Regenerate core, expose privateHost and temperature

package.json

@@ -22,9 +22,9 @@
     "lint": "eslint src/ --ext .ts",
     "repl": "npm run build && node utils/replInit.ts",
     "test:integration:node": "TEST_ENV=node jest src/integration/ -c jest.config.integration-node.js --detectOpenHandles --runInBand --bail --forceExit",
-    "test:integration-local:node": "TEST_ENV=node node src/integration/integrationRunner.js",
+    "test:integration-local:node": "TEST_ENV=node node utils/integrationRunner.js",
     "test:integration:edge": "TEST_ENV=edge jest src/integration/ -c jest.config.integration-edge.js --detectOpenHandles --runInBand --bail --forceExit",
-    "test:integration-local:edge": "TEST_ENV=edge node src/integration/integrationRunner.js",
+    "test:integration-local:edge": "TEST_ENV=edge node utils/integrationRunner.js",
     "test:integration:cleanup": "npm run build && node utils/cleanupResources.ts",
     "test:external-app-local": "chmod +x ts-external-app-test/local-external-app.sh && ts-external-app-test/local-external-app.sh",
     "test:unit": "jest src/"

src/assistant/data/chat.ts

@@ -130,12 +130,15 @@ export const messagesValidation = (options: ChatOptions): MessageModel[] => {
  * @throws An Error if the model is not one of the available models as outlined in {@link ChatModelEnum}.
  */
 export const modelValidation = (options: ChatOptions) => {
+  const allowedModels = Object.values(ChatModelEnum);
   // Make sure passed string for 'model' matches one of the Enum values; default to Gpt4o
   let model: ChatModelEnum = ChatModelEnum.Gpt4o;
   if (options.model) {
-    if (!Object.values(ChatModelEnum).toString().includes(options.model)) {
+    if (!allowedModels.includes(options.model as ChatModelEnum)) {
       throw new Error(
-        'Invalid model specified. Must be one of "gpt-4o" or "claude-3-5-sonnet"'
+        `Invalid model specified. Must be one of ${allowedModels
+          .map((m) => `"${m}"`)
+          .join(', ')}:`
       );
     } else {
       model = options.model as ChatModelEnum;

src/assistant/data/chatStream.ts

@@ -5,11 +5,7 @@ import {
 import type { PineconeConfiguration } from '../../data';
 import { buildUserAgent, getFetch, ChatStream } from '../../utils';
 import { AsstDataOperationsProvider } from './asstDataOperationsProvider';
-import type {
-  ChatOptions,
-  ContextOptions,
-  StreamedChatResponse,
-} from './types';
+import type { ChatOptions, StreamedChatResponse } from './types';
 import { handleApiError } from '../../errors';
 import { ReadableStream } from 'node:stream/web';
 import { Readable } from 'node:stream';

src/assistant/data/types.ts

@@ -131,6 +131,12 @@ export interface ChatOptions {
    * If empty, the assistant will default to using 'gpt-4o' model.
    */
   model?: string;
+  /**
+   * Controls the randomness of the model's output: lower values make responses more deterministic,
+   * while higher values increase creativity and variability. If the model does not support a temperature parameter, the parameter will be ignored.
+   */
+  temperature?: number;
+
   /**
    * A filter against which documents can be retrieved.
    */
@@ -160,6 +166,7 @@ type ChatOptionsType = keyof ChatOptions;
 export const ChatOptionsType: ChatOptionsType[] = [
   'messages',
   'model',
+  'temperature',
   'filter',
   'jsonResponse',
   'includeHighlights',
@@ -181,6 +188,11 @@ export interface ChatCompletionOptions {
    * If empty, the assistant will default to using 'gpt-4o' model.
    */
   model?: string;
+  /**
+   * Controls the randomness of the model's output: lower values make responses more deterministic,
+   * while higher values increase creativity and variability. If the model does not support a temperature parameter, the parameter will be ignored.
+   */
+  temperature?: number;
   /**
    * A filter against which documents can be retrieved.
    */
@@ -192,6 +204,7 @@ type ChatCompletionOptionsType = keyof ChatCompletionOptions;
 export const ChatCompletionOptionsType: ChatCompletionOptionsType[] = [
   'messages',
   'model',
+  'temperature',
   'filter',
 ];
 

src/integration/assistant-data/chat.test.ts

@@ -203,7 +203,7 @@ describe('Chat error paths', () => {
         model: 'invalid',
       })
     ).rejects.toThrow(
-      'Invalid model: "invalid". Must be one of: "gpt-4o", "claude-3-5-sonnet"'
+      'Invalid model: "invalid". Must be one of: "gpt-4o", "gpt-4.1", "o4-mini", "claude-3-5-sonnet", "claude-3-7-sonnet", "gemini-2.5-pro"'
     );
   });
 

src/integration/data/vectors/query.test.ts

@@ -88,7 +88,8 @@ describe('query tests on serverless index', () => {
         if (results.usage) {
           expect(results.usage.readUnits).toBeDefined();
         }
-      }
+      },
+      240000
     );
   });
 
@@ -110,7 +111,8 @@ describe('query tests on serverless index', () => {
         if (results.usage) {
           expect(results.usage.readUnits).toBeDefined();
         }
-      }
+      },
+      240000
     );
   });
 });

src/pinecone-generated-ts-fetch/assistant_data/models/Chat.ts

@@ -50,6 +50,12 @@ export interface Chat {
      * @memberof Chat
      */
     model?: ChatModelEnum;
+    /**
+     * Controls the randomness of the model's output: lower values make responses more deterministic, while higher values increase creativity and variability. If the model does not support a temperature parameter, the parameter will be ignored.
+     * @type {number}
+     * @memberof Chat
+     */
+    temperature?: number;
     /**
      * Optionally filter which documents can be retrieved using the following metadata fields.
      * @type {object}
@@ -82,7 +88,11 @@ export interface Chat {
  */
 export const ChatModelEnum = {
     Gpt4o: 'gpt-4o',
-    Claude35Sonnet: 'claude-3-5-sonnet'
+    Gpt41: 'gpt-4.1',
+    O4Mini: 'o4-mini',
+    Claude35Sonnet: 'claude-3-5-sonnet',
+    Claude37Sonnet: 'claude-3-7-sonnet',
+    Gemini25Pro: 'gemini-2.5-pro'
 } as const;
 export type ChatModelEnum = typeof ChatModelEnum[keyof typeof ChatModelEnum];
 
@@ -110,6 +120,7 @@ export function ChatFromJSONTyped(json: any, ignoreDiscriminator: boolean): Chat
         'messages': ((json['messages'] as Array<any>).map(MessageModelFromJSON)),
         'stream': !exists(json, 'stream') ? undefined : json['stream'],
         'model': !exists(json, 'model') ? undefined : json['model'],
+        'temperature': !exists(json, 'temperature') ? undefined : json['temperature'],
         'filter': !exists(json, 'filter') ? undefined : json['filter'],
         'jsonResponse': !exists(json, 'json_response') ? undefined : json['json_response'],
         'includeHighlights': !exists(json, 'include_highlights') ? undefined : json['include_highlights'],
@@ -129,6 +140,7 @@ export function ChatToJSON(value?: Chat | null): any {
         'messages': ((value.messages as Array<any>).map(MessageModelToJSON)),
         'stream': value.stream,
         'model': value.model,
+        'temperature': value.temperature,
         'filter': value.filter,
         'json_response': value.jsonResponse,
         'include_highlights': value.includeHighlights,

src/pinecone-generated-ts-fetch/assistant_data/models/SearchCompletions.ts

@@ -44,6 +44,12 @@ export interface SearchCompletions {
      * @memberof SearchCompletions
      */
     model?: SearchCompletionsModelEnum;
+    /**
+     * Controls the randomness of the model's output: lower values make responses more deterministic, while higher values increase creativity and variability. If the model does not support a temperature parameter, the parameter will be ignored.
+     * @type {number}
+     * @memberof SearchCompletions
+     */
+    temperature?: number;
     /**
      * Optionally filter which documents can be retrieved using the following metadata fields.
      * @type {object}
@@ -58,7 +64,11 @@ export interface SearchCompletions {
  */
 export const SearchCompletionsModelEnum = {
     Gpt4o: 'gpt-4o',
-    Claude35Sonnet: 'claude-3-5-sonnet'
+    Gpt41: 'gpt-4.1',
+    O4Mini: 'o4-mini',
+    Claude35Sonnet: 'claude-3-5-sonnet',
+    Claude37Sonnet: 'claude-3-7-sonnet',
+    Gemini25Pro: 'gemini-2.5-pro'
 } as const;
 export type SearchCompletionsModelEnum = typeof SearchCompletionsModelEnum[keyof typeof SearchCompletionsModelEnum];
 
@@ -86,6 +96,7 @@ export function SearchCompletionsFromJSONTyped(json: any, ignoreDiscriminator: b
         'messages': ((json['messages'] as Array<any>).map(MessageModelFromJSON)),
         'stream': !exists(json, 'stream') ? undefined : json['stream'],
         'model': !exists(json, 'model') ? undefined : json['model'],
+        'temperature': !exists(json, 'temperature') ? undefined : json['temperature'],
         'filter': !exists(json, 'filter') ? undefined : json['filter'],
     };
 }
@@ -102,6 +113,7 @@ export function SearchCompletionsToJSON(value?: SearchCompletions | null): any {
         'messages': ((value.messages as Array<any>).map(MessageModelToJSON)),
         'stream': value.stream,
         'model': value.model,
+        'temperature': value.temperature,
         'filter': value.filter,
     };
 }

src/pinecone-generated-ts-fetch/db_control/models/IndexModel.ts

@@ -68,6 +68,12 @@ export interface IndexModel {
      * @memberof IndexModel
      */
     host: string;
+    /**
+     * The private endpoint URL of an index.
+     * @type {string}
+     * @memberof IndexModel
+     */
+    privateHost?: string;
     /**
      * 
      * @type {DeletionProtection}
@@ -147,6 +153,7 @@ export function IndexModelFromJSONTyped(json: any, ignoreDiscriminator: boolean)
         'dimension': !exists(json, 'dimension') ? undefined : json['dimension'],
         'metric': json['metric'],
         'host': json['host'],
+        'privateHost': !exists(json, 'private_host') ? undefined : json['private_host'],
         'deletionProtection': !exists(json, 'deletion_protection') ? undefined : DeletionProtectionFromJSON(json['deletion_protection']),
         'tags': !exists(json, 'tags') ? undefined : json['tags'],
         'embed': !exists(json, 'embed') ? undefined : ModelIndexEmbedFromJSON(json['embed']),
@@ -169,6 +176,7 @@ export function IndexModelToJSON(value?: IndexModel | null): any {
         'dimension': value.dimension,
         'metric': value.metric,
         'host': value.host,
+        'private_host': value.privateHost,
         'deletion_protection': DeletionProtectionToJSON(value.deletionProtection),
         'tags': value.tags,
         'embed': ModelIndexEmbedToJSON(value.embed),

src/pinecone-generated-ts-fetch/db_data/apis/VectorOperationsApi.ts

@@ -279,7 +279,7 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Search a namespace using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.  For guidance and examples, see [Search](https://docs.pinecone.io/guides/search/semantic-search).
+     * Search a namespace using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.  For guidance, examples, and limits, see [Search](https://docs.pinecone.io/guides/search/search-overview).
      * Search with a vector
      */
     async queryVectorsRaw(requestParameters: QueryVectorsRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<runtime.ApiResponse<QueryResponse>> {
@@ -309,7 +309,7 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Search a namespace using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.  For guidance and examples, see [Search](https://docs.pinecone.io/guides/search/semantic-search).
+     * Search a namespace using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.  For guidance, examples, and limits, see [Search](https://docs.pinecone.io/guides/search/search-overview).
      * Search with a vector
      */
     async queryVectors(requestParameters: QueryVectorsRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<QueryResponse> {
@@ -318,7 +318,7 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query.   Searching with text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding). Searching with a query vector or record ID is supported for all indexes.   For guidance and examples, see [Search](https://docs.pinecone.io/guides/search/semantic-search).
+     * Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query.   Searching with text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding). Searching with a query vector or record ID is supported for all indexes.   For guidance, examples, and limits, see [Search](https://docs.pinecone.io/guides/search/search-overview).
      * Search with text
      */
     async searchRecordsNamespaceRaw(requestParameters: SearchRecordsNamespaceRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<runtime.ApiResponse<SearchRecordsResponse>> {
@@ -352,7 +352,7 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query.   Searching with text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding). Searching with a query vector or record ID is supported for all indexes.   For guidance and examples, see [Search](https://docs.pinecone.io/guides/search/semantic-search).
+     * Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query.   Searching with text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding). Searching with a query vector or record ID is supported for all indexes.   For guidance, examples, and limits, see [Search](https://docs.pinecone.io/guides/search/search-overview).
      * Search with text
      */
     async searchRecordsNamespace(requestParameters: SearchRecordsNamespaceRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<SearchRecordsResponse> {
@@ -400,7 +400,7 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Upsert text into a namespace. Pinecone converts the text to vectors automatically using the hosted embedding model associated with the index.  Upserting text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/reference/api/2025-01/control-plane/create_for_model).  For guidance and examples, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data#upsert-text).
+     * Upsert text into a namespace. Pinecone converts the text to vectors automatically using the hosted embedding model associated with the index.  Upserting text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/reference/api/2025-01/control-plane/create_for_model).  For guidance, examples, and limits, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data).
      * Upsert text
      */
     async upsertRecordsNamespaceRaw(requestParameters: UpsertRecordsNamespaceRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<runtime.ApiResponse<void>> {
@@ -434,15 +434,15 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Upsert text into a namespace. Pinecone converts the text to vectors automatically using the hosted embedding model associated with the index.  Upserting text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/reference/api/2025-01/control-plane/create_for_model).  For guidance and examples, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data#upsert-text).
+     * Upsert text into a namespace. Pinecone converts the text to vectors automatically using the hosted embedding model associated with the index.  Upserting text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/reference/api/2025-01/control-plane/create_for_model).  For guidance, examples, and limits, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data).
      * Upsert text
      */
     async upsertRecordsNamespace(requestParameters: UpsertRecordsNamespaceRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<void> {
         await this.upsertRecordsNamespaceRaw(requestParameters, initOverrides);
     }
 
     /**
-     * Upsert vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value.  For guidance and examples, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data#upsert-vectors).
+     * Upsert vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value.  For guidance, examples, and limits, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data).
      * Upsert vectors
      */
     async upsertVectorsRaw(requestParameters: UpsertVectorsRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<runtime.ApiResponse<UpsertResponse>> {
@@ -472,7 +472,7 @@ export class VectorOperationsApi extends runtime.BaseAPI {
     }
 
     /**
-     * Upsert vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value.  For guidance and examples, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data#upsert-vectors).
+     * Upsert vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value.  For guidance, examples, and limits, see [Upsert data](https://docs.pinecone.io/guides/index-data/upsert-data).
      * Upsert vectors
      */
     async upsertVectors(requestParameters: UpsertVectorsRequest, initOverrides?: RequestInit | runtime.InitOverrideFunction): Promise<UpsertResponse> {

src/pinecone-generated-ts-fetch/db_data/models/DeleteRequest.ts

@@ -38,8 +38,7 @@ export interface DeleteRequest {
      */
     namespace?: string;
     /**
-     * If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying ids to delete in the ids param or using delete_all=True. See [Understanding metadata](https://docs.pinecone.io/guides/index-data/indexing-overview#metadata).
-     * Serverless indexes do not support delete by metadata. Instead, you can use the `list` operation to fetch the vector IDs based on their common ID prefix and then delete the records by ID.
+     * If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying ids to delete in the ids param or using delete_all=True. See [Delete data](https://docs.pinecone.io/guides/manage-data/delete-data#delete-records-by-metadata).
      * @type {object}
      * @memberof DeleteRequest
      */

src/pinecone-generated-ts-fetch/db_data/models/UpsertRequest.ts

@@ -27,7 +27,7 @@ import {
  */
 export interface UpsertRequest {
     /**
-     * An array containing the vectors to upsert. Recommended batch limit is 100 vectors.
+     * An array containing the vectors to upsert. Recommended batch limit is up to 1000 vectors.
      * @type {Array<Vector>}
      * @memberof UpsertRequest
      */