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Querying Receipts using RAG
Integrating LLMs and Vector Search for Intelligent Queries
In today’s digital age, managing and extracting information from receipts can be a tedious task. But what if you could query your receipts intelligently, just like searching through a database? With the power of RAG (Retrieval-Augmented Generation), integrating Large Language Models (LLMs) and Vector Search, this becomes a reality. By transforming your receipts into searchable data and combining them with advanced AI models, you can ask complex questions and get accurate, context-aware answers in real-time. In this article, I’ll walk you through how to leverage RAG to make receipt querying smarter, faster, and more intuitive!
How RAG Works
Before diving into the code, let’s first get a clearer picture of how RAG works. Check out the diagram below:
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- Your private documents (receipts in this article) are transformed into word vector embeddings, a process known as embedding.
- Once the embeddings are created, they’re stored in vector databases like ChromaDB or saved directly on storage.