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fix(openai): 更新openai依赖版本并修复嵌入模型处理逻辑

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修复openai客户端中嵌入模型处理逻辑，当模型名称包含"jina"时不使用base64编码
移除平台相关头信息以解决兼容性问题
更新package.json中openai依赖版本至5.12.0
This commit is contained in:
icarus 2025-08-06 21:16:30 +08:00
parent 9f856bd696
commit 8fa91b35b3
2 changed files with 347 additions and 3 deletions
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344
.yarn/patches/openai-npm-5.12.0-a06a6369b2.patch vendored Normal file
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@ -0,0 +1,344 @@
diff --git a/client.js b/client.js
index 22cc08d77ce849842a28f684c20dd5738152efa4..0c20f96405edbe7724b87517115fa2a61934b343 100644
--- a/client.js
+++ b/client.js
@@ -444,7 +444,7 @@ class OpenAI {
'User-Agent': this.getUserAgent(),
'X-Stainless-Retry-Count': String(retryCount),
...(options.timeout ? { 'X-Stainless-Timeout': String(Math.trunc(options.timeout / 1000)) } : {}),
- ...(0, detect_platform_1.getPlatformHeaders)(),
+ // ...(0, detect_platform_1.getPlatformHeaders)(),
'OpenAI-Organization': this.organization,
'OpenAI-Project': this.project,
},
diff --git a/client.mjs b/client.mjs
index 7f1af99fb30d2cae03eea6687b53e6c7828faceb..fd66373a5eff31a5846084387a3fd97956c9ad48 100644
--- a/client.mjs
+++ b/client.mjs
@@ -1,43 +1,41 @@
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
var _OpenAI_instances, _a, _OpenAI_encoder, _OpenAI_baseURLOverridden;
-import { __classPrivateFieldGet, __classPrivateFieldSet } from "./internal/tslib.mjs";
-import { uuid4 } from "./internal/utils/uuid.mjs";
-import { validatePositiveInteger, isAbsoluteURL, safeJSON } from "./internal/utils/values.mjs";
-import { sleep } from "./internal/utils/sleep.mjs";
-import { castToError, isAbortError } from "./internal/errors.mjs";
-import { getPlatformHeaders } from "./internal/detect-platform.mjs";
-import * as Shims from "./internal/shims.mjs";
-import * as Opts from "./internal/request-options.mjs";
-import * as qs from "./internal/qs/index.mjs";
-import { VERSION } from "./version.mjs";
+import { APIPromise } from "./core/api-promise.mjs";
import * as Errors from "./core/error.mjs";
import * as Pagination from "./core/pagination.mjs";
import * as Uploads from "./core/uploads.mjs";
-import * as API from "./resources/index.mjs";
-import { APIPromise } from "./core/api-promise.mjs";
-import { Batches, } from "./resources/batches.mjs";
-import { Completions, } from "./resources/completions.mjs";
-import { Embeddings, } from "./resources/embeddings.mjs";
-import { Files, } from "./resources/files.mjs";
-import { Images, } from "./resources/images.mjs";
-import { Models } from "./resources/models.mjs";
-import { Moderations, } from "./resources/moderations.mjs";
-import { Webhooks } from "./resources/webhooks.mjs";
+import { isRunningInBrowser } from "./internal/detect-platform.mjs";
+import { castToError, isAbortError } from "./internal/errors.mjs";
+import { buildHeaders } from "./internal/headers.mjs";
+import * as qs from "./internal/qs/index.mjs";
+import * as Opts from "./internal/request-options.mjs";
+import * as Shims from "./internal/shims.mjs";
+import { __classPrivateFieldGet, __classPrivateFieldSet } from "./internal/tslib.mjs";
+import { readEnv } from "./internal/utils/env.mjs";
+import { formatRequestDetails, loggerFor, parseLogLevel, } from "./internal/utils/log.mjs";
+import { sleep } from "./internal/utils/sleep.mjs";
+import { uuid4 } from "./internal/utils/uuid.mjs";
+import { isAbsoluteURL, isEmptyObj, safeJSON, validatePositiveInteger } from "./internal/utils/values.mjs";
import { Audio } from "./resources/audio/audio.mjs";
+import { Batches, } from "./resources/batches.mjs";
import { Beta } from "./resources/beta/beta.mjs";
import { Chat } from "./resources/chat/chat.mjs";
+import { Completions, } from "./resources/completions.mjs";
import { Containers, } from "./resources/containers/containers.mjs";
+import { Embeddings, } from "./resources/embeddings.mjs";
import { Evals, } from "./resources/evals/evals.mjs";
+import { Files, } from "./resources/files.mjs";
import { FineTuning } from "./resources/fine-tuning/fine-tuning.mjs";
import { Graders } from "./resources/graders/graders.mjs";
+import { Images, } from "./resources/images.mjs";
+import * as API from "./resources/index.mjs";
+import { Models } from "./resources/models.mjs";
+import { Moderations, } from "./resources/moderations.mjs";
import { Responses } from "./resources/responses/responses.mjs";
import { Uploads as UploadsAPIUploads, } from "./resources/uploads/uploads.mjs";
import { VectorStores, } from "./resources/vector-stores/vector-stores.mjs";
-import { isRunningInBrowser } from "./internal/detect-platform.mjs";
-import { buildHeaders } from "./internal/headers.mjs";
-import { readEnv } from "./internal/utils/env.mjs";
-import { formatRequestDetails, loggerFor, parseLogLevel, } from "./internal/utils/log.mjs";
-import { isEmptyObj } from "./internal/utils/values.mjs";
+import { Webhooks } from "./resources/webhooks.mjs";
+import { VERSION } from "./version.mjs";
/**
* API Client for interfacing with the OpenAI API.
*/
@@ -441,7 +439,7 @@ export class OpenAI {
'User-Agent': this.getUserAgent(),
'X-Stainless-Retry-Count': String(retryCount),
...(options.timeout ? { 'X-Stainless-Timeout': String(Math.trunc(options.timeout / 1000)) } : {}),
- ...getPlatformHeaders(),
+ // ...getPlatformHeaders(),
'OpenAI-Organization': this.organization,
'OpenAI-Project': this.project,
},
diff --git a/core/error.js b/core/error.js
index c302cc356f0f24b50c3f5a0aa3ea0b79ae1e9a8d..164ee2ee31cd7eea8f70139e25d140b763e91d36 100644
--- a/core/error.js
+++ b/core/error.js
@@ -40,7 +40,7 @@ class APIError extends OpenAIError {
if (!status || !headers) {
return new APIConnectionError({ message, cause: (0, errors_1.castToError)(errorResponse) });
}
- const error = errorResponse?.['error'];
+ const error = errorResponse?.['error'] || errorResponse;
if (status === 400) {
return new BadRequestError(status, error, message, headers);
}
diff --git a/core/error.mjs b/core/error.mjs
index 75f5b0c328cc4894478f3490a00dbf6abd96fc12..269f46f96e9fad1f7a1649a3810562abc7fae37f 100644
--- a/core/error.mjs
+++ b/core/error.mjs
@@ -36,7 +36,7 @@ export class APIError extends OpenAIError {
if (!status || !headers) {
return new APIConnectionError({ message, cause: castToError(errorResponse) });
}
- const error = errorResponse?.['error'];
+ const error = errorResponse?.['error'] || errorResponse;
if (status === 400) {
return new BadRequestError(status, error, message, headers);
}
diff --git a/resources/embeddings.js b/resources/embeddings.js
index 2404264d4ba0204322548945ebb7eab3bea82173..93b9e286f62101b5aa7532e96ddba61f682ece3f 100644
--- a/resources/embeddings.js
+++ b/resources/embeddings.js
@@ -5,52 +5,64 @@ exports.Embeddings = void 0;
const resource_1 = require("../core/resource.js");
const utils_1 = require("../internal/utils.js");
class Embeddings extends resource_1.APIResource {
- /**
- * Creates an embedding vector representing the input text.
- *
- * @example
- * ```ts
- * const createEmbeddingResponse =
- * await client.embeddings.create({
- * input: 'The quick brown fox jumped over the lazy dog',
- * model: 'text-embedding-3-small',
- * });
- * ```
- */
- create(body, options) {
- const hasUserProvidedEncodingFormat = !!body.encoding_format;
- // No encoding_format specified, defaulting to base64 for performance reasons
- // See https://github.com/openai/openai-node/pull/1312
- let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
- if (hasUserProvidedEncodingFormat) {
- (0, utils_1.loggerFor)(this._client).debug('embeddings/user defined encoding_format:', body.encoding_format);
- }
- const response = this._client.post('/embeddings', {
- body: {
- ...body,
- encoding_format: encoding_format,
- },
- ...options,
- });
- // if the user specified an encoding_format, return the response as-is
- if (hasUserProvidedEncodingFormat) {
- return response;
- }
- // in this stage, we are sure the user did not specify an encoding_format
- // and we defaulted to base64 for performance reasons
- // we are sure then that the response is base64 encoded, let's decode it
- // the returned result will be a float32 array since this is OpenAI API's default encoding
- (0, utils_1.loggerFor)(this._client).debug('embeddings/decoding base64 embeddings from base64');
- return response._thenUnwrap((response) => {
- if (response && response.data) {
- response.data.forEach((embeddingBase64Obj) => {
- const embeddingBase64Str = embeddingBase64Obj.embedding;
- embeddingBase64Obj.embedding = (0, utils_1.toFloat32Array)(embeddingBase64Str);
- });
- }
- return response;
- });
+ /**
+ * Creates an embedding vector representing the input text.
+ *
+ * @example
+ * ```ts
+ * const createEmbeddingResponse =
+ * await client.embeddings.create({
+ * input: 'The quick brown fox jumped over the lazy dog',
+ * model: 'text-embedding-3-small',
+ * });
+ * ```
+ */
+ create(body, options) {
+ const hasUserProvidedEncodingFormat = !!body.encoding_format;
+ // No encoding_format specified, defaulting to base64 for performance reasons
+ // See https://github.com/openai/openai-node/pull/1312
+ let encoding_format = hasUserProvidedEncodingFormat
+ ? body.encoding_format
+ : "base64";
+ if (body.model.includes("jina")) {
+ encoding_format = undefined;
+ }
+ if (hasUserProvidedEncodingFormat) {
+ (0, utils_1.loggerFor)(this._client).debug(
+ "embeddings/user defined encoding_format:",
+ body.encoding_format
+ );
}
+ const response = this._client.post("/embeddings", {
+ body: {
+ ...body,
+ encoding_format: encoding_format,
+ },
+ ...options,
+ });
+ // if the user specified an encoding_format, return the response as-is
+ if (hasUserProvidedEncodingFormat || body.model.includes("jina")) {
+ return response;
+ }
+ // in this stage, we are sure the user did not specify an encoding_format
+ // and we defaulted to base64 for performance reasons
+ // we are sure then that the response is base64 encoded, let's decode it
+ // the returned result will be a float32 array since this is OpenAI API's default encoding
+ (0, utils_1.loggerFor)(this._client).debug(
+ "embeddings/decoding base64 embeddings from base64"
+ );
+ return response._thenUnwrap((response) => {
+ if (response && response.data && typeof response.data[0]?.embedding === 'string') {
+ response.data.forEach((embeddingBase64Obj) => {
+ const embeddingBase64Str = embeddingBase64Obj.embedding;
+ embeddingBase64Obj.embedding = (0, utils_1.toFloat32Array)(
+ embeddingBase64Str
+ );
+ });
+ }
+ return response;
+ });
+ }
}
exports.Embeddings = Embeddings;
//# sourceMappingURL=embeddings.js.map
diff --git a/resources/embeddings.mjs b/resources/embeddings.mjs
index 19dcaef578c194a89759c4360073cfd4f7dd2cbf..42c903fadb03c707356a983603ff09e4152ecf11 100644
--- a/resources/embeddings.mjs
+++ b/resources/embeddings.mjs
@@ -2,51 +2,61 @@
import { APIResource } from "../core/resource.mjs";
import { loggerFor, toFloat32Array } from "../internal/utils.mjs";
export class Embeddings extends APIResource {
- /**
- * Creates an embedding vector representing the input text.
- *
- * @example
- * ```ts
- * const createEmbeddingResponse =
- * await client.embeddings.create({
- * input: 'The quick brown fox jumped over the lazy dog',
- * model: 'text-embedding-3-small',
- * });
- * ```
- */
- create(body, options) {
- const hasUserProvidedEncodingFormat = !!body.encoding_format;
- // No encoding_format specified, defaulting to base64 for performance reasons
- // See https://github.com/openai/openai-node/pull/1312
- let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
- if (hasUserProvidedEncodingFormat) {
- loggerFor(this._client).debug('embeddings/user defined encoding_format:', body.encoding_format);
- }
- const response = this._client.post('/embeddings', {
- body: {
- ...body,
- encoding_format: encoding_format,
- },
- ...options,
- });
- // if the user specified an encoding_format, return the response as-is
- if (hasUserProvidedEncodingFormat) {
- return response;
- }
- // in this stage, we are sure the user did not specify an encoding_format
- // and we defaulted to base64 for performance reasons
- // we are sure then that the response is base64 encoded, let's decode it
- // the returned result will be a float32 array since this is OpenAI API's default encoding
- loggerFor(this._client).debug('embeddings/decoding base64 embeddings from base64');
- return response._thenUnwrap((response) => {
- if (response && response.data) {
- response.data.forEach((embeddingBase64Obj) => {
- const embeddingBase64Str = embeddingBase64Obj.embedding;
- embeddingBase64Obj.embedding = toFloat32Array(embeddingBase64Str);
- });
- }
- return response;
- });
+ /**
+ * Creates an embedding vector representing the input text.
+ *
+ * @example
+ * ```ts
+ * const createEmbeddingResponse =
+ * await client.embeddings.create({
+ * input: 'The quick brown fox jumped over the lazy dog',
+ * model: 'text-embedding-3-small',
+ * });
+ * ```
+ */
+ create(body, options) {
+ const hasUserProvidedEncodingFormat = !!body.encoding_format;
+ // No encoding_format specified, defaulting to base64 for performance reasons
+ // See https://github.com/openai/openai-node/pull/1312
+ let encoding_format = hasUserProvidedEncodingFormat
+ ? body.encoding_format
+ : "base64";
+ if (body.model.includes("jina")) {
+ encoding_format = undefined;
+ }
+ if (hasUserProvidedEncodingFormat) {
+ loggerFor(this._client).debug(
+ "embeddings/user defined encoding_format:",
+ body.encoding_format
+ );
}
+ const response = this._client.post("/embeddings", {
+ body: {
+ ...body,
+ encoding_format: encoding_format,
+ },
+ ...options,
+ });
+ // if the user specified an encoding_format, return the response as-is
+ if (hasUserProvidedEncodingFormat || body.model.includes("jina")) {
+ return response;
+ }
+ // in this stage, we are sure the user did not specify an encoding_format
+ // and we defaulted to base64 for performance reasons
+ // we are sure then that the response is base64 encoded, let's decode it
+ // the returned result will be a float32 array since this is OpenAI API's default encoding
+ loggerFor(this._client).debug(
+ "embeddings/decoding base64 embeddings from base64"
+ );
+ return response._thenUnwrap((response) => {
+ if (response && response.data && typeof response.data[0]?.embedding === 'string') {
+ response.data.forEach((embeddingBase64Obj) => {
+ const embeddingBase64Str = embeddingBase64Obj.embedding;
+ embeddingBase64Obj.embedding = toFloat32Array(embeddingBase64Str);
+ });
+ }
+ return response;
+ });
+ }
}
//# sourceMappingURL=embeddings.mjs.map

6
package.json
View File

@ -216,7 +216,7 @@
"motion": "^12.10.5",
"notion-helper": "^1.3.22",
"npx-scope-finder": "^1.2.0",
"openai": "^5.12.0",
"openai": "patch:openai@npm%3A5.12.0#~/.yarn/patches/openai-npm-5.12.0-a06a6369b2.patch",
"p-queue": "^8.1.0",
"pdf-lib": "^1.17.1",
"playwright": "^1.52.0",
@ -273,10 +273,10 @@
"@langchain/openai@npm:^0.3.16": "patch:@langchain/openai@npm%3A0.3.16#~/.yarn/patches/@langchain-openai-npm-0.3.16-e525b59526.patch",
"@langchain/openai@npm:>=0.1.0 <0.4.0": "patch:@langchain/openai@npm%3A0.3.16#~/.yarn/patches/@langchain-openai-npm-0.3.16-e525b59526.patch",
"libsql@npm:^0.4.4": "patch:libsql@npm%3A0.4.7#~/.yarn/patches/libsql-npm-0.4.7-444e260fb1.patch",
"openai@npm:^4.77.0": "patch:openai@npm%3A5.1.0#~/.yarn/patches/openai-npm-5.1.0-0e7b3ccb07.patch",
"openai@npm:^4.77.0": "patch:openai@npm%3A5.12.0#~/.yarn/patches/openai-npm-5.12.0-a06a6369b2.patch",
"pkce-challenge@npm:^4.1.0": "patch:pkce-challenge@npm%3A4.1.0#~/.yarn/patches/pkce-challenge-npm-4.1.0-fbc51695a3.patch",
"app-builder-lib@npm:26.0.13": "patch:app-builder-lib@npm%3A26.0.13#~/.yarn/patches/app-builder-lib-npm-26.0.13-a064c9e1d0.patch",
"openai@npm:^4.87.3": "patch:openai@npm%3A5.1.0#~/.yarn/patches/openai-npm-5.1.0-0e7b3ccb07.patch",
"openai@npm:^4.87.3": "patch:openai@npm%3A5.12.0#~/.yarn/patches/openai-npm-5.12.0-a06a6369b2.patch",
"app-builder-lib@npm:26.0.15": "patch:app-builder-lib@npm%3A26.0.15#~/.yarn/patches/app-builder-lib-npm-26.0.15-360e5b0476.patch",
"@langchain/core@npm:^0.3.26": "patch:@langchain/core@npm%3A0.3.44#~/.yarn/patches/@langchain-core-npm-0.3.44-41d5c3cb0a.patch",
"node-abi": "4.12.0",