A FastAPI application that uses Retrieval-Augmented Generation (RAG) with a large language model (LLM) to create an interactive chatbot. This chatbot leverages PostgreSQL vector store for efficient document retrieval and supports text and CSV data sources for initialization.
This project demonstrates how to integrate text embeddings using nomic-embed-text and granite-embedding models with PostgreSQL and pgvector. You can perform similarity searches, text analysis, and more.
Implementing PostgreSQL as a search engine with a recency-weighted hybrid search, all within Docker.
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