippet': 'Apple Inc., formerly Apple Computer, Inc., American ' 'manufacturer of personal computers, smartphones, ' 'tablet computers, computer peripherals, and computer ' '...', 'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony ' 'Ive Tim Cook Angela Ahrendts', 'Date': '1976 - present'}, 'imageUrl': |
2,999 | '1976 - present'}, 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3liELlhrMz3Wpsox29U8jJ3L8qETR0hBWHXbFnwjwQc34zwZvFELst2E&s', 'position': 3}, {'title': 'AAPL: Apple Inc Stock Price Quote - NASDAQ GS - ' 'Bloomberg.com', 'link': 'https://www.bloomberg.com/quote/AAPL:US', 'snippet': 'AAPL:USNASDAQ GS. Apple Inc. COMPANY INFO ; Open. ' '170.09 ; Prev Close. 169.59 ; Volume. 48,425,696 ; ' 'Market Cap. 2.667T ; Day Range. 167.54170.35.', 'position': 4}, {'title': 'Apple Inc. (AAPL) Company Profile & Facts - Yahoo ' 'Finance', 'link': 'https://finance.yahoo.com/quote/AAPL/profile/', 'snippet': 'Apple Inc. designs, manufactures, and markets ' 'smartphones, personal computers, tablets, wearables, ' 'and accessories worldwide. The company offers ' 'iPhone, a line ...', 'position': 5}, {'title': 'Apple Inc. (AAPL) Stock Price, News, Quote & History - ' 'Yahoo Finance', 'link': 'https://finance.yahoo.com/quote/AAPL', 'snippet': 'Find the latest Apple Inc. (AAPL) stock quote, ' 'history, news and other vital information to help ' 'you with your stock trading and investing.', 'position': 6}], 'peopleAlsoAsk': [{'question': 'What does Apple Inc do?', 'snippet': 'Apple Inc. (Apple) designs, manufactures and ' 'markets smartphones, personal\n' 'computers, tablets, wearables and accessories ' 'and sells a range of related\n' | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: '1976 - present'}, 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3liELlhrMz3Wpsox29U8jJ3L8qETR0hBWHXbFnwjwQc34zwZvFELst2E&s', 'position': 3}, {'title': 'AAPL: Apple Inc Stock Price Quote - NASDAQ GS - ' 'Bloomberg.com', 'link': 'https://www.bloomberg.com/quote/AAPL:US', 'snippet': 'AAPL:USNASDAQ GS. Apple Inc. COMPANY INFO ; Open. ' '170.09 ; Prev Close. 169.59 ; Volume. 48,425,696 ; ' 'Market Cap. 2.667T ; Day Range. 167.54170.35.', 'position': 4}, {'title': 'Apple Inc. (AAPL) Company Profile & Facts - Yahoo ' 'Finance', 'link': 'https://finance.yahoo.com/quote/AAPL/profile/', 'snippet': 'Apple Inc. designs, manufactures, and markets ' 'smartphones, personal computers, tablets, wearables, ' 'and accessories worldwide. The company offers ' 'iPhone, a line ...', 'position': 5}, {'title': 'Apple Inc. (AAPL) Stock Price, News, Quote & History - ' 'Yahoo Finance', 'link': 'https://finance.yahoo.com/quote/AAPL', 'snippet': 'Find the latest Apple Inc. (AAPL) stock quote, ' 'history, news and other vital information to help ' 'you with your stock trading and investing.', 'position': 6}], 'peopleAlsoAsk': [{'question': 'What does Apple Inc do?', 'snippet': 'Apple Inc. (Apple) designs, manufactures and ' 'markets smartphones, personal\n' 'computers, tablets, wearables and accessories ' 'and sells a range of related\n' |
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