llama-index-vector-stores-qdrant
llama-index vector_stores qdrant integration
Decision gist · record as of 2026-08-14
Yes, if you are already using LlamaIndex and need a vector store backend. Low install friction, active maintenance, MIT license, and no known vulnerabilities make it a straightforward choice. Verify that Qdrant server setup aligns with your deployment model before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (and below 3.14).
- Qdrant server or in-memory instance must be available or configured.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most production deployments.
last release 2026-08-13 (1 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 332,216 downloads/mo, #7,509 on PyPI
Alternatives
Verify before relying
pip install llama-index-vector-stores-qdrant
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
vector_store = QdrantVectorStore(client=client, collection_name="my_collection")- Whether Qdrant server setup or configuration is required before this integration can be used
- Performance characteristics and scalability limits for large embedding datasets
- Compatibility matrix with specific versions of llama-index-core and qdrant-client
What it is and what it does
This package provides a LlamaIndex integration layer for Qdrant, a vector database designed for similarity search and retrieval-augmented generation (RAG). It acts as a bridge between LlamaIndex's document processing and embedding pipeline and Qdrant's vector storage and query engine, allowing you to persist embeddings and perform semantic searches efficiently.
The package wraps Qdrant's gRPC client (via qdrant-client) and exposes it through LlamaIndex's standard vector store interface. You use it to store document embeddings in Qdrant and retrieve semantically similar results during inference, typically as part of a larger RAG system. It requires Python 3.10+, depends on llama-index-core for the integration framework, and handles the gRPC communication with Qdrant transparently.
Use it for
- Store embeddings from LlamaIndex document processing into Qdrant for persistent semantic search across documents
- Build RAG pipelines where LlamaIndex retrieves relevant context from Qdrant before passing it to an LLM
- Perform similarity search on embeddings without managing Qdrant client code directly in your application
- Scale embedding storage across multiple documents while maintaining LlamaIndex's high-level API
- Integrate Qdrant as a backend for LlamaIndex-based chatbots or question-answering systems
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using LlamaIndex and need a vector store backend.
Low install friction, active maintenance, MIT license, and no known vulnerabilities make it a straightforward choice. Verify that Qdrant server setup aligns with your deployment model before committing.
Install
llama-index-vector-stores-qdrant on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance as of 2026-08-13, released 1 day ago. Depends on grpcio, llama-index-core, and qdrant-client.
Requires Python 3.10 or later (and below 3.14). Qdrant server or in-memory instance must be available or configured.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most production deployments.
Quickstart
pip install llama-index-vector-stores-qdrant
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
vector_store = QdrantVectorStore(client=client, collection_name="my_collection")Verify before relying
- Whether Qdrant server setup or configuration is required before this integration can be used
- Performance characteristics and scalability limits for large embedding datasets
- Compatibility matrix with specific versions of llama-index-core and qdrant-client
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesgrpciollama-index-coreqdrant-client |
| Maintenance | Actively maintained 1 days since the last release |
| First released | |
| Downloads | 332,216 / month, #7,509 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_vector_stores_qdrant-0.10.3-py3-none-any.whl
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See also llama-index-retrievers-bm25 · llama-index-vector-stores-chroma · llama-index-vector-stores-faiss · llama-index-vector-stores-lancedb · llama-index-vector-stores-milvus · llama-index-vector-stores-redis · mempalace · llama-index-vector-stores-pinecone · qdrant-client · llama-index-vector-stores-postgres