Question Answering with LangChain and Qdrant without boilerplate
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load.
With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
Building applications with Large Language Models don’t have to be complicated. A lot has been going on recently to simplify the development, so you can utilize already pre-trained models and support even complex pipelines with a few lines of code. LangChain provides unified interfaces to different libraries, so you can avoid writing boilerplate code and focus on the value you want to bring.
Read the full article on how to combine LangChain with Qdrant to create an LLM generative app with vector search included by the link
