What can I automate with the Pinecone integration?

Read and write the Pinecone vector database: create indexes, embed text, upsert and search vectors, and rerank the results. Lodol supports 20 actions for Pinecone, which you can combine with any other app in a workflow.

How do I connect Pinecone to Lodol?

Authenticate Pinecone once from your workspace and every action and trigger it supports becomes available to drop into a workflow. There is no glue code to write or maintain.

All integrations

Pinecone

DatabasesAI Search & Memory

Read and write the Pinecone vector database: create indexes, embed text, upsert and search vectors, and rerank the results.

Get Started Browse all integrations

20 actions

Actions

Steps your workflow can run in Pinecone.

List indexesList every index in the Pinecone project, with each one's dimension, distance metric, region and readiness. Returns the names, which is the value every other action's 'Index' field takes.

Describe indexRead one index's configuration: its dimension, distance metric, cloud and region, tags, deletion protection, and the host it is served from. 'ready' says whether it can be written to yet - a freshly created index takes a moment.

Create indexCreate a serverless index for vectors you supply yourself - the kind 'Upsert vectors' and 'Search with a vector' work with. Give it the width of the vectors you will store and how similarity should be measured; neither can be changed afterwards. The index takes a moment to become ready.

Create index with an embedding modelCreate a serverless index that turns text into vectors for you. Nothing has to compute an embedding before writing or before searching: 'Upsert text records' sends the text and 'Search records' sends the question. Choose which field of your records holds the text - the default is 'chunk_text'.

Configure indexChange an existing index's deletion protection or its tags. An index's dimension, metric, cloud and region are fixed when it is created and are not offered here. Turn deletion protection off with this before 'Delete index' will run.

Delete indexDelete an index and every vector in it. This cannot be undone. If deletion protection is on, run 'Configure index' first to turn it off. Deletion is asynchronous - the index leaves 'List indexes' a moment later.

List modelsList the embedding and reranking models Pinecone hosts, with each one's dimensions, supported metrics, input limits and parameters. Run this to find a model name for 'Generate embeddings', 'Rerank documents' or a new integrated index.

Generate embeddingsTurn text into vectors with one of Pinecone's hosted models, without an index. 'embeddings' comes back as the plain arrays 'Upsert vectors' and 'Search with a vector' take, so this chains straight into either.

Rerank documentsRe-order a list of documents by how well they answer a question, using a hosted reranking model. Useful straight after a search: a vector search finds candidates quickly and a reranker puts the genuinely relevant ones on top. Plain strings are accepted as documents.

Get index statisticsCount what an index holds: the total number of vectors, and the number in each namespace, plus the dimension and metric the index was built with. The quickest way to confirm an upsert landed.

Upsert vectorsWrite vectors into a namespace. Each one is an id, an array of numbers as wide as the index, and optional metadata to filter on later. Writing an id that is already there replaces it, so this is safe to re-run.

Search with a vectorFind the vectors nearest to one you supply, or nearest to a vector already in the index. Returns each match's id and similarity score, most similar first, with its metadata unless you turn that off. Give a vector or an id, not both.

Fetch vectorsRead vectors back by id, with their numbers and metadata. Ids the index does not have are reported in 'missing_ids' rather than failing the step, so a typo is visible instead of looking like an empty index.

Update vectorChange one stored vector's numbers, its metadata, or both, without rewriting the rest of it. Metadata given here is merged into what is already stored, so keys you leave out are kept.

Delete vectorsDelete vectors by id, or empty a whole namespace. This cannot be undone. Pinecone can also delete by metadata filter, but only on pod-based indexes, so that is not offered here - search for what you want first and delete the ids it returns.

List record IDsList the ids stored in one namespace, in sorted order, optionally narrowed to those starting with a prefix. This is how you find an id when you do not already have one - the vectors themselves come from 'Fetch vectors'.

List namespacesList every namespace in an index, with how many records each one holds and how much space it uses. A namespace exists as soon as something is written into it, so there is nothing to create.

Delete namespaceDelete a namespace and every record in it. This cannot be undone. Use it to clear out a whole partition of an index without deleting the index itself.

Upsert text recordsWrite text straight into an index built with 'Create index with an embedding model' - Pinecone embeds it for you, so nothing has to produce a vector first. Each record needs an '_id' and the index's text field; anything else on it is stored as metadata. Writing an id again replaces it.

Search recordsSearch a namespace with a question in plain text, and optionally rerank the results in the same call. Searching by text needs an index built with 'Create index with an embedding model'; searching by vector or by the id of a stored record works on any index. Give exactly one of the three.