Safety | ü¶úÔ∏èüîó Langchain






Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesAdaptersDebuggingDeploymentEvaluationFallbacksLangSmithRun LLMs locallyModel comparisonPrivacyPydantic compatibilitySafetyAmazon Comprehend Moderation ChainConstitutional chainHugging Face prompt injection identificationLogical Fallacy chainModeration chainMoreGuidesSafetySafetyOne of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.Amazon Comprehend moderation chain: Use Amazon Comprehend to detect and handle Personally Identifiable Information (PII) and toxicity.Constitutional chain: Prompt the model with a set of principles which should guide the model behavior.Hugging Face prompt injection identification: Detect and handle prompt injection attacks. Logical Fallacy chain: Checks the model output against logical fallacies to correct any deviation.Moderation chain: Check if any output text is harmful and flag it.PreviousPydantic compatibilityNextAmazon Comprehend Moderation ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer. ->: Safety | ü¶úÔ∏èüîó Langchain






Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesAdaptersDebuggingDeploymentEvaluationFallbacksLangSmithRun LLMs locallyModel comparisonPrivacyPydantic compatibilitySafetyAmazon Comprehend Moderation ChainConstitutional chainHugging Face prompt injection identificationLogical Fallacy chainModeration chainMoreGuidesSafetySafetyOne of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.Amazon Comprehend moderation chain: Use Amazon Comprehend to detect and handle Personally Identifiable Information (PII) and toxicity.Constitutional chain: Prompt the model with a set of principles which should guide the model behavior.Hugging Face prompt injection identification: Detect and handle prompt injection attacks. Logical Fallacy chain: Checks the model output against logical fallacies to correct any deviation.Moderation chain: Check if any output text is harmful and flag it.PreviousPydantic compatibilityNextAmazon Comprehend Moderation ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.