Developer Builds Hybrid RAG API Using Semantic Search, Reranking, and Caching
A software developer has detailed the architecture behind a production-ready PDF question-answering API built with FastAPI, Qdrant, PostgreSQL, Redis, and LiteLLM. The system combines dense semantic…
Read the full articleYou might also wanna read
Hybrid Search and Re-Ranking: Improving RAG Accuracy Beyond Semantic Search
When semantic search isn't enough for the RAG
towardsdatascience.com·1mo ago
Building a Minimal RAG System from Scratch: PDF to Highlighted Answers in ~100 Lines of Python
Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the s
towardsdatascience.com·1mo agoRAG in Document Management Systems: Turning Enterprise Documents into Intelligent Knowledge
The adoption of advanced language models (LLMs) has revolutionized the way companies interact with data. However, these models have a critic
Production RAG Implementation: Lessons from Processing 13+ Million Documents
Lessons learned from building RAG systems for Usul AI and enterprise clients, processing over 13 million pages.
blog.abdellatif.io·9mo ago
Local AI Knowledge Base: Dockerized RAG Solution for Private Document Querying
A production-ready, 100% offline RAG Knowledge Base using Docker, Llama 3, and Ollama. Chat with your documents privately. Enterprise archit
github.com·7mo agoA More Powerful, Code-First Knowledge Base Experience on the DigitalOcean Gradient™ AI Platform
Building production-ready retrieval-augmented generation (RAG) systems can be complex, time-consuming, and often requires months of engineer
Comments
Sign in to join the conversation.
No comments yet. Be the first.