text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersDoctran Extract PropertiesDoctran Interrogate DocumentsDoctran Translate Documentshtml2textOpenAI Functions Metadata TaggerLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument transformersDocument transformersğŸ“„ï¸� Doctran Extract PropertiesWe can extract useful features of documents using the Doctran library, which uses OpenAI's function calling feature to extract specific metadata.ğŸ“„ï¸� Doctran Interrogate DocumentsDocuments used in a vector store knowledge base are typically stored in narrative or conversational format. However, most user queries are in question format. If we convert documents into Q&A format before vectorizing them, we can increase the liklihood of retrieving relevant documents, and decrease the liklihood of retrieving irrelevant documents.ğŸ“„ï¸� Doctran Translate DocumentsComparing documents through embeddings has the benefit of working across multiple languages. "Harrison says hello" and "Harrison dice hola" will occupy similar positions in the vector space because they have the same meaning semantically.ğŸ“„ï¸� html2texthtml2text is a Python script that converts a page of HTML into clean, easy-to-read plain ASCII text.ğŸ“„ï¸� OpenAI Functions Metadata TaggerIt can often be useful to tag ingested documents with structured metadata, such as the title, tone, or length of a document, to allow for more targeted similarity search later. However, for large numbers of documents, performing this labelling process manually can be tedious.PreviousYouTube transcriptsNextDoctran Extract