RocketRide Vector

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A RocketRide vector store node that stores embedded document chunks and retrieves them by keyword or semantic similarity from the managed tenant database. Pick it over the SQL and graph nodes for retrieval-augmented document search.

What it does

The node writes documents from the documents lane into a pgvector-backed table, replacing existing chunks for the same object IDs. Questions can produce matching documents, answers, or enriched questions through the three configured question outputs. Use it when retrieval should flow through a pipeline; unlike the tool-capable sibling stores, this node registers no agent tools or raw-SQL execution surface.

Lanes

Lane in	Lane out	Description
documents	—	Stores document chunks in the configured vector table.
questions	documents	Returns matching documents.
questions	answers	Returns matching documents as answers.
questions	questions	Enriches a question with matching documents.

Configuration

The single cloud profile provides a table, cosine similarity, a score threshold, and HNSW index defaults. RocketRide provisions a per-tenant database for its managed database nodes, and this node resolves it from the signed-in RocketRide identity instead of a host, user, password, or database name you enter. Bind an embedding module for semantic search; the embedding dimension is taken from the first stored document rather than from a configuration field.

Table

Table defaults to rocketride and is the PostgreSQL table that holds the chunks and embeddings. Choose a distinct table when separate corpora need separate retrieval indexes or retention behavior. The node accepts only an unquoted PostgreSQL identifier: it must start with a letter or underscore, use only letters, digits, and underscores, and be at most 63 characters. Invalid names are rejected during configuration validation and at startup.

Score threshold and similarity metric

Score threshold defaults to 0.5; it is the minimum returned similarity score. Raise it when loose matches are polluting downstream context, and lower it when relevant documents are being excluded. Scores are calculated from the configured metric: cosine uses 1 - distance, L2 uses 1 / (1 + distance), and inner product negates the returned distance. Regardless of this setting, the store drops results below its fixed 0.20 minimum similarity floor.

Similarity Metric defaults to cosine; l2 and inner_product are also accepted. Select the metric that matches the embeddings and expected notion of closeness before the table is first written, because it chooses the HNSW operator class used for the index. An unsupported value prevents startup.

HNSW m and ef_construction

The table's HNSW index is created on first write. HNSW m defaults to 16 and controls the graph degree; HNSW ef_construction defaults to 64 and controls the candidate list used while building it. Higher values can improve search quality at the cost of a more expensive, larger index. Values are clamped to pgvector's supported ranges (m 2–100 and ef_construction 4–1,000), and ef_construction is raised to at least twice m. These values do not rebuild an index that already exists.

Notes

Storage and retrieval behavior

The node creates the table on the first write and creates a metric-compatible HNSW index then. pgvector cannot create that index for embeddings wider than 2,000 dimensions, so the node warns and searches without the index in that case. Keyword search uses a content LIKE match; semantic search needs an embedding bound to the question and raises if none is available. Missing tables produce empty search results rather than an error.

Deleted objects are excluded by default, while document rendering reassembles stored chunks by chunkId. The store removes all existing chunks for incoming object IDs before inserting the replacement chunks, preventing duplicate data for a re-ingested object.

Schema

Field	Type	Description	Default
rrvector.collection	string	Table Name of the table to store vectors in your RocketRide cloud database.	"rocketride"
rrvector.hnsw_ef_construction	integer	HNSW ef_construction HNSW build-time candidate list size used when the index is first created.	64
rrvector.hnsw_m	integer	HNSW m HNSW graph degree (max connections per layer) used when the index is first created.	16
rrvector.profile	string	RocketRide cloud database Connect to...	"cloud"
rrvector.provider	string		const: "rocketride_vector"
rrvector.score	number	Score threshold Minimum similarity score for a document to be returned by search.	0.5
rrvector.similarity	string	Similarity Metric The similarity metric to use for vector search. Also selects the HNSW index operator class.	"cosine"

Dependencies

• psycopg2-binary
• pgvector