diff --git a/.agents/skills/create-justification/SKILL.md b/.agents/skills/create-justification/SKILL.md deleted file mode 100644 index 78a2aaea9..000000000 --- a/.agents/skills/create-justification/SKILL.md +++ /dev/null @@ -1,56 +0,0 @@ ---- -name: create-justification -description: Create a new justification file in .agents/justifications/ with a unique three-word ID, frontmatter, and formatted title -license: MIT -compatibility: opencode -metadata: - audience: agents - workflow: decision-making ---- - -## What I do - -I help you create new justification files in the `.agents/justifications/` directory. Each justification file gets: - -- A unique three-word identifier (e.g., `swift-emerald-river`) -- Frontmatter with the current date and formatted title -- Content you provide - -## How to use - -Run the script with a slug and content: - -```bash -npx tsx scripts/create-justification.ts "decision-name" "Justification content here" -``` - -Or use heredoc for multi-line content: - -```bash -npx tsx scripts/create-justification.ts "decision-name" << HEREDOC -Multi-line -justification content -goes here -HEREDOC -``` - -## File format - -Files are created as: `{three-word-id}-{slug}.md` - -Example: `swift-emerald-river-decision-name.md` - -The file includes frontmatter: - -```markdown ---- -date: 2026-01-13 -title: Decision Name ---- - -Your content here -``` - -## When to use me - -Use this skill when you need to document the reasoning or justification for a decision, approach, or architectural choice. The unique ID ensures no filename conflicts, and the frontmatter provides metadata for organization. diff --git a/.agents/skills/create-plan/SKILL.md b/.agents/skills/create-plan/SKILL.md deleted file mode 100644 index 8ceb2ef8c..000000000 --- a/.agents/skills/create-plan/SKILL.md +++ /dev/null @@ -1,56 +0,0 @@ ---- -name: create-plan -description: Create a new plan file in .agents/plans/ with a unique three-word ID, frontmatter, and formatted title -license: MIT -compatibility: opencode -metadata: - audience: agents - workflow: planning ---- - -## What I do - -I help you create new plan files in the `.agents/plans/` directory. Each plan file gets: - -- A unique three-word identifier (e.g., `happy-blue-moon`) -- Frontmatter with the current date and formatted title -- Content you provide - -## How to use - -Run the script with a slug and content: - -```bash -npx tsx scripts/create-plan.ts "feature-name" "Plan content here" -``` - -Or use heredoc for multi-line content: - -```bash -npx tsx scripts/create-plan.ts "feature-name" << HEREDOC -Multi-line -plan content -goes here -HEREDOC -``` - -## File format - -Files are created as: `{three-word-id}-{slug}.md` - -Example: `happy-blue-moon-feature-name.md` - -The file includes frontmatter: - -```markdown ---- -date: 2026-01-13 -title: Feature Name ---- - -Your content here -``` - -## When to use me - -Use this skill when you need to create a new plan document for a feature, task, or project. The unique ID ensures no filename conflicts, and the frontmatter provides metadata for organization. diff --git a/.agents/skills/create-scratch/SKILL.md b/.agents/skills/create-scratch/SKILL.md deleted file mode 100644 index e44e4779d..000000000 --- a/.agents/skills/create-scratch/SKILL.md +++ /dev/null @@ -1,56 +0,0 @@ ---- -name: create-scratch -description: Create a new scratch file in .agents/scratches/ with a unique three-word ID, frontmatter, and formatted title -license: MIT -compatibility: opencode -metadata: - audience: agents - workflow: exploration ---- - -## What I do - -I help you create new scratch files in the `.agents/scratches/` directory. Each scratch file gets: - -- A unique three-word identifier (e.g., `calm-teal-cloud`) -- Frontmatter with the current date and formatted title -- Content you provide - -## How to use - -Run the script with a slug and content: - -```bash -npx tsx scripts/create-scratch.ts "note-name" "Scratch content here" -``` - -Or use heredoc for multi-line content: - -```bash -npx tsx scripts/create-scratch.ts "note-name" << HEREDOC -Multi-line -scratch content -goes here -HEREDOC -``` - -## File format - -Files are created as: `{three-word-id}-{slug}.md` - -Example: `calm-teal-cloud-note-name.md` - -The file includes frontmatter: - -```markdown ---- -date: 2026-01-13 -title: Note Name ---- - -Your content here -``` - -## When to use me - -Use this skill when you need to create a temporary note, exploration document, or scratch pad for ideas. The unique ID ensures no filename conflicts, and the frontmatter provides metadata for organization. diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 55ed7f27d..e3b1007da 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,6 +15,7 @@ jobs: build_app: name: Build App runs-on: ubuntu-latest + timeout-minutes: 60 steps: - name: Checkout uses: actions/checkout@v4 @@ -32,6 +33,7 @@ jobs: build_docker: name: Build Docker Image runs-on: ubuntu-latest + timeout-minutes: 60 steps: - name: Checkout uses: actions/checkout@v4 diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index d74f30387..b5ac9017a 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -11,6 +11,7 @@ jobs: analyze: name: Analyze runs-on: ubuntu-latest + timeout-minutes: 60 permissions: actions: read contents: read diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 80d188964..00132070d 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -8,6 +8,7 @@ on: jobs: deploy: runs-on: ubuntu-latest + timeout-minutes: 60 steps: - name: Checkout code diff --git a/.github/workflows/issue-labeler.yml b/.github/workflows/issue-labeler.yml index 34d7a478f..d8589bf1b 100644 --- a/.github/workflows/issue-labeler.yml +++ b/.github/workflows/issue-labeler.yml @@ -7,6 +7,7 @@ on: jobs: label-when-assigned: runs-on: ubuntu-latest + timeout-minutes: 10 steps: - name: Label issue uses: actions/github-script@v6 diff --git a/.github/workflows/issue-opened.yml b/.github/workflows/issue-opened.yml index 92b559d11..fd4a60151 100644 --- a/.github/workflows/issue-opened.yml +++ b/.github/workflows/issue-opened.yml @@ -7,6 +7,7 @@ on: jobs: label_issues: runs-on: ubuntu-latest + timeout-minutes: 10 permissions: issues: write steps: diff --git a/.github/workflows/pr-labeler.yml b/.github/workflows/pr-labeler.yml index 15fe7cbfa..5c3eeaac2 100644 --- a/.github/workflows/pr-labeler.yml +++ b/.github/workflows/pr-labeler.yml @@ -13,6 +13,7 @@ jobs: contents: read pull-requests: write runs-on: ubuntu-latest + timeout-minutes: 10 steps: - uses: actions/labeler@v4 with: diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 50137d2e1..f28fc4e1f 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -14,6 +14,7 @@ jobs: build_and_publish_platform_containers: name: Build and publish platform containers runs-on: ${{ matrix.os }} + timeout-minutes: 60 strategy: fail-fast: false matrix: @@ -78,6 +79,7 @@ jobs: create_and_publish_manifest: name: Create and publish manifest runs-on: ubuntu-latest + timeout-minutes: 60 needs: build_and_publish_platform_containers steps: - name: Checkout diff --git a/.github/workflows/semantic-pull-requests.yml b/.github/workflows/semantic-pull-requests.yml index 0dab3392d..34e731436 100644 --- a/.github/workflows/semantic-pull-requests.yml +++ b/.github/workflows/semantic-pull-requests.yml @@ -15,6 +15,7 @@ jobs: validate-pr: name: Validate PR title runs-on: ubuntu-latest + timeout-minutes: 10 steps: - uses: amannn/action-semantic-pull-request@v5 id: lint_pr_title diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index c9c12ce59..aed53da8c 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -7,6 +7,7 @@ on: jobs: stale: runs-on: ubuntu-latest + timeout-minutes: 10 permissions: issues: write pull-requests: write diff --git a/.github/workflows/translations-force-pull.yml b/.github/workflows/translations-force-pull.yml index 5d804df63..b0893a2aa 100644 --- a/.github/workflows/translations-force-pull.yml +++ b/.github/workflows/translations-force-pull.yml @@ -18,6 +18,7 @@ jobs: pull_translations: name: Force pull translations runs-on: ubuntu-latest + timeout-minutes: 10 environment: Translations permissions: contents: write diff --git a/.github/workflows/translations-pull.yml b/.github/workflows/translations-pull.yml index 0e3703438..ea65548f2 100644 --- a/.github/workflows/translations-pull.yml +++ b/.github/workflows/translations-pull.yml @@ -16,6 +16,7 @@ jobs: pull_translations: name: Pull translations runs-on: ubuntu-latest + timeout-minutes: 10 environment: Translations permissions: contents: write diff --git a/.github/workflows/translations-upload.yml b/.github/workflows/translations-upload.yml index 0e80def07..b81b6375f 100644 --- a/.github/workflows/translations-upload.yml +++ b/.github/workflows/translations-upload.yml @@ -14,6 +14,7 @@ jobs: extract_translations: name: Extract and upload translations runs-on: ubuntu-latest + timeout-minutes: 30 environment: Translations permissions: contents: write diff --git a/apps/docs/content/docs/developers/api/documents.mdx b/apps/docs/content/docs/developers/api/documents.mdx index dbe2e6a85..bd526e828 100644 --- a/apps/docs/content/docs/developers/api/documents.mdx +++ b/apps/docs/content/docs/developers/api/documents.mdx @@ -28,35 +28,62 @@ Each document contains one or more PDF files, a list of recipients, and the fiel A document object contains the following properties: -| Property | Type | Description | -| --------------- | -------------- | -------------------------------------------------------------- | -| `id` | string | Unique identifier (e.g., `envelope_abc123`) | -| `type` | string | `DOCUMENT` or `TEMPLATE` | -| `status` | string | Current status: `DRAFT`, `PENDING`, `COMPLETED`, or `REJECTED` | -| `title` | string | Document title | -| `source` | string | How the document was created: `DOCUMENT`, `TEMPLATE`, `API` | -| `visibility` | string | Who can view: `EVERYONE`, `ADMIN`, `MANAGER_AND_ABOVE` | -| `externalId` | string \| null | Your custom identifier for the document | -| `createdAt` | string | ISO 8601 timestamp | -| `updatedAt` | string | ISO 8601 timestamp | -| `completedAt` | string \| null | Timestamp when all recipients completed signing | -| `deletedAt` | string \| null | Timestamp if soft-deleted | -| `recipients` | array | List of recipients and their signing status | -| `fields` | array | Signature and form fields on the document | -| `envelopeItems` | array | PDF files attached to the document | -| `documentMeta` | object | Email settings, redirect URL, signing options | +| Property | Type | Description | +| ------------------- | -------------- | -------------------------------------------------------------------------------------------- | +| `id` | string | Unique identifier (e.g., `envelope_abc123`) | +| `secondaryId` | string | Legacy identifier in prefixed form (`document_123` for documents, `template_123` for templates) | +| `internalVersion` | number | Internal envelope schema version | +| `type` | string | `DOCUMENT` or `TEMPLATE` | +| `status` | string | Current status: `DRAFT`, `PENDING`, `COMPLETED`, `REJECTED`, or `CANCELLED` | +| `title` | string | Document title | +| `source` | string | How the document was created: `DOCUMENT`, `TEMPLATE`, `TEMPLATE_DIRECT_LINK` | +| `visibility` | string | Who can view: `EVERYONE`, `ADMIN`, `MANAGER_AND_ABOVE` | +| `templateType` | string | Template visibility: `PUBLIC`, `PRIVATE`, or `ORGANISATION` (only meaningful for templates) | +| `externalId` | string \| null | Your custom identifier for the document | +| `userId` | number | ID of the user who owns the document | +| `teamId` | number | ID of the team the document belongs to | +| `folderId` | string \| null | ID of the folder containing the document | +| `templateId` | number \| null | Legacy ID of the template this document was created from | +| `authOptions` | object \| null | Access and action authentication requirements | +| `formValues` | object \| null | Pre-filled form values | +| `publicTitle` | string | Public title shown on profile and direct-link pages | +| `publicDescription` | string | Public description shown on profile and direct-link pages | +| `createdAt` | string | ISO 8601 timestamp | +| `updatedAt` | string | ISO 8601 timestamp | +| `completedAt` | string \| null | Timestamp when all recipients completed signing | +| `deletedAt` | string \| null | Timestamp if soft-deleted | +| `recipients` | array | List of recipients and their signing status | +| `fields` | array | Signature and form fields on the document | +| `envelopeItems` | array | PDF files attached to the document | +| `directLink` | object \| null | Direct-link signing configuration (`id`, `token`, `enabled`, `directTemplateRecipientId`) | +| `team` | object | Owning team (`id`, `url`) | +| `user` | object | Document owner (`id`, `name`, `email`) | +| `documentMeta` | object | Email settings, redirect URL, signing options | + +Documents created through the API have `source: "DOCUMENT"` — there is no separate `API` source value. To tag documents created by your integration, set `externalId` when creating them. ### Example Document Object ```json { "id": "envelope_abc123xyz", + "secondaryId": "document_123", + "internalVersion": 2, "type": "DOCUMENT", "status": "PENDING", - "source": "API", + "source": "DOCUMENT", "visibility": "EVERYONE", + "templateType": "PRIVATE", "title": "Service Agreement", "externalId": "contract-2025-001", + "userId": 1, + "teamId": 1, + "folderId": null, + "templateId": null, + "authOptions": null, + "formValues": null, + "publicTitle": "", + "publicDescription": "", "createdAt": "2025-01-15T10:30:00.000Z", "updatedAt": "2025-01-15T10:35:00.000Z", "completedAt": null, @@ -73,23 +100,41 @@ A document object contains the following properties: ], "fields": [ { - "id": "field_123", + "id": 123, + "secondaryId": "field_abc123", "type": "SIGNATURE", + "recipientId": 1, + "envelopeId": "envelope_abc123xyz", + "envelopeItemId": "envelope_item_xyz", "page": 1, - "positionX": 10, - "positionY": 80, - "width": 30, - "height": 5, - "recipientId": 1 + "positionX": "10", + "positionY": "80", + "width": "30", + "height": "5", + "customText": "", + "inserted": false, + "fieldMeta": null } ], "envelopeItems": [ { "id": "envelope_item_xyz", + "envelopeId": "envelope_abc123xyz", + "documentDataId": "doc_data_abc123", "title": "contract.pdf", "order": 1 } ], + "directLink": null, + "team": { + "id": 1, + "url": "your-team" + }, + "user": { + "id": 1, + "name": "Jane Smith", + "email": "jane@example.com" + }, "documentMeta": { "subject": "Please sign this document", "message": "Hi, please review and sign this agreement.", @@ -99,6 +144,8 @@ A document object contains the following properties: } ``` +Field position and size values are stored as decimals and serialized as strings in API responses. + ## List Documents Retrieve a paginated list of documents. @@ -114,7 +161,7 @@ GET /envelope | `page` | integer | Page number (default: 1) | | `perPage` | integer | Results per page (default: 10, max: 100) | | `type` | string | Filter by `DOCUMENT` or `TEMPLATE` | -| `status` | string | Filter by status: `DRAFT`, `PENDING`, `COMPLETED`, `REJECTED` | +| `status` | string | Filter by status: `DRAFT`, `PENDING`, `COMPLETED`, `REJECTED`, `CANCELLED` | | `source` | string | Filter by creation source | | `folderId` | string | Filter by folder ID | | `orderByColumn` | string | Sort field (only `createdAt` supported) | @@ -154,8 +201,8 @@ const response = await fetch(`${BASE_URL}/envelope`, { }, }); -const { data, pagination } = await response.json(); -console.log(`Found ${pagination.totalItems} documents`); +const { data, count } = await response.json(); +console.log(`Found ${count} documents`); // Filter by status const pendingResponse = await fetch( @@ -197,12 +244,10 @@ const pendingDocs = await pendingResponse.json(); ] } ], - "pagination": { - "page": 1, - "perPage": 10, - "totalPages": 5, - "totalItems": 42 - } + "count": 42, + "currentPage": 1, + "perPage": 10, + "totalPages": 5 } ``` @@ -628,6 +673,72 @@ The response includes signing URLs for each recipient: --- +## Cancel Document + +Cancel a pending document. This changes its status from `PENDING` to `CANCELLED`. + +``` +POST /envelope/cancel +``` + +### Request Body + +| Field | Type | Required | Description | +| ------------ | ------ | -------- | ----------------------------------- | +| `envelopeId` | string | Yes | Document ID | +| `reason` | string | No | Reason for cancelling the document | + +### Code Examples + + + +```bash +curl -X POST "https://app.documenso.com/api/v2/envelope/cancel" \ + -H "Authorization: api_xxxxxxxxxxxxxxxx" \ + -H "Content-Type: application/json" \ + -d '{ + "envelopeId": "envelope_abc123", + "reason": "The agreement is no longer needed." + }' +``` + + +```typescript +const response = await fetch('https://app.documenso.com/api/v2/envelope/cancel', { + method: 'POST', + headers: { + Authorization: 'api_xxxxxxxxxxxxxxxx', + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + envelopeId: 'envelope_abc123', + reason: 'The agreement is no longer needed.', + }), +}); + +const { success } = await response.json(); +``` + + + +### Response + +```json +{ + "success": true +} +``` + +### Behavior + +- Only documents in `PENDING` status can be cancelled. Other statuses return `400`. +- Cancellation is not idempotent. Cancelling the same document again returns `400`. +- The document owner and team members with `MANAGER` or higher permissions can cancel it. Requests for documents you cannot view return `404`; requests for visible documents without sufficient permissions return `401`. +- A successful cancellation fires the `DOCUMENT_CANCELLED` webhook. +- Cancellation emails are sent only to eligible non-CC, non-rejected recipients who were sent or opened the document. + +--- + ## Delete Document Delete a document. Completed documents cannot be deleted. @@ -670,7 +781,7 @@ const response = await fetch('https://app.documenso.com/api/v2/envelope/delete', const { success } = await response.json(); -```` +``` @@ -680,7 +791,7 @@ const { success } = await response.json(); { "success": true } -```` +``` --- @@ -694,9 +805,11 @@ POST /envelope/get-many ### Request Body -| Field | Type | Required | Description | -| ------------- | ----- | -------- | --------------------- | -| `envelopeIds` | array | Yes | Array of document IDs | +| Field | Type | Required | Description | +| ---------- | ------ | -------- | ---------------------------------------------------------------------------- | +| `ids` | object | Yes | ID selector containing `type` and `ids` | +| `ids.type` | string | Yes | `envelopeId`, `documentId`, or `templateId` | +| `ids.ids` | array | Yes | 1-20 IDs: strings for `envelopeId`; numbers for `documentId` or `templateId` | ### Code Examples @@ -707,12 +820,17 @@ curl -X POST "https://app.documenso.com/api/v2/envelope/get-many" \ -H "Authorization: api_xxxxxxxxxxxxxxxx" \ -H "Content-Type: application/json" \ -d '{ - "envelopeIds": ["envelope_abc123", "envelope_def456", "envelope_ghi789"] + "ids": { + "type": "envelopeId", + "ids": ["envelope_abc123", "envelope_def456", "envelope_ghi789"] + } }' ``` ```typescript +const requestedIds = ['envelope_abc123', 'envelope_def456', 'envelope_ghi789']; + const response = await fetch('https://app.documenso.com/api/v2/envelope/get-many', { method: 'POST', headers: { @@ -720,16 +838,36 @@ const response = await fetch('https://app.documenso.com/api/v2/envelope/get-many 'Content-Type': 'application/json', }, body: JSON.stringify({ - envelopeIds: ['envelope_abc123', 'envelope_def456', 'envelope_ghi789'], + ids: { + type: 'envelopeId', + ids: requestedIds, + }, }), }); -const documents = await response.json(); +const { data } = await response.json(); -```` +``` +### Response + +```json +{ + "data": [ + { + "id": "envelope_abc123", + "type": "DOCUMENT", + "status": "PENDING", + "title": "Service Agreement" + } + ] +} +``` + +The endpoint silently omits envelopes you cannot access instead of returning `404`. Compare `data.length` with `requestedIds.length` to detect omissions. + --- ## Document Statuses @@ -740,6 +878,7 @@ const documents = await response.json(); | `PENDING` | Document has been sent. Waiting for recipients to sign. | | `COMPLETED` | All recipients have signed. Document is sealed. | | `REJECTED` | A recipient rejected the document. | +| `CANCELLED` | The document was cancelled by its owner or a team member with `MANAGER` or higher permissions. | ### Status Transitions @@ -747,11 +886,13 @@ const documents = await response.json(); flowchart LR DRAFT --> PENDING --> COMPLETED PENDING --> REJECTED + PENDING --> CANCELLED ``` - **DRAFT to PENDING**: Call the distribute endpoint - **PENDING to COMPLETED**: All recipients complete their signing - **PENDING to REJECTED**: A recipient rejects the document +- **PENDING to CANCELLED**: The document owner or a team member with `MANAGER` or higher permissions cancels the document You cannot modify recipients or fields after a document moves to `PENDING` status. @@ -773,8 +914,8 @@ flowchart LR | Parameter | Values | Description | | ---------- | ------------------------------------------- | ------------------------- | | `type` | `DOCUMENT`, `TEMPLATE` | Filter by envelope type | -| `status` | `DRAFT`, `PENDING`, `COMPLETED`, `REJECTED` | Filter by status | -| `source` | `DOCUMENT`, `TEMPLATE`, `API` | Filter by creation source | +| `status` | `DRAFT`, `PENDING`, `COMPLETED`, `REJECTED`, `CANCELLED` | Filter by status | +| `source` | `DOCUMENT`, `TEMPLATE`, `TEMPLATE_DIRECT_LINK` | Filter by creation source | | `folderId` | string | Filter by folder | ### Sorting @@ -800,10 +941,10 @@ async function getAllPendingDocuments() { }, ); - const { data, pagination } = await response.json(); + const { data, currentPage, totalPages } = await response.json(); documents.push(...data); - hasMore = page < pagination.totalPages; + hasMore = currentPage < totalPages; page++; } diff --git a/apps/docs/content/docs/developers/api/migrate-to-envelopes.mdx b/apps/docs/content/docs/developers/api/migrate-to-envelopes.mdx index 2bd5c8568..5a719e90c 100644 --- a/apps/docs/content/docs/developers/api/migrate-to-envelopes.mdx +++ b/apps/docs/content/docs/developers/api/migrate-to-envelopes.mdx @@ -119,7 +119,7 @@ Full reference in the [V2 OpenAPI reference](https://openapi.documenso.com). | ------------------------------------------------- | ----------------------------------------------------- | | `GET /api/v2/document` | `GET /api/v2/envelope` | | `GET /api/v2/document/{documentId}` | `GET /api/v2/envelope/{envelopeId}` | -| `POST /api/v2/document/get-many` | `POST /api/v2/envelope/get-many` | +| `POST /api/v2/document/get-many` | `POST /api/v2/envelope/get-many` (body changes from `documentIds: number[]` to `ids: { type: "documentId"; ids: number[] }`) | | `POST /api/v2/document/create` | `POST /api/v2/envelope/create` | | `POST /api/v2/document/create/beta` | `POST /api/v2/envelope/create` | | `POST /api/v2/document/update` | `POST /api/v2/envelope/update` | @@ -140,7 +140,7 @@ Full reference in the [V2 OpenAPI reference](https://openapi.documenso.com). | ------------------------------------- | ------------------------------------------------ | | `GET /api/v2/template` | `GET /api/v2/envelope` (with `type=TEMPLATE`) | | `GET /api/v2/template/{templateId}` | `GET /api/v2/envelope/{envelopeId}` | -| `POST /api/v2/template/get-many` | `POST /api/v2/envelope/get-many` | +| `POST /api/v2/template/get-many` | `POST /api/v2/envelope/get-many` (body changes from `templateIds: number[]` to `ids: { type: "templateId"; ids: number[] }`) | | `POST /api/v2/template/create` | `POST /api/v2/envelope/create` (`type=TEMPLATE`) | | `POST /api/v2/template/create/beta` | `POST /api/v2/envelope/create` (`type=TEMPLATE`) | | `POST /api/v2/template/update` | `POST /api/v2/envelope/update` | diff --git a/apps/docs/content/docs/developers/api/rate-limits.mdx b/apps/docs/content/docs/developers/api/rate-limits.mdx index 95b0a68fe..878db97b6 100644 --- a/apps/docs/content/docs/developers/api/rate-limits.mdx +++ b/apps/docs/content/docs/developers/api/rate-limits.mdx @@ -11,6 +11,12 @@ Documenso enforces rate limits on all API endpoints to ensure service stability. ## HTTP Rate Limits +The rate limit applies to: + +- `/api/v1/*` +- `/api/v2/*` +- `/api/v2-beta/*` + **Limit:** 1000 requests per minute per IP address **Response:** 429 Too Many Requests @@ -19,7 +25,7 @@ Documenso enforces rate limits on all API endpoints to ensure service stability. this value, in which case you can be rate-limited before reaching the global limit. -### Rate Limit Response +### Global per-IP 429 Response ```json { @@ -27,10 +33,22 @@ Documenso enforces rate limits on all API endpoints to ensure service stability. } ``` - - No rate limit headers are currently provided. When you receive a 429 response, wait at least 60 - seconds before retrying. - +### Rate Limit Headers + +Responses from `/api/v1/*`, `/api/v2/*`, and `/api/v2-beta/*` include these headers. The only +exception is CORS preflight (`OPTIONS`) requests, which are answered before the rate limiter runs +and carry no rate limit headers: + +| Header | Description | +| ----------------------- | ---------------------------------------------------------------------- | +| `X-RateLimit-Limit` | Maximum requests allowed in the current global window | +| `X-RateLimit-Remaining` | Requests remaining in the current global window | +| `X-RateLimit-Reset` | End of the current global window, as a Unix epoch timestamp in seconds | + +A 429 response from a windowed limiter also includes `Retry-After`, in seconds, with a minimum +value of `1`. The global API limit uses fixed, epoch-aligned one-minute buckets, so the actual wait +until the next window is between 1 and 60 seconds. Honor `Retry-After` exactly instead of sleeping +for a fixed 60 seconds. See the [Retry-After handling example](/docs/developers/examples/common-workflows#error-handling-patterns). ## Resource Limits @@ -44,24 +62,55 @@ Beyond HTTP rate limits, your account has usage limits based on your subscriptio | Total Recipients | 10 | Unlimited | Unlimited | Unlimited | | Direct Templates | 3 | Unlimited | Unlimited | Unlimited | -### Error Response +### Organisation Limit 429 Responses -When you exceed a resource limit: +Organisation windowed limits and organisation monthly quotas produce 429 responses whose body +shape depends on the API version, and neither matches the global per-IP limiter's +`{ "error": "..." }` body. + +On `/api/v1/*`, the body contains only a message: ```json { - "error": "You have reached your document limit for this month. Please upgrade your plan.", - "code": "LIMIT_EXCEEDED", - "statusCode": 400 + "message": "Too many requests, please try again later. Contact support if you require higher limits." } ``` +On `/api/v2/*` and `/api/v2-beta/*`, the body is a structured error object: + +```json +{ + "message": "Too many requests, please try again later. Contact support if you require higher limits.", + "code": "TOO_MANY_REQUESTS", + "data": { + "code": "TOO_MANY_REQUESTS", + "httpStatus": 429, + "appError": { + "code": "TOO_MANY_REQUESTS", + "message": "Too many requests, please try again later. Contact support if you require higher limits." + } + } +} +``` + +Organisation windowed limit responses include the `X-RateLimit-*` headers and `Retry-After` for +their own window. Monthly quota responses carry no quota-specific rate limit headers or +`Retry-After` because the quota is not a time window; rely on the status code and message instead. + ## Error Codes -| Code | Status | Description | -| ------------------- | ------ | ----------------------------- | -| `TOO_MANY_REQUESTS` | 429 | HTTP rate limit exceeded | -| `LIMIT_EXCEEDED` | 400 | Resource usage limit exceeded | +| Code | Status | Description | +| ------------------- | ------ | ------------------------------------------------------------------ | +| `TOO_MANY_REQUESTS` | 429 | Global per-IP, organisation windowed, or monthly quota exceeded | +| `LIMIT_EXCEEDED` | 400 | Resource usage limit exceeded | + +There are three sources of `TOO_MANY_REQUESTS` responses: + +1. The global per-IP limit, returning the `{ "error": "..." }` body shown above. +2. Organisation windowed rate limits for the `api`, `document`, and `email` counters. +3. Organisation monthly quotas for the same three counters. Every authenticated API request + consumes the `api` counter, so any endpoint can return this 429 once the monthly API quota is + exhausted — not just envelope-related ones. --- diff --git a/apps/docs/content/docs/developers/examples/common-workflows.mdx b/apps/docs/content/docs/developers/examples/common-workflows.mdx index 704bf415f..5bdf32cdf 100644 --- a/apps/docs/content/docs/developers/examples/common-workflows.mdx +++ b/apps/docs/content/docs/developers/examples/common-workflows.mdx @@ -1000,9 +1000,12 @@ async function fetchWithRetry( // Retry on rate limit if (response.status === 429) { const retryAfter = response.headers.get('Retry-After'); - const delay = retryAfter ? parseInt(retryAfter) * 1000 : baseDelayMs * Math.pow(2, attempt); + // Honor Retry-After exactly; the cap only applies to the exponential fallback. + const delay = retryAfter + ? parseInt(retryAfter) * 1000 + : Math.min(baseDelayMs * Math.pow(2, attempt), maxDelayMs); console.log(`Rate limited, waiting ${delay}ms...`); - await new Promise((resolve) => setTimeout(resolve, Math.min(delay, maxDelayMs))); + await new Promise((resolve) => setTimeout(resolve, delay)); continue; } diff --git a/apps/docs/package.json b/apps/docs/package.json index be8dfc789..9539148d0 100644 --- a/apps/docs/package.json +++ b/apps/docs/package.json @@ -10,12 +10,12 @@ "postinstall": "fumadocs-mdx" }, "dependencies": { - "fumadocs-core": "16.5.0", - "fumadocs-mdx": "14.2.6", - "fumadocs-ui": "16.5.0", + "fumadocs-core": "16.14.3", + "fumadocs-mdx": "15.2.3", + "fumadocs-ui": "16.14.3", "lucide-react": "^0.563.0", "mermaid": "^11.12.2", - "next": "16.2.6", + "next": "16.3.0", "next-plausible": "^3.12.5", "next-themes": "^0.4.6", "react": "^19.2.4", diff --git a/apps/openpage-api/package.json b/apps/openpage-api/package.json index 4efc1dff8..1b7c14350 100644 --- a/apps/openpage-api/package.json +++ b/apps/openpage-api/package.json @@ -12,7 +12,7 @@ "dependencies": { "@documenso/prisma": "*", "luxon": "^3.7.2", - "next": "16.2.6" + "next": "16.3.0" }, "devDependencies": { "@types/node": "^20", diff --git a/apps/remix/app/components/embed/embed-document-signing-page-v1.tsx b/apps/remix/app/components/embed/embed-document-signing-page-v1.tsx index 669f5274c..c8726ae22 100644 --- a/apps/remix/app/components/embed/embed-document-signing-page-v1.tsx +++ b/apps/remix/app/components/embed/embed-document-signing-page-v1.tsx @@ -1,6 +1,7 @@ import { useThrottleFn } from '@documenso/lib/client-only/hooks/use-throttle-fn'; import { APP_I18N_OPTIONS } from '@documenso/lib/constants/i18n'; import { PDF_VIEWER_PAGE_SELECTOR } from '@documenso/lib/constants/pdf-viewer'; +import { AppError } from '@documenso/lib/errors/app-error'; import { ZSignDocumentEmbedDataSchema } from '@documenso/lib/types/embed-document-sign-schema'; import { isFieldUnsignedAndRequired } from '@documenso/lib/utils/advanced-fields-helpers'; import { getDocumentDataUrlForPdfViewer } from '@documenso/lib/utils/envelope-download'; @@ -32,6 +33,7 @@ import { useEffect, useId, useLayoutEffect, useMemo, useState } from 'react'; import { BrandingLogo } from '~/components/general/branding-logo'; import PDFViewerLazy from '~/components/general/pdf-viewer/pdf-viewer-lazy'; import { injectCss } from '~/utils/css-vars'; +import { getSigningCompletionErrorMessage } from '~/utils/toast-error-messages'; import { DocumentSigningAttachmentsPopover } from '../general/document-signing/document-signing-attachments-popover'; import { useRequiredDocumentSigningContext } from '../general/document-signing/document-signing-provider'; @@ -162,9 +164,12 @@ export const EmbedSignDocumentV1ClientPage = ({ ); } + const error = AppError.parseError(err); + const toastMessage = getSigningCompletionErrorMessage(error.code); + toast({ - title: _(msg`Something went wrong`), - description: _(msg`We were unable to submit this document at this time. Please try again later.`), + title: _(toastMessage.title), + description: _(toastMessage.description), variant: 'destructive', }); } diff --git a/apps/remix/app/components/embed/multisign/multi-sign-document-signing-view.tsx b/apps/remix/app/components/embed/multisign/multi-sign-document-signing-view.tsx index b144d1c46..15902715d 100644 --- a/apps/remix/app/components/embed/multisign/multi-sign-document-signing-view.tsx +++ b/apps/remix/app/components/embed/multisign/multi-sign-document-signing-view.tsx @@ -26,6 +26,7 @@ import { useState } from 'react'; import { match, P } from 'ts-pattern'; import PDFViewerLazy from '~/components/general/pdf-viewer/pdf-viewer-lazy'; +import { getSigningCompletionErrorMessage } from '~/utils/toast-error-messages'; import { useRequiredDocumentSigningContext } from '../../general/document-signing/document-signing-provider'; import { DocumentSigningRejectDialog } from '../../general/document-signing/document-signing-reject-dialog'; @@ -141,9 +142,12 @@ export const MultiSignDocumentSigningView = ({ } catch (err) { onDocumentError?.(); + const error = AppError.parseError(err); + const toastMessage = getSigningCompletionErrorMessage(error.code); + toast({ - title: _(msg`Error`), - description: _(msg`Failed to complete the document. Please try again.`), + title: _(toastMessage.title), + description: _(toastMessage.description), variant: 'destructive', }); } finally { diff --git a/apps/remix/app/components/general/document-signing/document-signing-complete-dialog.tsx b/apps/remix/app/components/general/document-signing/document-signing-complete-dialog.tsx index ee4b0b626..41abb63f1 100644 --- a/apps/remix/app/components/general/document-signing/document-signing-complete-dialog.tsx +++ b/apps/remix/app/components/general/document-signing/document-signing-complete-dialog.tsx @@ -14,6 +14,7 @@ import { } from '@documenso/ui/primitives/dialog'; import { Form, FormControl, FormField, FormItem, FormLabel, FormMessage } from '@documenso/ui/primitives/form/form'; import { Input } from '@documenso/ui/primitives/input'; +import { useToast } from '@documenso/ui/primitives/use-toast'; import { zodResolver } from '@hookform/resolvers/zod'; import { Trans, useLingui } from '@lingui/react/macro'; import type { Field, Recipient } from '@prisma/client'; @@ -27,6 +28,8 @@ import { useEmbedSigningContext } from '~/components/embed/embed-signing-context import { AccessAuth2FAForm } from '~/components/general/document-signing/access-auth-2fa-form'; import { DocumentSigningDisclosure } from '~/components/general/document-signing/document-signing-disclosure'; +import { getSigningCompletionErrorMessage } from '~/utils/toast-error-messages'; + import { useRequiredDocumentSigningAuthContext } from './document-signing-auth-provider'; export type DocumentSigningCompleteDialogProps = { @@ -85,7 +88,8 @@ export const DocumentSigningCompleteDialog = ({ position, disableNameInput = false, }: DocumentSigningCompleteDialogProps) => { - const { t } = useLingui(); + const { t, i18n } = useLingui(); + const { toast } = useToast(); const [showDialog, setShowDialog] = useState(false); @@ -174,6 +178,18 @@ export const DocumentSigningCompleteDialog = ({ return; } + + // This dialog owns the completion error toast for every signing surface + // so the user gets a specific, actionable message. Callers should run + // their own side effects (e.g. embeds posting document-error) and + // rethrow rather than toasting themselves. + const toastMessage = getSigningCompletionErrorMessage(err.code); + + toast({ + title: i18n._(toastMessage.title), + description: i18n._(toastMessage.description), + variant: 'destructive', + }); } }; diff --git a/apps/remix/app/components/general/document-signing/document-signing-form.tsx b/apps/remix/app/components/general/document-signing/document-signing-form.tsx index cbe467f36..2d3b70540 100644 --- a/apps/remix/app/components/general/document-signing/document-signing-form.tsx +++ b/apps/remix/app/components/general/document-signing/document-signing-form.tsx @@ -1,3 +1,4 @@ +import { AppError } from '@documenso/lib/errors/app-error'; import type { DocumentAndSender } from '@documenso/lib/server-only/document/get-document-by-token'; import type { TRecipientAccessAuth } from '@documenso/lib/types/document-auth'; import { isFieldUnsignedAndRequired } from '@documenso/lib/utils/advanced-fields-helpers'; @@ -19,6 +20,8 @@ import { useId, useMemo, useState } from 'react'; import { Controller, useForm } from 'react-hook-form'; import { useNavigate } from 'react-router'; +import { getSigningCompletionErrorMessage } from '~/utils/toast-error-messages'; + import { AssistantConfirmationDialog, type NextSigner } from '../../dialogs/assistant-confirmation-dialog'; import { DocumentSigningCompleteDialog } from './document-signing-complete-dialog'; import { useRequiredDocumentSigningContext } from './document-signing-provider'; @@ -100,9 +103,12 @@ export const DocumentSigningForm = ({ try { await completeDocument({ nextSigner }); } catch (err) { + const error = AppError.parseError(err); + const toastMessage = getSigningCompletionErrorMessage(error.code); + toast({ - title: _(msg`Error`), - description: _(msg`An error occurred while completing the document. Please try again.`), + title: _(toastMessage.title), + description: _(toastMessage.description), variant: 'destructive', }); diff --git a/apps/remix/app/components/general/envelope-signing/envelope-signing-complete-dialog.tsx b/apps/remix/app/components/general/envelope-signing/envelope-signing-complete-dialog.tsx index 53f125c7f..29f1dbeee 100644 --- a/apps/remix/app/components/general/envelope-signing/envelope-signing-complete-dialog.tsx +++ b/apps/remix/app/components/general/envelope-signing/envelope-signing-complete-dialog.tsx @@ -148,15 +148,11 @@ export const EnvelopeSignerCompleteDialog = () => { const error = AppError.parseError(err); if (error.code !== AppErrorCode.TWO_FACTOR_AUTH_FAILED) { - toast({ - title: t`Something went wrong`, - description: t`We were unable to submit this document at this time. Please try again later.`, - variant: 'destructive', - }); - onDocumentError?.(); } + // Rethrow so DocumentSigningCompleteDialog can handle 2FA retries and + // toast a specific completion error message. throw err; } }; @@ -224,14 +220,11 @@ export const EnvelopeSignerCompleteDialog = () => { } } catch (err) { console.log('err', err); - toast({ - title: t`Something went wrong`, - description: t`We were unable to submit this document at this time. Please try again later.`, - variant: 'destructive', - }); onDocumentError?.(); + // Rethrow so DocumentSigningCompleteDialog can toast a specific + // completion error message. throw err; } }; diff --git a/apps/remix/app/utils/toast-error-messages.ts b/apps/remix/app/utils/toast-error-messages.ts index 462f42694..49e4ceb62 100644 --- a/apps/remix/app/utils/toast-error-messages.ts +++ b/apps/remix/app/utils/toast-error-messages.ts @@ -42,6 +42,51 @@ export const getDirectTemplateErrorMessage = (code: string): ToastMessageDescrip })); }; +/** + * Toast messages for errors thrown while a recipient attempts to complete + * (sign) a document, so the user knows whether retrying can help and what to + * do next. + */ +export const getSigningCompletionErrorMessage = (code: string): ToastMessageDescriptor => { + return match(code) + .with(AppErrorCode.NOT_FOUND, () => ({ + title: msg`Document no longer available`, + description: msg`This document can no longer be signed. It may have been removed by the sender, or your signing access may have been revoked. Please contact the sender for a new signing link.`, + })) + .with(AppErrorCode.RECIPIENT_HAS_UNSIGNED_FIELDS, () => ({ + title: msg`Some fields were not saved`, + description: msg`One or more of your required fields have not been saved. Please refresh the page, complete any empty required fields, and try again.`, + })) + .with(AppErrorCode.RECIPIENT_OUT_OF_TURN, () => ({ + title: msg`It's not your turn to sign yet`, + description: msg`This document is signed in a set order and other recipients must sign before you. You will receive an email when it is your turn.`, + })) + .with(AppErrorCode.RECIPIENT_EXPIRED, () => ({ + title: msg`Signing link expired`, + description: msg`Your signing link has expired. Please contact the sender to request a new one.`, + })) + .with(AppErrorCode.ENVELOPE_COMPLETED, () => ({ + title: msg`Document already completed`, + description: msg`This document has already been completed and no further signatures can be added.`, + })) + .with(AppErrorCode.ENVELOPE_REJECTED, () => ({ + title: msg`Document rejected`, + description: msg`This document has been rejected by a recipient and can no longer be signed.`, + })) + .with(AppErrorCode.ENVELOPE_CANCELLED, () => ({ + title: msg`Document cancelled`, + description: msg`This document has been cancelled by the sender and can no longer be signed. Please contact the sender if you believe this is a mistake.`, + })) + .with(AppErrorCode.ENVELOPE_DRAFT, () => ({ + title: msg`Document not ready`, + description: msg`This document has not been sent for signing yet. Please wait for the sender to send it before signing.`, + })) + .otherwise(() => ({ + title: msg`Something went wrong`, + description: msg`We were unable to submit this document at this time. Please try again later.`, + })); +}; + export const getUploadErrorMessage = (code: string): ToastMessageDescriptor => { return match(code) .with(AppErrorCode.TOO_MANY_REQUESTS, () => FAIR_USE_LIMIT_EXCEEDED_ERROR_MESSAGE) diff --git a/apps/remix/package.json b/apps/remix/package.json index 4674c249d..b4e522572 100644 --- a/apps/remix/package.json +++ b/apps/remix/package.json @@ -97,7 +97,7 @@ "@types/react-dom": "^19.2.3", "@types/ua-parser-js": "^0.7.39", "cross-env": "^10.1.0", - "esbuild": "^0.27.0", + "esbuild": "^0.28.1", "remix-flat-routes": "^0.8.5", "rollup": "^4.53.3", "tsx": "^4.23.1", diff --git a/docker/Dockerfile b/docker/Dockerfile index fa2be3083..0b5923a05 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -76,7 +76,6 @@ COPY --from=builder /app/out/json/ . COPY --from=builder /app/out/package-lock.json ./package-lock.json COPY --from=builder /app/lingui.config.ts ./lingui.config.ts -COPY --from=builder /app/patches ./patches RUN npm ci @@ -115,8 +114,6 @@ WORKDIR /app COPY --from=builder --chown=nodejs:nodejs /app/out/json/ . # Copy the tailwind config files across COPY --from=builder --chown=nodejs:nodejs /app/out/full/packages/tailwind-config ./packages/tailwind-config -# Copy the patches across -COPY --from=builder --chown=nodejs:nodejs /app/patches ./patches RUN npm ci --only=production diff --git a/package-lock.json b/package-lock.json index c17553bfc..c32ca8122 100644 --- a/package-lock.json +++ b/package-lock.json @@ -13,7 +13,7 @@ "packages/*" ], "dependencies": { - "@ai-sdk/google-vertex": "3.0.81", + "@ai-sdk/google-vertex": "5.0.48", "@documenso/prisma": "*", "@libpdf/core": "^0.4.1", "@lingui/conf": "^5.6.0", @@ -47,7 +47,7 @@ "@radix-ui/react-toggle": "^1.1.14", "@radix-ui/react-toggle-group": "^1.1.15", "@radix-ui/react-tooltip": "^1.2.12", - "ai": "^5.0.104", + "ai": "^7.0.58", "cron-parser": "^5.5.0", "fflate": "^0.8.3", "luxon": "^3.7.2", @@ -55,6 +55,7 @@ "posthog-node": "4.18.0", "react": "^19.2.7", "react-dom": "^19.2.7", + "sharp": "0.35.3", "typescript": "5.6.2", "zod": "^3.25.76" }, @@ -76,7 +77,7 @@ "@types/react-dom": "^19.2.3", "dotenv": "^17.2.3", "dotenv-cli": "^11.0.0", - "esbuild": "^0.27.0", + "esbuild": "^0.28.1", "husky": "^9.1.7", "inngest": "^3.54.0", "inngest-cli": "^1.17.9", @@ -110,12 +111,12 @@ "version": "0.0.0", "hasInstallScript": true, "dependencies": { - "fumadocs-core": "16.5.0", - "fumadocs-mdx": "14.2.6", - "fumadocs-ui": "16.5.0", + "fumadocs-core": "16.14.3", + "fumadocs-mdx": "15.2.3", + "fumadocs-ui": "16.14.3", "lucide-react": "^0.563.0", "mermaid": "^11.12.2", - "next": "16.2.6", + "next": "16.3.0", "next-plausible": "^3.12.5", "next-themes": "^0.4.6", "react": "^19.2.4", @@ -143,117 +144,6 @@ "undici-types": ">=7.24.0 <7.24.7" } }, - "apps/docs/node_modules/chokidar": { - "version": "5.0.0", - "resolved": "https://registry.npmjs.org/chokidar/-/chokidar-5.0.0.tgz", - "integrity": "sha512-TQMmc3w+5AxjpL8iIiwebF73dRDF4fBIieAqGn9RGCWaEVwQ6Fb2cGe31Yns0RRIzii5goJ1Y7xbMwo1TxMplw==", - "license": "MIT", - "dependencies": { - "readdirp": "^5.0.0" - }, - "engines": { - "node": ">= 20.19.0" - }, - "funding": { - "url": "https://paulmillr.com/funding/" - } - }, - "apps/docs/node_modules/fumadocs-mdx": { - "version": "14.2.6", - "resolved": "https://registry.npmjs.org/fumadocs-mdx/-/fumadocs-mdx-14.2.6.tgz", - "integrity": "sha512-T8i5IllZ6OGaZ3/4Wwjl1zovvypSsr6Cco9ZACvoABLqpqTQ2TDfrW1nBt1o9YUKyfzkwDnjKdrnrq/nDexfcg==", - "license": "MIT", - "dependencies": { - "@mdx-js/mdx": "^3.1.1", - "@standard-schema/spec": "^1.1.0", - "chokidar": "^5.0.0", - "esbuild": "^0.27.2", - "estree-util-value-to-estree": "^3.5.0", - "js-yaml": "^4.1.1", - "mdast-util-to-markdown": "^2.1.2", - "picocolors": "^1.1.1", - "picomatch": "^4.0.3", - "remark-mdx": "^3.1.1", - "tinyexec": "^1.0.2", - "tinyglobby": "^0.2.15", - 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"node_modules/systeminformation": { - "version": "5.31.11", - "resolved": "https://registry.npmjs.org/systeminformation/-/systeminformation-5.31.11.tgz", - "integrity": "sha512-I6O7iaUj23AXRgCPDDnvi3xHvdOLp4+1YMbF+X194lJwY1NeWojgHJPhslVKcmTtrLTguRk3QJK+xEdTiI3P0w==", + "version": "5.33.1", + "resolved": "https://registry.npmjs.org/systeminformation/-/systeminformation-5.33.1.tgz", + "integrity": "sha512-DEN6ICHk3Tk0Uf/hrAHh7xlt7iL5CJFBtPZinA0H62DrGG/KPKqq/Nzj6lCXPS4Ay/sf/14zNnk9LpqKzBIc+w==", "license": "MIT", "os": [ "darwin", @@ -29394,7 +29298,7 @@ "systeminformation": "lib/cli.js" }, "engines": { - "node": ">=8.0.0" + "node": ">=10.0.0" }, "funding": { "type": "Buy me a coffee", @@ -29563,9 +29467,9 @@ } }, "node_modules/tapable": { - "version": "2.3.0", - "resolved": "https://registry.npmjs.org/tapable/-/tapable-2.3.0.tgz", - "integrity": "sha512-g9ljZiwki/LfxmQADO3dEY1CbpmXT5Hm2fJ+QaGKwSXUylMybePR7/67YW7jOrrvjEgL1Fmz5kzyAjWVWLlucg==", + "version": "2.3.3", + "resolved": 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"version": "5.0.3", + "resolved": "https://registry.npmjs.org/vfile-location/-/vfile-location-5.0.3.tgz", + "integrity": "sha512-5yXvWDEgqeiYiBe1lbxYF7UMAIm/IcopxMHrMQDq3nvKcjPKIhZklUKL+AE7J7uApI4kwe2snsK+eI6UTj9EHg==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "vfile": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/vfile-message": { "version": "4.0.3", "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-4.0.3.tgz", @@ -31310,6 +31224,16 @@ "defaults": "^1.0.3" } }, + "node_modules/web-namespaces": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/web-namespaces/-/web-namespaces-2.0.1.tgz", + "integrity": "sha512-bKr1DkiNa2krS7qxNtdrtHAmzuYGFQLiQ13TsorsdT6ULTkPLKuu5+GsFpDlg6JFjUTwX2DyhMPG2be8uPrqsQ==", + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/wooorm" + } + }, "node_modules/web-streams-polyfill": { "version": "3.3.3", "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-3.3.3.tgz", @@ -31634,9 +31558,9 @@ } }, "node_modules/yoctocolors": { - "version": "2.1.2", - "resolved": "https://registry.npmjs.org/yoctocolors/-/yoctocolors-2.1.2.tgz", - "integrity": "sha512-CzhO+pFNo8ajLM2d2IW/R93ipy99LWjtwblvC1RsoSUMZgyLbYFr221TnSNT7GjGdYui6P459mw9JH/g/zW2ug==", + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/yoctocolors/-/yoctocolors-2.2.0.tgz", + "integrity": "sha512-xYqdZFUK/VYazNl/oCDYN+3WloWQwMfZxBoiNt6qNyk+xfOdi598muWE42rNZFp1kNOiqW936q5RhUdnpqElSg==", "license": "MIT", "engines": { "node": ">=18" @@ -31651,6 +31575,48 @@ "integrity": "sha512-0LPOt3AxKqMdFBZA3HBAt/t/8vIKq7VaQYbuA8WxCgung+p9TVyKRYdpvCb80HcdTN2NkbIKbhNwKUfm3tQywQ==", "license": "MIT" }, + "node_modules/yuku-analyzer": { + "version": "0.8.4", + "resolved": "https://registry.npmjs.org/yuku-analyzer/-/yuku-analyzer-0.8.4.tgz", + "integrity": "sha512-793VvcTjw38e2kjWEx+4R9M1A6CUIS+cRQqi6eHKAnn9rt2mufMEf5/0ZPhVamByEaKQrUsHaDkSuXRm7HKGiw==", + "license": "MIT", + "dependencies": { + "@yuku-toolchain/types": "^0.8.4", + "yuku-ast": "^0.8.4" + }, + "optionalDependencies": { + "@yuku-analyzer/binding-android-arm64": "0.8.4", + "@yuku-analyzer/binding-darwin-arm64": "0.8.4", + "@yuku-analyzer/binding-darwin-x64": "0.8.4", + "@yuku-analyzer/binding-freebsd-x64": "0.8.4", + "@yuku-analyzer/binding-linux-arm-gnu": "0.8.4", + "@yuku-analyzer/binding-linux-arm-musl": "0.8.4", + "@yuku-analyzer/binding-linux-arm64-gnu": "0.8.4", + "@yuku-analyzer/binding-linux-arm64-musl": "0.8.4", + "@yuku-analyzer/binding-linux-x64-gnu": "0.8.4", + "@yuku-analyzer/binding-linux-x64-musl": "0.8.4", + "@yuku-analyzer/binding-win32-arm64": "0.8.4", + "@yuku-analyzer/binding-win32-x64": "0.8.4" + } + }, + "node_modules/yuku-ast": { + "version": "0.8.4", + "resolved": "https://registry.npmjs.org/yuku-ast/-/yuku-ast-0.8.4.tgz", + "integrity": "sha512-s7EWfWIQkaGmsGnyr/BU0jli9YTN5TvrKIsSmALyRD9elumDQInuhv0BrVObENKVCxr9W3Ikmnx5u02KvfuUmw==", + "license": "MIT", + "dependencies": { + "@yuku-toolchain/types": "^0.8.4" + } + }, + "node_modules/zbsearch": { + "version": "3.3.4", + "resolved": "https://registry.npmjs.org/zbsearch/-/zbsearch-3.3.4.tgz", + "integrity": "sha512-xGsv9rIwrili/fpLpVwmnCovEcvaAJg1ey+3Ur0+m3x1mnGoVO71iAwn4op420QLGNsQJNmScZjFq5TQ+cRi/g==", + "license": "Apache-2.0", + "engines": { + "node": ">= 20.0.0" + } + }, "node_modules/zod": { "version": "3.25.76", "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz", @@ -32345,7 +32311,7 @@ "version": "0.0.0", "license": "MIT", "dependencies": { - "@ai-sdk/google-vertex": "3.0.81", + "@ai-sdk/google-vertex": "5.0.48", "@aws-sdk/client-s3": "^3.998.0", "@aws-sdk/client-sesv2": "^3.998.0", "@aws-sdk/cloudfront-signer": "^3.998.0", @@ -32373,7 +32339,7 @@ "@sindresorhus/slugify": "^3.0.0", "@team-plain/typescript-sdk": "^5.11.0", "@vvo/tzdb": "^6.196.0", - "ai": "^5.0.104", + "ai": "^7.0.58", "bullmq": "^5.71.1", "colord": "^2.9.3", "csv-parse": "^6.1.0", @@ -32397,7 +32363,7 @@ "posthog-node": "4.18.0", "react": "^19.2.7", "remeda": "^2.32.0", - "sharp": "0.34.5", + "sharp": "0.35.3", "stripe": "^12.18.0", "ts-pattern": "^5.9.0", "zod": "^3.25.76" diff --git a/package.json b/package.json index 6e012a297..ee0fc81fb 100644 --- a/package.json +++ b/package.json @@ -64,7 +64,7 @@ "@ts-rest/serverless": "^3.52.1", "dotenv": "^17.2.3", "dotenv-cli": "^11.0.0", - "esbuild": "^0.27.0", + "esbuild": "^0.28.1", "husky": "^9.1.7", "inngest": "^3.54.0", "inngest-cli": "^1.17.9", @@ -89,7 +89,7 @@ "zod-prisma-types": "3.3.5" }, "dependencies": { - "@ai-sdk/google-vertex": "3.0.81", + "@ai-sdk/google-vertex": "5.0.48", "@documenso/prisma": "*", "@libpdf/core": "^0.4.1", "@lingui/conf": "^5.6.0", @@ -122,7 +122,7 @@ "@radix-ui/react-toggle": "^1.1.14", "@radix-ui/react-toggle-group": "^1.1.15", "@radix-ui/react-tooltip": "^1.2.12", - "ai": "^5.0.104", + "ai": "^7.0.58", "cron-parser": "^5.5.0", "fflate": "^0.8.3", "luxon": "^3.7.2", @@ -130,12 +130,14 @@ "posthog-node": "4.18.0", "react": "^19.2.7", "react-dom": "^19.2.7", + "sharp": "0.35.3", "typescript": "5.6.2", "@marsidev/react-turnstile": "^1.5.0", "zod": "^3.25.76" }, "overrides": { "lodash": "4.18.1", + "brace-expansion@1": "^1.1.18", "pdfjs-dist": "5.4.296", "postcss": "^8.5.19", "react": "$react", diff --git a/packages/lib/errors/app-error.ts b/packages/lib/errors/app-error.ts index 56c068215..79d5235b2 100644 --- a/packages/lib/errors/app-error.ts +++ b/packages/lib/errors/app-error.ts @@ -43,6 +43,20 @@ export enum AppErrorCode { */ RECIPIENT_ALREADY_SIGNED = 'RECIPIENT_ALREADY_SIGNED', + /** + * A completion request was made for a recipient that still has required + * fields which have not been inserted. Usually indicates the client's field + * state is out of sync with the server (e.g. a field insert failed to + * persist before submission). + */ + RECIPIENT_HAS_UNSIGNED_FIELDS = 'RECIPIENT_HAS_UNSIGNED_FIELDS', + + /** + * A completion request was made by a recipient in a sequential signing flow + * before the preceding recipients have signed. + */ + RECIPIENT_OUT_OF_TURN = 'RECIPIENT_OUT_OF_TURN', + /** * A signer recipient does not have a signature field assigned. Thrown when * distributing an envelope or using a direct template where at least one @@ -99,6 +113,8 @@ export const genericErrorCodeToTrpcErrorCodeMap: Record { - const envelope = await prisma.envelope.findFirstOrThrow({ + const envelope = await prisma.envelope.findFirst({ where: { ...unsafeBuildEnvelopeIdQuery(id, EnvelopeType.DOCUMENT), recipients: { @@ -78,10 +78,23 @@ export const completeDocumentWithToken = async ({ }, }); + // The most common cause is a stale signing page: the document was deleted, + // or the recipient was removed, after the link was opened. Surface a + // NOT_FOUND instead of leaking a Prisma P2025 as a 500. + if (!envelope) { + throw new AppError(AppErrorCode.NOT_FOUND, { + message: 'Document not found for the provided signing token', + statusCode: 404, + }); + } + const legacyDocumentId = mapSecondaryIdToDocumentId(envelope.secondaryId); if (envelope.recipients.length === 0) { - throw new Error(`Document ${envelope.id} has no recipient with token ${token}`); + throw new AppError(AppErrorCode.NOT_FOUND, { + message: `Document ${envelope.id} has no recipient with the provided token`, + statusCode: 404, + }); } const [recipient] = envelope.recipients; @@ -98,7 +111,19 @@ export const completeDocumentWithToken = async ({ } if (envelope.status !== DocumentStatus.PENDING) { - throw new Error(`Document ${envelope.id} must be pending`); + const envelopeStatusErrorCode: Record = { + [DocumentStatus.DRAFT]: AppErrorCode.ENVELOPE_DRAFT, + [DocumentStatus.COMPLETED]: AppErrorCode.ENVELOPE_COMPLETED, + [DocumentStatus.REJECTED]: AppErrorCode.ENVELOPE_REJECTED, + [DocumentStatus.CANCELLED]: AppErrorCode.ENVELOPE_CANCELLED, + // Unreachable: guarded by the status check above. + [DocumentStatus.PENDING]: AppErrorCode.INVALID_REQUEST, + }; + + throw new AppError(envelopeStatusErrorCode[envelope.status], { + message: `Document ${envelope.id} must be pending to be completed, found ${envelope.status}`, + statusCode: 400, + }); } assertRecipientNotExpired(recipient); @@ -116,7 +141,10 @@ export const completeDocumentWithToken = async ({ }); if (!isRecipientsTurn) { - throw new Error(`Recipient ${recipient.id} attempted to complete the document before it was their turn`); + throw new AppError(AppErrorCode.RECIPIENT_OUT_OF_TURN, { + message: `Recipient ${recipient.id} attempted to complete the document before it was their turn`, + statusCode: 400, + }); } } @@ -279,7 +307,10 @@ export const completeDocumentWithToken = async ({ } if (fieldsContainUnsignedRequiredField(fields)) { - throw new Error(`Recipient ${recipient.id} has unsigned fields`); + throw new AppError(AppErrorCode.RECIPIENT_HAS_UNSIGNED_FIELDS, { + message: `Recipient ${recipient.id} has unsigned fields`, + statusCode: 400, + }); } await prisma.$transaction(async (tx) => { diff --git a/packages/trpc/server/envelope-router/cancel-envelope.types.ts b/packages/trpc/server/envelope-router/cancel-envelope.types.ts index 06a6637fb..59ab827c1 100644 --- a/packages/trpc/server/envelope-router/cancel-envelope.types.ts +++ b/packages/trpc/server/envelope-router/cancel-envelope.types.ts @@ -8,13 +8,14 @@ export const cancelEnvelopeMeta: TrpcRouteMeta = { method: 'POST', path: '/envelope/cancel', summary: 'Cancel envelope', + description: 'Cancel a pending envelope', tags: ['Envelope'], }, }; export const ZCancelEnvelopeRequestSchema = z.object({ - envelopeId: z.string(), - reason: z.string().optional(), + envelopeId: z.string().describe('The ID of the envelope to cancel.'), + reason: z.string().describe('The reason for cancelling the envelope.').optional(), }); export const ZCancelEnvelopeResponseSchema = ZSuccessResponseSchema; diff --git a/packages/trpc/server/envelope-router/delete-envelope.types.ts b/packages/trpc/server/envelope-router/delete-envelope.types.ts index 654d24c6d..8eaf7db9e 100644 --- a/packages/trpc/server/envelope-router/delete-envelope.types.ts +++ b/packages/trpc/server/envelope-router/delete-envelope.types.ts @@ -8,12 +8,13 @@ export const deleteEnvelopeMeta: TrpcRouteMeta = { method: 'POST', path: '/envelope/delete', summary: 'Delete envelope', + description: 'Delete an envelope', tags: ['Envelope'], }, }; export const ZDeleteEnvelopeRequestSchema = z.object({ - envelopeId: z.string(), + envelopeId: z.string().describe('The ID of the envelope to delete.'), }); export const ZDeleteEnvelopeResponseSchema = ZSuccessResponseSchema; diff --git a/packages/trpc/server/envelope-router/update-envelope.types.ts b/packages/trpc/server/envelope-router/update-envelope.types.ts index c08da086f..a4a89f9ea 100644 --- a/packages/trpc/server/envelope-router/update-envelope.types.ts +++ b/packages/trpc/server/envelope-router/update-envelope.types.ts @@ -12,24 +12,32 @@ export const updateEnvelopeMeta: TrpcRouteMeta = { method: 'POST', path: '/envelope/update', summary: 'Update envelope', + description: 'Update envelope properties and settings', tags: ['Envelope'], }, }; export const ZUpdateEnvelopeRequestSchema = z.object({ - envelopeId: z.string(), + envelopeId: z.string().describe('The ID of the envelope to update.'), data: z .object({ title: ZDocumentTitleSchema.optional(), externalId: ZDocumentExternalIdSchema.nullish(), visibility: ZDocumentVisibilitySchema.optional(), - globalAccessAuth: z.array(ZDocumentAccessAuthTypesSchema).optional(), - globalActionAuth: z.array(ZDocumentActionAuthTypesSchema).optional(), - folderId: z.string().nullish(), - templateType: z.nativeEnum(TemplateType).optional(), + globalAccessAuth: z + .array(ZDocumentAccessAuthTypesSchema) + .describe('The authentication methods required to access the envelope.') + .optional(), + globalActionAuth: z + .array(ZDocumentActionAuthTypesSchema) + .describe('The authentication methods required to sign the envelope.') + .optional(), + folderId: z.string().describe('The ID of the folder containing the envelope.').nullish(), + templateType: z.nativeEnum(TemplateType).describe('The template type.').optional(), }) + .describe('The envelope properties to update.') .optional(), - meta: ZDocumentMetaUpdateSchema.optional(), + meta: ZDocumentMetaUpdateSchema.describe('The email and signing settings to update.').optional(), }); export const ZUpdateEnvelopeResponseSchema = ZEnvelopeLiteSchema; diff --git a/packages/trpc/server/recipient-router/router.ts b/packages/trpc/server/recipient-router/router.ts index 215984706..29fff7d9c 100644 --- a/packages/trpc/server/recipient-router/router.ts +++ b/packages/trpc/server/recipient-router/router.ts @@ -603,7 +603,7 @@ export const recipientRouter = router({ // can't complete via this route — they go through the CSC sync sign // flow (`enterprise.csc.signEnvelope`). This route returns the redirect URL // for the credential-scope OAuth round-trip. - const envelope = await prisma.envelope.findFirstOrThrow({ + const envelope = await prisma.envelope.findFirst({ where: { ...unsafeBuildEnvelopeIdQuery({ type: 'documentId', id: documentId }, EnvelopeType.DOCUMENT), recipients: { some: { token } }, @@ -611,6 +611,16 @@ export const recipientRouter = router({ select: { signatureLevel: true, internalVersion: true }, }); + // The most common cause is a stale signing page: the document was + // deleted, or the recipient was removed, after the link was opened. + // Surface a NOT_FOUND instead of leaking a Prisma P2025 as a 500. + if (!envelope) { + throw new AppError(AppErrorCode.NOT_FOUND, { + message: 'Document not found for the provided signing token', + statusCode: 404, + }); + } + if (isTspEnvelope(envelope)) { return await prepareCscRecipientSigning({ recipientToken: token, diff --git a/packages/ui/components/document/envelope-recipient-field-tooltip.tsx b/packages/ui/components/document/envelope-recipient-field-tooltip.tsx index b9a79586e..74c60ed13 100644 --- a/packages/ui/components/document/envelope-recipient-field-tooltip.tsx +++ b/packages/ui/components/document/envelope-recipient-field-tooltip.tsx @@ -2,16 +2,27 @@ import { getBoundingClientRect } from '@documenso/lib/client-only/get-bounding-c import { PDF_VIEWER_PAGE_SELECTOR } from '@documenso/lib/constants/pdf-viewer'; import { Trans, useLingui } from '@lingui/react/macro'; import type { Field, Recipient } from '@prisma/client'; -import { SigningStatus } from '@prisma/client'; -import { ClockIcon, EyeOffIcon, LockIcon } from 'lucide-react'; -import { useCallback, useEffect, useState } from 'react'; +import { FieldType, SigningStatus } from '@prisma/client'; +import { + CalendarDaysIcon, + CheckSquareIcon, + ChevronDownIcon, + ContactIcon, + DiscIcon, + EyeOffIcon, + HashIcon, + LockIcon, + MailIcon, + TypeIcon, + UserIcon, +} from 'lucide-react'; +import { type ElementType, useCallback, useEffect, useState } from 'react'; import { isTemplateRecipientEmailPlaceholder } from '../../../lib/constants/template'; import { extractInitials } from '../../../lib/utils/recipient-formatter'; import { SignatureIcon } from '../../icons/signature'; import { cn } from '../../lib/utils'; import { Avatar, AvatarFallback } from '../../primitives/avatar'; -import { Badge } from '../../primitives/badge'; import { FRIENDLY_FIELD_TYPE } from '../../primitives/document-flow/types'; import { PopoverHover } from '../../primitives/popover'; @@ -27,16 +38,18 @@ interface EnvelopeRecipientFieldTooltipProps { showRecipientColors?: boolean; } -const getRecipientDisplayText = (recipient: { name: string; email: string }) => { - if (recipient.name && !isTemplateRecipientEmailPlaceholder(recipient.email)) { - return `${recipient.name} (${recipient.email})`; - } - - if (recipient.name && isTemplateRecipientEmailPlaceholder(recipient.email)) { - return recipient.name; - } - - return recipient.email; +const FIELD_TYPE_ICONS: Record = { + [FieldType.SIGNATURE]: SignatureIcon, + [FieldType.FREE_SIGNATURE]: SignatureIcon, + [FieldType.INITIALS]: ContactIcon, + [FieldType.TEXT]: TypeIcon, + [FieldType.DATE]: CalendarDaysIcon, + [FieldType.EMAIL]: MailIcon, + [FieldType.NAME]: UserIcon, + [FieldType.NUMBER]: HashIcon, + [FieldType.RADIO]: DiscIcon, + [FieldType.CHECKBOX]: CheckSquareIcon, + [FieldType.DROPDOWN]: ChevronDownIcon, }; /** @@ -50,6 +63,8 @@ export function EnvelopeRecipientFieldTooltip({ }: EnvelopeRecipientFieldTooltipProps) { const { t } = useLingui(); + const FieldIcon = FIELD_TYPE_ICONS[field.type]; + const [hideField, setHideField] = useState(!showRecipientTooltip); const [coords, setCoords] = useState({ @@ -138,54 +153,64 @@ export function EnvelopeRecipientFieldTooltip({ } contentProps={{ - className: 'relative flex mb-4 w-fit flex-col p-4 text-sm', + className: 'flex w-64 flex-col overflow-hidden p-0 text-sm', + sideOffset: 20, + onOpenAutoFocus: (event) => event.preventDefault(), }} > - {showFieldStatus && ( - - {field?.fieldMeta?.readOnly ? ( - <> - - Read Only - - ) : field.recipient.signingStatus === SigningStatus.SIGNED ? ( - <> - - Signed - - ) : ( - <> - - Pending - - )} - - )} +
+ -

- +

{t(FRIENDLY_FIELD_TYPE[field.type])} field - -

+

-

{getRecipientDisplayText(field.recipient)}

+ {showFieldStatus && ( +
+ {field?.fieldMeta?.readOnly ? ( + <> + + + Read Only + + + ) : field.recipient.signingStatus === SigningStatus.SIGNED ? ( + <> + + + Signed + + + ) : ( + <> + + + Pending + + + )} +
+ )} +
- +
+
+

{field.recipient.name || field.recipient.email}

+ + {!isTemplateRecipientEmailPlaceholder(field.recipient.email) && field.recipient.name && ( +

{field.recipient.email}

+ )} +
+ + +
); diff --git a/patches/@ai-sdk+google-vertex+3.0.81.patch b/patches/@ai-sdk+google-vertex+3.0.81.patch deleted file mode 100644 index fee5b1fdb..000000000 --- a/patches/@ai-sdk+google-vertex+3.0.81.patch +++ /dev/null @@ -1,884 +0,0 @@ -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.mts b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.mts -index b4a9a4c..382b922 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.mts -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.mts -@@ -1,4 +1,4 @@ --import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - import { anthropicTools } from '@ai-sdk/anthropic/internal'; - -@@ -20,17 +20,17 @@ interface GoogleCredentials { - privateKeyId?: string; - } - --type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); -+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); - --interface GoogleVertexAnthropicProvider extends ProviderV2 { -+interface GoogleVertexAnthropicProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Creates a model for text generation. - */ -- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Anthropic-specific computer use tool. - */ -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.ts b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.ts -index b4a9a4c..382b922 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.ts -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.d.ts -@@ -1,4 +1,4 @@ --import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - import { anthropicTools } from '@ai-sdk/anthropic/internal'; - -@@ -20,17 +20,17 @@ interface GoogleCredentials { - privateKeyId?: string; - } - --type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); -+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); - --interface GoogleVertexAnthropicProvider extends ProviderV2 { -+interface GoogleVertexAnthropicProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Creates a model for text generation. - */ -- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Anthropic-specific computer use tool. - */ -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js -index f23e9a5..eee11de 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js -@@ -32,7 +32,7 @@ var import_provider_utils3 = require("@ai-sdk/provider-utils"); - var import_provider_utils = require("@ai-sdk/provider-utils"); - - // src/version.ts --var VERSION = true ? "3.0.81" : "0.0.0-test"; -+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test"; - - // src/edge/google-vertex-auth-edge.ts - var loadCredentials = async () => { -@@ -181,6 +181,7 @@ function createVertexAnthropic(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.chat = createChatModel; - provider.messages = createChatModel; -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js.map b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js.map -index cb63175..ed91fdc 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.js.map -@@ -1 +1 @@ --{"version":3,"sources":["../../../src/anthropic/edge/index.ts","../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n createVertexAnthropic,\n vertexAnthropic,\n} from './google-vertex-anthropic-provider-edge';\nexport type {\n GoogleVertexAnthropicProviderSettings,\n GoogleVertexAnthropicProvider,\n} from './google-vertex-anthropic-provider-edge';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n * Optional. The Google credentials for the Google Cloud service account. If\n * not provided, the Google Vertex provider will use environment variables to\n * load the credentials.\n */\n googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\n * Default Google Vertex AI Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n }\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n LanguageModelV2,\n NoSuchModelError,\n ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n */\n project?: string;\n\n /**\n * Google Cloud region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.languageModel = createChatModel;\n provider.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return 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-\ No newline at end of file -+{"version":3,"sources":["../../../src/anthropic/edge/index.ts","../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n createVertexAnthropic,\n vertexAnthropic,\n} from './google-vertex-anthropic-provider-edge';\nexport type {\n GoogleVertexAnthropicProviderSettings,\n GoogleVertexAnthropicProvider,\n} from './google-vertex-anthropic-provider-edge';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider 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The Google credentials for the Google Cloud service account. If\n * not provided, the Google Vertex provider will use environment variables to\n * load the credentials.\n */\n googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\n * Default Google Vertex AI Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n }\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n */\n project?: string;\n\n /**\n * Google Cloud region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 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-\ No newline at end of file -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs -index 3719f5f..726fb5d 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs -@@ -10,7 +10,7 @@ import { - } from "@ai-sdk/provider-utils"; - - // src/version.ts --var VERSION = true ? "3.0.81" : "0.0.0-test"; -+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test"; - - // src/edge/google-vertex-auth-edge.ts - var loadCredentials = async () => { -@@ -167,6 +167,7 @@ function createVertexAnthropic(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.chat = createChatModel; - provider.messages = createChatModel; -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs.map b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs.map -index 9200347..ba38267 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/edge/index.mjs.map -@@ -1 +1 @@ --{"version":3,"sources":["../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n * Optional. 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Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n }\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n LanguageModelV2,\n NoSuchModelError,\n ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. 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-\ No newline at end of file -+{"version":3,"sources":["../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n * Optional. The Google credentials for the Google Cloud service account. If\n * not provided, the Google Vertex provider will use environment variables to\n * load the credentials.\n */\n googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\n * Default Google Vertex AI Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n }\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n */\n project?: string;\n\n /**\n * Google Cloud region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 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-\ No newline at end of file -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.mts b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.mts -index 0650bf2..948aa4e 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.mts -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.mts -@@ -1,19 +1,19 @@ - import { GoogleAuthOptions } from 'google-auth-library'; --import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - import { anthropicTools } from '@ai-sdk/anthropic/internal'; - --type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); -+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); - --interface GoogleVertexAnthropicProvider extends ProviderV2 { -+interface GoogleVertexAnthropicProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Creates a model for text generation. - */ -- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Anthropic-specific computer use tool. - */ -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.ts b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.ts -index 0650bf2..948aa4e 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.ts -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.d.ts -@@ -1,19 +1,19 @@ - import { GoogleAuthOptions } from 'google-auth-library'; --import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - import { anthropicTools } from '@ai-sdk/anthropic/internal'; - --type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); -+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {}); - --interface GoogleVertexAnthropicProvider extends ProviderV2 { -+interface GoogleVertexAnthropicProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Creates a model for text generation. - */ -- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2; -+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3; - /** - Anthropic-specific computer use tool. - */ -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js -index 2f15ff7..e4811ee 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js -@@ -91,6 +91,7 @@ function createVertexAnthropic(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.chat = createChatModel; - provider.messages = createChatModel; -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js.map b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js.map -index c93b564..01468bb 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.js.map -@@ -1 +1 @@ --{"version":3,"sources":["../../src/anthropic/index.ts","../../src/anthropic/google-vertex-anthropic-provider-node.ts","../../src/google-vertex-auth-google-auth-library.ts","../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n vertexAnthropic,\n createVertexAnthropic,\n} from './google-vertex-anthropic-provider-node';\nexport type {\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings,\n} from './google-vertex-anthropic-provider-node';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from '../google-vertex-auth-google-auth-library';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from 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The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import {\n LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n */\n project?: string;\n\n /**\n * Google Cloud region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return provider;\n}\n"],"mappings":";;;;;;;;;;;;;;;;;;;;AAAA;AAAA;AAAA,+BAAAA;AAAA,EAAA;AAAA;AAAA;;;ACAA,IAAAC,yBAAwB;;;ACAxB,iCAA8C;AAE9C,IAAI,eAAkC;AACtC,IAAI,cAAwC;AAE5C,SAAS,QAAQ,SAA4B;AAC3C,MAAI,CAAC,gBAAgB,YAAY,aAAa;AAC5C,mBAAe,IAAI,sCAAW;AAAA,MAC5B,QAAQ,CAAC,gDAAgD;AAAA,MACzD,GAAG;AAAA,IACL,CAAC;AACD,kBAAc;AAAA,EAChB;AACA,SAAO;AACT;AAEA,eAAsB,kBAAkB,SAA6B;AACnE,QAAM,OAAO,QAAQ,WAAW,CAAC,CAAC;AAClC,QAAM,SAAS,MAAM,KAAK,UAAU;AACpC,QAAM,QAAQ,MAAM,OAAO,eAAe;AAC1C,UAAO,+BAAO,UAAS;AACzB;;;ACrBA,sBAIO;AACP,4BAKO;AACP,sBAGO;AAmDA,SAAS,sBACd,UAAiD,CAAC,GACnB;AAnEjC;AAoEE,QAAM,eAAW,2CAAoB;AAAA,IACnC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AACD,QAAM,cAAU,2CAAoB;AAAA,IAClC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AAED,QAAM,WACJ,qDAAqB,QAAQ,OAAO,MAApC,YACA,WAAW,aAAa,WAAW,KAAK,WAAW,GAAG,yCAAyC,OAAO,cAAc,QAAQ;AAE9H,QAAM,kBAAkB,CAAC,YAA+C;AAjF1E,QAAAC;AAkFI,eAAI,+CAA+B,SAAS;AAAA,MAC1C,UAAU;AAAA,MACV;AAAA,MACA,UAASA,MAAA,QAAQ,YAAR,OAAAA,MAAmB,CAAC;AAAA,MAC7B,OAAO,QAAQ;AAAA,MAEf,iBAAiB,CAACC,UAAS,gBACzB,GAAGA,QAAO,IAAI,OAAO,IACnB,cAAc,qBAAqB,YACrC;AAAA,MACF,sBAAsB,UAAQ;AAE5B,cAAM,EAAE,OAAO,GAAG,KAAK,IAAI;AAC3B,eAAO;AAAA,UACL,GAAG;AAAA,UACH,mBAAmB;AAAA,QACrB;AAAA,MACF;AAAA;AAAA,MAEA,eAAe,OAAO,CAAC;AAAA,IACzB,CAAC;AAAA;AAEH,QAAM,WAAW,SAAU,SAA+C;AACxE,QAAI,YAAY;AACd,YAAM,IAAI;AAAA,QACR;AAAA,MACF;AAAA,IACF;AAEA,WAAO,gBAAgB,OAAO;AAAA,EAChC;AAEA,WAAS,uBAAuB;AAChC,WAAS,gBAAgB;AACzB,WAAS,OAAO;AAChB,WAAS,WAAW;AAEpB,WAAS,qBAAqB,CAAC,YAAoB;AACjD,UAAM,IAAI,iCAAiB,EAAE,SAAS,WAAW,qBAAqB,CAAC;AAAA,EACzE;AACA,WAAS,aAAa,CAAC,YAAoB;AACzC,UAAM,IAAI,iCAAiB,EAAE,SAAS,WAAW,aAAa,CAAC;AAAA,EACjE;AAEA,WAAS,QAAQ;AAEjB,SAAO;AACT;;;AF3GO,SAASC,uBACd,UAAiD,CAAC,GACnB;AAC/B,SAAO,sBAA8B;AAAA,IACnC,GAAG;AAAA,IACH,SAAS,aAAa;AAAA,MACpB,eAAe,UAAU,MAAM;AAAA,QAC7B,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,GAAI,UAAM,gCAAQ,QAAQ,OAAO;AAAA,IACnC;AAAA,EACF,CAAC;AACH;AAKO,IAAM,kBAAkBA,uBAAsB;","names":["createVertexAnthropic","import_provider_utils","_a","baseURL","createVertexAnthropic"]} -\ No newline at end of file -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs -index d3bb8e6..ac0adf7 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs -@@ -72,6 +72,7 @@ function createVertexAnthropic(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.chat = createChatModel; - provider.messages = createChatModel; -diff --git a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs.map b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs.map -index 16f8ed3..0876a26 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/anthropic/index.mjs.map -@@ -1 +1 @@ --{"version":3,"sources":["../../src/anthropic/google-vertex-anthropic-provider-node.ts","../../src/google-vertex-auth-google-auth-library.ts","../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from '../google-vertex-auth-google-auth-library';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from './google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import {\n LanguageModelV2,\n NoSuchModelError,\n ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. 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The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import {\n LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n */\n project?: string;\n\n /**\n * Google Cloud region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return provider;\n}\n"],"mappings":";AAAA,SAAS,eAAe;;;ACAxB,SAAS,kBAAqC;AAE9C,IAAI,eAAkC;AACtC,IAAI,cAAwC;AAE5C,SAAS,QAAQ,SAA4B;AAC3C,MAAI,CAAC,gBAAgB,YAAY,aAAa;AAC5C,mBAAe,IAAI,WAAW;AAAA,MAC5B,QAAQ,CAAC,gDAAgD;AAAA,MACzD,GAAG;AAAA,IACL,CAAC;AACD,kBAAc;AAAA,EAChB;AACA,SAAO;AACT;AAEA,eAAsB,kBAAkB,SAA6B;AACnE,QAAM,OAAO,QAAQ,WAAW,CAAC,CAAC;AAClC,QAAM,SAAS,MAAM,KAAK,UAAU;AACpC,QAAM,QAAQ,MAAM,OAAO,eAAe;AAC1C,UAAO,+BAAO,UAAS;AACzB;;;ACrBA;AAAA,EAEE;AAAA,OAEK;AACP;AAAA,EAGE;AAAA,EACA;AAAA,OACK;AACP;AAAA,EACE;AAAA,EACA;AAAA,OACK;AAmDA,SAAS,sBACd,UAAiD,CAAC,GACnB;AAnEjC;AAoEE,QAAM,WAAW,oBAAoB;AAAA,IACnC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AACD,QAAM,UAAU,oBAAoB;AAAA,IAClC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AAED,QAAM,WACJ,0BAAqB,QAAQ,OAAO,MAApC,YACA,WAAW,aAAa,WAAW,KAAK,WAAW,GAAG,yCAAyC,OAAO,cAAc,QAAQ;AAE9H,QAAM,kBAAkB,CAAC,YAA+C;AAjF1E,QAAAA;AAkFI,eAAI,+BAA+B,SAAS;AAAA,MAC1C,UAAU;AAAA,MACV;AAAA,MACA,UAASA,MAAA,QAAQ,YAAR,OAAAA,MAAmB,CAAC;AAAA,MAC7B,OAAO,QAAQ;AAAA,MAEf,iBAAiB,CAACC,UAAS,gBACzB,GAAGA,QAAO,IAAI,OAAO,IACnB,cAAc,qBAAqB,YACrC;AAAA,MACF,sBAAsB,UAAQ;AAE5B,cAAM,EAAE,OAAO,GAAG,KAAK,IAAI;AAC3B,eAAO;AAAA,UACL,GAAG;AAAA,UACH,mBAAmB;AAAA,QACrB;AAAA,MACF;AAAA;AAAA,MAEA,eAAe,OAAO,CAAC;AAAA,IACzB,CAAC;AAAA;AAEH,QAAM,WAAW,SAAU,SAA+C;AACxE,QAAI,YAAY;AACd,YAAM,IAAI;AAAA,QACR;AAAA,MACF;AAAA,IACF;AAEA,WAAO,gBAAgB,OAAO;AAAA,EAChC;AAEA,WAAS,uBAAuB;AAChC,WAAS,gBAAgB;AACzB,WAAS,OAAO;AAChB,WAAS,WAAW;AAEpB,WAAS,qBAAqB,CAAC,YAAoB;AACjD,UAAM,IAAI,iBAAiB,EAAE,SAAS,WAAW,qBAAqB,CAAC;AAAA,EACzE;AACA,WAAS,aAAa,CAAC,YAAoB;AACzC,UAAM,IAAI,iBAAiB,EAAE,SAAS,WAAW,aAAa,CAAC;AAAA,EACjE;AAEA,WAAS,QAAQ;AAEjB,SAAO;AACT;;;AF3GO,SAASC,uBACd,UAAiD,CAAC,GACnB;AAC/B,SAAO,sBAA8B;AAAA,IACnC,GAAG;AAAA,IACH,SAAS,aAAa;AAAA,MACpB,eAAe,UAAU,MAAM;AAAA,QAC7B,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,GAAI,MAAM,QAAQ,QAAQ,OAAO;AAAA,IACnC;AAAA,EACF,CAAC;AACH;AAKO,IAAM,kBAAkBA,uBAAsB;","names":["_a","baseURL","createVertexAnthropic"]} -\ No newline at end of file -diff --git a/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.mts b/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.mts -index d82459e..020874d 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.mts -+++ b/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.mts -@@ -1,10 +1,10 @@ --import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider'; - import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - - type GoogleVertexImageModelId = 'imagen-3.0-generate-001' | 'imagen-3.0-generate-002' | 'imagen-3.0-fast-generate-001' | 'imagen-4.0-generate-preview-06-06' | 'imagen-4.0-fast-generate-preview-06-06' | 'imagen-4.0-ultra-generate-preview-06-06' | (string & {}); - --type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); -+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); - - declare const googleVertexTools: { - googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, { -@@ -31,20 +31,20 @@ declare const googleVertexTools: { - }>; - }; - --interface GoogleVertexProvider extends ProviderV2 { -+interface GoogleVertexProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexModelId): LanguageModelV2; -- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2; -+ (modelId: GoogleVertexModelId): LanguageModelV3; -+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3; - /** - * Creates a model for image generation. - */ -- image(modelId: GoogleVertexImageModelId): ImageModelV2; -+ image(modelId: GoogleVertexImageModelId): ImageModelV3; - /** - Creates a model for image generation. - */ -- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2; -+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3; - tools: typeof googleVertexTools; - } - interface GoogleVertexProviderSettings$1 { -@@ -56,6 +56,12 @@ interface GoogleVertexProviderSettings$1 { - Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`. - */ - project?: string; -+ /** -+ * API key for Express Mode authentication. When provided, uses simplified -+ * authentication with `x-goog-api-key` header instead of OAuth. -+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`. -+ */ -+ apiKey?: string; - /** - * Headers to use for requests. Can be: - * - A headers object -diff --git a/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.ts b/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.ts -index d82459e..020874d 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.ts -+++ b/node_modules/@ai-sdk/google-vertex/dist/edge/index.d.ts -@@ -1,10 +1,10 @@ --import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider'; - import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - - type GoogleVertexImageModelId = 'imagen-3.0-generate-001' | 'imagen-3.0-generate-002' | 'imagen-3.0-fast-generate-001' | 'imagen-4.0-generate-preview-06-06' | 'imagen-4.0-fast-generate-preview-06-06' | 'imagen-4.0-ultra-generate-preview-06-06' | (string & {}); - --type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); -+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); - - declare const googleVertexTools: { - googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, { -@@ -31,20 +31,20 @@ declare const googleVertexTools: { - }>; - }; - --interface GoogleVertexProvider extends ProviderV2 { -+interface GoogleVertexProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexModelId): LanguageModelV2; -- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2; -+ (modelId: GoogleVertexModelId): LanguageModelV3; -+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3; - /** - * Creates a model for image generation. - */ -- image(modelId: GoogleVertexImageModelId): ImageModelV2; -+ image(modelId: GoogleVertexImageModelId): ImageModelV3; - /** - Creates a model for image generation. - */ -- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2; -+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3; - tools: typeof googleVertexTools; - } - interface GoogleVertexProviderSettings$1 { -@@ -56,6 +56,12 @@ interface GoogleVertexProviderSettings$1 { - Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`. - */ - project?: string; -+ /** -+ * API key for Express Mode authentication. When provided, uses simplified -+ * authentication with `x-goog-api-key` header instead of OAuth. -+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`. -+ */ -+ apiKey?: string; - /** - * Headers to use for requests. Can be: - * - A headers object -diff --git a/node_modules/@ai-sdk/google-vertex/dist/edge/index.js b/node_modules/@ai-sdk/google-vertex/dist/edge/index.js -index 5061210..e47d6de 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/edge/index.js -+++ b/node_modules/@ai-sdk/google-vertex/dist/edge/index.js -@@ -33,7 +33,7 @@ var import_internal2 = require("@ai-sdk/google/internal"); - var import_provider_utils4 = require("@ai-sdk/provider-utils"); - - // src/version.ts --var VERSION = true ? "3.0.81" : "0.0.0-test"; -+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test"; - - // src/google-vertex-embedding-model.ts - var import_provider = require("@ai-sdk/provider"); -@@ -103,7 +103,7 @@ var googleVertexEmbeddingProviderOptions = import_v42.z.object({ - // src/google-vertex-embedding-model.ts - var GoogleVertexEmbeddingModel = class { - constructor(modelId, config) { -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - this.maxEmbeddingsPerCall = 2048; - this.supportsParallelCalls = true; - this.modelId = modelId; -@@ -196,7 +196,7 @@ var GoogleVertexImageModel = class { - constructor(modelId, config) { - this.modelId = modelId; - this.config = config; -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list - this.maxImagesPerCall = 4; - } -@@ -318,12 +318,28 @@ function createVertex(options = {}) { - environmentVariableName: "GOOGLE_VERTEX_LOCATION", - description: "Google Vertex location" - }); -+ const loadApiKey = () => (0, import_provider_utils4.loadSetting)({ -+ settingValue: options.apiKey, -+ settingName: "apiKey", -+ environmentVariableName: "GOOGLE_VERTEX_API_KEY", -+ description: "Google Vertex API key" -+ }); -+ const isExpressMode = () => { -+ try { -+ const apiKey = loadApiKey(); -+ return apiKey != null && apiKey !== ""; -+ } catch (e) { -+ return false; -+ } -+ }; - const loadBaseURL = () => { -- var _a; - const region = loadVertexLocation(); - const project = loadVertexProject(); -+ if (options.baseURL) { -+ return (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL); -+ } - const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`; -- return (_a = (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; -+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; - }; - const createConfig = (name) => { - const getHeaders = async () => { -@@ -366,6 +382,7 @@ function createVertex(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.textEmbeddingModel = createEmbeddingModel; - provider.image = createImageModel; -@@ -484,12 +501,21 @@ async function generateAuthToken(credentials) { - function createVertex2(options = {}) { - return createVertex({ - ...options, -- headers: async () => ({ -- Authorization: `Bearer ${await generateAuthToken( -- options.googleCredentials -- )}`, -- ...await (0, import_provider_utils6.resolve)(options.headers) -- }) -+ headers: async () => { -+ const resolvedHeaders = await (0, import_provider_utils6.resolve)(options.headers); -+ if (options.apiKey) { -+ return { -+ "x-goog-api-key": options.apiKey, -+ ...resolvedHeaders -+ }; -+ } -+ return { -+ Authorization: `Bearer ${await generateAuthToken( -+ options.googleCredentials -+ )}`, -+ ...resolvedHeaders -+ }; -+ } - }); - } - var vertex = createVertex2(); -diff --git a/node_modules/@ai-sdk/google-vertex/dist/edge/index.js.map b/node_modules/@ai-sdk/google-vertex/dist/edge/index.js.map -index 7162bdc..dbdd593 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/edge/index.js.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/edge/index.js.map -@@ -1 +1 @@ --{"version":3,"sources":["../../src/edge/index.ts","../../src/edge/google-vertex-provider-edge.ts","../../src/google-vertex-provider.ts","../../src/version.ts","../../src/google-vertex-embedding-model.ts","../../src/google-vertex-error.ts","../../src/google-vertex-embedding-options.ts","../../src/google-vertex-image-model.ts","../../src/google-vertex-tools.ts","../../src/edge/google-vertex-auth-edge.ts"],"sourcesContent":["export { createVertex, vertex } from './google-vertex-provider-edge';\nexport type {\n GoogleVertexProviderSettings,\n GoogleVertexProvider,\n} from './google-vertex-provider-edge';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from '../google-vertex-provider';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from './google-vertex-auth-edge';\n\nexport type { GoogleVertexProvider };\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n * Optional. The Google credentials for the Google Cloud service account. If\n * not provided, the Google Vertex provider will use environment variables to\n * load the credentials.\n */\n googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\n loadSetting,\n resolve,\n Resolvable,\n withoutTrailingSlash,\n withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleVertexConfig } from './google-vertex-config';\nimport { GoogleVertexEmbeddingModel } from './google-vertex-embedding-model';\nimport { GoogleVertexEmbeddingModelId } from './google-vertex-embedding-options';\nimport { GoogleVertexImageModel } from './google-vertex-image-model';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\nimport { GoogleVertexModelId } from './google-vertex-options';\nimport { googleVertexTools } from './google-vertex-tools';\n\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\n /**\nYour Google Vertex location. Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n */\n location?: string;\n\n /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n */\n project?: string;\n\n /**\n * Headers to use for requests. Can be:\n * - A headers object\n * - A Promise that resolves to a headers object\n * - A function that returns a headers object\n * - A function that returns a Promise of a headers object\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadBaseURL = () => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return (\n withoutTrailingSlash(options.baseURL) ??\n `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV2,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV2 {\n readonly specificationVersion = 'v2';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV2 {\n readonly specificationVersion = 'v2';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\n urlContext: googleTools.urlContext,\n fileSearch: googleTools.fileSearch,\n codeExecution: googleTools.codeExecution,\n vertexRagStore: googleTools.vertexRagStore,\n};\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const 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-\ No newline at end of file -+{"version":3,"sources":["../../src/edge/index.ts","../../src/edge/google-vertex-provider-edge.ts","../../src/google-vertex-provider.ts","../../src/version.ts","../../src/google-vertex-embedding-model.ts","../../src/google-vertex-error.ts","../../src/google-vertex-embedding-options.ts","../../src/google-vertex-image-model.ts","../../src/google-vertex-tools.ts","../../src/edge/google-vertex-auth-edge.ts"],"sourcesContent":["export { createVertex, vertex } from './google-vertex-provider-edge';\nexport type {\n GoogleVertexProviderSettings,\n GoogleVertexProvider,\n} from './google-vertex-provider-edge';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from '../google-vertex-provider';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from './google-vertex-auth-edge';\n\nexport type { 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If\n * not provided, the Google Vertex provider will use environment variables to\n * load the credentials.\n */\n googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => {\n const resolvedHeaders = await resolve(options.headers);\n\n // Express Mode - Use API key authentication\n if (options.apiKey) {\n return {\n 'x-goog-api-key': options.apiKey,\n ...resolvedHeaders,\n };\n }\n\n // OAuth Mode - Use Bearer token authentication\n return {\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...resolvedHeaders,\n };\n },\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV3, LanguageModelV3, ProviderV3 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\n loadSetting,\n resolve,\n Resolvable,\n withoutTrailingSlash,\n withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleVertexConfig } from './google-vertex-config';\nimport { GoogleVertexEmbeddingModel } from './google-vertex-embedding-model';\nimport { GoogleVertexEmbeddingModelId } from './google-vertex-embedding-options';\nimport { GoogleVertexImageModel } from './google-vertex-image-model';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\nimport { GoogleVertexModelId } from './google-vertex-options';\nimport { googleVertexTools } from './google-vertex-tools';\n\nexport interface GoogleVertexProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV3;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV3;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV3;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\n /**\nYour Google Vertex location. Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n */\n location?: string;\n\n /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n */\n project?: string;\n\n /**\n * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: string;\n\n /**\n * Headers to use for requests. Can be:\n * - A headers object\n * - A Promise that resolves to a headers object\n * - A function that returns a headers object\n * - A function that returns a Promise of a headers object\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadApiKey = () =>\n loadSetting({\n settingValue: options.apiKey,\n settingName: 'apiKey',\n environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n description: 'Google Vertex API key',\n });\n\n const isExpressMode = () => {\n try {\n const apiKey = loadApiKey();\n return apiKey != null && apiKey !== '';\n } catch {\n return false;\n }\n };\n\n const loadBaseURL = (): string => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // If baseURL is explicitly provided, use it\n if (options.baseURL) {\n return withoutTrailingSlash(options.baseURL) as string;\n }\n\n // Express Mode uses generativelanguage.googleapis.com\n // if (isExpressMode()) {\n // return `https://generativelanguage.googleapis.com/v1beta/projects/${project}/locations/${region}/publishers/google`;\n // }\n\n // OAuth mode: For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV3,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV3 {\n readonly specificationVersion = 'v3';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV3 {\n readonly specificationVersion = 'v3';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\n urlContext: googleTools.urlContext,\n fileSearch: googleTools.fileSearch,\n codeExecution: googleTools.codeExecution,\n vertexRagStore: googleTools.vertexRagStore,\n};\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n 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-\ No newline at end of file -diff --git a/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs b/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs -index 98f21bd..861ab39 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs -+++ b/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs -@@ -12,7 +12,7 @@ import { - } from "@ai-sdk/provider-utils"; - - // src/version.ts --var VERSION = true ? "3.0.81" : "0.0.0-test"; -+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test"; - - // src/google-vertex-embedding-model.ts - import { -@@ -90,7 +90,7 @@ var googleVertexEmbeddingProviderOptions = z2.object({ - // src/google-vertex-embedding-model.ts - var GoogleVertexEmbeddingModel = class { - constructor(modelId, config) { -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - this.maxEmbeddingsPerCall = 2048; - this.supportsParallelCalls = true; - this.modelId = modelId; -@@ -189,7 +189,7 @@ var GoogleVertexImageModel = class { - constructor(modelId, config) { - this.modelId = modelId; - this.config = config; -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list - this.maxImagesPerCall = 4; - } -@@ -311,12 +311,28 @@ function createVertex(options = {}) { - environmentVariableName: "GOOGLE_VERTEX_LOCATION", - description: "Google Vertex location" - }); -+ const loadApiKey = () => loadSetting({ -+ settingValue: options.apiKey, -+ settingName: "apiKey", -+ environmentVariableName: "GOOGLE_VERTEX_API_KEY", -+ description: "Google Vertex API key" -+ }); -+ const isExpressMode = () => { -+ try { -+ const apiKey = loadApiKey(); -+ return apiKey != null && apiKey !== ""; -+ } catch (e) { -+ return false; -+ } -+ }; - const loadBaseURL = () => { -- var _a; - const region = loadVertexLocation(); - const project = loadVertexProject(); -+ if (options.baseURL) { -+ return withoutTrailingSlash(options.baseURL); -+ } - const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`; -- return (_a = withoutTrailingSlash(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; -+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; - }; - const createConfig = (name) => { - const getHeaders = async () => { -@@ -359,6 +375,7 @@ function createVertex(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.textEmbeddingModel = createEmbeddingModel; - provider.image = createImageModel; -@@ -482,12 +499,21 @@ async function generateAuthToken(credentials) { - function createVertex2(options = {}) { - return createVertex({ - ...options, -- headers: async () => ({ -- Authorization: `Bearer ${await generateAuthToken( -- options.googleCredentials -- )}`, -- ...await resolve4(options.headers) -- }) -+ headers: async () => { -+ const resolvedHeaders = await resolve4(options.headers); -+ if (options.apiKey) { -+ return { -+ "x-goog-api-key": options.apiKey, -+ ...resolvedHeaders -+ }; -+ } -+ return { -+ Authorization: `Bearer ${await generateAuthToken( -+ options.googleCredentials -+ )}`, -+ ...resolvedHeaders -+ }; -+ } - }); - } - var vertex = createVertex2(); -diff --git a/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs.map b/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs.map -index 75d535e..d072547 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/edge/index.mjs.map -@@ -1 +1 @@ --{"version":3,"sources":["../../src/edge/google-vertex-provider-edge.ts","../../src/google-vertex-provider.ts","../../src/version.ts","../../src/google-vertex-embedding-model.ts","../../src/google-vertex-error.ts","../../src/google-vertex-embedding-options.ts","../../src/google-vertex-image-model.ts","../../src/google-vertex-tools.ts","../../src/edge/google-vertex-auth-edge.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from '../google-vertex-provider';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from './google-vertex-auth-edge';\n\nexport type { GoogleVertexProvider };\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n * Optional. The Google credentials for the Google Cloud service account. If\n * not provided, the Google Vertex provider will use environment variables to\n * load the credentials.\n */\n googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\n loadSetting,\n resolve,\n Resolvable,\n withoutTrailingSlash,\n withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleVertexConfig } from './google-vertex-config';\nimport { GoogleVertexEmbeddingModel } from './google-vertex-embedding-model';\nimport { GoogleVertexEmbeddingModelId } from './google-vertex-embedding-options';\nimport { GoogleVertexImageModel } from './google-vertex-image-model';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\nimport { GoogleVertexModelId } from './google-vertex-options';\nimport { googleVertexTools } from './google-vertex-tools';\n\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\n /**\nYour Google Vertex location. Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n */\n location?: string;\n\n /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n */\n project?: string;\n\n /**\n * Headers to use for requests. Can be:\n * - A headers object\n * - A Promise that resolves to a headers object\n * - A function that returns a headers object\n * - A function that returns a Promise of a headers object\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadBaseURL = () => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return (\n withoutTrailingSlash(options.baseURL) ??\n `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV2,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV2 {\n readonly specificationVersion = 'v2';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV2 {\n readonly specificationVersion = 'v2';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\n urlContext: googleTools.urlContext,\n fileSearch: googleTools.fileSearch,\n codeExecution: googleTools.codeExecution,\n vertexRagStore: googleTools.vertexRagStore,\n};\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n 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Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n */\n location?: string;\n\n /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n */\n project?: string;\n\n /**\n * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: string;\n\n /**\n * Headers to use for requests. Can be:\n * - A headers object\n * - A Promise that resolves to a headers object\n * - A function that returns a headers object\n * - A function that returns a Promise of a headers object\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadApiKey = () =>\n loadSetting({\n settingValue: options.apiKey,\n settingName: 'apiKey',\n environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n description: 'Google Vertex API key',\n });\n\n const isExpressMode = () => {\n try {\n const apiKey = loadApiKey();\n return apiKey != null && apiKey !== '';\n } catch {\n return false;\n }\n };\n\n const loadBaseURL = (): string => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // If baseURL is explicitly provided, use it\n if (options.baseURL) {\n return withoutTrailingSlash(options.baseURL) as string;\n }\n\n // Express Mode uses generativelanguage.googleapis.com\n // if (isExpressMode()) {\n // return `https://generativelanguage.googleapis.com/v1beta/projects/${project}/locations/${region}/publishers/google`;\n // }\n\n // OAuth mode: For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV3,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV3 {\n readonly specificationVersion = 'v3';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV3 {\n readonly specificationVersion = 'v3';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\n urlContext: googleTools.urlContext,\n fileSearch: googleTools.fileSearch,\n codeExecution: googleTools.codeExecution,\n vertexRagStore: googleTools.vertexRagStore,\n};\n","import {\n loadOptionalSetting,\n loadSetting,\n withUserAgentSuffix,\n getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n /**\n * The client email for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n */\n clientEmail: string;\n\n /**\n * The private key for the Google Cloud service account. Defaults to the\n * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n */\n privateKey: string;\n\n /**\n * Optional. The private key ID for the Google Cloud service account. Defaults\n * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n */\n privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise => {\n try {\n return {\n clientEmail: loadSetting({\n settingValue: undefined,\n settingName: 'clientEmail',\n environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n description: 'Google client email',\n }),\n privateKey: loadSetting({\n settingValue: undefined,\n settingName: 'privateKey',\n environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n description: 'Google private key',\n }),\n privateKeyId: loadOptionalSetting({\n settingValue: undefined,\n environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n }),\n };\n } catch (error: any) {\n throw new Error(`Failed to load Google credentials: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}`;\n const encoder = new TextEncoder();\n const data = encoder.encode(signingInput);\n\n const signature = await crypto.subtle.sign(\n 'RSASSA-PKCS1-v1_5',\n privateKey,\n data,\n );\n\n const signatureBase64 = base64url(\n String.fromCharCode(...new Uint8Array(signature)),\n );\n\n return `${base64url(JSON.stringify(header))}.${base64url(\n JSON.stringify(payload),\n )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n try {\n const creds = credentials || (await loadCredentials());\n const jwt = await buildJwt(creds);\n\n const response = await fetch('https://oauth2.googleapis.com/token', {\n method: 'POST',\n headers: withUserAgentSuffix(\n { 'Content-Type': 'application/x-www-form-urlencoded' },\n `ai-sdk/google-vertex/${VERSION}`,\n getRuntimeEnvironmentUserAgent(),\n ),\n body: new URLSearchParams({\n grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n assertion: jwt,\n }),\n });\n\n if (!response.ok) {\n throw new Error(`Token request failed: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n 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-\ No newline at end of file -diff --git a/node_modules/@ai-sdk/google-vertex/dist/index.d.mts b/node_modules/@ai-sdk/google-vertex/dist/index.d.mts -index 8d77cd2..6714ed9 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/index.d.mts -+++ b/node_modules/@ai-sdk/google-vertex/dist/index.d.mts -@@ -1,6 +1,6 @@ - import { z } from 'zod/v4'; - import { GoogleAuthOptions } from 'google-auth-library'; --import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider'; - import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - -@@ -24,7 +24,7 @@ declare const vertexImageProviderOptionsSchema: z.ZodObject<{ - }, z.core.$strip>; - type GoogleVertexImageProviderOptions = z.infer; - --type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); -+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); - - declare const googleVertexTools: { - googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, { -@@ -51,20 +51,20 @@ declare const googleVertexTools: { - }>; - }; - --interface GoogleVertexProvider extends ProviderV2 { -+interface GoogleVertexProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexModelId): LanguageModelV2; -- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2; -+ (modelId: GoogleVertexModelId): LanguageModelV3; -+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3; - /** - * Creates a model for image generation. - */ -- image(modelId: GoogleVertexImageModelId): ImageModelV2; -+ image(modelId: GoogleVertexImageModelId): ImageModelV3; - /** - Creates a model for image generation. - */ -- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2; -+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3; - tools: typeof googleVertexTools; - } - interface GoogleVertexProviderSettings$1 { -@@ -76,6 +76,12 @@ interface GoogleVertexProviderSettings$1 { - Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`. - */ - project?: string; -+ /** -+ * API key for Express Mode authentication. When provided, uses simplified -+ * authentication with `x-goog-api-key` header instead of OAuth. -+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`. -+ */ -+ apiKey?: string; - /** - * Headers to use for requests. Can be: - * - A headers object -diff --git a/node_modules/@ai-sdk/google-vertex/dist/index.d.ts b/node_modules/@ai-sdk/google-vertex/dist/index.d.ts -index 8d77cd2..6714ed9 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/index.d.ts -+++ b/node_modules/@ai-sdk/google-vertex/dist/index.d.ts -@@ -1,6 +1,6 @@ - import { z } from 'zod/v4'; - import { GoogleAuthOptions } from 'google-auth-library'; --import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider'; -+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider'; - import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils'; - import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils'; - -@@ -24,7 +24,7 @@ declare const vertexImageProviderOptionsSchema: z.ZodObject<{ - }, z.core.$strip>; - type GoogleVertexImageProviderOptions = z.infer; - --type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); -+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {}); - - declare const googleVertexTools: { - googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, { -@@ -51,20 +51,20 @@ declare const googleVertexTools: { - }>; - }; - --interface GoogleVertexProvider extends ProviderV2 { -+interface GoogleVertexProvider extends ProviderV3 { - /** - Creates a model for text generation. - */ -- (modelId: GoogleVertexModelId): LanguageModelV2; -- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2; -+ (modelId: GoogleVertexModelId): LanguageModelV3; -+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3; - /** - * Creates a model for image generation. - */ -- image(modelId: GoogleVertexImageModelId): ImageModelV2; -+ image(modelId: GoogleVertexImageModelId): ImageModelV3; - /** - Creates a model for image generation. - */ -- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2; -+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3; - tools: typeof googleVertexTools; - } - interface GoogleVertexProviderSettings$1 { -@@ -76,6 +76,12 @@ interface GoogleVertexProviderSettings$1 { - Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`. - */ - project?: string; -+ /** -+ * API key for Express Mode authentication. When provided, uses simplified -+ * authentication with `x-goog-api-key` header instead of OAuth. -+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`. -+ */ -+ apiKey?: string; - /** - * Headers to use for requests. Can be: - * - A headers object -diff --git a/node_modules/@ai-sdk/google-vertex/dist/index.js b/node_modules/@ai-sdk/google-vertex/dist/index.js -index 7079679..773069e 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/index.js -+++ b/node_modules/@ai-sdk/google-vertex/dist/index.js -@@ -55,7 +55,7 @@ var import_internal2 = require("@ai-sdk/google/internal"); - var import_provider_utils4 = require("@ai-sdk/provider-utils"); - - // src/version.ts --var VERSION = true ? "3.0.81" : "0.0.0-test"; -+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test"; - - // src/google-vertex-embedding-model.ts - var import_provider = require("@ai-sdk/provider"); -@@ -125,7 +125,7 @@ var googleVertexEmbeddingProviderOptions = import_v42.z.object({ - // src/google-vertex-embedding-model.ts - var GoogleVertexEmbeddingModel = class { - constructor(modelId, config) { -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - this.maxEmbeddingsPerCall = 2048; - this.supportsParallelCalls = true; - this.modelId = modelId; -@@ -218,7 +218,7 @@ var GoogleVertexImageModel = class { - constructor(modelId, config) { - this.modelId = modelId; - this.config = config; -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list - this.maxImagesPerCall = 4; - } -@@ -340,12 +340,28 @@ function createVertex(options = {}) { - environmentVariableName: "GOOGLE_VERTEX_LOCATION", - description: "Google Vertex location" - }); -+ const loadApiKey = () => (0, import_provider_utils4.loadSetting)({ -+ settingValue: options.apiKey, -+ settingName: "apiKey", -+ environmentVariableName: "GOOGLE_VERTEX_API_KEY", -+ description: "Google Vertex API key" -+ }); -+ const isExpressMode = () => { -+ try { -+ const apiKey = loadApiKey(); -+ return apiKey != null && apiKey !== ""; -+ } catch (e) { -+ return false; -+ } -+ }; - const loadBaseURL = () => { -- var _a; - const region = loadVertexLocation(); - const project = loadVertexProject(); -+ if (options.baseURL) { -+ return (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL); -+ } - const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`; -- return (_a = (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; -+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; - }; - const createConfig = (name) => { - const getHeaders = async () => { -@@ -388,6 +404,7 @@ function createVertex(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.textEmbeddingModel = createEmbeddingModel; - provider.image = createImageModel; -@@ -400,12 +417,21 @@ function createVertex(options = {}) { - function createVertex2(options = {}) { - return createVertex({ - ...options, -- headers: async () => ({ -- Authorization: `Bearer ${await generateAuthToken( -- options.googleAuthOptions -- )}`, -- ...await (0, import_provider_utils5.resolve)(options.headers) -- }) -+ headers: async () => { -+ const resolvedHeaders = await (0, import_provider_utils5.resolve)(options.headers); -+ if (options.apiKey) { -+ return { -+ "x-goog-api-key": options.apiKey, -+ ...resolvedHeaders -+ }; -+ } -+ return { -+ Authorization: `Bearer ${await generateAuthToken( -+ options.googleAuthOptions -+ )}`, -+ ...resolvedHeaders -+ }; -+ } - }); - } - var vertex = createVertex2(); -diff --git a/node_modules/@ai-sdk/google-vertex/dist/index.js.map b/node_modules/@ai-sdk/google-vertex/dist/index.js.map -index 7e93232..474dc37 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/index.js.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/index.js.map -@@ -1 +1 @@ --{"version":3,"sources":["../src/index.ts","../src/google-vertex-provider-node.ts","../src/google-vertex-auth-google-auth-library.ts","../src/google-vertex-provider.ts","../src/version.ts","../src/google-vertex-embedding-model.ts","../src/google-vertex-error.ts","../src/google-vertex-embedding-options.ts","../src/google-vertex-image-model.ts","../src/google-vertex-tools.ts"],"sourcesContent":["export type { GoogleVertexImageProviderOptions } from './google-vertex-image-model';\nexport { createVertex, vertex } from './google-vertex-provider-node';\nexport type {\n GoogleVertexProvider,\n GoogleVertexProviderSettings,\n} from './google-vertex-provider-node';\nexport { VERSION } from './version';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from './google-vertex-auth-google-auth-library';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from './google-vertex-provider';\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport type { GoogleVertexProvider };\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\n loadSetting,\n resolve,\n Resolvable,\n withoutTrailingSlash,\n withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleVertexConfig } from './google-vertex-config';\nimport { GoogleVertexEmbeddingModel } from './google-vertex-embedding-model';\nimport { GoogleVertexEmbeddingModelId } from './google-vertex-embedding-options';\nimport { GoogleVertexImageModel } from './google-vertex-image-model';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\nimport { GoogleVertexModelId } from './google-vertex-options';\nimport { googleVertexTools } from './google-vertex-tools';\n\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\n /**\nYour Google Vertex location. Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n */\n location?: string;\n\n /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n */\n project?: string;\n\n /**\n * Headers to use for requests. Can be:\n * - A headers object\n * - A Promise that resolves to a headers object\n * - A function that returns a headers object\n * - A function that returns a Promise of a headers object\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadBaseURL = () => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return (\n withoutTrailingSlash(options.baseURL) ??\n `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV2,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV2 {\n readonly specificationVersion = 'v2';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV2 {\n readonly specificationVersion = 'v2';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\n urlContext: googleTools.urlContext,\n fileSearch: googleTools.fileSearch,\n codeExecution: googleTools.codeExecution,\n vertexRagStore: 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-\ No newline at end of file -+{"version":3,"sources":["../src/index.ts","../src/google-vertex-provider-node.ts","../src/google-vertex-auth-google-auth-library.ts","../src/google-vertex-provider.ts","../src/version.ts","../src/google-vertex-embedding-model.ts","../src/google-vertex-error.ts","../src/google-vertex-embedding-options.ts","../src/google-vertex-image-model.ts","../src/google-vertex-tools.ts"],"sourcesContent":["export type { GoogleVertexImageProviderOptions } from './google-vertex-image-model';\nexport { createVertex, vertex } from './google-vertex-provider-node';\nexport type {\n GoogleVertexProvider,\n GoogleVertexProviderSettings,\n} from './google-vertex-provider-node';\nexport { VERSION } from './version';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from './google-vertex-auth-google-auth-library';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n 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'' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV3,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV3 {\n readonly specificationVersion = 'v3';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV3 {\n readonly specificationVersion = 'v3';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = 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"3.0.81" : "0.0.0-test"; -+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test"; - - // src/google-vertex-embedding-model.ts - import { -@@ -111,7 +111,7 @@ var googleVertexEmbeddingProviderOptions = z2.object({ - // src/google-vertex-embedding-model.ts - var GoogleVertexEmbeddingModel = class { - constructor(modelId, config) { -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - this.maxEmbeddingsPerCall = 2048; - this.supportsParallelCalls = true; - this.modelId = modelId; -@@ -210,7 +210,7 @@ var GoogleVertexImageModel = class { - constructor(modelId, config) { - this.modelId = modelId; - this.config = config; -- this.specificationVersion = "v2"; -+ this.specificationVersion = "v3"; - // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list - this.maxImagesPerCall = 4; - } -@@ -332,12 +332,28 @@ function createVertex(options = {}) { - environmentVariableName: "GOOGLE_VERTEX_LOCATION", - description: "Google Vertex location" - }); -+ const loadApiKey = () => loadSetting({ -+ settingValue: options.apiKey, -+ settingName: "apiKey", -+ environmentVariableName: "GOOGLE_VERTEX_API_KEY", -+ description: "Google Vertex API key" -+ }); -+ const isExpressMode = () => { -+ try { -+ const apiKey = loadApiKey(); -+ return apiKey != null && apiKey !== ""; -+ } catch (e) { -+ return false; -+ } -+ }; - const loadBaseURL = () => { -- var _a; - const region = loadVertexLocation(); - const project = loadVertexProject(); -+ if (options.baseURL) { -+ return withoutTrailingSlash(options.baseURL); -+ } - const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`; -- return (_a = withoutTrailingSlash(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; -+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`; - }; - const createConfig = (name) => { - const getHeaders = async () => { -@@ -380,6 +396,7 @@ function createVertex(options = {}) { - } - return createChatModel(modelId); - }; -+ provider.specificationVersion = "v3"; - provider.languageModel = createChatModel; - provider.textEmbeddingModel = createEmbeddingModel; - provider.image = createImageModel; -@@ -392,12 +409,21 @@ function createVertex(options = {}) { - function createVertex2(options = {}) { - return createVertex({ - ...options, -- headers: async () => ({ -- Authorization: `Bearer ${await generateAuthToken( -- options.googleAuthOptions -- )}`, -- ...await resolve4(options.headers) -- }) -+ headers: async () => { -+ const resolvedHeaders = await resolve4(options.headers); -+ if (options.apiKey) { -+ return { -+ "x-goog-api-key": options.apiKey, -+ ...resolvedHeaders -+ }; -+ } -+ return { -+ Authorization: `Bearer ${await generateAuthToken( -+ options.googleAuthOptions -+ )}`, -+ ...resolvedHeaders -+ }; -+ } - }); - } - var vertex = createVertex2(); -diff --git a/node_modules/@ai-sdk/google-vertex/dist/index.mjs.map b/node_modules/@ai-sdk/google-vertex/dist/index.mjs.map -index 19e04d6..0238ee3 100644 ---- a/node_modules/@ai-sdk/google-vertex/dist/index.mjs.map -+++ b/node_modules/@ai-sdk/google-vertex/dist/index.mjs.map -@@ -1 +1 @@ --{"version":3,"sources":["../src/google-vertex-provider-node.ts","../src/google-vertex-auth-google-auth-library.ts","../src/google-vertex-provider.ts","../src/version.ts","../src/google-vertex-embedding-model.ts","../src/google-vertex-error.ts","../src/google-vertex-embedding-options.ts","../src/google-vertex-image-model.ts","../src/google-vertex-tools.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from './google-vertex-auth-google-auth-library';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from './google-vertex-provider';\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n Optional. 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You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadBaseURL = () => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return (\n withoutTrailingSlash(options.baseURL) ??\n `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV2,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV2 {\n readonly specificationVersion = 'v2';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV2 {\n readonly specificationVersion = 'v2';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = 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Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n */\n location?: string;\n\n /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n */\n project?: string;\n\n /**\n * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: string;\n\n /**\n * Headers to use for requests. Can be:\n * - A headers object\n * - A Promise that resolves to a headers object\n * - A function that returns a headers object\n * - A function that returns a Promise of a headers object\n */\n headers?: Resolvable>;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n // for testing\n generateId?: () => string;\n\n /**\nBase URL for the Google Vertex API calls.\n */\n baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n const loadVertexProject = () =>\n loadSetting({\n settingValue: options.project,\n settingName: 'project',\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n description: 'Google Vertex project',\n });\n\n const loadVertexLocation = () =>\n loadSetting({\n settingValue: options.location,\n settingName: 'location',\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n description: 'Google Vertex location',\n });\n\n const loadApiKey = () =>\n loadSetting({\n settingValue: options.apiKey,\n settingName: 'apiKey',\n environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n description: 'Google Vertex API key',\n });\n\n const isExpressMode = () => {\n try {\n const apiKey = loadApiKey();\n return apiKey != null && apiKey !== '';\n } catch {\n return false;\n }\n };\n\n const loadBaseURL = (): string => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // If baseURL is explicitly provided, use it\n if (options.baseURL) {\n return withoutTrailingSlash(options.baseURL) as string;\n }\n\n // Express Mode uses generativelanguage.googleapis.com\n // if (isExpressMode()) {\n // return `https://generativelanguage.googleapis.com/v1beta/projects/${project}/locations/${region}/publishers/google`;\n // }\n\n // OAuth mode: For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\n // Google Cloud Storage URLs:\n /^gs:\\/\\/.*$/,\n ],\n }),\n });\n };\n\n const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n const createImageModel = (modelId: GoogleVertexImageModelId) =>\n new GoogleVertexImageModel(modelId, createConfig('image'));\n\n const provider = function (modelId: GoogleVertexModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Vertex AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.tools = googleVertexTools;\n\n return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n EmbeddingModelV3,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n resolve,\n parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n GoogleVertexEmbeddingModelId,\n googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV3 {\n readonly specificationVersion = 'v3';\n readonly modelId: GoogleVertexEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleVertexConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n modelId: GoogleVertexEmbeddingModelId,\n config: GoogleVertexConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url,\n headers: mergedHeaders,\n body: {\n instances: values.map(value => ({\n content: value,\n task_type: googleOptions.taskType,\n title: googleOptions.title,\n })),\n parameters: {\n outputDimensionality: googleOptions.outputDimensionality,\n autoTruncate: googleOptions.autoTruncate,\n },\n },\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleVertexTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.predictions.map(\n prediction => prediction.embeddings.values,\n ),\n usage: {\n tokens: response.predictions.reduce(\n (tokenCount, prediction) =>\n tokenCount + prediction.embeddings.statistics.token_count,\n 0,\n ),\n },\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n predictions: z.array(\n z.object({\n embeddings: z.object({\n values: z.array(z.number()),\n statistics: z.object({\n token_count: z.number(),\n }),\n }),\n }),\n ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n});\n\nexport type GoogleVertexErrorData = z.infer;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n {\n errorSchema: googleVertexErrorDataSchema,\n errorToMessage: data => data.error.message,\n },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n | 'textembedding-gecko'\n | 'textembedding-gecko@001'\n | 'textembedding-gecko@003'\n | 'textembedding-gecko-multilingual'\n | 'textembedding-gecko-multilingual@001'\n | 'text-multilingual-embedding-002'\n | 'text-embedding-004'\n | 'text-embedding-005'\n | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. The title of the document being embedded.\n * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n * Helps the model produce better embeddings by providing additional context.\n */\n title: z.string().optional(),\n\n /**\n * Optional. When set to true, input text will be truncated. When set to false,\n * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n */\n autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n typeof googleVertexEmbeddingProviderOptions\n>;\n","import { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\n createJsonResponseHandler,\n parseProviderOptions,\n postJsonToApi,\n resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable>;\n fetch?: typeof fetch;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV3 {\n readonly specificationVersion = 'v3';\n // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n readonly maxImagesPerCall = 4;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleVertexImageModelId,\n private config: GoogleVertexImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n const vertexImageOptions = await parseProviderOptions({\n provider: 'vertex',\n providerOptions,\n schema: vertexImageProviderOptionsSchema,\n });\n\n const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\n },\n };\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleVertexFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n vertexImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images:\n response.predictions?.map(\n ({ bytesBase64Encoded }) => bytesBase64Encoded,\n ) ?? [],\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n providerMetadata: {\n vertex: {\n images:\n response.predictions?.map(prediction => {\n const {\n // normalize revised prompt property\n prompt: revisedPrompt,\n } = prediction;\n\n return { ...(revisedPrompt != null && { revisedPrompt }) };\n }) ?? [],\n },\n },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n predictions: z\n .array(\n z.object({\n bytesBase64Encoded: z.string(),\n mimeType: z.string(),\n prompt: z.string().nullish(),\n }),\n )\n .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n negativePrompt: z.string().nullish(),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n safetySetting: z\n .enum([\n 'block_low_and_above',\n 'block_medium_and_above',\n 'block_only_high',\n 'block_none',\n ])\n .nullish(),\n addWatermark: z.boolean().nullish(),\n storageUri: z.string().nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = 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-\ No newline at end of file diff --git a/scripts/create-justification.ts b/scripts/create-justification.ts deleted file mode 100644 index 04f48d70c..000000000 --- a/scripts/create-justification.ts +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env node -import { mkdirSync, readFileSync, writeFileSync } from 'fs'; -import { join } from 'path'; - -import { generateId } from './utils/generate-id'; - -const JUSTIFICATIONS_DIR = join(process.cwd(), '.agents', 'justifications'); - -const main = () => { - const args = process.argv.slice(2); - - if (args.length === 0) { - console.error('Usage: npx tsx scripts/create-justification.ts "file-slug" [content]'); - console.error(' or: npx tsx scripts/create-justification.ts "file-slug" << HEREDOC'); - process.exit(1); - } - - const slug = args[0]; - let content = ''; - - // Check if content is provided as second argument - if (args.length > 1) { - content = args.slice(1).join(' '); - } else { - // Read from stdin (heredoc) - try { - const stdin = readFileSync(0, 'utf-8'); - content = stdin.trim(); - } catch (error) { - console.error('Error reading from stdin:', error); - process.exit(1); - } - } - - if (!content) { - console.error('Error: No content provided'); - process.exit(1); - } - - // Generate unique ID - const id = generateId(); - const filename = `${id}-${slug}.md`; - const filepath = join(JUSTIFICATIONS_DIR, filename); - - // Format title from slug (kebab-case to Title Case) - const title = slug - .split('-') - .map((word) => word.charAt(0).toUpperCase() + word.slice(1)) - .join(' '); - - // Get current date in ISO format - const date = new Date().toISOString().split('T')[0]; - - // Create frontmatter - const frontmatter = `--- -date: ${date} -title: ${title} ---- - -`; - - // Ensure directory exists - mkdirSync(JUSTIFICATIONS_DIR, { recursive: true }); - - // Write file with frontmatter - writeFileSync(filepath, frontmatter + content, 'utf-8'); - - console.log(`Created justification: ${filepath}`); - console.log(`ID: ${id}`); - console.log(`Filename: ${filename}`); -}; - -main(); diff --git a/scripts/create-plan.ts b/scripts/create-plan.ts deleted file mode 100644 index 647205935..000000000 --- a/scripts/create-plan.ts +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env node -import { mkdirSync, readFileSync, writeFileSync } from 'fs'; -import { join } from 'path'; - -import { generateId } from './utils/generate-id'; - -const PLANS_DIR = join(process.cwd(), '.agents', 'plans'); - -const main = () => { - const args = process.argv.slice(2); - - if (args.length === 0) { - console.error('Usage: npx tsx scripts/create-plan.ts "file-slug" [content]'); - console.error(' or: npx tsx scripts/create-plan.ts "file-slug" << HEREDOC'); - process.exit(1); - } - - const slug = args[0]; - let content = ''; - - // Check if content is provided as second argument - if (args.length > 1) { - content = args.slice(1).join(' '); - } else { - // Read from stdin (heredoc) - try { - const stdin = readFileSync(0, 'utf-8'); - content = stdin.trim(); - } catch (error) { - console.error('Error reading from stdin:', error); - process.exit(1); - } - } - - if (!content) { - console.error('Error: No content provided'); - process.exit(1); - } - - // Generate unique ID - const id = generateId(); - const filename = `${id}-${slug}.md`; - const filepath = join(PLANS_DIR, filename); - - // Format title from slug (kebab-case to Title Case) - const title = slug - .split('-') - .map((word) => word.charAt(0).toUpperCase() + word.slice(1)) - .join(' '); - - // Get current date in ISO format - const date = new Date().toISOString().split('T')[0]; - - // Create frontmatter - const frontmatter = `--- -date: ${date} -title: ${title} ---- - -`; - - // Ensure directory exists - mkdirSync(PLANS_DIR, { recursive: true }); - - // Write file with frontmatter - writeFileSync(filepath, frontmatter + content, 'utf-8'); - - console.log(`Created plan: ${filepath}`); - console.log(`ID: ${id}`); - console.log(`Filename: ${filename}`); -}; - -main(); diff --git a/scripts/create-scratch.ts b/scripts/create-scratch.ts deleted file mode 100644 index fe77644ce..000000000 --- a/scripts/create-scratch.ts +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env node -import { mkdirSync, readFileSync, writeFileSync } from 'fs'; -import { join } from 'path'; - -import { generateId } from './utils/generate-id'; - -const SCRATCHES_DIR = join(process.cwd(), '.agents', 'scratches'); - -const main = () => { - const args = process.argv.slice(2); - - if (args.length === 0) { - console.error('Usage: npx tsx scripts/create-scratch.ts "file-slug" [content]'); - console.error(' or: npx tsx scripts/create-scratch.ts "file-slug" << HEREDOC'); - process.exit(1); - } - - const slug = args[0]; - let content = ''; - - // Check if content is provided as second argument - if (args.length > 1) { - content = args.slice(1).join(' '); - } else { - // Read from stdin (heredoc) - try { - const stdin = readFileSync(0, 'utf-8'); - content = stdin.trim(); - } catch (error) { - console.error('Error reading from stdin:', error); - process.exit(1); - } - } - - if (!content) { - console.error('Error: No content provided'); - process.exit(1); - } - - // Generate unique ID - const id = generateId(); - const filename = `${id}-${slug}.md`; - const filepath = join(SCRATCHES_DIR, filename); - - // Format title from slug (kebab-case to Title Case) - const title = slug - .split('-') - .map((word) => word.charAt(0).toUpperCase() + word.slice(1)) - .join(' '); - - // Get current date in ISO format - const date = new Date().toISOString().split('T')[0]; - - // Create frontmatter - const frontmatter = `--- -date: ${date} -title: ${title} ---- - -`; - - // Ensure directory exists - mkdirSync(SCRATCHES_DIR, { recursive: true }); - - // Write file with frontmatter - writeFileSync(filepath, frontmatter + content, 'utf-8'); - - console.log(`Created scratch: ${filepath}`); - console.log(`ID: ${id}`); - console.log(`Filename: ${filename}`); -}; - -main(); diff --git a/scripts/utils/generate-id.ts b/scripts/utils/generate-id.ts deleted file mode 100644 index d2c950076..000000000 --- a/scripts/utils/generate-id.ts +++ /dev/null @@ -1,84 +0,0 @@ -/** - * Generates a unique identifier using three simple words. - * Falls back to unix timestamp if word generation fails. - */ -export const generateId = (): string => { - const adjectives = [ - 'happy', - 'bright', - 'swift', - 'calm', - 'bold', - 'clever', - 'gentle', - 'quick', - 'sharp', - 'warm', - 'cool', - 'fresh', - 'solid', - 'clear', - 'sweet', - 'wild', - 'quiet', - 'loud', - 'smooth', - ]; - - const nouns = [ - 'moon', - 'star', - 'ocean', - 'river', - 'forest', - 'mountain', - 'cloud', - 'wave', - 'stone', - 'flower', - 'bird', - 'wind', - 'light', - 'shadow', - 'fire', - 'earth', - 'sky', - 'tree', - 'leaf', - 'rock', - ]; - - const colors = [ - 'blue', - 'red', - 'green', - 'yellow', - 'purple', - 'orange', - 'pink', - 'cyan', - 'amber', - 'emerald', - 'violet', - 'indigo', - 'coral', - 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