Qdrant

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A RocketRide vector store node backed by Qdrant, for storing embedded documents and retrieving them by semantic or keyword search.

What it does

Stores pre-embedded document chunks in a Qdrant collection and retrieves them by semantic (vector) similarity or full-text keyword search. Works with both self-hosted Qdrant and Qdrant Cloud. The node is also a tool node (classType: ["store", "tool"] with the invoke capability), so agents in the same pipeline can call its search, upsert, and delete tools directly.

Uses the official qdrant_client Python SDK over REST (prefer_grpc=False, 60-second timeout). If the configured host has no scheme, http:// is prepended automatically.

Documents must be run through an embedding node before reaching this node: the store does not compute embeddings on the data lane and raises an error if a chunk arrives without one.

The collection is created automatically on first write, including payload indexes on meta.nodeId, meta.objectId, meta.parent, meta.permissionId, meta.isDeleted, meta.isTable, and a full-text index on content (word tokenizer, lowercase, token length 2-15). Writing chunk 0 of an object first deletes all existing points with the same objectId, so re-ingesting a document replaces it rather than duplicating it. Upserts are batched: a flush happens every 500 points or when the accumulated payload exceeds the payload limit (32 MiB by default).

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Configuration

Lanes

Lane in	Lane out	Description
documents	(none)	Ingest pre-embedded documents into the collection
questions	documents	Return matching documents
questions	answers	Return matching documents as an answer
questions	questions	Enrich the question with matching documents for downstream nodes

Fields

Field	Type	Description
serverName	string	Default "qdrant". Namespace for agent-facing tool names, e.g. 'qdrant' exposes tools as qdrant.search / qdrant.upsert / qdrant.delete. Change this when running multiple Qdrant nodes in the same pipeline so their tool names do not collide.
profile	string	Default "cloud". Connect to...
provider	string	Default "qdrant".

Profiles

Profile	Default host	Default port
Qdrant cloud server (default)	(your Qdrant Cloud endpoint)	6333
Your own Qdrant server	localhost	6333

Save-time validation

When the configuration is saved, the node validates it with a fast read-only probe (get_collections, 10-second timeout):

• The collection name must match ^[A-Za-z0-9._-]{1,255}$: no / or spaces.
• Port 0 is rejected explicitly.
• Scheme is inferred when the host has none: http for localhost or 127.*, https for *.qdrant.io hosts or port 443.
• Qdrant Cloud with a wrong port does not return an HTTP status: the probe simply times out after 10 seconds and surfaces a timeout warning.

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Agent tools

Tools are exposed under the configured serverName namespace (default qdrant).

search

Semantic similarity search over the collection.

Argument	Type	Description
query	string	Required. The search query text.
top_k	integer	Maximum results to return (default 10, capped at 100).
filter	object	Optional metadata filter; supports objectId, nodeId, and parent keys.

Requires an embedding provider bound to the node (the embedding sub-block in the configuration). If semantic search fails, the tool falls back to keyword search. Returns {"results": [{content, score, metadata}], "total": n} on success or {"success": false, "error": "..."} on failure.

upsert

Add or update documents in the collection.

Each item in the documents array requires content and object_id (used for deduplication) and may include metadata, plus optional pre-computed embedding and embedding_model to skip automatic embedding. Without a pre-computed vector the node's bound embedding provider is used; if none is configured the call fails. Returns {"success": true, "count": n, "skipped": n}.

delete

Delete documents by ID.

Takes a required object_ids string array and removes all points whose meta.objectId matches. Returns {"success": true, "deleted_count": n}.

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Scoring and relevance

Raw Qdrant scores are rescaled to [0, 1] before filtering. For Cosine similarity the rescaling is (score + 1) / 2; for other metrics a sigmoid is applied. Results below a hard floor of 0.20 are always dropped as noise, and the configured retrieval score threshold is applied on top of that in the rescaled space (applied by the base store, not passed to Qdrant as score_threshold).

The similarity metric defaults to Cosine; Euclid, Dot, and Manhattan are also accepted. Any other value raises a configuration error at startup. Semantic search uses exact search (SearchParams(exact=True)) and does not support a non-zero result offset; keyword search and plain filtered gets use Qdrant scroll and do support offsets.

Deletion of source files is handled as a soft delete: chunks are marked meta.isDeleted: true and excluded from all searches by default. They are only returned when the filter explicitly asks for deleted documents. A document that reappears is marked active again.

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Authentication

Self-hosted Qdrant typically needs no credentials: leave apikey empty. For Qdrant Cloud, set host to your instance endpoint (<instance>.<region>.qdrant.io) and provide the cluster API key in apikey. The key is passed as the api_key argument of the Qdrant client.

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Notes

• Qdrant's full-text tokenizer does not support all languages out of the box. Chinese, Japanese, and Korean are not enabled in the default Qdrant build and require building Qdrant from source with the --features multiling-chinese,multiling-japanese,multiling-korean flags. This only affects keyword search over the content payload; semantic search uses the embeddings you provide and is unaffected.
• Rendering a full document rehydrates all of its chunks in chunkId order, fetching them in batches of up to 32 MiB and joining them, so out-of-order storage is handled transparently.

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Schema

Field	Type	Description	Default
qdrant.profile	string	Type of Qdrant host Connect to...	"cloud"
qdrant.provider	string		const: "qdrant"
qdrant.serverName	string	Tool Server Name Namespace for agent-facing tool names, e.g. 'qdrant' exposes tools as qdrant.search / qdrant.upsert / qdrant.delete. Change this when running multiple Qdrant nodes in the same pipeline so their tool names do not collide.	"qdrant"
vector.cloud.host		Enter the server IP address e.g. ..qdrant.io
vector.cloud.port			6333
vector.local.host			"localhost"
vector.local.port			6333

Dependencies

• grpcio
• grpcio-tools
• portalocker
• pydantic
• urllib3
• httpx
• qdrant_client
• numpy