I'm trying to connect my company's internal wiki to a chatbot. In the context of a Django (AI backend), how do you handle the data ingestion pipeline for Retrieval-Augmented Generation (RAG) so it stays updated in real-time?
3 answers
Setting up a RAG pipeline in a Django (AI backend) requires a robust synchronization strategy. I recommend using Django signals; every time a wiki page model is saved, trigger a Celery task that re-generates the embeddings and updates your vector database (like Chroma or Pinecone). This ensures your AI always has the "ground truth" data. One thing to watch out for is document chunking. In our system, we use LangChain’s RecursiveCharacterTextSplitter within a Django management command to process legacy data. It’s vital to keep the metadata in your vector store synced with your Django primary keys so you can easily link back to the original source.
Deborah, do you run the embedding model locally on your Django (AI backend) server, or do you find it more cost-effective to use an API like OpenAI’s text-embedding-3?
Make sure to implement a "relevance score" threshold so your AI doesn't try to answer based on loosely related but ultimately unhelpful document chunks.
Exactly, Paul. In our Django (AI backend), we filter out any chunks with a similarity score below 0.7. This significantly reduced "hallucinations" where the AI would try to force an answer from irrelevant text.
Steven, for our Django (AI backend), we use the OpenAI API for embeddings. While running a local model like Llama or BERT saves on API costs, the maintenance of a GPU-enabled worker and the latency hit usually don't make sense unless you have massive data volume or strict privacy requirements. Using an API allows you to scale the "intelligence" of your RAG system without worrying about the underlying hardware, which is a huge benefit for smaller development teams.