Vector stores

Get a vector store file batch

GET /openai/v1/vector_stores/{vector_store_id}/file_batches/{batch_id}

Retrieves a vector store file batch by its identifier.

路径参数

vector_store_id string 必需

The ID of the vector store that the file batch belongs to.

batch_id string 必需

The ID of the file batch being retrieved.

响应

The request has succeeded.

正文

Content-Type: application/json

id string 必需

The identifier, which can be referenced in API endpoints.

object enum 必需

The object type, which is always vector_store.file_batch.

vector_store.file_batch

created_at integer 必需

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

vector_store_id string 必需

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

status enum 必需

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

in_progresscompletedcancelledfailed

file_counts OpenAI.VectorStoreFileBatchObjectFileCounts 必需

in_progress integer 必需

completed integer 必需

failed integer 必需

cancelled integer 必需

total integer 必需

请求

curl https://api.openai.com/v1/vector_stores/vs_abc123/file_batches/vsfb_abc123 \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "OpenAI-Beta: assistants=v2"

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
vector_store_file_batch = client.vector_stores.file_batches.retrieve(
    batch_id="vsfb_abc123",
    vector_store_id="vs_abc123",
)
print(vector_store_file_batch.id)

import OpenAI from "openai";
const openai = new OpenAI();

async function main() {
  const vectorStoreFileBatch = await openai.vectorStores.fileBatches.retrieve(
    "vsfb_abc123",
    { vector_store_id: "vs_abc123" }
  );
  console.log(vectorStoreFileBatch);
}

main();

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const vectorStoreFileBatch = await client.vectorStores.fileBatches.retrieve('vsfb_abc123', {
  vector_store_id: 'vs_abc123',
});

console.log(vectorStoreFileBatch.id);

package main

import (
    "context"
    "fmt"

    "github.com/openai/openai-go"
    "github.com/openai/openai-go/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("My API Key"),
    )
    vectorStoreFileBatch, err := client.VectorStores.FileBatches.Get(
        context.TODO(),
        "vs_abc123",
        "vsfb_abc123",
    )
    if err != nil {
        panic(err.Error())
    }
    fmt.Printf("%+v
", vectorStoreFileBatch.ID)
}

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.vectorstores.filebatches.FileBatchRetrieveParams;
import com.openai.models.vectorstores.filebatches.VectorStoreFileBatch;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        FileBatchRetrieveParams params = FileBatchRetrieveParams.builder()
            .vectorStoreId("vs_abc123")
            .batchId("vsfb_abc123")
            .build();
        VectorStoreFileBatch vectorStoreFileBatch = client.vectorStores().fileBatches().retrieve(params);
    }
}

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

vector_store_file_batch = openai.vector_stores.file_batches.retrieve("vsfb_abc123", vector_store_id: "vs_abc123")

puts(vector_store_file_batch)

响应

{
  "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,
  }
}