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Vector Stores
File Batches

Create vector store file batch

vector_stores.file_batches.create(strvector_store_id, FileBatchCreateParams**kwargs) -> VectorStoreFileBatch
POST/vector_stores/{vector_store_id}/file_batches

Create a vector store file batch.

ParametersExpand Collapse
vector_store_id: str
attributes: Optional[Dict[str, Union[str, float, bool]]]

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters, booleans, or numbers.

Accepts one of the following:
str
float
bool
chunking_strategy: Optional[FileChunkingStrategyParam]

The chunking strategy used to chunk the file(s). If not set, will use the auto strategy. Only applicable if file_ids is non-empty.

Accepts one of the following:
class AutoFileChunkingStrategyParam: …

The default strategy. This strategy currently uses a max_chunk_size_tokens of 800 and chunk_overlap_tokens of 400.

type: Literal["auto"]

Always auto.

class StaticFileChunkingStrategyObjectParam: …

Customize your own chunking strategy by setting chunk size and chunk overlap.

chunk_overlap_tokens: int

The number of tokens that overlap between chunks. The default value is 400.

Note that the overlap must not exceed half of max_chunk_size_tokens.

max_chunk_size_tokens: int

The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 and the maximum value is 4096.

minimum100
maximum4096
type: Literal["static"]

Always static.

file_ids: Optional[SequenceNotStr[str]]

A list of File IDs that the vector store should use. Useful for tools like file_search that can access files. If attributes or chunking_strategy are provided, they will be applied to all files in the batch. The maximum batch size is 2000 files. Mutually exclusive with files.

files: Optional[Iterable[File]]

A list of objects that each include a file_id plus optional attributes or chunking_strategy. Use this when you need to override metadata for specific files. The global attributes or chunking_strategy will be ignored and must be specified for each file. The maximum batch size is 2000 files. Mutually exclusive with file_ids.

file_id: str

A File ID that the vector store should use. Useful for tools like file_search that can access files.

attributes: Optional[Dict[str, Union[str, float, bool]]]

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters, booleans, or numbers.

Accepts one of the following:
str
float
bool
chunking_strategy: Optional[FileChunkingStrategyParam]

The chunking strategy used to chunk the file(s). If not set, will use the auto strategy. Only applicable if file_ids is non-empty.

Accepts one of the following:
class AutoFileChunkingStrategyParam: …

The default strategy. This strategy currently uses a max_chunk_size_tokens of 800 and chunk_overlap_tokens of 400.

type: Literal["auto"]

Always auto.

class StaticFileChunkingStrategyObjectParam: …

Customize your own chunking strategy by setting chunk size and chunk overlap.

chunk_overlap_tokens: int

The number of tokens that overlap between chunks. The default value is 400.

Note that the overlap must not exceed half of max_chunk_size_tokens.

max_chunk_size_tokens: int

The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 and the maximum value is 4096.

minimum100
maximum4096
type: Literal["static"]

Always static.

ReturnsExpand Collapse
class VectorStoreFileBatch: …

A batch of files attached to a vector store.

id: str

The identifier, which can be referenced in API endpoints.

created_at: int

The Unix timestamp (in seconds) for when the vector store files batch was created.

file_counts: FileCounts
cancelled: int

The number of files that where cancelled.

completed: int

The number of files that have been processed.

failed: int

The number of files that have failed to process.

in_progress: int

The number of files that are currently being processed.

total: int

The total number of files.

object: Literal["vector_store.files_batch"]

The object type, which is always vector_store.file_batch.

status: Literal["in_progress", "completed", "cancelled", "failed"]

The status of the vector store files batch, which can be either in_progress, completed, cancelled or failed.

Accepts one of the following:
"in_progress"
"completed"
"cancelled"
"failed"
vector_store_id: str

The ID of the vector store that the File is attached to.

Create vector store file batch

from openai import OpenAI
client = OpenAI()

vector_store_file_batch = client.vector_stores.file_batches.create(
  vector_store_id="vs_abc123",
  files=[
    {
      "file_id": "file-abc123",
      "attributes": {"category": "finance"},
    },
    {
      "file_id": "file-abc456",
      "chunking_strategy": {
        "type": "static",
        "max_chunk_size_tokens": 1200,
        "chunk_overlap_tokens": 200,
      },
    },
  ],
)
print(vector_store_file_batch)

{
  "id": "vsfb_abc123",
  "object": "vector_store.file_batch",
  "created_at": 1699061776,
  "vector_store_id": "vs_abc123",
  "status": "in_progress",
  "file_counts": {
    "in_progress": 1,
    "completed": 1,
    "failed": 0,
    "cancelled": 0,
    "total": 0,
  }
}
Returns Examples
{
  "id": "vsfb_abc123",
  "object": "vector_store.file_batch",
  "created_at": 1699061776,
  "vector_store_id": "vs_abc123",
  "status": "in_progress",
  "file_counts": {
    "in_progress": 1,
    "completed": 1,
    "failed": 0,
    "cancelled": 0,
    "total": 0,
  }
}