ModelAi/node_modules/openai/resources/vector-stores/file-batches.js

"use strict";
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
Object.defineProperty(exports, "__esModule", { value: true });
exports.FileBatches = void 0;
const resource_1 = require("../../core/resource.js");
const pagination_1 = require("../../core/pagination.js");
const headers_1 = require("../../internal/headers.js");
const sleep_1 = require("../../internal/utils/sleep.js");
const Util_1 = require("../../lib/Util.js");
const path_1 = require("../../internal/utils/path.js");
class FileBatches extends resource_1.APIResource {
    /**
     * Create a vector store file batch.
     */
    create(vectorStoreID, body, options) {
        return this._client.post((0, path_1.path) `/vector_stores/${vectorStoreID}/file_batches`, {
            body,
            ...options,
            headers: (0, headers_1.buildHeaders)([{ 'OpenAI-Beta': 'assistants=v2' }, options?.headers]),
        });
    }
    /**
     * Retrieves a vector store file batch.
     */
    retrieve(batchID, params, options) {
        const { vector_store_id } = params;
        return this._client.get((0, path_1.path) `/vector_stores/${vector_store_id}/file_batches/${batchID}`, {
            ...options,
            headers: (0, headers_1.buildHeaders)([{ 'OpenAI-Beta': 'assistants=v2' }, options?.headers]),
        });
    }
    /**
     * Cancel a vector store file batch. This attempts to cancel the processing of
     * files in this batch as soon as possible.
     */
    cancel(batchID, params, options) {
        const { vector_store_id } = params;
        return this._client.post((0, path_1.path) `/vector_stores/${vector_store_id}/file_batches/${batchID}/cancel`, {
            ...options,
            headers: (0, headers_1.buildHeaders)([{ 'OpenAI-Beta': 'assistants=v2' }, options?.headers]),
        });
    }
    /**
     * Create a vector store batch and poll until all files have been processed.
     */
    async createAndPoll(vectorStoreId, body, options) {
        const batch = await this.create(vectorStoreId, body);
        return await this.poll(vectorStoreId, batch.id, options);
    }
    /**
     * Returns a list of vector store files in a batch.
     */
    listFiles(batchID, params, options) {
        const { vector_store_id, ...query } = params;
        return this._client.getAPIList((0, path_1.path) `/vector_stores/${vector_store_id}/file_batches/${batchID}/files`, (pagination_1.CursorPage), { query, ...options, headers: (0, headers_1.buildHeaders)([{ 'OpenAI-Beta': 'assistants=v2' }, options?.headers]) });
    }
    /**
     * Wait for the given file batch to be processed.
     *
     * Note: this will return even if one of the files failed to process, you need to
     * check batch.file_counts.failed_count to handle this case.
     */
    async poll(vectorStoreID, batchID, options) {
        const headers = (0, headers_1.buildHeaders)([
            options?.headers,
            {
                'X-Stainless-Poll-Helper': 'true',
                'X-Stainless-Custom-Poll-Interval': options?.pollIntervalMs?.toString() ?? undefined,
            },
        ]);
        while (true) {
            const { data: batch, response } = await this.retrieve(batchID, { vector_store_id: vectorStoreID }, {
                ...options,
                headers,
            }).withResponse();
            switch (batch.status) {
                case 'in_progress':
                    let sleepInterval = 5000;
                    if (options?.pollIntervalMs) {
                        sleepInterval = options.pollIntervalMs;
                    }
                    else {
                        const headerInterval = response.headers.get('openai-poll-after-ms');
                        if (headerInterval) {
                            const headerIntervalMs = parseInt(headerInterval);
                            if (!isNaN(headerIntervalMs)) {
                                sleepInterval = headerIntervalMs;
                            }
                        }
                    }
                    await (0, sleep_1.sleep)(sleepInterval);
                    break;
                case 'failed':
                case 'cancelled':
                case 'completed':
                    return batch;
            }
        }
    }
    /**
     * Uploads the given files concurrently and then creates a vector store file batch.
     *
     * The concurrency limit is configurable using the `maxConcurrency` parameter.
     */
    async uploadAndPoll(vectorStoreId, { files, fileIds = [] }, options) {
        if (files == null || files.length == 0) {
            throw new Error(`No \`files\` provided to process. If you've already uploaded files you should use \`.createAndPoll()\` instead`);
        }
        const configuredConcurrency = options?.maxConcurrency ?? 5;
        // We cap the number of workers at the number of files (so we don't start any unnecessary workers)
        const concurrencyLimit = Math.min(configuredConcurrency, files.length);
        const client = this._client;
        const fileIterator = files.values();
        const allFileIds = [...fileIds];
        // This code is based on this design. The libraries don't accommodate our environment limits.
        // https://stackoverflow.com/questions/40639432/what-is-the-best-way-to-limit-concurrency-when-using-es6s-promise-all
        async function processFiles(iterator) {
            for (let item of iterator) {
                const fileObj = await client.files.create({ file: item, purpose: 'assistants' }, options);
                allFileIds.push(fileObj.id);
            }
        }
        // Start workers to process results
        const workers = Array(concurrencyLimit).fill(fileIterator).map(processFiles);
        // Wait for all processing to complete.
        await (0, Util_1.allSettledWithThrow)(workers);
        return await this.createAndPoll(vectorStoreId, {
            file_ids: allFileIds,
        });
    }
}
exports.FileBatches = FileBatches;
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