{"library":"llama-index-vector-stores-qdrant","type":"library","category":null,"description":"The `llama-index-vector-stores-qdrant` library provides an integration for using Qdrant as a vector store within the LlamaIndex framework. It enables users to store and retrieve vector embeddings efficiently for building Retrieval-Augmented Generation (RAG) applications. This integration supports various Qdrant features, including hybrid search capabilities, and is part of the LlamaIndex v0.10.0 ecosystem which adopted a modular architecture with separate integration packages.","language":"python","status":"active","version":"0.10.0","tags":["LlamaIndex","Qdrant","Vector Store","RAG","LLM","Embeddings","Hybrid Search","AI"],"last_verified":"Sun May 24","install":[{"cmd":"pip install llama-index-vector-stores-qdrant qdrant-client","imports":["from llama_index.vector_stores.qdrant import QdrantVectorStore","from qdrant_client import QdrantClient","from qdrant_client import AsyncQdrantClient","from llama_index.core import ServiceContext"]},{"cmd":"pip install llama-index-vector-stores-qdrant qdrant-client fastembed","imports":[]}],"homepage":"https://llamaindex.ai","github":null,"docs":null,"changelog":null,"pypi":"https://pypi.org/project/llama-index-vector-stores-qdrant/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":75,"avg_install_s":24,"avg_import_s":9.5,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/llama-index-vector-stores-qdrant/compatibility"}}