ceven

Pinecone

Connects your AI workflows to a high performance vector database to store long term memory and perform semantic search across millions of records.

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Why use Ceven?

  1. AI native Pinecone integration

    • Describe the outcome and Ceven picks the right Pinecone calls, fills the parameters, and checks the result.
    • Structured, agent friendly tool schemas so each call runs reliably instead of by guesswork.
    • Rich coverage for reading, writing, and querying your Pinecone data, across all 48 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke Pinecone access.
    • Per user and per environment credentials instead of shared keys.

  3. Agent optimized design

    • Actions are tuned from real success and error rates so reliability climbs over time.
    • Full execution logs so you always know what ran in Pinecone, when, and on whose behalf.
    • The agent pauses and asks when Pinecone is unclear instead of plowing ahead.

  4. Enterprise grade security

    • Fine grained access so you control which agents and people can reach Pinecone.
    • Least privilege by default, read scopes first and only the writes a workflow needs.
    • A full audit trail of every Pinecone action to support review and sign off.

Supported tools

Every action Ceven's agents can run on Pinecone, and when to use it.

Create index
Use this when you need to initialize a new vector database index for storing and querying embeddings.
Query vectors
Perform semantic search within an index using a query vector to retrieve the most similar items.
Upsert records
Use this when you need to add or update records with automatic text to vector conversion in a namespace.
Update vector
Overwrite vector values or metadata for a specific record ID to keep your memory current.
Rerank documents
Order retrieved documents by semantic relevance to a user query using hosted reranking models.
Generate embeddings
Convert raw text into vector representations for use in similarity matching or storage.
Create namespace
Use this when you need to organize vectors into isolated partitions for different users or projects.
List indexes
Pull all indexes in your project along with their current configurations and status.
Describe index stats
Pull vector counts per namespace and index fullness to monitor storage capacity.
Start bulk import
Trigger an asynchronous import of vectors from S3, GCS, or Azure Blob Storage.
Create backup
Preserve the current state of an index including all vectors and metadata for recovery.
Delete index
Permanently remove an index and all contained data from your project.
Cancel Bulk Import
Tool to cancel a bulk import operation in Pinecone. Use when you need to stop an ongoing import operation that is not yet finished.
Configure Index
Tool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags. Use when you need to scale an index vertically or horizontally, enable/disable deletion protection, or update tags. The change is asy
Create Index with Embedding Model
Tool to create a Pinecone index with integrated embedding model for automatic vectorization. Use when you need to set up a new index that automatically converts text to vectors using a pre configured embedding model.
Create Index from Backup
Tool to create an index from a backup. Use when you need to restore or duplicate index data from a previously saved backup.
Delete Namespace
Tool to permanently delete a namespace from a serverless index. Use when you need to remove an entire namespace and all its data. This operation is irreversible and only supported on serverless indexes.
Describe Backup
Tool to retrieve detailed information about a specific backup. Use when you need to check backup status, configuration, or metadata.
Describe Bulk Import
Tool to describe a specific bulk import operation in Pinecone. Use when you need to retrieve detailed information about an import's status, progress, timing, and any errors.
Describe Restore Job
Tool to get detailed information about a specific restore job in Pinecone. Use when you need to check the status, progress, or metadata of a restore operation.
Get Model Information
Tool to retrieve detailed information about a specific model hosted by Pinecone. Use when you need to understand model capabilities for embedding and reranking operations.
List Bulk Imports
Tool to list all recent and ongoing bulk import operations in Pinecone. Use when you need to monitor or track the status of data import jobs. Supports pagination with a default limit of 100 imports per page.
List Collections
Tool to list all collections in a Pinecone project (pod based indexes only). Use when you need to view available collections.
List Index Backups
Tool to list all backups for a specific Pinecone index. Use when you need to view available backups for an index. Supports pagination via limit and paginationToken parameters.
List Available Models
Tool to list all available embedding and reranking models hosted by Pinecone. Use when you need to discover available models or filter by model type (embed/rerank) or vector type (dense/sparse).
List Namespaces
Tool to list all namespaces in a serverless Pinecone index. Use when you need to discover available namespaces for data organization. Returns up to 100 namespaces by default with pagination support.
List Project Backups
Tool to list all backups for indexes in a Pinecone project. Use when you need to retrieve backup information across all project indexes. Supports pagination with limit and paginationToken parameters.
List Restore Jobs
Tool to list all restore jobs for a project with pagination support. Use when you need to view the status of restore operations or track restore progress.
List Vectors
Tool to list vector IDs in a Pinecone serverless index. Use when you need to browse or retrieve vector identifiers from a namespace. Supports filtering by prefix and pagination for large result sets.
Search Records in Namespace
Tool to search records within a Pinecone namespace using text, vector, or ID query. Use when you need to find similar records based on embeddings or record IDs. Results can optionally be reranked for relevance.

30 actions · scroll to see them all

Frequently asked questions

Ceven uses Pinecone hosted embedding models to streamline the process. When you use the upsert records action, the agent sends the raw text to the Pinecone embedding endpoint, which returns the vector representation and stores it in the specified index. This removes the need for you to manage separate embedding models like OpenAI or Cohere. If you prefer to use your own vectors, you can use the update vector action to push pre computed embeddings directly. The workflow ensures that the dimensions of your vectors match the configuration of your index to prevent API errors during the ingestion process.
Yes, you should use namespaces for this purpose. Namespaces allow you to partition the vectors within a single index, ensuring that a query for one client never retrieves data from another. Ceven provides a create namespace action that lets you programmatically isolate data. When querying, the agent specifies the namespace ID to narrow the search space. This is more cost effective than creating a separate index for every client and allows for faster management of data lifecycles, such as deleting a single client namespace without affecting the rest of your database.
One important quirk is that Pinecone offers deletion protection for certain index configurations. If deletion protection is enabled, a request to delete the index will fail until the setting is toggled off. Additionally, if there are pending collections or active bulk imports, the deletion might be delayed or blocked. Ceven handles this by first checking the index configuration via the describe index stats call. If the agent detects that deletion protection is active, it will notify you or attempt to configure the index to allow deletion before proceeding with the final removal command.
Standard vector search retrieves the most similar items based on cosine similarity, but this can sometimes miss the nuance of a specific question. Ceven uses the rerank documents action to take the top results from a Pinecone query and pass them through a cross encoder model. This model looks at the query and the document together to assign a more accurate relevance score. This two stage process gives you the speed of vector search with the precision of a deep learning ranker, which is critical for RAG applications where the LLM needs the most exact context.
For millions of vectors, using individual upsert calls is too slow. Ceven uses the start bulk import action to connect Pinecone directly to your cloud storage in S3, GCS, or Azure. You provide the bucket path and the file format, and Pinecone pulls the data asynchronously. The agent then monitors the progress using the describe bulk import tool. Once the status changes to completed, the data is immediately available for querying. This method bypasses the standard API rate limits for writes and is the recommended way to initialize large scale knowledge bases.
Yes, Ceven can manage the restore process using the create index from backup action. First, the agent lists available backups to find the correct snapshot ID. Once selected, it triggers the restore job, which creates a brand new index populated with the data from that backup. Because restoring a large index takes time, the agent uses the describe restore job tool to poll for completion. Once the index is live, the agent can update your workflow configuration to point to the new index name, ensuring minimal downtime for your AI applications.
Serverless indexes are designed for ease of use and scale, where you pay for what you use without managing infrastructure. Pod based indexes provide more control over the hardware and are often used for very specific latency requirements. Ceven supports both, but some actions like list collections are only available for pod based indexes. When you create an index through Ceven, the agent helps you choose the right type based on your expected load. If you are unsure, serverless is usually the better choice for most AI memory tasks due to the lack of manual scaling needs.
Ceven implements an intelligent retry mechanism with exponential backoff to handle Pinecone rate limits. If the agent receives a 429 error during a heavy upsert or query load, it pauses the workflow and retries the request after a short delay. For high volume data movement, the agent automatically suggests switching to bulk import instead of individual API calls. This ensures that your production workflows do not crash during traffic spikes and that data integrity is maintained even when pushing the limits of your current Pinecone tier.

Alternatives to Pinecone

Other tools that solve a similar problem. Ceven supports these too, so you can switch or run more than one at once.

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