langchain 在OpenAI的document_loaders/audio中，出现了AttributeError: 'str'对象没有'text'属性,我为...
Note: This code is pulled directly from document loaders chapter of Langchain Chat With Your Data course with Harrison Chase and Andrew Ng. It downloads an audio file of a public youtube video and generates a transcript. In a Jupyter notebook, configure your Azure OpenAI environment variables ...
langchain 在OpenAI的document_loaders/audio中，出现了AttributeError: 'str'对象没有'text'属性,我为...
items items array of object items User's email items.createUser string Email of the user triggering the flow Document ID items.documentId string Single unique ID for all generated documents Flow timestamp items.triggerTime string Timestamp when the flow was triggered. Questionnaire name ...
Next, the code checks if the MediaUrl0 property in the request body is undefined, suggesting that the message does not contain a document. If that is the case, the code calls the handleIncomingMessage function, and passes the request object as an argument, to generate a response message. Th...
python langchain.document_loaders PyPDFDirectoryLoader抛出PdfReadError查看哪些PDF文件已损坏。然后将其从...
vectorstore file. The function then loads an object of type HuggingFaceHubEmbeddings using the HuggingFaceHubEmbeddings() method. It then loads a FAISS vectorstore using the FAISS.load_local() method. The function then finds the most similar documents to the query using the faiss.similarity_...
NamePathTypeDescription items items array of object status items.status string The status of the job (Completed, Timeout) documentId items.documentId string Unique document ID DefinitionsstringThis is the basic data type 'string'.objectThis is the type 'object'.In...
(MDPPermissions.NoChanges); //Create a FieldMDPOptionSpec object that stores //signature field lock dictionary information. FieldMDPOptionSpec fieldMDPOptionsSpec = new FieldMDPOptionSpec(); //Lock all fields in the PDF document fieldMDPOptionsSpec.setAction(Field...
It uses thePGVectorclass from LangChain to ingest the embeddings, with their document chunks and metadata, into Amazon Aurora PostgreSQL and create a semantic search index on all the embedding vectors. Second, in real time and for each new question, ...