Case Study
RAG Knowledge Base Application
Private knowledge base with Retrieval-Augmented Generation and secure chat.
Project Details
About This Project
Scope: Centralize documents and enable smart answers via RAG.
Aims: Reduce support load and enable instant, accurate knowledge retrieval.
Features: Document ingestion, embeddings, vector search, citations, role-based access, chat with sources.
Stack Used: FastAPI, LangChain, LangGraph, OpenAI, UpsonicAI, pgvector (PostgreSQL), Docker, AWS S3.
Outcomes: Reliable answers with transparent citations and fine-grained access.
Aims: Reduce support load and enable instant, accurate knowledge retrieval.
Features: Document ingestion, embeddings, vector search, citations, role-based access, chat with sources.
Stack Used: FastAPI, LangChain, LangGraph, OpenAI, UpsonicAI, pgvector (PostgreSQL), Docker, AWS S3.
Outcomes: Reliable answers with transparent citations and fine-grained access.
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