Overview

• Upload pdf to aws S3 , using aws-sdk
• send S3 data into pine cone using /create-chat

Passing into PineconeDB

• take fileKey from s3 load the PDF (loadS3IntoPinecone)
• Splits pdf to smaller chunks (loadS3IntoPinecone)
• Vectorise the chunks (loadS3IntoPinecone)
• embed the vectors with its metadata (loadS3IntoPinecone)
• Store embedded vectors to pineconeDB (loadS3IntoPinecone)

Generate an entry in NeonDB to store

• insert a chat entry for chat table

• each chat entry is a table which will be used later to store a conversation content as an entry in a message table

• Generate a query which will be vectorised

• Query PineconeDB and compare vectors in DB with query vector

• Extract the most similar vector meta data

• Feed metadata into Openai

PineconeDB

• index is like the DB
• in each index there are namespaces
• in each namespeace there are vectors spaces
• index(DB) => namespace(table) => vector(rows)
• each pdf will have its own name space

/api/create-chat

• checks for auth with clerk
• sends file_key and file_name through req body
• loads takes the pdf stores in s3
• and then stores into pine cone by embedding vectors of the chunks of pdf
• in pineconeDB each file_name is a name space
• inserts record into neonDB
• each pdf has a chat,
• each chat has a message table relation

/api/chat

• takes the chatId, and references the chat
• creates a context by feeding the user mesasge
• a context is created by when the user message is being query by pineconeDB and find the closest matching vector of the pdf when the pdf has been uploaded
• this context string template is then spend to openAi as a prompt
• and then we will stream data to the user
• when data is streamed , will create a new row in messageSchema