qdrant-vector-search
Category: knowledge-and-rag · License: MIT · Version: 1.0.0
Tags: RAG, Vector Search, Qdrant, Semantic Search, Embeddings, Similarity Search, HNSW, Production, Distributed
When to use it
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.Example requests
Ask your agent in plain language, for example:- “How do I set up Qdrant with payload filtering for my paper abstracts?”
- “I have 2.3M PubMed abstracts embedded with BAAI/bge-base-en-v1.5 (768-d) in embeddings.parquet, with fields year, journal and mesh_terms. I need to load them into a self-hosted Docker instance and query for nearest neighbors restricted to year >= 2020 and a given journal. My filtered queries are taking several seconds. What should I do?”
- “We’re building an internal literature QA system for our lab. Each chunk needs both a dense embedding and a BM25-style sparse representation, and we want to combine them with metadata filters like publication year. Data must stay on our own servers and be able to scale to tens of millions of chunks. Which vector store and architecture would you use, and how do I implement it?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole knowledge-and-rag category with /plugin install knowledge-and-rag@research-agent-skills.