Qdrant Vector Store¶

Import: from selectools.rag.stores import QdrantVectorStore Stability: beta Added in: v0.21.0

QdrantVectorStore wraps the official qdrant-client to give you a self-hosted or Qdrant Cloud-backed vector store. It auto-creates collections, supports cosine similarity by default, and lets you filter searches on metadata via Qdrant's payload indexing.

qdrant_quick.py

from selectools.embeddings import OpenAIEmbeddingProvider
from selectools.rag import Document
from selectools.rag.stores import QdrantVectorStore

embedder = OpenAIEmbeddingProvider()
store = QdrantVectorStore(
    embedder=embedder,
    collection_name="my_docs",
    url="http://localhost:6333",
)

store.add_documents([
    Document(text="Qdrant is a vector search engine.", metadata={"category": "infra"}),
    Document(text="It supports REST and gRPC.", metadata={"category": "infra"}),
])

# search() takes a query embedding, not a string — embed the query first
query_vec = embedder.embed_query("vector search")
results = store.search(query_vec, top_k=2)

Install¶

pip install "selectools[rag]"

qdrant-client>=1.7.0 is part of the [rag] extras.

You also need a running Qdrant instance. The simplest way:

docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

Or sign up for Qdrant Cloud and get a managed instance.

Constructor¶

QdrantVectorStore(
    embedder: EmbeddingProvider,
    collection_name: str = "selectools",
    url: str = "http://localhost:6333",
    api_key: str | None = None,
    prefer_grpc: bool = True,
    **qdrant_kwargs,
)

Parameter	Description
`embedder`	Any `EmbeddingProvider`. Used to compute vectors for both `add_documents()` and `search()`.
`collection_name`	Qdrant collection. Auto-created on first `add_documents()` if it doesn't exist.
`url`	Qdrant server URL. Use `https://...` for cloud.
`api_key`	Optional API key for Qdrant Cloud or authenticated servers.
`prefer_grpc`	When `True` (default) the client uses gRPC for lower-latency vector ops.
`**qdrant_kwargs`	Additional arguments forwarded to `qdrant_client.QdrantClient`.

Cloud Configuration¶

import os

store = QdrantVectorStore(
    embedder=OpenAIEmbedder(),
    collection_name="prod_docs",
    url="https://my-cluster.qdrant.io",
    api_key=os.environ["QDRANT_API_KEY"],
)

Metadata Filtering¶

Document metadata is stored as Qdrant payload, so you can filter searches at the database level. Use qdrant_client.models.Filter constructs and pass them via **search_kwargs (the store forwards them to the underlying client).

API Reference¶

Method	Description
`add_documents(docs)`	Embed documents and upsert into the collection
`search(query, top_k)`	Cosine similarity search
`delete(ids)`	Delete documents by ID
`clear()`	Delete the entire collection

#	Script	Description
78	`78_qdrant_vector_store.py`	Qdrant quickstart with metadata filtering