Pinecone

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A RocketRide vector-store node that stores embedded document chunks in a Pinecone index and retrieves them by semantic or keyword search. Use it when a pipeline or agent needs a Pinecone-backed document store.

About Pinecone

Pinecone is a managed vector database for applications that search embedding data. It organizes vectors into indexes and supports vector queries with metadata filters. Use it when Pinecone is the service your application uses for retrieval and you want RocketRide to store and query the same index.

What it does

The node accepts embedded documents on its documents lane and can retrieve matching documents for incoming questions. It also exposes the store to an agent as tools. It creates a Pinecone index when documents are first added, and rejects incoming chunks without embeddings. Pick it over a self-managed store when the retrieval corpus belongs in a Pinecone index and its selected deployment mode is already part of the intended Pinecone setup.

The driver treats its Collection setting as the Pinecone index name. When it has to create that index, the supplied embeddings determine its dimension and the selected similarity setting determines its metric.

Lanes

Lane in	Lane out	Description
documents	—	Store embedded document chunks.
questions	documents	Return matching documents.
questions	answers	Return matching documents as answers.
questions	questions	Enrich questions with matching documents.

As a tool

The configured tool-server name defaults to pinecone.

Function	Description
search	Searches the store for a non-empty query; accepts optional top_k and metadata filter, and returns matching content, metadata, and scores.
upsert	Adds or updates a non-empty documents array. Each document requires content and an object ID; it can provide an embedding and embedding model or use the bound embedding provider.
delete	Deletes documents for a non-empty object_ids array and returns the deleted count.

search requires a bound embedding provider for semantic similarity search. The three functions return a failure object when their required input or an embedding cannot be obtained.

search accepts an optional filter object honoring only objectId, nodeId and parent; any other key is rejected. upsert accepts an optional metadata object storing nodeId, parent and chunkId, defaulting to "vectordb_tool", "/" and 0 respectively.

Profiles

Default: Pinecone Serverless Dense Index (serverless-dense).

Profile	Deployment mode	Context
Pinecone Pod-Based Index	pod-based	Creates a pod-based index.
Pinecone Serverless Dense Index (default)	serverless-dense	Creates a serverless index.

Configuration

Choose the profile that matches the target index, then provide the API key and collection name. New indexes use the selected profile; an existing index is checked against it during the save-time probe. The profile selection matters at creation time, while the key and collection select which existing Pinecone resource this node connects to.

Index profile

Pinecone Serverless Dense Index is the default profile and creates an index with the driver's serverless specification; Pinecone Pod-Based Index uses its pod-based specification. Select the one that matches an index you already have, or the kind of index you intend the node to create. The save-time probe compares an existing index's specification with the selected profile and warns if they do not agree; change the profile rather than assuming the node can change the existing index's deployment type.

Collection

The collection is the Pinecone index name and defaults to rocketride. Validation requires lowercase letters, numbers, and hyphens only; it rejects leading or trailing hyphens, consecutive hyphens, and names over 45 characters. Choose a name that identifies the one index this node will use. Change it to direct the node to a different Pinecone index, not to split a corpus inside one index. A validation warning flags a name that cannot be used by the configured service.

Similarity and retrieval score

The runtime accepts cosine, euclidean, or dotproduct as the similarity setting, defaulting to cosine; another value stops initialization. This value becomes the metric when the driver creates an index, so make it agree with the embedding data and leave an existing index's metric alone. Choosing the wrong metric changes ranking and can make good matches appear weak.

Although the configuration value score is read with a default of 0.5, the semantic-search filtering code reads the separately named threshhold_search attribute, whose initial value is 0.0; therefore the configured score is not applied by this implementation. Do not rely on this setting to suppress weak Pinecone results. If relevance needs stricter filtering, apply a downstream constraint or use a store whose retrieval threshold is enforced until this implementation changes.

API key

The API key is trimmed before the Pinecone client is created. It is needed both to open the store and for the save-time probe that lists indexes. Rotate or replace it when authentication fails rather than changing the collection name; the probe can also reveal a key that reaches Pinecone but cannot inspect the chosen index. Keep one key per intended service boundary when the node should not see every index available to a broader application credential.

Tool Server Name

The tool-server name defaults to pinecone and prefixes the three agent functions. Change it when multiple Pinecone stores are connected to the same agent so their functions do not share a namespace.

Authentication

Set the API key for the selected Pinecone profile. The runtime creates its client with that key, and the save-time probe uses it to list indexes and check an existing index's deployment mode.

Notes

Search and document lifecycle

Semantic search requires an embedding and does not accept a non-zero offset. Keyword search adds a content filter to the metadata filters. Re-ingesting an object deletes its existing chunks before replacement chunks are upserted. The store can mark chunks deleted or active, and its default filters exclude marked-deleted chunks.

The node retrieves semantic results with the requested limit (or 25 when no limit is supplied). It runs the collection-existence and embedding-model checks before searching, which makes a missing index or incompatible model fail before results are returned.

Index creation is intentionally opinionated: the serverless branch supplies an AWS us-east-1 specification, while the pod branch supplies one p1.x1 pod in us-east1-gcp. Those are creation-time defaults in this node, not settings that alter an existing index. Choose an already compatible index when those defaults are not appropriate for the intended deployment.

Rendering and paths

Rendering retrieves an object's chunks in chunkId order and sends joined text to the callback in renderChunkSize groups. For path listing, the driver requests enough records for the requested offset and limit, then slices the results locally.

Metadata filters include document identity, parent path, permissions, table information, and deletion state. The driver defaults isDeleted to false in its Pinecone filter, so a soft-deleted source does not appear in normal retrieval. That makes reactivating or explicitly including deleted material a separate caller choice rather than an accidental result of broad searching.

Upstream docs

• Pinecone documentation

Schema

Field	Type	Description	Default
pinecone.collection	string	Collection Enter the name of the collection. Accepted are: Lower case, alphanumeric characters, hyphens	"rocketride"
pinecone.profile	string	Type of Pinecone Connection Connect to...	"pod-based"
pinecone.provider	string		const: "pinecone"
pinecone.serverName	string	Tool Server Name Namespace for agent-facing tool names, e.g. 'pinecone' exposes tools as pinecone.search / pinecone.upsert / pinecone.delete. Change this when running multiple Pinecone nodes in the same pipeline so their tool names do not collide.	"pinecone"

Dependencies

• pinecone
• pinecone-plugin-assistant
• pinecone-plugin-interface