Create a new OpenAI vector store — a collection of processed files that can be used with the file_search tool.

Use this when you need a new vector store to hold uploaded documents for semantic search or retrieval-augmented generation (RAG).

You can optionally provide file_ids to attach already-uploaded files, a chunking strategy, and metadata (up to 16 key-value pairs).

Retrieve details of a specific OpenAI vector store by its ID.

Use this to check the status, file counts, metadata, and other properties of an existing vector store.

Modify an existing OpenAI vector store — rename it, update metadata, or change the expiration policy.

Use this when you need to update properties of an existing vector store without recreating it.

Permanently delete an OpenAI vector store by its ID.

This will remove the vector store and all its associated files. This action cannot be undone.

List all OpenAI vector stores with pagination support.

Returns a paginated list of vector stores in your project. Use 'after' or 'before' cursors to navigate pages.

Search an OpenAI vector store for relevant chunks based on a query and optional file-attribute filters.

Use this to perform semantic search across the documents in a vector store. You can provide a single query string or an array of queries, and optionally filter by file attributes (e.g., department = "engineering").

Results include ranked search hits with content snippets, file IDs, and relevance scores.

List all files in your OpenAI project with pagination and optional purpose filter.

Returns metadata about each uploaded file including its ID, filename, size, purpose, and creation timestamp. Use the 'purpose' parameter to filter by type (e.g., "assistants", "fine-tune", "batch").

Retrieve metadata about a specific file by its ID, including filename, size, purpose, and status.

Permanently delete a file and remove it from all vector stores.

This action cannot be undone. The file will be deleted from OpenAI's storage.

Retrieve the raw content of a file by its ID.

Returns the file content as-is (text, JSON, JSONL, etc. depending on the file type). For large files, consider using the file's metadata first to check its size.

Attach a previously uploaded file to a vector store.

The file must already exist in your OpenAI project (use openai_upload_file to upload first). Once attached, the file will be chunked and indexed for semantic search.

For multi-file ingestion, prefer openai_create_vector_store_file_batch to minimize per-vector-store write requests.

List all files attached to a vector store with pagination and optional status filter.

Shows each file's processing status (in_progress, completed, failed, cancelled) so you can monitor ingestion progress.

Retrieve details of a specific file attached to a vector store, including its processing status and any errors.

Remove a file from a vector store (does NOT delete the underlying OpenAI file).

After removal, the file will no longer be searchable in this vector store. To delete the file entirely, use openai_delete_file.

Retrieve the parsed text content of a vector store file.

Returns the chunks of text that were extracted from the file and indexed in the vector store. Useful for inspecting what content is available for search.

Update or clear the attributes (up to 16 key-value pairs) on a vector store file.

Attributes can be used to filter and organize files within a vector store. Pass null to clear all attributes.

Create a batch of files to attach to a vector store.

This is the recommended way to attach multiple files at once — it minimizes per-vector-store write requests and is more efficient than attaching files one by one.

Retrieve the status and details of a file batch, including file counts by status.

Cancel processing of a file batch as soon as possible.

Use this to stop ingestion of files that are still being processed. Already-processed files will remain attached.

List all files in a specific file batch with pagination and optional status filter.

Use this to inspect which files in a batch have been processed, are still in progress, or have failed.

Upload a local file to OpenAI.

The file is uploaded using multipart/form-data. Once uploaded, you can attach the returned file ID to a vector store using openai_attach_file_to_vector_store.

Supported purposes:

• assistants: Used in the Assistants API

• batch: Used in the Batch API

• fine-tune: Used for fine-tuning

• vision: Images for vision fine-tuning

• user_data: Flexible file type for any purpose

• evals: Used for eval data sets

Individual files can be up to 512 MB.