Building Document Chatbots with Vector Store Injest & Query

📚 Uploading files is a major strength of using Line Chain and Flow Wise.

💡 In the previous video, a document chatbot was created using Flow Wise to upload and store files.

⚠️ While this solution is suitable for simple use cases, it is not efficient for uploading large files or multiple files.

📚 Building production ready document chatbots involves uploading and storing documentation in a vector store.

🔎 A separate chat flow is created to query the database and interact with the chatbot.

💾 Data stored in an in-memory vector store needs to be persisted in a proper database like Pinecone.

🏬 Initializing an index with specified dimensions for an openai embedding.

📝 Creating a chat flow to upload and store documents in the database.

🔗 Connecting the chat flow to a Vector store node using the Pinecone API.

✅ Set up the Flowise index by entering the index name and API key

📄 Add a document loader and text splitter to feed documents into Pinecone

🔑 Connect an embedding node and provide the OpenAI API key

📁 When running the ingestion chat flow, the Vector store gets updated with the uploaded document.

⏳ The process of uploading and storing the document in the database takes some time.

🔍 A separate chat flow is created to interact with the database and ask questions.

📝 This tutorial demonstrates how to set up and use the Vector Store feature in Flowise AI, specifically with the Pinecone index for similarity search.

🔑 To integrate the Vector Store, the OpenAI API key and Pinecone API key need to be provided.

💡 The Vector Store allows for converting questions into embeddings and performing similarity searches on a database.

📚 The tutorial demonstrates how to use a document QA chat flow to fetch information from a database.

💻 Data can be updated or added to the database by running the ingest chat flow.

💾 Storing data in a third-party database ensures persistence and prevents data loss.