diff --git a/apps/docs/components/icons.tsx b/apps/docs/components/icons.tsx index 606e5b0faf6..9ef127b7958 100644 --- a/apps/docs/components/icons.tsx +++ b/apps/docs/components/icons.tsx @@ -2412,6 +2412,36 @@ export function ImageIcon(props: SVGProps) { ) } +export function EmbeddingsIcon(props: SVGProps) { + return ( + + {/* Rays sit below the nodes in weight, but not so far below that they + wash out to loose dots at the 14px search-row size. */} + + + + + + + + ) +} + export function TypeformIcon(props: SVGProps) { return ( = { elasticsearch: ElasticsearchIcon, elevenlabs: ElevenLabsIcon, emailbison: EmailBisonIcon, + embeddings: EmbeddingsIcon, enrich: EnrichSoIcon, enrichment: EnrichmentIcon, enrow: EnrowIcon, diff --git a/apps/docs/content/docs/en/integrations/embeddings.mdx b/apps/docs/content/docs/en/integrations/embeddings.mdx new file mode 100644 index 00000000000..1759b946d91 --- /dev/null +++ b/apps/docs/content/docs/en/integrations/embeddings.mdx @@ -0,0 +1,130 @@ +--- +title: Embeddings +description: Generate embeddings +--- + +import { BlockInfoCard } from "@/components/ui/block-info-card" + + + +{/* MANUAL-CONTENT-START:intro */} +An embedding turns a piece of text into a list of numbers that captures its meaning. Two texts that mean similar things get similar numbers, so you can compare meaning directly instead of matching keywords. That is what powers semantic search, grouping related items, and spotting near-duplicates that are worded differently. + +The Embeddings block generates those numbers using OpenAI, Google Gemini, Cohere, or Mistral. Pick a provider, pick one of its models, pass in text, and get a vector back — one vector per input, in the order you supplied them. You can embed a single string or a list of strings in one call. + +Models differ in what they are good at and what they cost. `text-embedding-3-small` is the cost-efficient general choice, `gemini-embedding-001` gives the highest retrieval quality, `embed-v4.0` handles multilingual content, and `codestral-embed` is tuned for source code. Some models also let you trade vector size against quality, and some accept a task type so the vector is conditioned for how it will be used — the block only offers those controls for the models that actually support them. + +Two things worth knowing before you build on it. Vectors are only comparable when they come from the same model at the same size, so changing either means re-embedding everything you intend to compare. And input longer than the model's limit is shortened to fit rather than rejected, with a warning in the run, so chunk long documents yourself when the tail matters. + +Sim's knowledge bases embed separately, at a fixed vector width and from a smaller set of models. This block is for embedding text yourself inside a workflow. +{/* MANUAL-CONTENT-END */} + + +## Usage Instructions + +Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, Google Gemini, Cohere, and Mistral embedding models. + + + +## Actions + +### `embeddings_openai` + +Generate embeddings from text using OpenAI's embedding models + +#### Input + +| Parameter | Type | Required | Description | +| --------- | ---- | -------- | ----------- | +| `input` | string | Yes | Text to embed, or an array of texts to embed in one call | +| `model` | string | No | Embedding model to use | +| `taskType` | string | No | What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering | +| `dimensions` | number | No | Output dimensions, when the model supports truncation. Defaults to native. | +| `apiKey` | string | Yes | API key for the selected embedding provider | + +#### Output + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| `embeddings` | json | Generated embeddings | +| `model` | string | Model used | +| `provider` | string | Provider used | +| `dimensions` | number | Dimensionality of each vector | +| `usage` | json | Token usage | + +### `embeddings_gemini` + +Generate embeddings from text using Google's Gemini embedding models + +#### Input + +| Parameter | Type | Required | Description | +| --------- | ---- | -------- | ----------- | +| `input` | string | Yes | Text to embed, or an array of texts to embed in one call | +| `model` | string | No | Embedding model to use | +| `taskType` | string | No | What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering | +| `dimensions` | number | No | Output dimensions, when the model supports truncation. Defaults to native. | +| `apiKey` | string | Yes | API key for the selected embedding provider | + +#### Output + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| `embeddings` | json | Generated embeddings | +| `model` | string | Model used | +| `provider` | string | Provider used | +| `dimensions` | number | Dimensionality of each vector | +| `usage` | json | Token usage | + +### `embeddings_cohere` + +Generate embeddings from text using Cohere's embedding models + +#### Input + +| Parameter | Type | Required | Description | +| --------- | ---- | -------- | ----------- | +| `input` | string | Yes | Text to embed, or an array of texts to embed in one call | +| `model` | string | No | Embedding model to use | +| `taskType` | string | No | What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering | +| `dimensions` | number | No | Output dimensions, when the model supports truncation. Defaults to native. | +| `apiKey` | string | Yes | API key for the selected embedding provider | + +#### Output + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| `embeddings` | json | Generated embeddings | +| `model` | string | Model used | +| `provider` | string | Provider used | +| `dimensions` | number | Dimensionality of each vector | +| `usage` | json | Token usage | + +### `embeddings_mistral` + +Generate embeddings from text using Mistral's embedding models + +#### Input + +| Parameter | Type | Required | Description | +| --------- | ---- | -------- | ----------- | +| `input` | string | Yes | Text to embed, or an array of texts to embed in one call | +| `model` | string | No | Embedding model to use | +| `taskType` | string | No | What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering | +| `dimensions` | number | No | Output dimensions, when the model supports truncation. Defaults to native. | +| `apiKey` | string | Yes | API key for the selected embedding provider | + +#### Output + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| `embeddings` | json | Generated embeddings | +| `model` | string | Model used | +| `provider` | string | Provider used | +| `dimensions` | number | Dimensionality of each vector | +| `usage` | json | Token usage | + + diff --git a/apps/docs/content/docs/en/integrations/exa.mdx b/apps/docs/content/docs/en/integrations/exa.mdx index 509a74803c0..b74db293093 100644 --- a/apps/docs/content/docs/en/integrations/exa.mdx +++ b/apps/docs/content/docs/en/integrations/exa.mdx @@ -71,8 +71,6 @@ Search the web using Exa AI. Returns relevant search results with titles, URLs, | `startCrawlDate` | string | No | Deprecated: use startPublishedDate. Only include results crawled on or after this ISO 8601 date | | `endCrawlDate` | string | No | Deprecated: use endPublishedDate. Only include results crawled on or before this ISO 8601 date | | `apiKey` | string | Yes | Exa AI API Key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -120,8 +118,6 @@ Retrieve the contents of webpages using Exa AI. Returns the title, text content, | `livecrawlTimeout` | number | No | Live crawl timeout in milliseconds \(max 90000\). Default: 10000 | | `livecrawl` | string | No | Deprecated: use maxAgeHours instead. Live crawling mode: never, fallback, always, or preferred | | `apiKey` | string | Yes | Exa AI API Key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -162,8 +158,6 @@ Find webpages similar to a given URL using Exa AI. Deprecated by Exa in favor of | `livecrawlTimeout` | number | No | Live crawl timeout in milliseconds \(max 90000\). Default: 10000 | | `livecrawl` | string | No | Deprecated: use maxAgeHours instead. Live crawling mode: never, fallback, always, or preferred | | `apiKey` | string | Yes | Exa AI API Key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -191,8 +185,6 @@ Get an AI-generated answer to a question with citations from the web using Exa A | `text` | boolean | No | Include the full page text of each cited source \(default: false\). This does not affect the answer itself. | | `outputSchema` | json | No | JSON Schema describing the answer shape. When supplied, the answer is returned as a structured object instead of a string. | | `apiKey` | string | Yes | Exa AI API Key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -222,8 +214,6 @@ Run a deep research task with Exa Agent. Handles multi-step list building, enric | `systemPrompt` | string | No | Additional guidance for how the agent should behave or format its answer | | `previousRunId` | string | No | ID of a completed agent run to continue from, for follow-up questions | | `apiKey` | string | Yes | Exa AI API Key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/google_books.mdx b/apps/docs/content/docs/en/integrations/google_books.mdx index 24deff5c884..4004ccbf8f3 100644 --- a/apps/docs/content/docs/en/integrations/google_books.mdx +++ b/apps/docs/content/docs/en/integrations/google_books.mdx @@ -46,8 +46,6 @@ Search for books using the Google Books API | `startIndex` | number | No | Index of the first result to return \(for pagination\) | | `maxResults` | number | No | Maximum number of results to return \(1-40\) | | `langRestrict` | string | No | Restrict results to a specific language \(ISO 639-1 code\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -84,8 +82,6 @@ Get detailed information about a specific book volume | `apiKey` | string | Yes | Google Books API key | | `volumeId` | string | Yes | The ID of the volume to retrieve | | `projection` | string | No | Projection level \(full, lite\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/google_maps.mdx b/apps/docs/content/docs/en/integrations/google_maps.mdx index 18be2f935a3..00e8717ec6a 100644 --- a/apps/docs/content/docs/en/integrations/google_maps.mdx +++ b/apps/docs/content/docs/en/integrations/google_maps.mdx @@ -50,8 +50,6 @@ Get current air quality data for a location | `lat` | number | Yes | Latitude coordinate | | `lng` | number | Yes | Longitude coordinate | | `languageCode` | string | No | Language code for the response \(e.g., "en", "es"\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -93,8 +91,6 @@ Get directions and route information between two locations | `waypoints` | json | No | Array of intermediate waypoints | | `units` | string | No | Unit system: metric or imperial | | `language` | string | No | Language code for results \(e.g., en, es, fr\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -139,8 +135,6 @@ Calculate travel distance and time between multiple origins and destinations | `avoid` | string | No | Features to avoid: tolls, highways, or ferries | | `units` | string | No | Unit system: metric or imperial | | `language` | string | No | Language code for results \(e.g., en, es, fr\) | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -169,8 +163,6 @@ Get elevation data for a location | `apiKey` | string | Yes | Google Maps API key | | `lat` | number | Yes | Latitude coordinate | | `lng` | number | Yes | Longitude coordinate | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -193,8 +185,6 @@ Convert an address into geographic coordinates (latitude and longitude) | `address` | string | Yes | The address to geocode | | `language` | string | No | Language code for results \(e.g., en, es, fr\) | | `region` | string | No | Region bias as a ccTLD code \(e.g., us, uk\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -227,8 +217,6 @@ Geolocate a device using WiFi access points, cell towers, or IP address | `considerIp` | boolean | No | Whether to use IP address for geolocation \(default: true\) | | `cellTowers` | array | No | Array of cell tower objects with cellId, locationAreaCode, mobileCountryCode, mobileNetworkCode | | `wifiAccessPoints` | array | No | Array of WiFi access point objects with macAddress \(required\), signalStrength, etc. | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -250,8 +238,6 @@ Get detailed information about a specific place | `placeId` | string | Yes | Google Place ID | | `fields` | string | No | Comma-separated list of fields to return | | `language` | string | No | Language code for results \(e.g., en, es, fr\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -306,8 +292,6 @@ Search for places of a given type within a radius of a location | `rankPreference` | string | No | How to rank results: POPULARITY \(default\) or DISTANCE | | `languageCode` | string | No | Language code for the response \(e.g., en, es\) | | `regionCode` | string | No | Region bias as a ccTLD code \(e.g., us, uk\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -342,8 +326,6 @@ Search for places using a text query | `language` | string | No | Language code for results \(e.g., en, es, fr\) | | `region` | string | No | Region bias as a ccTLD code \(e.g., us, uk\) | | `pageToken` | string | No | Token from a previous search response to fetch the next page of results. Wait a couple seconds after receiving the token before using it, or the API returns INVALID_REQUEST | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -378,8 +360,6 @@ Get a daily pollen forecast (grass, tree, weed) for a location | `days` | number | No | Number of forecast days to return \(1-5, defaults to 1\) | | `languageCode` | string | No | Language code for the response \(e.g., "en", "es"\) | | `plantsDescription` | boolean | No | Include detailed plant descriptions \(defaults to true\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -413,8 +393,6 @@ Convert geographic coordinates (latitude and longitude) into a human-readable ad | `lat` | number | Yes | Latitude coordinate | | `lng` | number | Yes | Longitude coordinate | | `language` | string | No | Language code for results \(e.g., en, es, fr\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -439,8 +417,6 @@ Snap GPS coordinates to the nearest road segment | `apiKey` | string | Yes | Google Maps API key with Roads API enabled | | `path` | string | Yes | Pipe-separated list of lat,lng coordinates \(e.g., "60.170880,24.942795\|60.170879,24.942796"\) | | `interpolate` | boolean | No | Whether to interpolate additional points along the road | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -466,8 +442,6 @@ Get solar potential and panel insights for the building nearest a location | `lat` | number | Yes | Latitude coordinate | | `lng` | number | Yes | Longitude coordinate | | `requiredQuality` | string | No | Minimum imagery quality to accept \(HIGH, MEDIUM, or BASE\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -525,8 +499,6 @@ Get timezone information for a location | `lng` | number | Yes | Longitude coordinate | | `timestamp` | number | No | Unix timestamp to determine DST offset \(defaults to current time\) | | `language` | string | No | Language code for timezone name \(e.g., en, es, fr\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -552,8 +524,6 @@ Validate and standardize a postal address | `regionCode` | string | No | ISO 3166-1 alpha-2 country code \(e.g., "US", "CA"\) | | `locality` | string | No | City or locality name | | `enableUspsCass` | boolean | No | Enable USPS CASS validation for US addresses | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/google_pagespeed.mdx b/apps/docs/content/docs/en/integrations/google_pagespeed.mdx index 41628ee0221..85342f1345d 100644 --- a/apps/docs/content/docs/en/integrations/google_pagespeed.mdx +++ b/apps/docs/content/docs/en/integrations/google_pagespeed.mdx @@ -55,8 +55,6 @@ Analyze a webpage for performance, accessibility, SEO, and best practices using | `category` | string | No | Lighthouse categories to analyze \(comma-separated\): performance, accessibility, best-practices, seo | | `strategy` | string | No | Analysis strategy: desktop or mobile | | `locale` | string | No | Locale for results \(e.g., en, fr, de\) | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/google_translate.mdx b/apps/docs/content/docs/en/integrations/google_translate.mdx index d58efeb2c2e..6045f3479d3 100644 --- a/apps/docs/content/docs/en/integrations/google_translate.mdx +++ b/apps/docs/content/docs/en/integrations/google_translate.mdx @@ -43,8 +43,6 @@ Translate text between languages using the Google Cloud Translation API. Support | `target` | string | Yes | Target language code \(e.g., "es", "fr", "de", "ja"\) | | `source` | string | No | Source language code. If omitted, the API will auto-detect the source language. | | `format` | string | No | Format of the text: "text" for plain text, "html" for HTML content | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -63,8 +61,6 @@ Detect the language of text using the Google Cloud Translation API. | --------- | ---- | -------- | ----------- | | `apiKey` | string | Yes | Google Cloud API key with Cloud Translation API enabled | | `text` | string | Yes | The text to detect the language of | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/linkup.mdx b/apps/docs/content/docs/en/integrations/linkup.mdx index 2ea9d4c7ae0..f31c687fa88 100644 --- a/apps/docs/content/docs/en/integrations/linkup.mdx +++ b/apps/docs/content/docs/en/integrations/linkup.mdx @@ -51,8 +51,6 @@ Search the web for information using Linkup | `includeDomains` | string | No | Comma-separated list of domain names to restrict search results to | | `includeInlineCitations` | boolean | No | Add inline citations to answers \(only applies when outputType is "sourcedAnswer"\) | | `includeSources` | boolean | No | Include sources in response | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/meta.json b/apps/docs/content/docs/en/integrations/meta.json index ef27475a3a9..53eea6c9171 100644 --- a/apps/docs/content/docs/en/integrations/meta.json +++ b/apps/docs/content/docs/en/integrations/meta.json @@ -67,6 +67,7 @@ "elasticsearch", "elevenlabs", "emailbison", + "embeddings", "enrich", "enrichment", "enrow", diff --git a/apps/docs/content/docs/en/integrations/openai.mdx b/apps/docs/content/docs/en/integrations/openai.mdx index e9072e6cdc5..2e14fc44b8c 100644 --- a/apps/docs/content/docs/en/integrations/openai.mdx +++ b/apps/docs/content/docs/en/integrations/openai.mdx @@ -45,7 +45,6 @@ Generate embeddings from text using OpenAI's embedding models | --------- | ---- | -------- | ----------- | | `input` | string | Yes | Text to generate embeddings for | | `model` | string | No | Model to use for embeddings | -| `encodingFormat` | string | No | The format to return the embeddings in | | `apiKey` | string | Yes | OpenAI API key | #### Output diff --git a/apps/docs/content/docs/en/integrations/perplexity.mdx b/apps/docs/content/docs/en/integrations/perplexity.mdx index fd8f9f2d0e8..d289aab2af0 100644 --- a/apps/docs/content/docs/en/integrations/perplexity.mdx +++ b/apps/docs/content/docs/en/integrations/perplexity.mdx @@ -49,8 +49,6 @@ Generate completions using Perplexity AI chat models | `max_tokens` | number | No | Maximum number of tokens to generate \(e.g., 1024, 2048, 4096\) | | `temperature` | number | No | Sampling temperature between 0 and 1 \(e.g., 0.0 for deterministic, 0.7 for creative\) | | `apiKey` | string | Yes | Perplexity API key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output @@ -80,8 +78,6 @@ Get ranked search results from Perplexity's continuously refreshed index with ad | `search_after_date` | string | No | Include only content published after this date \(format: MM/DD/YYYY\) | | `search_before_date` | string | No | Include only content published before this date \(format: MM/DD/YYYY\) | | `apiKey` | string | Yes | Perplexity API key | -| `pricing` | per_request | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/docs/content/docs/en/integrations/serper.mdx b/apps/docs/content/docs/en/integrations/serper.mdx index e850ed4b8ef..b59c8701bdd 100644 --- a/apps/docs/content/docs/en/integrations/serper.mdx +++ b/apps/docs/content/docs/en/integrations/serper.mdx @@ -47,8 +47,6 @@ A powerful web search tool that provides access to Google search results through | `hl` | string | No | Language code for search results \(e.g., "en", "es", "de", "fr"\) | | `type` | string | No | Type of search to perform \(e.g., "search", "news", "images", "videos", "places", "shopping"\) | | `apiKey` | string | Yes | Serper API Key | -| `pricing` | custom | No | No description | -| `rateLimit` | string | No | No description | #### Output diff --git a/apps/sim/app/api/tools/embeddings/route.test.ts b/apps/sim/app/api/tools/embeddings/route.test.ts new file mode 100644 index 00000000000..5d369690421 --- /dev/null +++ b/apps/sim/app/api/tools/embeddings/route.test.ts @@ -0,0 +1,176 @@ +/** + * @vitest-environment node + */ +import { createMockRequest, hybridAuthMockFns } from '@sim/testing' +import { beforeEach, describe, expect, it, vi } from 'vitest' + +const { mockEmbed } = vi.hoisted(() => ({ + mockEmbed: vi.fn(), +})) + +vi.mock('@/lib/embeddings', async () => { + const catalog = await import('@/lib/embeddings/catalog') + return { + embed: mockEmbed, + findEmbeddingModelInfo: catalog.findEmbeddingModelInfo, + getModelsForProvider: catalog.getModelsForProvider, + resolveDimensions: catalog.resolveDimensions, + } +}) + +import { POST } from '@/app/api/tools/embeddings/route' + +const baseBody = { + provider: 'openai', + model: 'text-embedding-3-small', + input: 'hello world', + apiKey: 'sk-test', +} + +function post(body: Record) { + return POST(createMockRequest('POST', body) as never, undefined as never) +} + +describe('POST /api/tools/embeddings', () => { + beforeEach(() => { + vi.clearAllMocks() + hybridAuthMockFns.mockCheckInternalAuth.mockResolvedValue({ + success: true, + userId: 'user-1', + authType: 'internal_jwt', + }) + mockEmbed.mockResolvedValue({ + embeddings: [[0.1, 0.2]], + totalTokens: 3, + isBYOK: true, + modelName: 'text-embedding-3-small', + pricingId: 'text-embedding-3-small', + dimensions: 1536, + }) + }) + + it('rejects an unauthenticated caller', async () => { + hybridAuthMockFns.mockCheckInternalAuth.mockResolvedValue({ success: false }) + const response = await post(baseBody) + expect(response.status).toBe(401) + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('embeds and returns the contract shape', async () => { + const response = await post(baseBody) + expect(response.status).toBe(200) + const json = await response.json() + expect(json).toMatchObject({ + success: true, + embeddings: [[0.1, 0.2]], + model: 'text-embedding-3-small', + provider: 'openai', + dimensions: 1536, + usage: { prompt_tokens: 3, total_tokens: 3 }, + __embeddingTokens: 3, + }) + }) + + it('rejects an unknown model', async () => { + const response = await post({ ...baseBody, model: 'not-a-real-model' }) + expect(response.status).toBe(400) + expect((await response.json()).error).toContain('Unsupported embedding model') + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('rejects a model that belongs to another provider', async () => { + const response = await post({ ...baseBody, provider: 'cohere', model: 'gemini-embedding-001' }) + expect(response.status).toBe(400) + expect((await response.json()).error).toContain('belongs to gemini, not cohere') + expect(mockEmbed).not.toHaveBeenCalled() + }) + + /** + * Regression: an unsupported `dimensions` used to escape as the generic 502 + * from the embed() catch, reporting a client input error as an upstream + * failure. + */ + it('rejects an unsupported dimension with 400, not 502', async () => { + const response = await post({ ...baseBody, dimensions: 777 }) + expect(response.status).toBe(400) + expect((await response.json()).error).toContain('does not support 777-dimensional output') + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('rejects any dimension for a model without Matryoshka support', async () => { + const response = await post({ + ...baseBody, + provider: 'mistral', + model: 'mistral-embed', + dimensions: 999, + }) + expect(response.status).toBe(400) + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('accepts a supported dimension', async () => { + const response = await post({ ...baseBody, dimensions: 512 }) + expect(response.status).toBe(200) + expect(mockEmbed).toHaveBeenCalledWith( + ['hello world'], + expect.objectContaining({ dimensions: 512 }) + ) + }) + + it('surfaces a provider failure as 502', async () => { + mockEmbed.mockRejectedValue(new Error('Embedding API failed: 429 Too Many Requests')) + const response = await post(baseBody) + expect(response.status).toBe(502) + expect((await response.json()).error).toContain('429') + }) + + it('splits a JSON-array input into separate texts', async () => { + await post({ ...baseBody, input: '["alpha","beta"]' }) + expect(mockEmbed).toHaveBeenCalledWith(['alpha', 'beta'], expect.anything()) + }) + + /** + * The contract bounds the array arm, but a JSON-encoded array reaches the + * route as a plain string and is only expanded after validation — so the + * bounds have to be re-applied to the normalized list or they hold for a + * native array body only. + */ + describe('JSON-encoded array bounds', () => { + it('rejects a JSON array that exceeds the input count limit', async () => { + const many = JSON.stringify(Array.from({ length: 1001 }, (_, i) => `t${i}`)) + const response = await post({ ...baseBody, input: many }) + + expect(response.status).toBe(400) + expect((await response.json()).error).toContain('cannot exceed 1000 texts') + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('rejects an empty JSON array instead of reporting success with no vectors', async () => { + const response = await post({ ...baseBody, input: '[]' }) + + expect(response.status).toBe(400) + expect((await response.json()).error).toContain('at least one text') + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('rejects a JSON array containing a blank entry', async () => { + const response = await post({ ...baseBody, input: '["ok"," "]' }) + + expect(response.status).toBe(400) + expect((await response.json()).error).toContain('entries cannot be empty') + expect(mockEmbed).not.toHaveBeenCalled() + }) + + it('accepts a JSON array within the bounds', async () => { + const response = await post({ ...baseBody, input: '["alpha","beta"]' }) + + expect(response.status).toBe(200) + expect(mockEmbed).toHaveBeenCalledWith(['alpha', 'beta'], expect.anything()) + }) + }) + + it('embeds a non-JSON string as a single text', async () => { + await post({ ...baseBody, input: 'just a sentence' }) + expect(mockEmbed).toHaveBeenCalledWith(['just a sentence'], expect.anything()) + }) +}) diff --git a/apps/sim/app/api/tools/embeddings/route.ts b/apps/sim/app/api/tools/embeddings/route.ts new file mode 100644 index 00000000000..ae215caeb4d --- /dev/null +++ b/apps/sim/app/api/tools/embeddings/route.ts @@ -0,0 +1,177 @@ +import { createLogger } from '@sim/logger' +import { getErrorMessage } from '@sim/utils/errors' +import { type NextRequest, NextResponse } from 'next/server' +import { + embeddingsToolContract, + MAX_EMBEDDING_INPUTS, + MAX_EMBEDDING_TOTAL_CHARS, +} from '@/lib/api/contracts/tools/embeddings' +import { getValidationErrorMessage, parseRequest, validationErrorResponse } from '@/lib/api/server' +import { checkInternalAuth } from '@/lib/auth/hybrid' +import { withRouteHandler } from '@/lib/core/utils/with-route-handler' +import { + DEFAULT_MODEL_BY_PROVIDER, + embed, + findEmbeddingModelInfo, + resolveDimensions, +} from '@/lib/embeddings' + +const logger = createLogger('EmbeddingsToolAPI') + +export const dynamic = 'force-dynamic' + +/** + * Accepts a single string, an array, or a JSON-encoded array from a reference + * expression. Probes for the opening bracket with a regex rather than `trim()`, + * which would copy the whole payload just to read one character. + */ +function normalizeInput(input: string | string[]): string[] { + if (Array.isArray(input)) return input + if (/^\s*\[/.test(input)) { + try { + const parsed = JSON.parse(input) + if (Array.isArray(parsed) && parsed.every((entry) => typeof entry === 'string')) { + return parsed + } + } catch { + // Not JSON — fall through and embed the raw string + } + } + return [input] +} + +export const POST = withRouteHandler(async (request: NextRequest) => { + const authResult = await checkInternalAuth(request, { requireWorkflowId: false }) + if (!authResult.success || !authResult.userId) { + return NextResponse.json({ success: false, error: 'Unauthorized' }, { status: 401 }) + } + + const parsed = await parseRequest( + embeddingsToolContract, + request, + {}, + { + validationErrorResponse: (error) => { + logger.warn('Invalid embeddings request', { issues: error.issues }) + return validationErrorResponse( + error, + getValidationErrorMessage(error, 'Invalid request data') + ) + }, + } + ) + if (!parsed.success) return parsed.response + + const { provider, apiKey, model, input, taskType, dimensions } = parsed.data.body + + const texts = normalizeInput(input) + + /** + * The contract bounds the array arm, but a JSON-encoded array arrives as a + * plain string and is only expanded here, after validation. Re-checking the + * normalized list is what makes the bounds hold for the reference-expression + * path too, rather than only for a native array body. + */ + if (texts.length === 0) { + return NextResponse.json( + { success: false, error: 'input must contain at least one text' }, + { status: 400 } + ) + } + if (texts.length > MAX_EMBEDDING_INPUTS) { + return NextResponse.json( + { + success: false, + error: `input cannot exceed ${MAX_EMBEDDING_INPUTS} texts, received ${texts.length}`, + }, + { status: 400 } + ) + } + /** + * Summing lengths is cheap and runs before the per-entry whitespace scan, so + * an oversized body is rejected without walking every character. + */ + const totalChars = texts.reduce((sum, text) => sum + text.length, 0) + if (totalChars > MAX_EMBEDDING_TOTAL_CHARS) { + return NextResponse.json( + { + success: false, + error: `Input is too large: ${totalChars} characters exceeds the ${MAX_EMBEDDING_TOTAL_CHARS} limit`, + }, + { status: 400 } + ) + } + + if (texts.some((text) => !/\S/.test(text))) { + return NextResponse.json( + { success: false, error: 'input entries cannot be empty' }, + { status: 400 } + ) + } + + const resolvedModel = model || DEFAULT_MODEL_BY_PROVIDER[provider] + const info = findEmbeddingModelInfo(resolvedModel) + if (!info) { + return NextResponse.json( + { success: false, error: `Unsupported embedding model: ${resolvedModel}` }, + { status: 400 } + ) + } + if (info.provider !== provider) { + return NextResponse.json( + { + success: false, + error: `Model ${resolvedModel} belongs to ${info.provider}, not ${provider}`, + }, + { status: 400 } + ) + } + + /** + * Resolved here as well as inside `embed()` so an unsupported `dimensions` + * is reported as the client error it is. The block's dropdown constrains the + * field, but a reference expression can put any value on the wire. + */ + try { + resolveDimensions(info, dimensions) + } catch (error) { + return NextResponse.json( + { success: false, error: getErrorMessage(error, 'Invalid dimensions') }, + { status: 400 } + ) + } + + logger.info(`Embedding ${texts.length} input(s) with ${provider}/${resolvedModel}`) + + try { + const result = await embed(texts, { + model: resolvedModel, + taskType, + dimensions, + apiKey, + /** + * Callers reach this route through a tool whose `request.modelInput` + * already projected `input` at the HTTP hop, so projecting again here + * would run the substitution over already-projected content. + */ + projectInputs: null, + }) + + return NextResponse.json({ + success: true, + embeddings: result.embeddings, + model: result.modelName, + provider, + dimensions: result.dimensions, + usage: { + prompt_tokens: result.totalTokens, + total_tokens: result.totalTokens, + }, + __embeddingTokens: result.totalTokens, + }) + } catch (error) { + const message = getErrorMessage(error, 'Embedding generation failed') + logger.error('Embedding generation failed', { error: message }) + return NextResponse.json({ success: false, error: message }, { status: 502 }) + } +}) diff --git a/apps/sim/blocks/blocks.test.ts b/apps/sim/blocks/blocks.test.ts index e595f30ddad..3fdeccc46fe 100644 --- a/apps/sim/blocks/blocks.test.ts +++ b/apps/sim/blocks/blocks.test.ts @@ -837,6 +837,87 @@ describe.concurrent('Blocks Module', () => { expect(getBlock('video_generator_v2')?.hideFromToolbar).toBe(true) }) + it('should keep the legacy openai block registered but out of discovery', () => { + const legacy = getBlock('openai') + const replacement = getBlock('embeddings') + + // Placed instances must keep resolving and executing. + expect(legacy).toBeDefined() + expect(legacy?.tools.access).toContain('openai_embeddings') + // ...while the block itself is gone from the toolbar, search, and mentions. + expect(legacy?.hideFromToolbar).toBe(true) + expect(legacy?.sunset).toEqual({ status: 'legacy', replacedBy: 'embeddings' }) + // The badge only renders when replacedBy resolves to a registered block. + expect(replacement).toBeDefined() + expect(replacement?.hideFromToolbar).not.toBe(true) + }) + + /** + * `openai_embeddings` is an alias of `embeddings_openai`, so the legacy + * block's runtime payload gained `provider` and `dimensions`. Undeclared, + * they were absent from the tag picker and unreferenceable downstream even + * though every run returned them. + */ + it('should declare every output the legacy openai block returns at runtime', () => { + const legacy = getBlock('openai') + const replacement = getBlock('embeddings') + + expect(Object.keys(legacy?.outputs ?? {}).sort()).toEqual([ + 'dimensions', + 'embeddings', + 'model', + 'provider', + 'usage', + ]) + expect(legacy?.outputs?.provider).toEqual({ + type: 'string', + description: 'Provider used', + }) + expect(legacy?.outputs?.dimensions).toEqual({ + type: 'number', + description: 'Dimensionality of each vector', + }) + // Both blocks run the same tool, so neither may expose fields the other lacks. + expect(Object.keys(legacy?.outputs ?? {}).sort()).toEqual( + Object.keys(replacement?.outputs ?? {}).sort() + ) + }) + + it('should offer every embeddings provider with a matching tool and model list', () => { + const block = getBlock('embeddings') + const providerSubBlock = block?.subBlocks.find((sb) => sb.id === 'provider') + const providerOptions = providerSubBlock?.options + const providerIds = Array.isArray(providerOptions) + ? providerOptions.map((option) => option.id) + : [] + + expect(providerSubBlock?.commandSearchable).toBe(true) + expect(providerSubBlock?.value?.()).toBe('openai') + expect(providerIds).toEqual(['openai', 'gemini', 'cohere', 'mistral']) + + for (const provider of providerIds) { + // Each provider routes to its own registered tool... + const toolId = block?.tools.config?.tool?.({ provider }) + expect(block?.tools.access).toContain(toolId) + // ...and has a model dropdown with at least one option. + const modelSubBlock = block?.subBlocks.find( + (sb) => sb.id === 'model' && sb.condition?.value === provider + ) + expect( + Array.isArray(modelSubBlock?.options) ? modelSubBlock.options.length : 0 + ).toBeGreaterThan(0) + } + }) + + it('should default an embeddings block saved before the provider field existed to openai', () => { + const block = getBlock('embeddings') + + // Serialization runs before variable resolution, so an absent provider + // must still resolve to the original OpenAI tool. + expect(block?.tools.config?.tool?.({})).toBe('embeddings_openai') + expect(block?.tools.config?.tool?.({ provider: 'gemini' })).toBe('embeddings_gemini') + }) + it('should mark the agent model combobox as command-searchable', () => { const agentBlock = getBlock('agent') const modelSubBlock = agentBlock?.subBlocks.find((sb) => sb.id === 'model') diff --git a/apps/sim/blocks/blocks/embeddings.test.ts b/apps/sim/blocks/blocks/embeddings.test.ts new file mode 100644 index 00000000000..92d9a5c5c53 --- /dev/null +++ b/apps/sim/blocks/blocks/embeddings.test.ts @@ -0,0 +1,266 @@ +/** + * @vitest-environment node + */ +import { describe, expect, it } from 'vitest' +import { DEFAULT_MODEL_BY_PROVIDER, EMBEDDING_MODELS } from '@/lib/embeddings/catalog' +import { EmbeddingsBlock, TOOL_ID_BY_PROVIDER } from '@/blocks/blocks/embeddings' + +/** + * The block derives its model, task-type, and dimension options from the + * catalog, so these assert the derivation still produces what the UI expects: + * one dropdown per provider/model, the catalog's own option sets, and the + * native size pre-selected. They also pin the provider-to-tool routing. + */ + +function subBlocksById(id: string) { + return EmbeddingsBlock.subBlocks.filter((sb) => sb.id === id) +} + +function optionIds(options: unknown): string[] { + return Array.isArray(options) ? options.map((option) => (option as { id: string }).id) : [] +} + +/** The provider a `{ field: 'provider', value: X }` condition selects. */ +function conditionProvider(subBlock: { condition?: unknown }): string | undefined { + const condition = subBlock.condition as + | { field?: string; value?: unknown; and?: { field?: string; value?: unknown } } + | undefined + return typeof condition?.value === 'string' ? condition.value : undefined +} + +function conditionModel(subBlock: { condition?: unknown }): string | undefined { + const condition = subBlock.condition as { and?: { field?: string; value?: unknown } } | undefined + return typeof condition?.and?.value === 'string' ? condition.and.value : undefined +} + +describe('Embeddings block', () => { + it('offers exactly the catalog models for each provider', () => { + const modelSubBlocks = subBlocksById('model') + const offered = new Map() + + for (const subBlock of modelSubBlocks) { + const provider = conditionProvider(subBlock) + expect(provider).toBeDefined() + offered.set(provider as string, optionIds(subBlock.options)) + } + + const expected = new Map() + for (const [modelId, info] of Object.entries(EMBEDDING_MODELS)) { + expected.set(info.provider, [...(expected.get(info.provider) ?? []), modelId]) + } + + expect([...offered.keys()].sort()).toEqual([...expected.keys()].sort()) + for (const [provider, models] of expected) { + expect(offered.get(provider)?.slice().sort()).toEqual(models.slice().sort()) + } + }) + + it('defaults each provider to a model that provider actually owns', () => { + for (const [provider, model] of Object.entries(DEFAULT_MODEL_BY_PROVIDER)) { + expect(EMBEDDING_MODELS[model]).toBeDefined() + expect(EMBEDDING_MODELS[model].provider).toBe(provider) + } + }) + + it('shows a task-type dropdown for exactly the models that support one', () => { + const withTaskTypes = Object.entries(EMBEDDING_MODELS) + .filter(([, info]) => info.supportedTaskTypes) + .map(([id]) => id) + + const subBlocks = subBlocksById('taskType') + expect(subBlocks.map(conditionModel).sort()).toEqual(withTaskTypes.slice().sort()) + + for (const subBlock of subBlocks) { + const model = conditionModel(subBlock) as string + expect(optionIds(subBlock.options)).toEqual([ + ...(EMBEDDING_MODELS[model].supportedTaskTypes ?? []), + ]) + } + }) + + it('shows a dimensions dropdown for exactly the models that support reduction', () => { + const withDimensions = Object.entries(EMBEDDING_MODELS) + .filter(([, info]) => info.supportedDimensions) + .map(([id]) => id) + + const subBlocks = subBlocksById('dimensions') + expect(subBlocks.map(conditionModel).sort()).toEqual(withDimensions.slice().sort()) + + for (const subBlock of subBlocks) { + const model = conditionModel(subBlock) as string + const info = EMBEDDING_MODELS[model] + expect(optionIds(subBlock.options)).toEqual( + (info.supportedDimensions ?? []).map((size) => String(size)) + ) + // The pre-selected value must be the model's native size. + expect(subBlock.value?.()).toBe(String(info.nativeDimensions)) + } + }) + + it('routes every provider to a tool it declares access to', () => { + for (const [provider, toolId] of Object.entries(TOOL_ID_BY_PROVIDER)) { + expect(EmbeddingsBlock.tools.access).toContain(toolId) + expect(EmbeddingsBlock.tools.config?.tool?.({ provider })).toBe(toolId) + } + expect(EmbeddingsBlock.tools.access).toHaveLength(Object.keys(TOOL_ID_BY_PROVIDER).length) + }) + + it('only forwards capabilities the selected model declares', () => { + const params = EmbeddingsBlock.tools.config?.params + + // ada-002 has neither task types nor reducible dimensions, so both are dropped + // even when stale sub-block values linger in a saved workflow. + expect( + params?.({ + provider: 'openai', + model: 'text-embedding-ada-002', + input: 'hello', + apiKey: 'k', + taskType: 'query', + dimensions: '256', + }) + ).toEqual({ apiKey: 'k', input: 'hello', model: 'text-embedding-ada-002' }) + + // gemini declares both, so both are forwarded — dimensions coerced to a number. + expect( + params?.({ + provider: 'gemini', + model: 'gemini-embedding-001', + input: 'hello', + apiKey: 'k', + taskType: 'query', + dimensions: '768', + }) + ).toEqual({ + apiKey: 'k', + input: 'hello', + model: 'gemini-embedding-001', + taskType: 'query', + dimensions: 768, + }) + }) + + /** + * Every per-model Dimensions dropdown shares the `dimensions` id and nothing + * clears a stored subblock value when its `dependsOn` fields change, so a + * reduction chosen for one model outlives a switch to another. + */ + /** + * The generic handler merges this result over the original inputs + * (`{ ...inputs, ...transformedParams }`), so omitting a stale key leaves the + * old value in place. Only an explicit `undefined` overrides it — asserting + * the merged result is what actually pins the behavior. + */ + describe('stale values are overridden, not merely omitted', () => { + const mergeLikeExecutor = (inputs: Record) => ({ + ...inputs, + ...EmbeddingsBlock.tools.config?.params?.(inputs), + }) + + it('overrides a dimension the selected model does not offer', () => { + const merged = mergeLikeExecutor({ + provider: 'openai', + model: 'text-embedding-3-small', + input: 'hello', + apiKey: 'k', + dimensions: '3072', + }) + + expect(merged.dimensions).toBeUndefined() + }) + + it('overrides a task type the selected model does not offer', () => { + const merged = mergeLikeExecutor({ + provider: 'openai', + model: 'text-embedding-3-small', + input: 'hello', + apiKey: 'k', + taskType: 'similarity', + }) + + expect(merged.taskType).toBeUndefined() + }) + + it('replaces a model left over from a previous provider', () => { + // Selecting Cohere with OpenAI's model still stored must not reach the + // route, which rejects it as a provider mismatch. + const merged = mergeLikeExecutor({ + provider: 'cohere', + model: 'text-embedding-3-large', + input: 'hello', + apiKey: 'k', + }) + + expect(merged.model).toBe('embed-v4.0') + }) + + it('keeps a model that does belong to the selected provider', () => { + const merged = mergeLikeExecutor({ + provider: 'openai', + model: 'text-embedding-3-large', + input: 'hello', + apiKey: 'k', + }) + + expect(merged.model).toBe('text-embedding-3-large') + }) + }) + + it('drops a dimension the newly selected model no longer offers', () => { + const params = EmbeddingsBlock.tools.config?.params + + // 3072 is valid for text-embedding-3-large but not for -3-small. + expect( + params?.({ + provider: 'openai', + model: 'text-embedding-3-small', + input: 'hello', + apiKey: 'k', + dimensions: '3072', + }) + ).toEqual({ apiKey: 'k', input: 'hello', model: 'text-embedding-3-small' }) + + // A model with no reduction support never forwards one. + expect( + params?.({ + provider: 'mistral', + model: 'mistral-embed', + input: 'hello', + apiKey: 'k', + dimensions: '512', + }) + ).toEqual({ apiKey: 'k', input: 'hello', model: 'mistral-embed' }) + }) + + it('drops a task type the newly selected model no longer offers', () => { + const params = EmbeddingsBlock.tools.config?.params + + // Gemini supports 'similarity'; Cohere does not. + expect( + params?.({ + provider: 'cohere', + model: 'embed-v4.0', + input: 'hello', + apiKey: 'k', + taskType: 'similarity', + }) + ).toEqual({ apiKey: 'k', input: 'hello', model: 'embed-v4.0' }) + + // A model with no task conditioning never forwards one. + expect( + params?.({ + provider: 'openai', + model: 'text-embedding-3-small', + input: 'hello', + apiKey: 'k', + taskType: 'query', + }) + ).toEqual({ apiKey: 'k', input: 'hello', model: 'text-embedding-3-small' }) + }) + + it('requires input text', () => { + expect(() => + EmbeddingsBlock.tools.config?.params?.({ provider: 'openai', apiKey: 'k' }) + ).toThrow('Input text is required') + }) +}) diff --git a/apps/sim/blocks/blocks/embeddings.ts b/apps/sim/blocks/blocks/embeddings.ts new file mode 100644 index 00000000000..5df2a357435 --- /dev/null +++ b/apps/sim/blocks/blocks/embeddings.ts @@ -0,0 +1,327 @@ +import { EmbeddingsIcon } from '@/components/icons' +/** + * Imported from the catalog module directly rather than the `@/lib/embeddings` + * barrel: the barrel re-exports the client, which reaches BYOK key lookup and + * `@sim/db`. Block configs are bundled for the browser, so only the pure + * catalog data may cross this boundary. + */ +import { + DEFAULT_MODEL_BY_PROVIDER, + EMBEDDING_CATALOG_PROVIDERS, + EMBEDDING_MODELS, + getModelsForProvider, +} from '@/lib/embeddings/catalog' +import type { EmbeddingCatalogProvider, EmbeddingTaskType } from '@/lib/embeddings/types' +import type { BlockConfig, BlockMeta, SubBlockConfig } from '@/blocks/types' +import { AuthMode, IntegrationType } from '@/blocks/types' +import type { EmbeddingsResponse } from '@/tools/embeddings/types' + +const TOOL_ID_BY_PROVIDER: Record = { + openai: 'embeddings_openai', + gemini: 'embeddings_gemini', + cohere: 'embeddings_cohere', + mistral: 'embeddings_mistral', +} + +const PROVIDER_LABELS: Record = { + openai: 'OpenAI', + gemini: 'Google Gemini', + cohere: 'Cohere', + mistral: 'Mistral', +} + +const TASK_TYPE_LABELS: Record = { + document: 'Document', + query: 'Query', + similarity: 'Semantic Similarity', + classification: 'Classification', + clustering: 'Clustering', +} + +/** + * Dropdowns are derived from the catalog rather than hand-copied, so adding a + * catalog model cannot leave this block stale. Every variant shares one + * sub-block id, so each is scoped by a `condition` naming the provider. + */ +const MODEL_SUB_BLOCKS: SubBlockConfig[] = EMBEDDING_CATALOG_PROVIDERS.map((provider) => ({ + id: 'model', + title: 'Model', + type: 'dropdown', + options: getModelsForProvider(provider).map((id) => ({ label: EMBEDDING_MODELS[id].label, id })), + value: () => DEFAULT_MODEL_BY_PROVIDER[provider], + condition: { field: 'provider', value: provider }, + dependsOn: ['provider'], +})) + +/** + * Task-type and dimension dropdowns, which are per-model rather than + * per-provider: the `condition` names both, and a model contributes a dropdown + * only for the capabilities the catalog says it has. + */ +const CAPABILITY_SUB_BLOCKS: SubBlockConfig[] = Object.entries(EMBEDDING_MODELS).flatMap( + ([model, info]) => { + const scope = { field: 'provider', value: info.provider, and: { field: 'model', value: model } } + const subBlocks: SubBlockConfig[] = [] + + if (info.supportedTaskTypes) { + subBlocks.push({ + id: 'taskType', + title: 'Task Type', + type: 'dropdown', + options: info.supportedTaskTypes.map((task) => ({ + label: TASK_TYPE_LABELS[task], + id: task, + })), + value: () => 'document', + condition: scope, + dependsOn: ['provider', 'model'], + }) + } + + if (info.supportedDimensions) { + subBlocks.push({ + id: 'dimensions', + title: 'Dimensions', + type: 'dropdown', + options: info.supportedDimensions.map((size) => ({ + label: size === info.nativeDimensions ? `${size} (default)` : String(size), + id: String(size), + })), + value: () => String(info.nativeDimensions), + condition: scope, + dependsOn: ['provider', 'model'], + }) + } + + return subBlocks + } +) + +export const EmbeddingsBlock: BlockConfig = { + type: 'embeddings', + name: 'Embeddings', + description: 'Generate embeddings', + authMode: AuthMode.ApiKey, + longDescription: + 'Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, Google Gemini, Cohere, and Mistral embedding models.', + category: 'tools', + integrationType: IntegrationType.AI, + docsLink: 'https://docs.sim.ai/integrations/embeddings', + bgColor: '#7B4DFF', + icon: EmbeddingsIcon, + subBlocks: [ + { + id: 'input', + title: 'Input Text', + type: 'long-input', + placeholder: 'Enter text to generate embeddings for', + required: true, + }, + { + id: 'provider', + title: 'Provider', + type: 'dropdown', + options: EMBEDDING_CATALOG_PROVIDERS.map((provider) => ({ + label: PROVIDER_LABELS[provider], + id: provider, + })), + commandSearchable: true, + value: () => 'openai', + }, + ...MODEL_SUB_BLOCKS, + ...CAPABILITY_SUB_BLOCKS, + /** + * One field for every provider. Sim stocks a hosted key for all four + * (`OPENAI_API_KEY`, `GEMINI_API_KEY`, `COHERE_API_KEY`, `MISTRAL_API_KEY`), + * so none of them needs the user to supply one on hosted Sim. + */ + { + id: 'apiKey', + title: 'API Key', + type: 'short-input', + placeholder: 'Enter your provider API key', + password: true, + required: true, + connectionDroppable: false, + hideWhenHosted: true, + }, + ], + tools: { + access: ['embeddings_openai', 'embeddings_gemini', 'embeddings_cohere', 'embeddings_mistral'], + config: { + /** + * Runs at serialization, before variable resolution, so this only ever + * does plain lookups — never coercion, which would destroy dynamic + * `` references. + */ + tool: (params) => { + const provider = params.provider as EmbeddingCatalogProvider + return TOOL_ID_BY_PROVIDER[provider] ?? TOOL_ID_BY_PROVIDER.openai + }, + /** + * Every per-provider dropdown shares one subblock id (`model`, + * `taskType`, `dimensions`) and nothing clears a stored value when its + * `dependsOn` fields change, so a choice made for one provider or model + * outlives a switch away from it. + * + * Each stale field is therefore rewritten to an explicit `undefined` + * rather than omitted. The generic handler merges this result over the + * original inputs (`{ ...inputs, ...transformedParams }`), so an omitted + * key leaves the stale value untouched — only an explicit `undefined` + * overrides it. + */ + params: (params) => { + const provider = (params.provider as EmbeddingCatalogProvider) || 'openai' + if (!params.input) { + throw new Error('Input text is required') + } + + /** A model saved under a previous provider must not survive the switch. */ + const savedModel = params.model as string | undefined + const model = + savedModel && EMBEDDING_MODELS[savedModel]?.provider === provider + ? savedModel + : DEFAULT_MODEL_BY_PROVIDER[provider] + + const info = EMBEDDING_MODELS[model] + const requested = + params.dimensions !== undefined && params.dimensions !== '' + ? Number(params.dimensions) + : undefined + const dimensions = + requested !== undefined && + !Number.isNaN(requested) && + info?.supportedDimensions?.includes(requested) + ? requested + : undefined + const taskType = + params.taskType && + info?.supportedTaskTypes?.includes(params.taskType as EmbeddingTaskType) + ? (params.taskType as EmbeddingTaskType) + : undefined + + return { + apiKey: params.apiKey, + input: params.input, + model, + taskType, + dimensions, + } + }, + }, + }, + inputs: { + input: { type: 'string', description: 'Text to embed, or an array of texts' }, + provider: { type: 'string', description: 'Embedding provider' }, + model: { type: 'string', description: 'Embedding model' }, + taskType: { type: 'string', description: 'What the embedding will be used for' }, + dimensions: { type: 'number', description: 'Output vector dimensions' }, + apiKey: { type: 'string', description: 'Provider API key' }, + }, + outputs: { + embeddings: { type: 'json', description: 'Generated embeddings' }, + model: { type: 'string', description: 'Model used' }, + provider: { type: 'string', description: 'Provider used' }, + dimensions: { type: 'number', description: 'Dimensionality of each vector' }, + usage: { type: 'json', description: 'Token usage' }, + }, +} + +export { TOOL_ID_BY_PROVIDER } + +export const EmbeddingsBlockMeta = { + tags: ['llm', 'vector-search'], + url: 'https://docs.sim.ai/integrations/embeddings', + templates: [ + { + icon: EmbeddingsIcon, + title: 'Document embedding pipeline', + prompt: + 'Build a workflow that watches a files folder, chunks each new document, generates embeddings, and upserts vectors into Pinecone with rich metadata for retrieval.', + modules: ['files', 'knowledge-base', 'agent', 'workflows'], + category: 'engineering', + tags: ['automation', 'sync'], + alsoIntegrations: ['pinecone'], + }, + { + icon: EmbeddingsIcon, + title: 'Knowledge base re-embedder', + prompt: + 'Create a scheduled workflow that finds documents whose embeddings are stale, regenerates them, and re-upserts the vectors into Pinecone so retrieval stays current.', + modules: ['scheduled', 'knowledge-base', 'agent', 'workflows'], + category: 'engineering', + tags: ['automation', 'sync', 'vector-search'], + alsoIntegrations: ['pinecone'], + }, + { + icon: EmbeddingsIcon, + title: 'Semantic duplicate detector', + prompt: + 'Build a workflow that reads new rows from a table, generates an embedding for each, compares them against existing rows by cosine similarity, and flags near-duplicates in an evaluation table.', + modules: ['tables', 'agent', 'workflows'], + category: 'engineering', + tags: ['engineering', 'analysis', 'vector-search'], + }, + { + icon: EmbeddingsIcon, + title: 'Product catalog semantic search', + prompt: + 'Create a workflow that embeds each product description from a table, upserts the vectors into Pinecone, and lets an incoming query return the closest matching products by similarity.', + modules: ['tables', 'agent', 'workflows'], + category: 'marketing', + tags: ['marketing', 'vector-search'], + alsoIntegrations: ['pinecone'], + }, + { + icon: EmbeddingsIcon, + title: 'Semantic ticket deduplication', + prompt: + 'Build a workflow that embeds each new support ticket, searches a Pinecone index of past tickets for near-duplicates, and links the new ticket to the matching thread instead of opening a fresh one.', + modules: ['agent', 'workflows'], + category: 'support', + tags: ['support', 'automation', 'vector-search'], + alsoIntegrations: ['pinecone'], + }, + { + icon: EmbeddingsIcon, + title: 'FAQ semantic router', + prompt: + 'Create a workflow that embeds an incoming question, compares it against embedded FAQ entries to find the closest match, and returns the canned answer when similarity is high or escalates to an agent when it is not.', + modules: ['agent', 'workflows'], + category: 'support', + tags: ['support', 'automation', 'vector-search'], + }, + { + icon: EmbeddingsIcon, + title: 'Embedding-based content clustering', + prompt: + 'Build a scheduled workflow that pulls recent feedback from a table, generates embeddings for each entry, clusters them by semantic similarity, and writes the themed groups with representative quotes back to a summary table.', + modules: ['scheduled', 'tables', 'agent', 'workflows'], + category: 'operations', + tags: ['analysis', 'automation', 'vector-search'], + }, + ], + skills: [ + { + name: 'embed-text', + description: + 'Generate an embedding vector for a piece of text to use in semantic search or similarity.', + content: + '# Embed Text\n\nConvert text into an embedding vector.\n\n## Steps\n1. Take the input text. If it is long, ensure it fits the model context; otherwise chunk it first.\n2. Choose a provider and model — text-embedding-3-small for cost-efficient general use, gemini-embedding-001 for the highest retrieval quality, embed-v4.0 for multilingual work, or codestral-embed for code. Keep the model consistent with any existing vectors it will be compared against.\n3. Set the task type to Query or Document when the model supports it, so the vector is conditioned for how it will be used.\n4. Generate the embedding.\n\n## Output\nReturn the embedding vector, the provider and model used, the dimensionality, and token usage. Vectors are only comparable when they come from the same model at the same dimensionality.', + }, + { + name: 'embed-documents-for-retrieval', + description: + 'Chunk and embed a set of documents so they can be upserted into a vector store for retrieval.', + content: + '# Embed Documents for Retrieval\n\nPrepare documents for semantic retrieval by chunking and embedding them.\n\n## Steps\n1. Split each document into reasonably sized chunks with light overlap so context is preserved.\n2. Embed each chunk with a single consistent model, using the Document task type where the model supports it.\n3. Pair each vector with its source metadata (document ID, chunk index, title) ready for upsert into the vector store.\n\n## Output\nReturn the embeddings with their associated metadata, the model used, and the dimensionality. Report how many chunks were produced and flag any chunk that failed to embed.', + }, + { + name: 'find-semantic-duplicates', + description: + 'Embed items and compare vectors by cosine similarity to flag near-duplicate content.', + content: + '# Find Semantic Duplicates\n\nDetect items that mean the same thing even when worded differently.\n\n## Steps\n1. Embed each candidate item with the same model and dimensionality used for the existing set.\n2. Compare each new vector against existing vectors using cosine similarity.\n3. Flag pairs above a similarity threshold (e.g. 0.9) as likely duplicates; treat lower scores as distinct.\n\n## Output\nReturn the flagged duplicate pairs with their similarity scores, sorted highest first, so they can be merged or deduplicated.', + }, + ], +} as const satisfies BlockMeta diff --git a/apps/sim/blocks/blocks/mongodb.ts b/apps/sim/blocks/blocks/mongodb.ts index 732017c4e64..74e62a190a3 100644 --- a/apps/sim/blocks/blocks/mongodb.ts +++ b/apps/sim/blocks/blocks/mongodb.ts @@ -995,7 +995,7 @@ export const MongoDBBlockMeta = { modules: ['scheduled', 'agent', 'workflows'], category: 'engineering', tags: ['engineering', 'sync'], - alsoIntegrations: ['pinecone', 'openai'], + alsoIntegrations: ['pinecone', 'embeddings'], }, { icon: MongoDBIcon, diff --git a/apps/sim/blocks/blocks/openai.ts b/apps/sim/blocks/blocks/openai.ts index 983130773a9..bed288a26a2 100644 --- a/apps/sim/blocks/blocks/openai.ts +++ b/apps/sim/blocks/blocks/openai.ts @@ -13,6 +13,13 @@ export const OpenAIBlock: BlockConfig = { docsLink: 'https://docs.sim.ai/integrations/openai', bgColor: '#000000', icon: OpenAIIcon, + /** + * Superseded by the multi-provider `embeddings` block. Left otherwise + * untouched so placed instances keep working exactly as they do today; it is + * only removed from the discovery surfaces. + */ + hideFromToolbar: true, + sunset: { status: 'legacy', replacedBy: 'embeddings' }, subBlocks: [ { id: 'input', @@ -52,6 +59,14 @@ export const OpenAIBlock: BlockConfig = { outputs: { embeddings: { type: 'json', description: 'Generated embeddings' }, model: { type: 'string', description: 'Model used' }, + /** + * `openai_embeddings` is an alias of `embeddings_openai`, so the runtime + * payload gained these two. Declaring them is purely additive — it does not + * change execution, and without it the tag picker cannot offer fields the + * block demonstrably returns. + */ + provider: { type: 'string', description: 'Provider used' }, + dimensions: { type: 'number', description: 'Dimensionality of each vector' }, usage: { type: 'json', description: 'Token usage' }, }, } diff --git a/apps/sim/blocks/blocks/pinecone.ts b/apps/sim/blocks/blocks/pinecone.ts index 3d01b42f702..e5d2cdaa87a 100644 --- a/apps/sim/blocks/blocks/pinecone.ts +++ b/apps/sim/blocks/blocks/pinecone.ts @@ -592,7 +592,7 @@ export const PineconeBlockMeta = { modules: ['scheduled', 'agent', 'workflows'], category: 'engineering', tags: ['engineering', 'sync'], - alsoIntegrations: ['openai'], + alsoIntegrations: ['embeddings'], }, { icon: PineconeIcon, @@ -602,7 +602,7 @@ export const PineconeBlockMeta = { modules: ['agent', 'workflows'], category: 'productivity', tags: ['research', 'enterprise'], - alsoIntegrations: ['openai'], + alsoIntegrations: ['embeddings'], }, { icon: PineconeIcon, @@ -622,7 +622,7 @@ export const PineconeBlockMeta = { modules: ['tables', 'agent', 'workflows'], category: 'engineering', tags: ['engineering', 'analysis'], - alsoIntegrations: ['openai'], + alsoIntegrations: ['embeddings'], }, { icon: PineconeIcon, @@ -641,7 +641,7 @@ export const PineconeBlockMeta = { modules: ['agent', 'workflows'], category: 'support', tags: ['support', 'automation'], - alsoIntegrations: ['openai'], + alsoIntegrations: ['embeddings'], }, { icon: PineconeIcon, @@ -651,7 +651,7 @@ export const PineconeBlockMeta = { modules: ['agent', 'workflows'], category: 'support', tags: ['support', 'vector-search', 'automation'], - alsoIntegrations: ['openai', 'zendesk'], + alsoIntegrations: ['embeddings', 'zendesk'], }, ], skills: [ diff --git a/apps/sim/blocks/blocks/qdrant.ts b/apps/sim/blocks/blocks/qdrant.ts index 6b8552b769a..98e25e66889 100644 --- a/apps/sim/blocks/blocks/qdrant.ts +++ b/apps/sim/blocks/blocks/qdrant.ts @@ -259,7 +259,7 @@ export const QdrantBlockMeta = { modules: ['files', 'agent', 'workflows'], category: 'engineering', tags: ['engineering', 'sync'], - alsoIntegrations: ['google_drive', 'openai'], + alsoIntegrations: ['google_drive', 'embeddings'], }, { icon: QdrantIcon, @@ -315,7 +315,7 @@ export const QdrantBlockMeta = { modules: ['agent', 'workflows'], category: 'support', tags: ['support', 'vector-search', 'automation'], - alsoIntegrations: ['openai', 'zendesk'], + alsoIntegrations: ['embeddings', 'zendesk'], }, ], skills: [ diff --git a/apps/sim/blocks/registry-maps.ts b/apps/sim/blocks/registry-maps.ts index 4ef1bad73a3..16385e85f0d 100644 --- a/apps/sim/blocks/registry-maps.ts +++ b/apps/sim/blocks/registry-maps.ts @@ -63,6 +63,7 @@ import { DynamoDBBlock, DynamoDBBlockMeta } from '@/blocks/blocks/dynamodb' import { ElasticsearchBlock, ElasticsearchBlockMeta } from '@/blocks/blocks/elasticsearch' import { ElevenLabsBlock, ElevenLabsBlockMeta } from '@/blocks/blocks/elevenlabs' import { EmailBisonBlock, EmailBisonBlockMeta } from '@/blocks/blocks/emailbison' +import { EmbeddingsBlock, EmbeddingsBlockMeta } from '@/blocks/blocks/embeddings' import { EnrichBlock, EnrichBlockMeta } from '@/blocks/blocks/enrich' import { EnrichmentBlock, EnrichmentBlockMeta } from '@/blocks/blocks/enrichment' import { EnrowBlock, EnrowBlockMeta } from '@/blocks/blocks/enrow' @@ -412,6 +413,7 @@ export const BLOCK_REGISTRY: Record = { elasticsearch: ElasticsearchBlock, elevenlabs: ElevenLabsBlock, emailbison: EmailBisonBlock, + embeddings: EmbeddingsBlock, enrich: EnrichBlock, enrichment: EnrichmentBlock, enrow: EnrowBlock, @@ -734,6 +736,7 @@ export const BLOCK_META_REGISTRY: Record = { elasticsearch: ElasticsearchBlockMeta, elevenlabs: ElevenLabsBlockMeta, emailbison: EmailBisonBlockMeta, + embeddings: EmbeddingsBlockMeta, enrich: EnrichBlockMeta, enrichment: EnrichmentBlockMeta, enrow: EnrowBlockMeta, diff --git a/apps/sim/components/icons.tsx b/apps/sim/components/icons.tsx index 606e5b0faf6..9ef127b7958 100644 --- a/apps/sim/components/icons.tsx +++ b/apps/sim/components/icons.tsx @@ -2412,6 +2412,36 @@ export function ImageIcon(props: SVGProps) { ) } +export function EmbeddingsIcon(props: SVGProps) { + return ( + + {/* Rays sit below the nodes in weight, but not so far below that they + wash out to loose dots at the 14px search-row size. */} + + + + + + + + ) +} + export function TypeformIcon(props: SVGProps) { return ( +export type EmbeddingsToolResponse = z.output +export type EmbeddingProvider = (typeof embeddingProviders)[number] +export type EmbeddingTaskTypeName = (typeof embeddingTaskTypes)[number] + +export const embeddingsToolContract = defineRouteContract({ + method: 'POST', + path: '/api/tools/embeddings', + body: embeddingsToolBodySchema, + response: { mode: 'json', schema: embeddingsToolResponseSchema }, +}) diff --git a/apps/sim/lib/core/utils/concurrency.ts b/apps/sim/lib/core/utils/concurrency.ts index 9156c3c83d2..6ce7ba9d6d6 100644 --- a/apps/sim/lib/core/utils/concurrency.ts +++ b/apps/sim/lib/core/utils/concurrency.ts @@ -3,12 +3,18 @@ * preserving input order in the result. Use to bound fan-out (e.g. per-row * object-storage reads) so a large batch doesn't issue every request at once. * - * Contract: `fn` MUST NOT reject. Results are awaited via `Promise.all`, so a - * single rejection fails the entire batch (e.g. one bad row would break a whole - * logs export/list page). Callers that materialize per-row data pass a total - * mapper (one that catches and returns a degraded value rather than throwing); - * keep it that way. If a future caller needs per-item isolation, add an - * allSettled-style variant rather than letting a throwing mapper through here. + * Failure is all-or-nothing: results are awaited via `Promise.all`, so a single + * rejection fails the whole batch. Pick the mapper to match what a partial + * result is worth to the caller. + * + * Callers that materialize per-row data (logs export/list pages) MUST pass a + * total mapper — one that catches and returns a degraded value rather than + * throwing — so one bad row cannot break an entire page. Callers whose result is + * only meaningful complete (embedding a batch, where a partial vector set is not + * a usable answer) may let the mapper throw and rely on the batch failing. + * + * Neither mode keeps the successes alongside a failure; a caller needing that + * should add an allSettled-style variant rather than bending this one. */ export async function mapWithConcurrency( items: readonly T[], diff --git a/apps/sim/lib/embeddings/catalog.test.ts b/apps/sim/lib/embeddings/catalog.test.ts new file mode 100644 index 00000000000..8f9e594aa1f --- /dev/null +++ b/apps/sim/lib/embeddings/catalog.test.ts @@ -0,0 +1,125 @@ +/** + * @vitest-environment node + */ +import { describe, expect, it } from 'vitest' +import { + EMBEDDING_MODELS, + getEmbeddingModelInfo, + getKbEligibleModels, + getModelsForProvider, + hasApproximateTokenCount, + KB_EMBEDDING_DIMENSIONS, + resolveDimensions, +} from '@/lib/embeddings/catalog' +import { EMBEDDING_MODEL_PRICING } from '@/providers/models' + +describe('embedding catalog', () => { + it('throws a named error for an unknown model', () => { + expect(() => getEmbeddingModelInfo('not-a-model')).toThrow( + 'Unsupported embedding model: not-a-model' + ) + }) + + it('gives every model a pricing entry so hosted-key billing cannot silently be free', () => { + for (const [modelId, info] of Object.entries(EMBEDDING_MODELS)) { + expect( + EMBEDDING_MODEL_PRICING[info.pricingId], + `missing pricing for ${modelId}` + ).toBeDefined() + } + }) + + it('offers the native size among the Matryoshka options, largest first', () => { + for (const [modelId, info] of Object.entries(EMBEDDING_MODELS)) { + if (!info.supportedDimensions) continue + /** + * The native size must be selectable — it is what the block pre-selects — + * but need not lead the list: codestral-embed defaults to 1536 and tops + * out at 3072, so its options straddle the default. + */ + expect( + info.supportedDimensions.includes(info.nativeDimensions), + `${modelId} does not offer its native size` + ).toBe(true) + // Descending order is what the block's dropdown renders. + expect([...info.supportedDimensions]).toEqual( + [...info.supportedDimensions].sort((a, b) => b - a) + ) + } + }) + + it('never declares a request token budget below its own per-input ceiling', () => { + for (const [modelId, info] of Object.entries(EMBEDDING_MODELS)) { + if (info.maxTokensPerRequest === undefined) continue + expect( + info.maxTokensPerRequest, + `${modelId} would truncate a maximal single input` + ).toBeGreaterThanOrEqual(info.maxInputTokens) + } + }) + + it('only marks a model KB-eligible when it can emit the fixed KB vector width', () => { + for (const modelId of getKbEligibleModels()) { + const info = EMBEDDING_MODELS[modelId] + const canEmit = + info.nativeDimensions === KB_EMBEDDING_DIMENSIONS || + info.supportedDimensions?.includes(KB_EMBEDDING_DIMENSIONS) + expect(canEmit, `${modelId} cannot emit ${KB_EMBEDDING_DIMENSIONS} dimensions`).toBe(true) + } + }) + + it('keeps the KB-eligible set to the three models knowledge bases already index with', () => { + // Widening this set changes which models KB_EMBEDDING_MODEL accepts, so it + // is a deliberate decision rather than a side effect of adding a provider. + expect(getKbEligibleModels().sort()).toEqual([ + 'gemini-embedding-001', + 'text-embedding-3-large', + 'text-embedding-3-small', + ]) + }) + + it('groups models under the provider that actually serves them', () => { + expect(getModelsForProvider('gemini')).toEqual(['gemini-embedding-001']) + expect(getModelsForProvider('cohere')).toEqual(['embed-v4.0']) + expect(getModelsForProvider('mistral')).toEqual(['mistral-embed', 'codestral-embed']) + }) +}) + +describe('resolveDimensions', () => { + const gemini = EMBEDDING_MODELS['gemini-embedding-001'] + const ada = EMBEDDING_MODELS['text-embedding-ada-002'] + + it('falls back to native when nothing is requested', () => { + expect(resolveDimensions(gemini)).toBe(3072) + expect(resolveDimensions(ada)).toBe(1536) + }) + + it('accepts a supported reduction', () => { + expect(resolveDimensions(gemini, 768)).toBe(768) + }) + + it('rejects an unsupported size and names what is allowed', () => { + expect(() => resolveDimensions(gemini, 999)).toThrow(/does not support 999/) + expect(() => resolveDimensions(ada, 256)).toThrow(/does not support 256/) + }) +}) + +/** + * Batching counts tokens with tiktoken, which only has encodings for OpenAI + * models; every other id falls back to cl100k. This flag records which models + * are counted approximately. It must not be used to shrink the ceiling — + * truncating below a provider's declared limit drops valid content silently, + * which is strictly worse than the visible rejection it would guard against. + */ +describe('hasApproximateTokenCount', () => { + it('is false only for tiktoken-native models', () => { + for (const [id, info] of Object.entries(EMBEDDING_MODELS)) { + expect(hasApproximateTokenCount(info), id).toBe(info.tokenizerProvider !== 'openai') + } + }) + + it('covers at least one model, so the flag stays meaningful', () => { + const approximate = Object.values(EMBEDDING_MODELS).filter(hasApproximateTokenCount) + expect(approximate.length).toBeGreaterThan(0) + }) +}) diff --git a/apps/sim/lib/embeddings/catalog.ts b/apps/sim/lib/embeddings/catalog.ts new file mode 100644 index 00000000000..69f5f7cb11c --- /dev/null +++ b/apps/sim/lib/embeddings/catalog.ts @@ -0,0 +1,234 @@ +import type { + EmbeddingCatalogProvider, + EmbeddingTaskType, + TokenizerProviderId, +} from '@/lib/embeddings/types' +import type { BYOKProviderId } from '@/tools/types' + +/** + * Single source of truth for embedding models across the platform: the + * knowledge-base indexing path, the Embeddings block, and pricing lookups all + * resolve model metadata from here. + */ + +export const DEFAULT_EMBEDDING_MODEL = 'text-embedding-3-small' + +/** + * Dimensionality every knowledge-base vector is stored at. The pgvector column + * is fixed at this width, so any model used for KB indexing must be able to + * emit vectors of exactly this size. + */ +export const KB_EMBEDDING_DIMENSIONS = 1536 as const + +/** + * OpenAI caps a single `/v1/embeddings` call at 300,000 tokens summed across all + * inputs, independent of the 8192-token per-input ceiling. + */ +const OPENAI_MAX_TOKENS_PER_REQUEST = 300_000 + +export interface EmbeddingModelInfo { + provider: EmbeddingCatalogProvider + /** Human-readable label for the block's model dropdown. */ + label: string + /** Pricing/billing label - must match an entry in EMBEDDING_MODEL_PRICING when billed. */ + pricingId: string + tokenizerProvider: TokenizerProviderId + /** Dimensionality the model emits when no reduction is requested. */ + nativeDimensions: number + /** + * Output dimensions the model can emit, largest first — the block renders this + * list in order. Omitted when the model has a fixed size. + * + * This is not required to lead with {@link nativeDimensions}: a model whose + * API default sits below its maximum (codestral-embed defaults to 1536 and + * tops out at 3072) offers sizes on both sides of its default. + */ + supportedDimensions?: readonly number[] + /** + * Task types this model can condition on, so the block only ever offers what + * the provider actually accepts. Omitted when the model has no task + * conditioning. + */ + supportedTaskTypes?: readonly EmbeddingTaskType[] + /** Provider's per-input token ceiling. Longer inputs are truncated to fit. */ + maxInputTokens: number + /** + * Provider's ceiling on tokens summed across every input in one request, which + * is a different limit from {@link maxInputTokens} and bounds how many inputs + * may share a batch. Omitted when the provider documents no such figure; the + * client then applies a conservative default rather than an invented number. + */ + maxTokensPerRequest?: number + /** + * Selectable for knowledge-base indexing. Requires the model to emit exactly + * KB_EMBEDDING_DIMENSIONS. + */ + kbEligible: boolean +} + +export const EMBEDDING_MODELS: Record = { + 'text-embedding-3-small': { + provider: 'openai', + label: 'text-embedding-3-small', + pricingId: 'text-embedding-3-small', + tokenizerProvider: 'openai', + nativeDimensions: 1536, + supportedDimensions: [1536, 1024, 768, 512, 256], + maxInputTokens: 8192, + maxTokensPerRequest: OPENAI_MAX_TOKENS_PER_REQUEST, + kbEligible: true, + }, + 'text-embedding-3-large': { + provider: 'openai', + label: 'text-embedding-3-large', + pricingId: 'text-embedding-3-large', + tokenizerProvider: 'openai', + nativeDimensions: 3072, + supportedDimensions: [3072, 1536, 1024, 768, 512, 256], + maxInputTokens: 8192, + maxTokensPerRequest: OPENAI_MAX_TOKENS_PER_REQUEST, + kbEligible: true, + }, + /** + * Superseded by the v3 models and not offered for knowledge bases, but kept + * in the catalog because the legacy Embeddings block still lists it and + * placed instances must keep resolving. + */ + 'text-embedding-ada-002': { + provider: 'openai', + label: 'text-embedding-ada-002', + pricingId: 'text-embedding-ada-002', + tokenizerProvider: 'openai', + nativeDimensions: 1536, + maxInputTokens: 8192, + maxTokensPerRequest: OPENAI_MAX_TOKENS_PER_REQUEST, + kbEligible: false, + }, + 'gemini-embedding-001': { + provider: 'gemini', + label: 'gemini-embedding-001', + pricingId: 'gemini-embedding-001', + tokenizerProvider: 'google', + nativeDimensions: 3072, + supportedDimensions: [3072, 1536, 768], + supportedTaskTypes: ['document', 'query', 'similarity', 'classification', 'clustering'], + maxInputTokens: 2048, + kbEligible: true, + }, + /** Cohere has no dedicated semantic-similarity input type, so it is not offered. */ + 'embed-v4.0': { + provider: 'cohere', + label: 'embed-v4.0', + pricingId: 'embed-v4.0', + tokenizerProvider: 'cohere', + nativeDimensions: 1536, + supportedDimensions: [1536, 1024, 512, 256], + supportedTaskTypes: ['document', 'query', 'classification', 'clustering'], + maxInputTokens: 128_000, + kbEligible: false, + }, + 'mistral-embed': { + provider: 'mistral', + label: 'mistral-embed', + pricingId: 'mistral-embed', + tokenizerProvider: 'mistral', + nativeDimensions: 1024, + maxInputTokens: 8192, + kbEligible: false, + }, + /** + * `output_dimension` tops out at 3072 while the API default is 1536, so the + * offered sizes straddle the default rather than starting at it. + */ + 'codestral-embed': { + provider: 'mistral', + label: 'codestral-embed', + pricingId: 'codestral-embed', + tokenizerProvider: 'mistral', + nativeDimensions: 1536, + supportedDimensions: [3072, 1536, 1024, 512, 256], + maxInputTokens: 8192, + kbEligible: false, + }, +} + +/** Providers a user can pick, in the order the block offers them. */ +export const EMBEDDING_CATALOG_PROVIDERS: readonly EmbeddingCatalogProvider[] = [ + 'openai', + 'gemini', + 'cohere', + 'mistral', +] as const + +/** + * Model each provider falls back to when the caller names none. Single source + * for the block's pre-selected value, the per-provider tools, and the route. + */ +export const DEFAULT_MODEL_BY_PROVIDER: Record = { + openai: DEFAULT_EMBEDDING_MODEL, + gemini: 'gemini-embedding-001', + cohere: 'embed-v4.0', + mistral: 'mistral-embed', +} + +/** + * BYOK provider id for a workspace-owned key. Differs from the embedding + * provider id for Gemini, whose keys are stored under the shared Google entry. + */ +export const BYOK_PROVIDER_IDS: Record = { + openai: 'openai', + gemini: 'google', + cohere: 'cohere', + mistral: 'mistral', +} + +export function getEmbeddingModelInfo(model: string): EmbeddingModelInfo { + const info = EMBEDDING_MODELS[model] + if (!info) { + throw new Error(`Unsupported embedding model: ${model}`) + } + return info +} + +export function findEmbeddingModelInfo(model: string): EmbeddingModelInfo | undefined { + return EMBEDDING_MODELS[model] +} + +export function getModelsForProvider(provider: EmbeddingCatalogProvider): string[] { + return Object.keys(EMBEDDING_MODELS).filter((id) => EMBEDDING_MODELS[id].provider === provider) +} + +/** Model ids selectable for knowledge-base indexing. */ +export function getKbEligibleModels(): string[] { + return Object.keys(EMBEDDING_MODELS).filter((id) => EMBEDDING_MODELS[id].kbEligible) +} + +/** + * True when a model's tokens cannot be counted exactly. + * + * Batching measures with tiktoken, which only has encodings for OpenAI models — + * every other id falls back to `cl100k_base`, so Gemini, Cohere, and Mistral + * ceilings are enforced in approximate units. The ceiling is still applied as + * declared rather than discounted to absorb the error: an undercount surfaces + * as a visible provider rejection, whereas silently shortening an embedding's + * input does not. + */ +export function hasApproximateTokenCount(info: EmbeddingModelInfo): boolean { + return info.tokenizerProvider !== 'openai' +} + +/** + * Resolves the dimensionality a request will actually produce, given an + * optional caller-requested reduction. + */ +export function resolveDimensions(info: EmbeddingModelInfo, requested?: number): number { + if (requested === undefined) return info.nativeDimensions + if (!info.supportedDimensions?.includes(requested)) { + throw new Error( + `${info.label} does not support ${requested}-dimensional output. Supported: ${ + info.supportedDimensions?.join(', ') ?? info.nativeDimensions + }` + ) + } + return requested +} diff --git a/apps/sim/lib/embeddings/client.test.ts b/apps/sim/lib/embeddings/client.test.ts new file mode 100644 index 00000000000..fcfdc38627f --- /dev/null +++ b/apps/sim/lib/embeddings/client.test.ts @@ -0,0 +1,386 @@ +/** + * @vitest-environment node + */ +import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest' +import { embed } from '@/lib/embeddings/client' + +/** + * Exercises the orchestrator end-to-end against a mocked transport: batching, + * per-provider item caps, input ordering, dimension resolution, and retry. + * Every call passes an explicit `apiKey` so BYOK/env/rotating-pool resolution + * (which needs a database) is bypassed. + */ + +const originalFetch = global.fetch + +function jsonResponse(body: unknown, status = 200): Response { + return { + ok: status >= 200 && status < 300, + status, + statusText: String(status), + json: async () => body, + text: async () => JSON.stringify(body), + } as Response +} + +function openAIBody(vectors: number[][], totalTokens = 5) { + return { + data: vectors.map((embedding) => ({ embedding })), + usage: { total_tokens: totalTokens }, + } +} + +let fetchMock: ReturnType + +beforeEach(() => { + fetchMock = vi.fn() + global.fetch = fetchMock as unknown as typeof fetch +}) + +afterEach(() => { + global.fetch = originalFetch + vi.restoreAllMocks() +}) + +describe('embed', () => { + it('sends one request for a small batch and returns its vectors', async () => { + fetchMock.mockResolvedValue(jsonResponse(openAIBody([[1, 2, 3]], 4))) + + const result = await embed(['hello'], { + model: 'text-embedding-3-small', + apiKey: 'sk-test', + }) + + expect(fetchMock).toHaveBeenCalledTimes(1) + const [url, init] = fetchMock.mock.calls[0] + expect(url).toBe('https://api.openai.com/v1/embeddings') + expect(JSON.parse((init as RequestInit).body as string)).toMatchObject({ + input: ['hello'], + model: 'text-embedding-3-small', + }) + expect(result.embeddings).toEqual([[1, 2, 3]]) + expect(result.totalTokens).toBe(4) + expect(result.dimensions).toBe(1536) + expect(result.pricingId).toBe('text-embedding-3-small') + }) + + it("splits past Gemini's 100-item cap and preserves input order across batches", async () => { + const inputs = Array.from({ length: 250 }, (_, i) => `text-${i}`) + let cursor = 0 + + fetchMock.mockImplementation(async (_url, init) => { + const body = JSON.parse((init as RequestInit).body as string) + const count = body.requests.length + // Each vector encodes its global input index so ordering is verifiable. + const embeddings = Array.from({ length: count }, (_, i) => ({ values: [cursor + i] })) + cursor += count + return jsonResponse({ embeddings }) + }) + + const result = await embed(inputs, { + model: 'gemini-embedding-001', + apiKey: 'g-test', + taskType: 'document', + }) + + expect(fetchMock).toHaveBeenCalledTimes(3) + const sentCounts = fetchMock.mock.calls.map( + ([, init]) => JSON.parse((init as RequestInit).body as string).requests.length + ) + expect(sentCounts).toEqual([100, 100, 50]) + expect(result.embeddings).toHaveLength(250) + // Native dimensionality means no reduction, so values pass through unnormalized. + expect(result.embeddings.map((v) => v[0])).toEqual(inputs.map((_, i) => i)) + }) + + it('estimates tokens when the provider omits usage', async () => { + fetchMock.mockResolvedValue(jsonResponse({ embeddings: [{ values: [1, 2] }] })) + + const result = await embed(['some text to embed'], { + model: 'gemini-embedding-001', + apiKey: 'g-test', + }) + + expect(result.totalTokens).toBeGreaterThan(0) + }) + + it("bills Gemini on its reported token count rather than tiktoken's guess", async () => { + fetchMock.mockResolvedValue( + jsonResponse({ + embeddings: [{ values: [1, 2] }], + usageMetadata: { promptTokenCount: 4321 }, + }) + ) + + const result = await embed(['some text to embed'], { + model: 'gemini-embedding-001', + apiKey: 'g-test', + }) + + expect(result.totalTokens).toBe(4321) + }) + + it('splits a long input list into several bounded requests', async () => { + fetchMock.mockImplementation(async (_url, init) => { + const body = JSON.parse((init as RequestInit).body as string) + return jsonResponse(openAIBody(body.input.map(() => [1]))) + }) + + /** + * 40 inputs of roughly 500 tokens each exceed the batch target several times + * over, so they must be spread across requests rather than sent as one. + * Every input still has to arrive exactly once, in order. + */ + const inputs = Array.from({ length: 40 }, (_, i) => `${i} ${'word '.repeat(500)}`) + const result = await embed(inputs, { model: 'text-embedding-3-small', apiKey: 'sk-test' }) + + expect(fetchMock.mock.calls.length).toBeGreaterThan(1) + const sent = fetchMock.mock.calls.flatMap( + ([, init]) => JSON.parse((init as RequestInit).body as string).input as string[] + ) + expect(sent).toEqual(inputs) + expect(result.embeddings).toHaveLength(40) + }) + + it('keeps a long Cohere input whole rather than cutting it to the batch budget', async () => { + fetchMock.mockImplementation(async (_url, init) => { + const body = JSON.parse((init as RequestInit).body as string) + return jsonResponse({ + embeddings: { float: body.texts.map(() => [1]) }, + meta: { billed_units: { input_tokens: 1 } }, + }) + }) + + /** + * Cohere accepts 128k tokens in one text — far above the conservative + * default request budget. That budget floors at the per-input ceiling, or + * this input would be silently cut to a fraction of its length. + */ + const long = 'word '.repeat(30_000) + await embed([long], { model: 'embed-v4.0', apiKey: 'co-test' }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body.texts[0]).toBe(long) + }) + + it('forwards a supported dimension reduction and reports it back', async () => { + fetchMock.mockResolvedValue(jsonResponse(openAIBody([[1, 2]]))) + + const result = await embed(['hello'], { + model: 'text-embedding-3-large', + apiKey: 'sk-test', + dimensions: 1024, + }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body.dimensions).toBe(1024) + expect(result.dimensions).toBe(1024) + }) + + /** + * Regression: the resolved dimensionality is reported back to the caller but + * must not reach the wire unless the caller asked to reduce. `ada-002` and + * `mistral-embed` reject the parameter outright, so sending it populated with + * the native size made every unreduced request to those models a 400. + */ + it('omits the dimension field when no reduction was requested', async () => { + fetchMock.mockResolvedValue(jsonResponse(openAIBody([[1, 2]]))) + + const result = await embed(['hello'], { + model: 'text-embedding-ada-002', + apiKey: 'sk-test', + }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body).not.toHaveProperty('dimensions') + expect(result.dimensions).toBe(1536) + }) + + it('omits the dimension field for a model without Matryoshka support', async () => { + fetchMock.mockResolvedValue( + jsonResponse({ + data: [{ embedding: [1, 2], index: 0 }], + usage: { total_tokens: 5 }, + }) + ) + + const result = await embed(['hello'], { model: 'mistral-embed', apiKey: 'key-test' }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body).not.toHaveProperty('output_dimension') + expect(result.dimensions).toBe(1024) + }) + + it('rejects an unsupported dimension before making a request', async () => { + await expect( + embed(['hello'], { model: 'text-embedding-3-small', apiKey: 'sk-test', dimensions: 999 }) + ).rejects.toThrow(/does not support 999/) + expect(fetchMock).not.toHaveBeenCalled() + }) + + it('rejects an unknown model before making a request', async () => { + await expect(embed(['hello'], { model: 'nope', apiKey: 'sk-test' })).rejects.toThrow( + 'Unsupported embedding model: nope' + ) + expect(fetchMock).not.toHaveBeenCalled() + }) + + it('surfaces a non-retryable provider error with its status', async () => { + fetchMock.mockResolvedValue(jsonResponse({ error: 'bad key' }, 401)) + + await expect( + embed(['hello'], { model: 'text-embedding-3-small', apiKey: 'sk-bad' }) + ).rejects.toThrow(/Embedding API failed: 401/) + // 401 is not retryable, so exactly one attempt is made. + expect(fetchMock).toHaveBeenCalledTimes(1) + }) + + it('retries a rate-limited request and succeeds on a later attempt', async () => { + fetchMock + .mockResolvedValueOnce(jsonResponse({ error: 'slow down' }, 429)) + .mockResolvedValueOnce(jsonResponse(openAIBody([[7, 8]]))) + + const result = await embed(['hello'], { + model: 'text-embedding-3-small', + apiKey: 'sk-test', + }) + + expect(fetchMock).toHaveBeenCalledTimes(2) + expect(result.embeddings).toEqual([[7, 8]]) + }) + + it('marks a caller-supplied key as BYOK so Sim does not bill for it', async () => { + fetchMock.mockResolvedValue(jsonResponse(openAIBody([[1]]))) + + const result = await embed(['hello'], { + model: 'text-embedding-3-small', + apiKey: 'sk-user-owned', + }) + + expect(result.isBYOK).toBe(true) + }) + + /** + * `batchByTokenLimit` truncates any single text above the limit it is given, + * so the limit has to be the selected model's own. One shared constant sent + * oversized input to the models with a lower ceiling and silently dropped + * content the models with a higher one would have accepted. + */ + describe('per-model token limits', () => { + it("truncates against Gemini's lower ceiling rather than a shared constant", async () => { + fetchMock.mockResolvedValue(jsonResponse({ embeddings: [{ values: [1] }] })) + // ~10k tokens: over Gemini's 2048 ceiling, but under the old 8000 constant, + // so this used to reach the provider whole and come back a 502. + const long = 'word '.repeat(8000) + + await embed([long], { model: 'gemini-embedding-001', apiKey: 'g-test' }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + const sent = body.requests[0].content.parts[0].text + expect(sent.length).toBeLessThan(long.length) + }) + + it("keeps text intact up to Cohere's much higher ceiling", async () => { + fetchMock.mockResolvedValue( + jsonResponse({ embeddings: { float: [[1]] }, meta: { billed_units: { input_tokens: 9 } } }) + ) + // Over the old 8000 constant, well under Cohere's 128k, so it must survive. + const long = 'word '.repeat(8000) + + await embed([long], { model: 'embed-v4.0', apiKey: 'c-test' }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body.texts[0]).toBe(long) + }) + }) + + /** + * The knowledge-base path rewrites resolved-secret plaintext back to + * placeholders before inputs reach a provider. The block path projects + * earlier, at the tool's HTTP hop, and passes null here so the substitution + * does not run twice over already-projected content. + */ + describe('resolved-secret projection', () => { + it('sends projected inputs, not the originals', async () => { + fetchMock.mockResolvedValue(jsonResponse(openAIBody([[1], [2]]))) + + await embed(['token is sk-live-123', 'harmless'], { + model: 'text-embedding-3-small', + apiKey: 'sk-test', + projectInputs: (values) => values.map((v) => v.replace('sk-live-123', '{{API_KEY}}')), + }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body.input).toEqual(['token is {{API_KEY}}', 'harmless']) + expect(JSON.stringify(body)).not.toContain('sk-live-123') + }) + + it('leaves inputs untouched when the caller passes null', async () => { + fetchMock.mockResolvedValue(jsonResponse(openAIBody([[1]]))) + + await embed(['already projected'], { + model: 'text-embedding-3-small', + apiKey: 'sk-test', + projectInputs: null, + }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body.input).toEqual(['already projected']) + }) + + it('estimates tokens from the projected values, not the originals', async () => { + // Gemini omits usage, so the token count is estimated from what was sent. + fetchMock.mockResolvedValue(jsonResponse({ embeddings: [{ values: [1, 2, 3] }] })) + + const result = await embed(['x'.repeat(400)], { + model: 'gemini-embedding-001', + apiKey: 'key-test', + projectInputs: () => ['tiny'], + }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + expect(body.requests[0].content.parts[0].text).toBe('tiny') + // 400 chars would estimate far higher; 'tiny' lands in single digits. + expect(result.totalTokens).toBeLessThan(10) + }) + + /** + * Projection changes length, and batching truncates whatever it measures. + * Batching the pre-projection text sized against a string that was never + * sent: a lengthening projection then exceeded the model's ceiling, and a + * shortening one discarded content that would have fit. + */ + it('batches the projected text, not the original', async () => { + fetchMock.mockResolvedValue(jsonResponse({ embeddings: [{ values: [1] }] })) + // Under Gemini's 2048 ceiling before projection, far over it after. + const short = 'secret' + + await embed([short], { + model: 'gemini-embedding-001', + apiKey: 'g-test', + projectInputs: () => ['word '.repeat(8000)], + }) + + const body = JSON.parse((fetchMock.mock.calls[0][1] as RequestInit).body as string) + const sent = body.requests[0].content.parts[0].text + // Truncated against the model ceiling, so the lengthened text cannot go out whole. + expect(sent.length).toBeLessThan('word '.repeat(8000).length) + }) + + it('projects once even when the request is retried', async () => { + const projectInputs = vi.fn((values: readonly string[]) => values.map(() => 'projected')) + fetchMock + .mockResolvedValueOnce(jsonResponse({ error: 'rate limited' }, 429)) + .mockResolvedValueOnce(jsonResponse(openAIBody([[1]]))) + + await embed(['secret'], { + model: 'text-embedding-3-small', + apiKey: 'sk-test', + projectInputs, + }) + + expect(fetchMock).toHaveBeenCalledTimes(2) + expect(projectInputs).toHaveBeenCalledTimes(1) + }) + }) +}) diff --git a/apps/sim/lib/embeddings/client.ts b/apps/sim/lib/embeddings/client.ts new file mode 100644 index 00000000000..038d0a33073 --- /dev/null +++ b/apps/sim/lib/embeddings/client.ts @@ -0,0 +1,288 @@ +import { createLogger } from '@sim/logger' +import { chunkArray } from '@sim/utils' +import { env, envNumber } from '@/lib/core/config/env' +import { mapWithConcurrency } from '@/lib/core/utils/concurrency' +import { + DEFAULT_EMBEDDING_MODEL, + type EmbeddingModelInfo, + getEmbeddingModelInfo, + hasApproximateTokenCount, + resolveDimensions, +} from '@/lib/embeddings/catalog' +import { resolveProviderKey } from '@/lib/embeddings/keys' +import { getAdapterFactory } from '@/lib/embeddings/providers' +import type { + EmbeddingProviderAdapter, + EmbeddingTaskType, + EmbedOptions, + EmbedResult, +} from '@/lib/embeddings/types' +import { isRetryableError, retryWithExponentialBackoff } from '@/lib/knowledge/documents/utils' +import { batchByTokenLimit, estimateTokenCount, truncateToTokenLimit } from '@/lib/tokenization' + +const logger = createLogger('EmbeddingClient') + +const MAX_CONCURRENT_BATCHES = envNumber(env.KB_CONFIG_CONCURRENCY_LIMIT, 50) +const EMBEDDING_REQUEST_TIMEOUT_MS = 60_000 + +/** + * Tokens this client aims to put in one request. Not a provider limit — every + * provider accepts at least this much, and OpenAI documents 300,000 — but the + * batch size the knowledge-base indexing path has run on in production. + * + * Kept here rather than raised to each provider's maximum so a request stays + * comfortably inside {@link EMBEDDING_REQUEST_TIMEOUT_MS}: a timed-out batch is + * retried three times, so large batches make a slow provider expensive to fail + * against. Raising this trades fewer round trips for costlier retries. + */ +const BATCH_TOKEN_TARGET = 8192 + +export class EmbeddingAPIError extends Error { + public status: number + + constructor(message: string, status: number) { + super(message) + this.name = 'EmbeddingAPIError' + this.status = status + } +} + +interface ResolvedProvider { + adapter: EmbeddingProviderAdapter + info: EmbeddingModelInfo + /** Model name as sent to the provider (an Azure deployment name when Azure is active). */ + modelName: string + /** Dimensionality the request will produce, for reporting and billing. */ + dimensions: number + isBYOK: boolean +} + +/** + * Azure OpenAI takes over for OpenAI models when fully configured, but only + * when the caller has not supplied its own key. A user-pasted OpenAI key must + * always go to OpenAI. + */ +function resolveAzureOverride(info: EmbeddingModelInfo, model: string) { + if (info.provider !== 'openai') return null + const apiKey = env.AZURE_OPENAI_API_KEY + const endpoint = env.AZURE_OPENAI_ENDPOINT + const apiVersion = env.AZURE_OPENAI_API_VERSION + if (!apiKey || !endpoint || !apiVersion) return null + /** + * Azure deployment names default to the embedding model name when + * `KB_OPENAI_MODEL_NAME` is unset — this matches the pre-existing + * convention where deployments are named after the model they host. + */ + return { apiKey, endpoint, apiVersion, deployment: env.KB_OPENAI_MODEL_NAME || model } +} + +async function resolveProvider(model: string, options: EmbedOptions): Promise { + const info = getEmbeddingModelInfo(model) + const dimensions = resolveDimensions(info, options.dimensions) + + if (!options.apiKey) { + const azure = resolveAzureOverride(info, model) + if (azure) { + return { + adapter: getAdapterFactory('azure-openai')({ + modelName: azure.deployment, + apiKey: azure.apiKey, + nativeDimensions: info.nativeDimensions, + endpoint: azure.endpoint, + apiVersion: azure.apiVersion, + }), + info, + modelName: azure.deployment, + dimensions, + isBYOK: false, + } + } + } + + const { apiKey, isBYOK } = options.apiKey + ? { apiKey: options.apiKey, isBYOK: true } + : await resolveProviderKey(info.provider, options.workspaceId) + + return { + adapter: getAdapterFactory(info.provider)({ + modelName: model, + apiKey, + nativeDimensions: info.nativeDimensions, + }), + info, + modelName: model, + dimensions, + isBYOK, + } +} + +/** `inputs` are already projected and batched by {@link embed}. */ +async function callEmbeddingAPI( + inputs: string[], + provider: ResolvedProvider, + taskType: EmbeddingTaskType, + /** + * The caller's explicit reduction, or undefined when none was requested. Kept + * distinct from `provider.dimensions` because a model without Matryoshka + * support rejects the parameter outright — sending it populated with the + * native size is a 400, not a no-op. + */ + requestedDimensions: number | undefined +): Promise<{ embeddings: number[][]; totalTokens: number }> { + return retryWithExponentialBackoff( + async () => { + const request = provider.adapter.buildRequest({ + inputs, + taskType, + dimensions: requestedDimensions, + }) + + const controller = new AbortController() + const timeout = setTimeout(() => controller.abort(), EMBEDDING_REQUEST_TIMEOUT_MS) + + const response = await fetch(request.apiUrl, { + method: 'POST', + headers: request.headers, + body: JSON.stringify(request.body), + signal: controller.signal, + }).finally(() => clearTimeout(timeout)) + + if (!response.ok) { + const errorText = await response.text() + throw new EmbeddingAPIError( + `Embedding API failed: ${response.status} ${response.statusText} - ${errorText}`, + response.status + ) + } + + const json = await response.json() + const embeddings = request.parse(json) + /** + * Fallback for a response that carries no usage block. Estimated with the + * provider's own tokenizer, which is approximate for every non-OpenAI + * model — see {@link hasApproximateTokenCount}. + */ + const totalTokens = + request.parseTokens?.(json) ?? + inputs.reduce( + (sum, text) => sum + estimateTokenCount(text, provider.info.tokenizerProvider).count, + 0 + ) + + return { embeddings, totalTokens } + }, + { + maxRetries: 3, + initialDelayMs: 1000, + maxDelayMs: 10000, + retryCondition: (error: unknown) => { + if (error instanceof EmbeddingAPIError) { + return error.status === 429 || error.status >= 500 + } + return isRetryableError(error) + }, + } + ) +} + +/** + * Generates embeddings for a batch of texts with token-aware batching, + * per-provider item caps, bounded concurrency, and retry on transient failures. + */ +export async function embed(texts: string[], options: EmbedOptions): Promise { + const model = options.model ?? DEFAULT_EMBEDDING_MODEL + const taskType = options.taskType ?? 'document' + const provider = await resolveProvider(model, options) + + /** + * Projected before batching, not after. The projector rewrites resolved-secret + * plaintext to placeholders, which changes length, and `batchByTokenLimit` + * measures and truncates whatever it is handed. Batching the pre-projection + * text would size against a different string than the one actually sent: a + * lengthening projection then exceeds the model's ceiling and the provider + * rejects it, and a shortening one discards content that would have fit. + * + * Doing it here also keeps projection to exactly once per call, so no retry + * can re-project already-projected content. + */ + const modelInputs = options.projectInputs ? options.projectInputs(texts) : texts + + /** + * Each input is held to the model's own per-input ceiling, exactly as declared. + * One shared constant sent oversized input to models with a lower limit and + * discarded content models with a higher one accept; discounting the ceiling + * to absorb tokenizer error would reintroduce the second harm. + * + * Truncation happens here rather than inside `batchByTokenLimit` so it occurs + * once, against the right limit, and is always warned about: a shortened + * embedding input is otherwise indistinguishable from a good one, both to the + * caller and in the vector it produces. + */ + const ceiling = provider.info.maxInputTokens + const boundedInputs = modelInputs.map((text) => { + if (estimateTokenCount(text, provider.info.tokenizerProvider).count <= ceiling) return text + logger.warn('Embedding input exceeds the model token limit and will be truncated', { + model, + maxInputTokens: ceiling, + chars: text.length, + approximateTokenCount: hasApproximateTokenCount(provider.info), + }) + return truncateToTokenLimit(text, ceiling, model) + }) + + /** + * How many tokens may share one request — a different limit from the per-input + * ceiling above, and the one that decides how many inputs go in a batch. + * + * Three bounds compose here: + * + * 1. {@link BATCH_TOKEN_TARGET} is what we actually aim for — an operational + * choice, not a provider limit (see its declaration for the reasoning). + * 2. A provider's documented summed-token cap, when it publishes one, is a + * hard ceiling the target can never exceed. + * 3. The per-input ceiling is a floor. A budget below it would make + * `batchByTokenLimit` truncate inputs the provider would have accepted — + * Cohere takes 128k tokens in one text, far above the target. + */ + const requestBudget = Math.max( + Math.min(provider.info.maxTokensPerRequest ?? BATCH_TOKEN_TARGET, BATCH_TOKEN_TARGET), + ceiling + ) + + const tokenBatches = batchByTokenLimit(boundedInputs, requestBudget, model) + const itemLimit = provider.adapter.maxItemsPerRequest + const batches = itemLimit + ? tokenBatches.flatMap((batch) => chunkArray(batch, itemLimit)) + : tokenBatches + + const batchResults = await mapWithConcurrency( + batches, + MAX_CONCURRENT_BATCHES, + async (batch, i) => { + try { + return await callEmbeddingAPI(batch, provider, taskType, options.dimensions) + } catch (error) { + logger.error(`Failed to generate embeddings for batch ${i + 1}/${batches.length}:`, error) + throw error + } + } + ) + + const embeddings: number[][] = [] + let totalTokens = 0 + for (const batch of batchResults) { + for (const vector of batch.embeddings) { + embeddings.push(vector) + } + totalTokens += batch.totalTokens + } + + return { + embeddings, + totalTokens, + isBYOK: provider.isBYOK, + modelName: provider.modelName, + pricingId: provider.info.pricingId, + dimensions: provider.dimensions, + } +} diff --git a/apps/sim/lib/embeddings/index.ts b/apps/sim/lib/embeddings/index.ts new file mode 100644 index 00000000000..998150e638e --- /dev/null +++ b/apps/sim/lib/embeddings/index.ts @@ -0,0 +1,13 @@ +/** + * Public surface of the embeddings module. Kept to what callers outside this + * directory actually use — the block imports `catalog` directly, because this + * barrel re-exports the client and would drag BYOK lookup and `@sim/db` into the + * browser bundle. + */ +export { + DEFAULT_MODEL_BY_PROVIDER, + findEmbeddingModelInfo, + resolveDimensions, +} from '@/lib/embeddings/catalog' +export { embed } from '@/lib/embeddings/client' +export type { EmbeddingTaskType, EmbedOptions, EmbedResult } from '@/lib/embeddings/types' diff --git a/apps/sim/lib/embeddings/keys.ts b/apps/sim/lib/embeddings/keys.ts new file mode 100644 index 00000000000..d04aa423552 --- /dev/null +++ b/apps/sim/lib/embeddings/keys.ts @@ -0,0 +1,86 @@ +import { createLogger } from '@sim/logger' +import { getBYOKKey } from '@/lib/api-key/byok' +import { getRotatingApiKey } from '@/lib/core/config/api-keys' +import { env } from '@/lib/core/config/env' +import { BYOK_PROVIDER_IDS } from '@/lib/embeddings/catalog' +import type { EmbeddingCatalogProvider } from '@/lib/embeddings/types' +import type { BYOKProviderId } from '@/tools/types' + +const logger = createLogger('EmbeddingKeys') + +export interface ResolvedEmbeddingKey { + apiKey: string + /** True when a workspace-owned key was used, meaning Sim does not bill for it. */ + isBYOK: boolean +} + +interface ProviderKeyConfig { + /** BYOK provider id used to look up a workspace-owned key. */ + byokProviderId: BYOKProviderId + /** Singular platform key, checked before the rotating pool. */ + envKey: string | undefined + /** Provider id for the rotating key pool, when one exists. */ + rotatingProvider?: string + missingKeyError: string +} + +/** + * Resolution order per provider is BYOK -> singular env key -> rotating pool. + * `env` is read lazily through a getter so tests that stub `env` still work. + */ +const PROVIDER_KEY_CONFIG: Record ProviderKeyConfig> = { + openai: () => ({ + byokProviderId: BYOK_PROVIDER_IDS.openai, + envKey: env.OPENAI_API_KEY, + rotatingProvider: 'openai', + missingKeyError: 'OPENAI_API_KEY is not configured', + }), + gemini: () => ({ + byokProviderId: BYOK_PROVIDER_IDS.gemini, + envKey: env.GEMINI_API_KEY, + rotatingProvider: 'gemini', + missingKeyError: + 'GEMINI_API_KEY (or GEMINI_API_KEY_1/2/3 for rotation) must be configured for Gemini embeddings', + }), + cohere: () => ({ + byokProviderId: BYOK_PROVIDER_IDS.cohere, + envKey: env.COHERE_API_KEY, + rotatingProvider: 'cohere', + missingKeyError: + 'COHERE_API_KEY (or COHERE_API_KEY_1/2/3 for rotation) must be configured for Cohere embeddings', + }), + mistral: () => ({ + byokProviderId: BYOK_PROVIDER_IDS.mistral, + envKey: env.MISTRAL_API_KEY, + missingKeyError: 'MISTRAL_API_KEY must be configured for Mistral embeddings', + }), +} + +export async function resolveProviderKey( + provider: EmbeddingCatalogProvider, + workspaceId?: string | null +): Promise { + const config = PROVIDER_KEY_CONFIG[provider]() + + if (workspaceId) { + const byokResult = await getBYOKKey(workspaceId, config.byokProviderId) + if (byokResult) { + logger.info(`Using workspace BYOK key for ${provider} embeddings`) + return { apiKey: byokResult.apiKey, isBYOK: true } + } + } + + if (config.envKey) { + return { apiKey: config.envKey, isBYOK: false } + } + + if (config.rotatingProvider) { + try { + return { apiKey: getRotatingApiKey(config.rotatingProvider), isBYOK: false } + } catch { + throw new Error(config.missingKeyError) + } + } + + throw new Error(config.missingKeyError) +} diff --git a/apps/sim/lib/embeddings/normalize.ts b/apps/sim/lib/embeddings/normalize.ts new file mode 100644 index 00000000000..8b470c15013 --- /dev/null +++ b/apps/sim/lib/embeddings/normalize.ts @@ -0,0 +1,18 @@ +/** + * Returns an L2-normalized copy of a vector, for providers that do not + * normalize a Matryoshka-reduced output themselves. + * + * Gemini does NOT auto-normalize when `outputDimensionality` is set below the + * native 3072 dimension on `gemini-embedding-001`. Cohere never documents + * whether it renormalizes a truncated vector, so its reduced output is + * normalized here too — the operation is idempotent, so it is a no-op on + * already-unit vectors and a correctness fix otherwise. Both keep cosine and + * inner-product similarity correct. + */ +export function l2Normalize(vector: number[]): number[] { + let sumSquares = 0 + for (const v of vector) sumSquares += v * v + const norm = Math.sqrt(sumSquares) + if (norm === 0) return vector + return vector.map((v) => v / norm) +} diff --git a/apps/sim/lib/embeddings/providers/azure-openai.ts b/apps/sim/lib/embeddings/providers/azure-openai.ts new file mode 100644 index 00000000000..2ebd93812b1 --- /dev/null +++ b/apps/sim/lib/embeddings/providers/azure-openai.ts @@ -0,0 +1,32 @@ +import { + OPENAI_MAX_ITEMS_PER_REQUEST, + type OpenAIEmbeddingResponse, +} from '@/lib/embeddings/providers/openai' +import type { AzureEmbeddingAdapterContext, EmbeddingAdapterFactory } from '@/lib/embeddings/types' + +/** + * Azure OpenAI embeddings. The model is selected by the deployment name in the + * URL rather than a `model` body field, so `modelName` here is the deployment. + */ +export const createAzureOpenAIAdapter: EmbeddingAdapterFactory = ({ + modelName, + apiKey, + endpoint, + apiVersion, +}) => ({ + maxItemsPerRequest: OPENAI_MAX_ITEMS_PER_REQUEST, + buildRequest: ({ inputs, dimensions }) => ({ + apiUrl: `${endpoint}/openai/deployments/${modelName}/embeddings?api-version=${apiVersion}`, + headers: { + 'api-key': apiKey, + 'Content-Type': 'application/json', + }, + body: { + input: inputs, + encoding_format: 'float', + ...(dimensions !== undefined && { dimensions }), + }, + parse: (json) => (json as OpenAIEmbeddingResponse).data.map((item) => item.embedding), + parseTokens: (json) => (json as OpenAIEmbeddingResponse).usage?.total_tokens, + }), +}) diff --git a/apps/sim/lib/embeddings/providers/cohere.ts b/apps/sim/lib/embeddings/providers/cohere.ts new file mode 100644 index 00000000000..381d6818da1 --- /dev/null +++ b/apps/sim/lib/embeddings/providers/cohere.ts @@ -0,0 +1,63 @@ +import { l2Normalize } from '@/lib/embeddings/normalize' +import type { EmbeddingAdapterFactory, EmbeddingTaskType } from '@/lib/embeddings/types' + +/** Cohere's v2 embed endpoint rejects requests with more than 96 texts. */ +const COHERE_MAX_ITEMS_PER_REQUEST = 96 + +/** + * Cohere has no dedicated semantic-similarity input type; `similarity` falls + * back to `search_document`, and the catalog does not offer it for this model. + */ +const COHERE_INPUT_TYPES: Record = { + document: 'search_document', + query: 'search_query', + similarity: 'search_document', + classification: 'classification', + clustering: 'clustering', +} + +interface CohereEmbeddingResponse { + embeddings: { float?: number[][] } + meta?: { billed_units?: { input_tokens?: number } } +} + +/** + * Cohere `/v2/embed`. `input_type` is required by the API, so a task type is + * always sent. + * + * Cohere documents `output_dimension` as Matryoshka truncation but never states + * whether it renormalizes the truncated vector. Rather than depend on an + * undocumented guarantee, reduced output is normalized locally: `l2Normalize` is + * idempotent, so this is a no-op if Cohere already returns unit vectors and a + * correctness fix if it does not. + */ +export const createCohereAdapter: EmbeddingAdapterFactory = ({ + modelName, + apiKey, + nativeDimensions, +}) => ({ + maxItemsPerRequest: COHERE_MAX_ITEMS_PER_REQUEST, + buildRequest: ({ inputs, taskType, dimensions }) => ({ + apiUrl: 'https://api.cohere.com/v2/embed', + headers: { + Authorization: `Bearer ${apiKey}`, + 'Content-Type': 'application/json', + }, + body: { + model: modelName, + texts: inputs, + input_type: COHERE_INPUT_TYPES[taskType], + embedding_types: ['float'], + ...(dimensions !== undefined && { output_dimension: dimensions }), + }, + parse: (json) => { + const vectors = (json as CohereEmbeddingResponse).embeddings?.float + if (!vectors) { + throw new Error('Cohere embed response did not include float embeddings') + } + const isReduced = dimensions !== undefined && dimensions < nativeDimensions + return isReduced ? vectors.map(l2Normalize) : vectors + }, + parseTokens: (json) => (json as CohereEmbeddingResponse).meta?.billed_units?.input_tokens, + }), +}) diff --git a/apps/sim/lib/embeddings/providers/gemini.ts b/apps/sim/lib/embeddings/providers/gemini.ts new file mode 100644 index 00000000000..e27ac7a6ecf --- /dev/null +++ b/apps/sim/lib/embeddings/providers/gemini.ts @@ -0,0 +1,66 @@ +import { l2Normalize } from '@/lib/embeddings/normalize' +import type { EmbeddingAdapterFactory, EmbeddingTaskType } from '@/lib/embeddings/types' + +/** + * Gemini rejects batch embedding requests above 100 items. Google does not state + * this in the `batchEmbedContents` reference, so the cap is set from the limit + * the API is observed to enforce rather than from a documented figure. + */ +const GEMINI_MAX_ITEMS_PER_REQUEST = 100 + +const GEMINI_TASK_TYPES: Record = { + document: 'RETRIEVAL_DOCUMENT', + query: 'RETRIEVAL_QUERY', + similarity: 'SEMANTIC_SIMILARITY', + classification: 'CLASSIFICATION', + clustering: 'CLUSTERING', +} + +interface GeminiEmbeddingResponse { + embeddings: Array<{ values: number[] }> + /** + * `EmbeddingUsageMetadata` on `BatchEmbedContentsResponse`. Optional because + * older API surfaces omit it; the caller then falls back to a local estimate. + */ + usageMetadata?: { promptTokenCount?: number } +} + +/** + * Gemini `batchEmbedContents`. Gemini does not normalize when the output is + * reduced below the model's native dimensionality, so vectors are normalized + * locally in that case. + * + * Token counts come from the response rather than a local estimate: tiktoken has + * no Gemini encoding and falls back to `cl100k_base`, which does not match how + * Gemini actually tokenizes, and this count is what knowledge-base runs bill on. + */ +export const createGeminiAdapter: EmbeddingAdapterFactory = ({ + modelName, + apiKey, + nativeDimensions, +}) => ({ + maxItemsPerRequest: GEMINI_MAX_ITEMS_PER_REQUEST, + buildRequest: ({ inputs, taskType, dimensions }) => { + const isReduced = dimensions !== undefined && dimensions < nativeDimensions + return { + apiUrl: `https://generativelanguage.googleapis.com/v1beta/models/${modelName}:batchEmbedContents`, + headers: { + 'Content-Type': 'application/json', + 'x-goog-api-key': apiKey, + }, + body: { + requests: inputs.map((text) => ({ + model: `models/${modelName}`, + content: { parts: [{ text }] }, + taskType: GEMINI_TASK_TYPES[taskType], + ...(dimensions !== undefined && { outputDimensionality: dimensions }), + })), + }, + parse: (json) => { + const values = (json as GeminiEmbeddingResponse).embeddings.map((item) => item.values) + return isReduced ? values.map(l2Normalize) : values + }, + parseTokens: (json) => (json as GeminiEmbeddingResponse).usageMetadata?.promptTokenCount, + } + }, +}) diff --git a/apps/sim/lib/embeddings/providers/index.ts b/apps/sim/lib/embeddings/providers/index.ts new file mode 100644 index 00000000000..6f197a68cf0 --- /dev/null +++ b/apps/sim/lib/embeddings/providers/index.ts @@ -0,0 +1,37 @@ +import { createAzureOpenAIAdapter } from '@/lib/embeddings/providers/azure-openai' +import { createCohereAdapter } from '@/lib/embeddings/providers/cohere' +import { createGeminiAdapter } from '@/lib/embeddings/providers/gemini' +import { createMistralAdapter } from '@/lib/embeddings/providers/mistral' +import { createOpenAIAdapter } from '@/lib/embeddings/providers/openai' +import type { + AzureEmbeddingAdapterContext, + EmbeddingAdapterFactory, + EmbeddingProviderKind, +} from '@/lib/embeddings/types' + +/** Azure's entry keeps its own context type so its routing fields stay required. */ +type AdapterFactoryFor = K extends 'azure-openai' + ? EmbeddingAdapterFactory + : EmbeddingAdapterFactory + +const ADAPTER_FACTORIES: { [K in EmbeddingProviderKind]: AdapterFactoryFor } = { + openai: createOpenAIAdapter, + 'azure-openai': createAzureOpenAIAdapter, + gemini: createGeminiAdapter, + cohere: createCohereAdapter, + mistral: createMistralAdapter, +} + +export function getAdapterFactory( + provider: K +): AdapterFactoryFor { + return ADAPTER_FACTORIES[provider] +} + +export { + createAzureOpenAIAdapter, + createCohereAdapter, + createGeminiAdapter, + createMistralAdapter, + createOpenAIAdapter, +} diff --git a/apps/sim/lib/embeddings/providers/mistral.ts b/apps/sim/lib/embeddings/providers/mistral.ts new file mode 100644 index 00000000000..fd256581970 --- /dev/null +++ b/apps/sim/lib/embeddings/providers/mistral.ts @@ -0,0 +1,33 @@ +import type { EmbeddingAdapterFactory } from '@/lib/embeddings/types' + +interface MistralEmbeddingResponse { + data: Array<{ embedding: number[]; index: number }> + usage?: { prompt_tokens?: number; total_tokens?: number } +} + +/** + * Mistral `/v1/embeddings`. The REST body field is `input` (singular), even + * though Mistral's SDK examples show `inputs`. Mistral has no task + * conditioning, so `taskType` is ignored. + */ +export const createMistralAdapter: EmbeddingAdapterFactory = ({ modelName, apiKey }) => ({ + buildRequest: ({ inputs, dimensions }) => ({ + apiUrl: 'https://api.mistral.ai/v1/embeddings', + headers: { + Authorization: `Bearer ${apiKey}`, + 'Content-Type': 'application/json', + }, + body: { + model: modelName, + input: inputs, + encoding_format: 'float', + ...(dimensions !== undefined && { output_dimension: dimensions }), + }, + parse: (json) => { + const { data } = json as MistralEmbeddingResponse + /** Mistral returns an `index` per item; sort so vectors match input order. */ + return [...data].sort((a, b) => a.index - b.index).map((item) => item.embedding) + }, + parseTokens: (json) => (json as MistralEmbeddingResponse).usage?.total_tokens, + }), +}) diff --git a/apps/sim/lib/embeddings/providers/openai.ts b/apps/sim/lib/embeddings/providers/openai.ts new file mode 100644 index 00000000000..4c24664a0a5 --- /dev/null +++ b/apps/sim/lib/embeddings/providers/openai.ts @@ -0,0 +1,33 @@ +import type { EmbeddingAdapterFactory } from '@/lib/embeddings/types' + +/** Shared by the OpenAI and Azure OpenAI adapters — Azure mirrors this wire shape exactly. */ +export interface OpenAIEmbeddingResponse { + data: Array<{ embedding: number[] }> + usage?: { prompt_tokens?: number; total_tokens?: number } +} + +/** OpenAI rejects an `input` array longer than 2048 entries. */ +export const OPENAI_MAX_ITEMS_PER_REQUEST = 2048 + +/** + * OpenAI `/v1/embeddings`. Omitting `dimensions` yields the model's native + * dimensionality, which is what callers who do not reduce should get. + */ +export const createOpenAIAdapter: EmbeddingAdapterFactory = ({ modelName, apiKey }) => ({ + maxItemsPerRequest: OPENAI_MAX_ITEMS_PER_REQUEST, + buildRequest: ({ inputs, dimensions }) => ({ + apiUrl: 'https://api.openai.com/v1/embeddings', + headers: { + Authorization: `Bearer ${apiKey}`, + 'Content-Type': 'application/json', + }, + body: { + input: inputs, + model: modelName, + encoding_format: 'float', + ...(dimensions !== undefined && { dimensions }), + }, + parse: (json) => (json as OpenAIEmbeddingResponse).data.map((item) => item.embedding), + parseTokens: (json) => (json as OpenAIEmbeddingResponse).usage?.total_tokens, + }), +}) diff --git a/apps/sim/lib/embeddings/providers/providers.test.ts b/apps/sim/lib/embeddings/providers/providers.test.ts new file mode 100644 index 00000000000..fda8907edba --- /dev/null +++ b/apps/sim/lib/embeddings/providers/providers.test.ts @@ -0,0 +1,174 @@ +/** + * @vitest-environment node + */ +import { describe, expect, it } from 'vitest' +import { l2Normalize } from '@/lib/embeddings/normalize' +import { + createCohereAdapter, + createGeminiAdapter, + createMistralAdapter, + createOpenAIAdapter, +} from '@/lib/embeddings/providers' + +const INPUTS = ['alpha', 'beta'] + +function norm(vector: number[]): number { + return Math.sqrt(vector.reduce((sum, v) => sum + v * v, 0)) +} + +describe('l2Normalize', () => { + it('scales a vector to unit length', () => { + expect(norm(l2Normalize([3, 4]))).toBeCloseTo(1) + }) + + it('leaves a zero vector alone rather than dividing by zero', () => { + expect(l2Normalize([0, 0, 0])).toEqual([0, 0, 0]) + }) +}) + +describe('OpenAI adapter', () => { + const adapter = createOpenAIAdapter({ + modelName: 'text-embedding-3-small', + apiKey: 'sk-test', + nativeDimensions: 1536, + }) + + it('omits dimensions when none is requested, so the model returns its native size', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + expect(request.apiUrl).toBe('https://api.openai.com/v1/embeddings') + expect(request.headers.Authorization).toBe('Bearer sk-test') + expect(request.body).not.toHaveProperty('dimensions') + }) + + it('sends dimensions when a reduction is requested', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document', dimensions: 512 }) + expect(request.body).toMatchObject({ dimensions: 512, model: 'text-embedding-3-small' }) + }) + + it('parses vectors and token usage', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + const json = { + data: [{ embedding: [1, 2] }, { embedding: [3, 4] }], + usage: { total_tokens: 7 }, + } + expect(request.parse(json)).toEqual([ + [1, 2], + [3, 4], + ]) + expect(request.parseTokens?.(json)).toBe(7) + }) +}) + +describe('Gemini adapter', () => { + const adapter = createGeminiAdapter({ + modelName: 'gemini-embedding-001', + apiKey: 'g-test', + nativeDimensions: 3072, + }) + + it('caps items per request at Gemini’s documented limit', () => { + expect(adapter.maxItemsPerRequest).toBe(100) + }) + + it('maps task types onto Gemini’s enum', () => { + const asDocument = adapter.buildRequest({ inputs: ['x'], taskType: 'document' }) + const asQuery = adapter.buildRequest({ inputs: ['x'], taskType: 'query' }) + expect(asDocument.body).toMatchObject({ + requests: [expect.objectContaining({ taskType: 'RETRIEVAL_DOCUMENT' })], + }) + expect(asQuery.body).toMatchObject({ + requests: [expect.objectContaining({ taskType: 'RETRIEVAL_QUERY' })], + }) + }) + + it('normalizes only when the output is reduced below native', () => { + const raw = { embeddings: [{ values: [3, 4] }] } + + // Reduced: Gemini does not normalize for us, so the adapter must. + const reduced = adapter.buildRequest({ inputs: ['x'], taskType: 'document', dimensions: 768 }) + expect(norm(reduced.parse(raw)[0])).toBeCloseTo(1) + + // Native: Gemini already returns unit vectors, so values pass through untouched. + const native = adapter.buildRequest({ inputs: ['x'], taskType: 'document', dimensions: 3072 }) + expect(native.parse(raw)[0]).toEqual([3, 4]) + }) +}) + +describe('Cohere adapter', () => { + const adapter = createCohereAdapter({ + modelName: 'embed-v4.0', + apiKey: 'co-test', + nativeDimensions: 1536, + }) + + it('caps items per request at Cohere’s documented limit', () => { + expect(adapter.maxItemsPerRequest).toBe(96) + }) + + it('always sends input_type, which the v2 API requires', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'query' }) + expect(request.apiUrl).toBe('https://api.cohere.com/v2/embed') + expect(request.body).toMatchObject({ + input_type: 'search_query', + embedding_types: ['float'], + texts: INPUTS, + }) + }) + + it('reads vectors out of the float embedding type and tokens from billed_units', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + const json = { + embeddings: { float: [[1, 2]] }, + meta: { billed_units: { input_tokens: 11 } }, + } + expect(request.parse(json)).toEqual([[1, 2]]) + expect(request.parseTokens?.(json)).toBe(11) + }) + + it('fails loudly when the requested embedding type is missing', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + expect(() => request.parse({ embeddings: {} })).toThrow(/did not include float embeddings/) + }) + + it('normalizes reduced output, which Cohere never documents as renormalized', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document', dimensions: 256 }) + const [vector] = request.parse({ embeddings: { float: [[3, 4]] } }) + expect(norm(vector)).toBeCloseTo(1) + }) + + it('leaves full-width output untouched', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document', dimensions: 1536 }) + expect(request.parse({ embeddings: { float: [[3, 4]] } })).toEqual([[3, 4]]) + }) +}) + +describe('Mistral adapter', () => { + const adapter = createMistralAdapter({ + modelName: 'mistral-embed', + apiKey: 'm-test', + nativeDimensions: 1024, + }) + + it('uses the singular `input` field the REST API documents', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + expect(request.apiUrl).toBe('https://api.mistral.ai/v1/embeddings') + expect(request.body).toMatchObject({ input: INPUTS, model: 'mistral-embed' }) + expect(request.body).not.toHaveProperty('inputs') + }) + + it('restores input order from the response index', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + const json = { + data: [ + { embedding: [9, 9], index: 1 }, + { embedding: [1, 1], index: 0 }, + ], + usage: { total_tokens: 4 }, + } + expect(request.parse(json)).toEqual([ + [1, 1], + [9, 9], + ]) + expect(request.parseTokens?.(json)).toBe(4) + }) +}) diff --git a/apps/sim/lib/embeddings/types.ts b/apps/sim/lib/embeddings/types.ts new file mode 100644 index 00000000000..91741ae67e5 --- /dev/null +++ b/apps/sim/lib/embeddings/types.ts @@ -0,0 +1,112 @@ +/** + * Provider-agnostic embedding types shared by the knowledge-base indexing path + * and the Embeddings block. Provider-specific wire formats are confined to + * `@/lib/embeddings/providers`. + */ + +export type EmbeddingProviderKind = 'openai' | 'azure-openai' | 'gemini' | 'cohere' | 'mistral' + +/** + * Providers a catalog model can belong to. Azure OpenAI is excluded because it + * is a transport override for OpenAI models rather than a provider users pick: + * no model is ever catalogued under it, and it resolves its own credentials. + */ +export type EmbeddingCatalogProvider = Exclude + +/** Provider id for `estimateTokenCount` so token counts match the embedding provider's tokenization. */ +export type TokenizerProviderId = 'openai' | 'google' | 'cohere' | 'mistral' + +/** + * What the embedding will be used for. Providers that support task-conditioned + * embeddings map these onto their own enum; providers that do not ignore it. + */ +export type EmbeddingTaskType = + | 'document' + | 'query' + | 'similarity' + | 'classification' + | 'clustering' + +export interface EmbeddingProviderRequest { + apiUrl: string + headers: Record + body: unknown + /** Extracts vectors from the provider's response, in input order. */ + parse: (json: unknown) => number[][] + /** Reads the provider's reported prompt-token count, when it reports one. */ + parseTokens?: (json: unknown) => number | undefined +} + +export interface BuildEmbeddingRequestOptions { + inputs: string[] + taskType: EmbeddingTaskType + /** Target output dimensions. Undefined means the model's native dimensionality. */ + dimensions?: number +} + +export interface EmbeddingProviderAdapter { + buildRequest: (options: BuildEmbeddingRequestOptions) => EmbeddingProviderRequest + /** Hard per-request item cap enforced by the provider (e.g. Gemini caps at 100). */ + maxItemsPerRequest?: number +} + +export interface EmbeddingAdapterContext { + /** Model name as the provider expects it on the wire (an Azure deployment name for Azure). */ + modelName: string + apiKey: string + /** Model's un-reduced dimensionality, so adapters can detect a Matryoshka reduction. */ + nativeDimensions: number +} + +/** + * Azure selects the model by deployment name in the URL, so it needs routing + * fields no other provider takes. Declared as its own context rather than as + * optional fields on the shared one, so a caller cannot construct the Azure + * adapter without them and silently produce an `undefined/...` URL. + */ +export interface AzureEmbeddingAdapterContext extends EmbeddingAdapterContext { + endpoint: string + apiVersion: string +} + +export type EmbeddingAdapterFactory = + (context: Ctx) => EmbeddingProviderAdapter + +export interface EmbedOptions { + /** Catalog model id. Defaults to the platform default when omitted. */ + model?: string + /** Workspace used to look up a BYOK key before falling back to platform keys. */ + workspaceId?: string | null + taskType?: EmbeddingTaskType + /** Target output dimensions. Undefined means the model's native dimensionality. */ + dimensions?: number + /** + * Caller-supplied key that bypasses BYOK/env/rotating-pool resolution entirely. + * Used by the Embeddings block when the user pastes their own key. + */ + apiKey?: string + /** + * Rewrites resolved-secret plaintext back to placeholders before the inputs + * reach a provider. + * + * Required rather than optional, and explicitly nullable, so a new caller has + * to decide: omitting it silently is exactly how this control goes missing. + * Pass `null` only when the inputs were already projected upstream — the tool + * path projects at the HTTP hop via `request.modelInput`, so passing a + * projector there too would project twice. + */ + projectInputs: ((values: readonly string[]) => string[]) | null +} + +export interface EmbedResult { + embeddings: number[][] + totalTokens: number + /** True when a workspace-owned key was used, meaning Sim does not bill for it. */ + isBYOK: boolean + /** Model name as sent to the provider. */ + modelName: string + /** Pricing identifier for use with `getEmbeddingModelPricing` / `calculateCost`. */ + pricingId: string + /** Dimensionality of the returned vectors. */ + dimensions: number +} diff --git a/apps/sim/lib/integrations/icon-mapping.ts b/apps/sim/lib/integrations/icon-mapping.ts index 628a6dd91be..36bd6521479 100644 --- a/apps/sim/lib/integrations/icon-mapping.ts +++ b/apps/sim/lib/integrations/icon-mapping.ts @@ -66,6 +66,7 @@ import { ElasticsearchIcon, ElevenLabsIcon, EmailBisonIcon, + EmbeddingsIcon, EnrichmentIcon, EnrichSoIcon, EnrowIcon, @@ -157,7 +158,6 @@ import { ObsidianIcon, OktaIcon, OnePasswordIcon, - OpenAIIcon, OutlookIcon, PackageSearchIcon, PagerDutyIcon, @@ -314,6 +314,7 @@ export const blockTypeToIconMap: Record = { elasticsearch: ElasticsearchIcon, elevenlabs: ElevenLabsIcon, emailbison: EmailBisonIcon, + embeddings: EmbeddingsIcon, enrich: EnrichSoIcon, enrichment: EnrichmentIcon, enrow: EnrowIcon, @@ -423,7 +424,6 @@ export const blockTypeToIconMap: Record = { okta: OktaIcon, onedrive: MicrosoftOneDriveIcon, onepassword: OnePasswordIcon, - openai: OpenAIIcon, outlook: OutlookIcon, pagerduty: PagerDutyIcon, parallel_ai: ParallelIcon, diff --git a/apps/sim/lib/integrations/integrations.json b/apps/sim/lib/integrations/integrations.json index a2d27494361..e616903b1d8 100644 --- a/apps/sim/lib/integrations/integrations.json +++ b/apps/sim/lib/integrations/integrations.json @@ -1,5 +1,5 @@ { - "updatedAt": "2026-08-02", + "updatedAt": "2026-08-06", "integrations": [ { "type": "onepassword", @@ -5715,14 +5715,14 @@ "tags": ["sales-engagement", "email-marketing", "automation"] }, { - "type": "openai", + "type": "embeddings", "slug": "embeddings", "name": "Embeddings", - "description": "Generate Open AI embeddings", - "longDescription": "Integrate Embeddings into the workflow. Can generate embeddings from text.", - "bgColor": "#000000", - "iconName": "OpenAIIcon", - "docsUrl": "https://docs.sim.ai/integrations/openai", + "description": "Generate embeddings", + "longDescription": "Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, Google Gemini, Cohere, and Mistral embedding models.", + "bgColor": "#7B4DFF", + "iconName": "EmbeddingsIcon", + "docsUrl": "https://docs.sim.ai/integrations/embeddings", "operations": [], "operationCount": 0, "triggers": [], diff --git a/apps/sim/lib/knowledge/embedding-models.ts b/apps/sim/lib/knowledge/embedding-models.ts index 5d837a1fbb8..b965570965b 100644 --- a/apps/sim/lib/knowledge/embedding-models.ts +++ b/apps/sim/lib/knowledge/embedding-models.ts @@ -1,17 +1,25 @@ /** - * Registry of embedding models supported by the platform. + * Knowledge-base view over the platform embedding catalog + * (`@/lib/embeddings/catalog`). Knowledge bases store every vector at a fixed + * width, so only catalog models flagged `kbEligible` are selectable here. * Selection happens server-side via the `KB_EMBEDDING_MODEL` env var; this - * registry exists to resolve provider, tokenizer, and pricing metadata at - * runtime for any model recorded on a knowledge base row. + * module resolves provider, tokenizer, and pricing metadata at runtime for any + * model recorded on a knowledge base row. */ -export const EMBEDDING_DIMENSIONS = 1536 as const +import { + DEFAULT_EMBEDDING_MODEL as CATALOG_DEFAULT_EMBEDDING_MODEL, + getEmbeddingModelInfo as getCatalogModelInfo, + getKbEligibleModels, + KB_EMBEDDING_DIMENSIONS, +} from '@/lib/embeddings/catalog' +import type { EmbeddingProviderKind, TokenizerProviderId } from '@/lib/embeddings/types' -export const DEFAULT_EMBEDDING_MODEL = 'text-embedding-3-small' +export const EMBEDDING_DIMENSIONS = KB_EMBEDDING_DIMENSIONS -export type EmbeddingProviderKind = 'openai' | 'azure-openai' | 'gemini' +export const DEFAULT_EMBEDDING_MODEL = CATALOG_DEFAULT_EMBEDDING_MODEL -export type TokenizerProviderId = 'openai' | 'google' +export type { EmbeddingProviderKind, TokenizerProviderId } export interface EmbeddingModelInfo { provider: EmbeddingProviderKind @@ -21,28 +29,36 @@ export interface EmbeddingModelInfo { tokenizerProvider: TokenizerProviderId } -export const SUPPORTED_EMBEDDING_MODELS: Partial> = { - 'text-embedding-3-small': { - provider: 'openai', - pricingId: 'text-embedding-3-small', - tokenizerProvider: 'openai', - }, - 'text-embedding-3-large': { - provider: 'openai', - pricingId: 'text-embedding-3-large', - tokenizerProvider: 'openai', - }, - 'gemini-embedding-001': { - provider: 'gemini', - pricingId: 'gemini-embedding-001', - tokenizerProvider: 'google', - }, +export const SUPPORTED_EMBEDDING_MODELS: Partial> = + Object.fromEntries( + getKbEligibleModels().map((id) => { + const info = getCatalogModelInfo(id) + return [ + id, + { + provider: info.provider, + pricingId: info.pricingId, + tokenizerProvider: info.tokenizerProvider, + }, + ] + }) + ) + +/** + * Throws unless `model` is selectable for knowledge-base indexing. Call before + * handing a model to `embed()` so an ineligible id fails here, naming the + * knowledge-base constraint, rather than deeper in the provider path. + */ +export function assertKbEmbeddingModel(model: string): void { + getEmbeddingModelInfo(model) } export function getEmbeddingModelInfo(model: string): EmbeddingModelInfo { const info = SUPPORTED_EMBEDDING_MODELS[model] if (!info) { - throw new Error(`Unsupported embedding model: ${model}`) + /** Surfaces the catalog's error for unknown ids, and a KB-specific one for ineligible models. */ + getCatalogModelInfo(model) + throw new Error(`Embedding model is not available for knowledge bases: ${model}`) } return info } diff --git a/apps/sim/lib/knowledge/embeddings.ts b/apps/sim/lib/knowledge/embeddings.ts index 037c5e4843a..94a0997d81e 100644 --- a/apps/sim/lib/knowledge/embeddings.ts +++ b/apps/sim/lib/knowledge/embeddings.ts @@ -1,190 +1,30 @@ import { createLogger } from '@sim/logger' import { getErrorMessage } from '@sim/utils/errors' -import { getBYOKKey } from '@/lib/api-key/byok' import { type BillingAttributionSnapshot, toBillingContext, } from '@/lib/billing/core/billing-attribution' import { recordUsage } from '@/lib/billing/core/usage-log' import { checkAndBillPayerOverageThreshold } from '@/lib/billing/threshold-billing' -import { getRotatingApiKey } from '@/lib/core/config/api-keys' -import { env, envNumber } from '@/lib/core/config/env' -import { isRetryableError, retryWithExponentialBackoff } from '@/lib/knowledge/documents/utils' +import { env } from '@/lib/core/config/env' +import { embed } from '@/lib/embeddings' import { + assertKbEmbeddingModel, DEFAULT_EMBEDDING_MODEL, EMBEDDING_DIMENSIONS, getEmbeddingModelInfo, SUPPORTED_EMBEDDING_MODELS, - type TokenizerProviderId, } from '@/lib/knowledge/embedding-models' import { projectKnowledgeModelInputs } from '@/lib/knowledge/model-input-provenance' -import { batchByTokenLimit, estimateTokenCount } from '@/lib/tokenization' +import { estimateTokenCount } from '@/lib/tokenization' import { calculateCost } from '@/providers/utils' const logger = createLogger('EmbeddingUtils') -const MAX_TOKENS_PER_REQUEST = 8000 -const MAX_CONCURRENT_BATCHES = envNumber(env.KB_CONFIG_CONCURRENCY_LIMIT, 50) -const EMBEDDING_REQUEST_TIMEOUT_MS = 60_000 - export { EMBEDDING_DIMENSIONS } from '@/lib/knowledge/embedding-models' -class EmbeddingAPIError extends Error { - public status: number - - constructor(message: string, status: number) { - super(message) - this.name = 'EmbeddingAPIError' - this.status = status - } -} - export type EmbeddingInputType = 'document' | 'query' -interface ProviderRequest { - apiUrl: string - headers: Record - body: unknown - parse: (json: unknown) => number[][] -} - -interface ResolvedProvider { - modelName: string - pricingId: string - isBYOK: boolean - /** Tokenizer used to estimate tokens when the API does not return a usage field. */ - tokenizerProvider: TokenizerProviderId - /** Hard per-request item cap enforced by the provider (e.g. Gemini caps at 100). */ - maxItemsPerRequest?: number - buildRequest: (inputs: string[], inputType: EmbeddingInputType) => ProviderRequest -} - -/** Gemini's `batchEmbedContents` rejects requests with more than 100 items. */ -const GEMINI_MAX_ITEMS_PER_REQUEST = 100 - -async function resolveOpenAIKey(workspaceId?: string | null): Promise<{ - apiKey: string - isBYOK: boolean -}> { - if (workspaceId) { - const byokResult = await getBYOKKey(workspaceId, 'openai') - if (byokResult) { - logger.info('Using workspace BYOK key for OpenAI embeddings') - return { apiKey: byokResult.apiKey, isBYOK: true } - } - } - if (env.OPENAI_API_KEY) { - return { apiKey: env.OPENAI_API_KEY, isBYOK: false } - } - try { - return { apiKey: getRotatingApiKey('openai'), isBYOK: false } - } catch { - throw new Error('OPENAI_API_KEY is not configured') - } -} - -async function resolveGeminiKey(workspaceId?: string | null): Promise<{ - apiKey: string - isBYOK: boolean -}> { - if (workspaceId) { - const byokResult = await getBYOKKey(workspaceId, 'google') - if (byokResult) { - logger.info('Using workspace BYOK key for Gemini embeddings') - return { apiKey: byokResult.apiKey, isBYOK: true } - } - } - if (env.GEMINI_API_KEY) { - return { apiKey: env.GEMINI_API_KEY, isBYOK: false } - } - try { - return { apiKey: getRotatingApiKey('gemini'), isBYOK: false } - } catch { - throw new Error( - 'GEMINI_API_KEY (or GEMINI_API_KEY_1/2/3 for rotation) must be configured for Gemini embeddings' - ) - } -} - -function buildOpenAIProvider(modelName: string, apiKey: string): ResolvedProvider['buildRequest'] { - return (inputs) => ({ - apiUrl: 'https://api.openai.com/v1/embeddings', - headers: { - Authorization: `Bearer ${apiKey}`, - 'Content-Type': 'application/json', - }, - body: { - input: inputs, - model: modelName, - encoding_format: 'float', - dimensions: EMBEDDING_DIMENSIONS, - }, - parse: (json) => { - const data = json as { data: Array<{ embedding: number[] }> } - return data.data.map((item) => item.embedding) - }, - }) -} - -function buildAzureOpenAIProvider( - deployment: string, - apiKey: string, - endpoint: string, - apiVersion: string -): ResolvedProvider['buildRequest'] { - return (inputs) => ({ - apiUrl: `${endpoint}/openai/deployments/${deployment}/embeddings?api-version=${apiVersion}`, - headers: { - 'api-key': apiKey, - 'Content-Type': 'application/json', - }, - body: { - input: inputs, - encoding_format: 'float', - dimensions: EMBEDDING_DIMENSIONS, - }, - parse: (json) => { - const data = json as { data: Array<{ embedding: number[] }> } - return data.data.map((item) => item.embedding) - }, - }) -} - -/** - * Gemini does NOT auto-normalize embeddings when `outputDimensionality` is set below the - * native 3072 dimension on `gemini-embedding-001`. Manually L2-normalize so cosine and - * inner-product similarity work correctly. - */ -function l2Normalize(vector: number[]): number[] { - let sumSquares = 0 - for (const v of vector) sumSquares += v * v - const norm = Math.sqrt(sumSquares) - if (norm === 0) return vector - return vector.map((v) => v / norm) -} - -function buildGeminiProvider(modelName: string, apiKey: string): ResolvedProvider['buildRequest'] { - return (inputs, inputType) => ({ - apiUrl: `https://generativelanguage.googleapis.com/v1beta/models/${modelName}:batchEmbedContents`, - headers: { - 'Content-Type': 'application/json', - 'x-goog-api-key': apiKey, - }, - body: { - requests: inputs.map((text) => ({ - model: `models/${modelName}`, - content: { parts: [{ text }] }, - taskType: inputType === 'query' ? 'RETRIEVAL_QUERY' : 'RETRIEVAL_DOCUMENT', - outputDimensionality: EMBEDDING_DIMENSIONS, - })), - }, - parse: (json) => { - const data = json as { embeddings: Array<{ values: number[] }> } - return data.embeddings.map((item) => l2Normalize(item.values)) - }, - }) -} - /** * Returns the embedding model to use for new knowledge bases. * Sourced from the `KB_EMBEDDING_MODEL` env var; falls back to the default if @@ -203,148 +43,6 @@ export function getConfiguredEmbeddingModel(): string { return DEFAULT_EMBEDDING_MODEL } -async function resolveProvider( - embeddingModel: string, - workspaceId?: string | null -): Promise { - const azureApiKey = env.AZURE_OPENAI_API_KEY - const azureEndpoint = env.AZURE_OPENAI_ENDPOINT - const azureApiVersion = env.AZURE_OPENAI_API_VERSION - const isOpenAIModel = SUPPORTED_EMBEDDING_MODELS[embeddingModel]?.provider === 'openai' - /** - * Azure deployment names default to the embedding model name when - * `KB_OPENAI_MODEL_NAME` is unset — this matches the pre-existing - * convention where deployments are named after the model they host. - */ - const azureDeploymentName = env.KB_OPENAI_MODEL_NAME || embeddingModel - const useAzure = Boolean(isOpenAIModel && azureApiKey && azureEndpoint && azureApiVersion) - - const info = getEmbeddingModelInfo(embeddingModel) - - if (useAzure) { - return { - modelName: azureDeploymentName, - pricingId: info.pricingId, - isBYOK: false, - tokenizerProvider: info.tokenizerProvider, - buildRequest: buildAzureOpenAIProvider( - azureDeploymentName, - azureApiKey!, - azureEndpoint!, - azureApiVersion! - ), - } - } - - if (info.provider === 'openai') { - const { apiKey, isBYOK } = await resolveOpenAIKey(workspaceId) - return { - modelName: embeddingModel, - pricingId: info.pricingId, - isBYOK, - tokenizerProvider: info.tokenizerProvider, - buildRequest: buildOpenAIProvider(embeddingModel, apiKey), - } - } - - if (info.provider === 'gemini') { - const { apiKey, isBYOK } = await resolveGeminiKey(workspaceId) - return { - modelName: embeddingModel, - pricingId: info.pricingId, - isBYOK, - tokenizerProvider: info.tokenizerProvider, - maxItemsPerRequest: GEMINI_MAX_ITEMS_PER_REQUEST, - buildRequest: buildGeminiProvider(embeddingModel, apiKey), - } - } - - throw new Error(`Unknown embedding provider for model ${embeddingModel}`) -} - -async function callEmbeddingAPI( - inputs: string[], - provider: ResolvedProvider, - inputType: EmbeddingInputType -): Promise<{ embeddings: number[][]; totalTokens: number }> { - const modelInputs = projectKnowledgeModelInputs(inputs) - return retryWithExponentialBackoff( - async () => { - const request = provider.buildRequest(modelInputs, inputType) - - const controller = new AbortController() - const timeout = setTimeout(() => controller.abort(), EMBEDDING_REQUEST_TIMEOUT_MS) - - const response = await fetch(request.apiUrl, { - method: 'POST', - headers: request.headers, - body: JSON.stringify(request.body), - signal: controller.signal, - }).finally(() => clearTimeout(timeout)) - - if (!response.ok) { - const errorText = await response.text() - throw new EmbeddingAPIError( - `Embedding API failed: ${response.status} ${response.statusText} - ${errorText}`, - response.status - ) - } - - const json = await response.json() - const embeddings = request.parse(json) - const usage = (json as { usage?: { total_tokens?: number } }).usage - const totalTokens = - usage?.total_tokens ?? - // Gemini does not return usage.total_tokens — estimate with the provider's tokenizer - modelInputs.reduce( - (sum, text) => sum + estimateTokenCount(text, provider.tokenizerProvider).count, - 0 - ) - - return { embeddings, totalTokens } - }, - { - maxRetries: 3, - initialDelayMs: 1000, - maxDelayMs: 10000, - retryCondition: (error: unknown) => { - if (error instanceof EmbeddingAPIError) { - return error.status === 429 || error.status >= 500 - } - return isRetryableError(error) - }, - } - ) -} - -function splitByItemLimit(items: T[], limit: number): T[][] { - if (items.length <= limit) return [items] - const result: T[][] = [] - for (let i = 0; i < items.length; i += limit) { - result.push(items.slice(i, i + limit)) - } - return result -} - -async function processWithConcurrency( - items: T[], - concurrency: number, - processor: (item: T, index: number) => Promise -): Promise { - const results: R[] = new Array(items.length) - let currentIndex = 0 - - const workers = Array.from({ length: Math.min(concurrency, items.length) }, async () => { - while (currentIndex < items.length) { - const index = currentIndex++ - results[index] = await processor(items[index], index) - } - }) - - await Promise.all(workers) - return results -} - export interface GenerateEmbeddingsResult { embeddings: number[][] totalTokens: number @@ -356,64 +54,52 @@ export interface GenerateEmbeddingsResult { /** * Generate embeddings for multiple texts with token-aware batching and parallel processing. + * + * Every knowledge-base vector is pinned to {@link EMBEDDING_DIMENSIONS} so it + * matches the fixed width of the pgvector column. */ export async function generateEmbeddings( texts: string[], embeddingModel: string = DEFAULT_EMBEDDING_MODEL, workspaceId?: string | null ): Promise { - const provider = await resolveProvider(embeddingModel, workspaceId) + assertKbEmbeddingModel(embeddingModel) - const tokenBatches = batchByTokenLimit(texts, MAX_TOKENS_PER_REQUEST, embeddingModel) - const batches = provider.maxItemsPerRequest - ? tokenBatches.flatMap((batch) => splitByItemLimit(batch, provider.maxItemsPerRequest!)) - : tokenBatches - - const batchResults = await processWithConcurrency( - batches, - MAX_CONCURRENT_BATCHES, - async (batch, i) => { - try { - return await callEmbeddingAPI(batch, provider, 'document') - } catch (error) { - logger.error(`Failed to generate embeddings for batch ${i + 1}/${batches.length}:`, error) - throw error - } - } - ) - - const allEmbeddings: number[][] = [] - let totalTokens = 0 - for (const batch of batchResults) { - for (const emb of batch.embeddings) { - allEmbeddings.push(emb) - } - totalTokens += batch.totalTokens - } + const result = await embed(texts, { + model: embeddingModel, + workspaceId, + taskType: 'document', + dimensions: EMBEDDING_DIMENSIONS, + projectInputs: projectKnowledgeModelInputs, + }) return { - embeddings: allEmbeddings, - totalTokens, - isBYOK: provider.isBYOK, - modelName: provider.modelName, - pricingId: provider.pricingId, + embeddings: result.embeddings, + totalTokens: result.totalTokens, + isBYOK: result.isBYOK, + modelName: result.modelName, + pricingId: result.pricingId, } } -/** - * Generate embedding for a single search query. - */ export async function generateSearchEmbedding( query: string, embeddingModel: string = DEFAULT_EMBEDDING_MODEL, workspaceId?: string | null ): Promise<{ embedding: number[]; isBYOK: boolean }> { - const provider = await resolveProvider(embeddingModel, workspaceId) + assertKbEmbeddingModel(embeddingModel) + + const result = await embed([query], { + model: embeddingModel, + workspaceId, + taskType: 'query', + dimensions: EMBEDDING_DIMENSIONS, + projectInputs: projectKnowledgeModelInputs, + }) - logger.info(`Using ${provider.modelName} for search embedding generation`) + logger.info(`Using ${result.modelName} for search embedding generation`) - const { embeddings } = await callEmbeddingAPI([query], provider, 'query') - return { embedding: embeddings[0], isBYOK: provider.isBYOK } + return { embedding: result.embeddings[0], isBYOK: result.isBYOK } } /** diff --git a/apps/sim/providers/models.ts b/apps/sim/providers/models.ts index 16d95621f79..2ec267a5439 100644 --- a/apps/sim/providers/models.ts +++ b/apps/sim/providers/models.ts @@ -4399,6 +4399,21 @@ export const EMBEDDING_MODEL_PRICING: Record = { output: 0.0, updatedAt: '2026-04-29', }, + 'embed-v4.0': { + input: 0.12, // $0.12 per 1M tokens + output: 0.0, + updatedAt: '2026-08-05', + }, + 'mistral-embed': { + input: 0.1, // $0.1 per 1M tokens + output: 0.0, + updatedAt: '2026-08-05', + }, + 'codestral-embed': { + input: 0.15, // $0.15 per 1M tokens + output: 0.0, + updatedAt: '2026-08-05', + }, } export function getEmbeddingModelPricing(modelId: string): ModelPricing | null { diff --git a/apps/sim/tools/embeddings/cohere.ts b/apps/sim/tools/embeddings/cohere.ts new file mode 100644 index 00000000000..ae3dbbe448d --- /dev/null +++ b/apps/sim/tools/embeddings/cohere.ts @@ -0,0 +1,9 @@ +import { createEmbeddingTool } from '@/tools/embeddings/factory' + +export const embeddingsCohereTool = createEmbeddingTool({ + id: 'embeddings_cohere', + name: 'Cohere Embeddings', + provider: 'cohere', + description: "Generate embeddings from text using Cohere's embedding models", + envKeyPrefix: 'COHERE_API_KEY', +}) diff --git a/apps/sim/tools/embeddings/factory.test.ts b/apps/sim/tools/embeddings/factory.test.ts new file mode 100644 index 00000000000..5f1b7221ce7 --- /dev/null +++ b/apps/sim/tools/embeddings/factory.test.ts @@ -0,0 +1,79 @@ +/** + * @vitest-environment node + */ +import { describe, expect, it } from 'vitest' +import { embeddingsCohereTool } from '@/tools/embeddings/cohere' +import { embeddingsGeminiTool } from '@/tools/embeddings/gemini' +import { embeddingsMistralTool } from '@/tools/embeddings/mistral' +import { embeddingsOpenAITool } from '@/tools/embeddings/openai' +import { embeddingsTool as legacyOpenAIEmbeddingsTool } from '@/tools/openai/embeddings' + +const ALL_TOOLS = [ + embeddingsOpenAITool, + embeddingsGeminiTool, + embeddingsCohereTool, + embeddingsMistralTool, + legacyOpenAIEmbeddingsTool, +] + +/** + * `input` is the only param that leaves as model-visible content. Without a + * `modelInput` declaration the projection layer fails open — a secret + * interpolated into the input would reach the provider as plaintext — so every + * tool built from the factory, including the legacy alias, must declare it. + */ +describe('embeddings tools model-input projection', () => { + it('declares a projecting model input on every tool', () => { + for (const tool of ALL_TOOLS) { + expect(tool.request.modelInput?.mode, `${tool.id} must project its model input`).toBe( + 'project' + ) + } + }) + + it('selects only the input field, never the API key', () => { + for (const tool of ALL_TOOLS) { + const modelInput = tool.request.modelInput + if (modelInput?.mode !== 'project') throw new Error(`${tool.id} does not project`) + + expect( + modelInput.select({ + input: 'embed me', + apiKey: 'sk-secret', + model: 'text-embedding-3-small', + } as never) + ).toEqual({ input: 'embed me' }) + } + }) + + /** + * The projection traverser recurses element-wise through arrays, so an array + * input needs no special selector — but the selected key must survive + * unchanged, since the block accepts both a string and a list of strings. + */ + it('selects an array input unchanged', () => { + const modelInput = embeddingsOpenAITool.request.modelInput + if (modelInput?.mode !== 'project') throw new Error('expected a projecting model input') + + expect(modelInput.select({ input: ['alpha', 'beta'], apiKey: 'sk-secret' } as never)).toEqual({ + input: ['alpha', 'beta'], + }) + }) + + /** + * Every selected key must exist in `tool.params` or the projection layer + * rejects the selection and the call fails closed. + */ + it('selects only keys the tool declares as params', () => { + for (const tool of ALL_TOOLS) { + const modelInput = tool.request.modelInput + if (modelInput?.mode !== 'project') throw new Error(`${tool.id} does not project`) + + for (const key of Object.keys(modelInput.select({ input: 'x' } as never))) { + expect(Object.hasOwn(tool.params, key), `${tool.id}.params must declare "${key}"`).toBe( + true + ) + } + } + }) +}) diff --git a/apps/sim/tools/embeddings/factory.ts b/apps/sim/tools/embeddings/factory.ts new file mode 100644 index 00000000000..1fbd70262d8 --- /dev/null +++ b/apps/sim/tools/embeddings/factory.ts @@ -0,0 +1,191 @@ +import type { EmbeddingProvider } from '@/lib/api/contracts/tools/embeddings' +import { BYOK_PROVIDER_IDS, DEFAULT_MODEL_BY_PROVIDER } from '@/lib/embeddings/catalog' +import { getEmbeddingModelPricing } from '@/providers/models' +import type { EmbeddingsParams, EmbeddingsResponse } from '@/tools/embeddings/types' +import type { ToolConfig } from '@/tools/types' + +/** Throttle applied only when a caller draws on Sim's hosted key pool. */ +const HOSTED_KEY_RATE_LIMIT = { + mode: 'per_request', + requestsPerMinute: 100, + burstMultiplier: 1, +} as const + +interface CreateEmbeddingToolOptions { + id: string + name: string + provider: EmbeddingProvider + description: string + /** Env var prefix for the hosted key pool. */ + envKeyPrefix: string +} + +/** + * Builds a provider-specific embeddings tool. Every provider shares the same + * params, transport, and output shape; only key resolution and the default + * model differ, so they are produced from one definition rather than copied. + */ +export function createEmbeddingTool({ + id, + name, + provider, + description, + envKeyPrefix, +}: CreateEmbeddingToolOptions): ToolConfig { + const defaultModel = DEFAULT_MODEL_BY_PROVIDER[provider] + + return { + id, + name, + description, + version: '1.0.0', + + params: { + input: { + type: 'string', + required: true, + visibility: 'user-or-llm', + description: 'Text to embed, or an array of texts to embed in one call', + }, + model: { + type: 'string', + required: false, + visibility: 'user-only', + description: 'Embedding model to use', + default: defaultModel, + }, + taskType: { + type: 'string', + required: false, + visibility: 'user-only', + description: + 'What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering', + }, + dimensions: { + type: 'number', + required: false, + visibility: 'user-only', + description: 'Output dimensions, when the model supports truncation. Defaults to native.', + }, + apiKey: { + type: 'string', + required: true, + visibility: 'user-only', + description: 'API key for the selected embedding provider', + }, + }, + + hosting: { + envKeyPrefix, + apiKeyParam: 'apiKey', + byokProviderId: BYOK_PROVIDER_IDS[provider], + /** + * Billed per input token with no markup, matching how the knowledge-base + * path bills the same models. + */ + pricing: { + type: 'custom', + getCost: (_params, output) => { + const tokens = output.__embeddingTokens + if (typeof tokens !== 'number' || Number.isNaN(tokens)) { + throw new Error('Embedding response missing token usage') + } + const model = typeof output.model === 'string' ? output.model : defaultModel + const pricing = getEmbeddingModelPricing(model) + if (!pricing) { + throw new Error(`No pricing configured for embedding model: ${model}`) + } + return { + cost: (tokens * pricing.input) / 1_000_000, + metadata: { model, totalTokens: tokens, inputPricePerMillion: pricing.input }, + } + }, + }, + rateLimit: HOSTED_KEY_RATE_LIMIT, + }, + + request: { + url: '/api/tools/embeddings', + method: 'POST', + /** + * `input` is the only param that leaves as model-visible content, so a + * secret interpolated into it is rewritten back to its placeholder before + * the request is built. Declaring this moves the tool from never + * projecting to projecting-or-failing-closed; it is inert on runs that + * resolved no secrets, since the trace registry is absent there. + */ + modelInput: { + mode: 'project' as const, + select: (params: EmbeddingsParams) => ({ input: params.input }), + }, + headers: () => ({ + 'Content-Type': 'application/json', + }), + body: (params: EmbeddingsParams) => ({ + provider, + apiKey: params.apiKey, + model: params.model || defaultModel, + input: params.input, + taskType: params.taskType, + dimensions: params.dimensions, + }), + }, + + transformResponse: async (response: Response) => { + const data = (await response.json()) as { + success?: boolean + error?: string + embeddings?: number[][] + model?: string + provider?: string + dimensions?: number + usage?: { prompt_tokens: number; total_tokens: number } + __embeddingTokens?: number + } + + if (!response.ok || data.success === false || data.error) { + return { + success: false, + error: data.error || 'Embedding generation failed', + output: { + embeddings: [], + model: data.model || '', + provider: data.provider || provider, + dimensions: 0, + usage: { prompt_tokens: 0, total_tokens: 0 }, + }, + } + } + + return { + success: true, + output: { + embeddings: data.embeddings || [], + model: data.model || '', + provider: data.provider || provider, + dimensions: data.dimensions ?? 0, + usage: data.usage || { prompt_tokens: 0, total_tokens: 0 }, + __embeddingTokens: data.__embeddingTokens, + }, + } + }, + + outputs: { + embeddings: { + type: 'json', + description: 'Generated embedding vectors, one per input, in input order', + }, + model: { type: 'string', description: 'Model used' }, + provider: { type: 'string', description: 'Provider used' }, + dimensions: { type: 'number', description: 'Dimensionality of each returned vector' }, + usage: { + type: 'json', + description: 'Token usage', + properties: { + prompt_tokens: { type: 'number', description: 'Tokens in the input' }, + total_tokens: { type: 'number', description: 'Total tokens billed' }, + }, + }, + }, + } +} diff --git a/apps/sim/tools/embeddings/gemini.ts b/apps/sim/tools/embeddings/gemini.ts new file mode 100644 index 00000000000..d9e7866206c --- /dev/null +++ b/apps/sim/tools/embeddings/gemini.ts @@ -0,0 +1,9 @@ +import { createEmbeddingTool } from '@/tools/embeddings/factory' + +export const embeddingsGeminiTool = createEmbeddingTool({ + id: 'embeddings_gemini', + name: 'Gemini Embeddings', + provider: 'gemini', + description: "Generate embeddings from text using Google's Gemini embedding models", + envKeyPrefix: 'GEMINI_API_KEY', +}) diff --git a/apps/sim/tools/embeddings/index.ts b/apps/sim/tools/embeddings/index.ts new file mode 100644 index 00000000000..c58ca83ab3a --- /dev/null +++ b/apps/sim/tools/embeddings/index.ts @@ -0,0 +1,6 @@ +export { embeddingsCohereTool } from '@/tools/embeddings/cohere' +export { createEmbeddingTool } from '@/tools/embeddings/factory' +export { embeddingsGeminiTool } from '@/tools/embeddings/gemini' +export { embeddingsMistralTool } from '@/tools/embeddings/mistral' +export { embeddingsOpenAITool } from '@/tools/embeddings/openai' +export type { EmbeddingsParams, EmbeddingsResponse } from '@/tools/embeddings/types' diff --git a/apps/sim/tools/embeddings/mistral.ts b/apps/sim/tools/embeddings/mistral.ts new file mode 100644 index 00000000000..f9a3b2cb626 --- /dev/null +++ b/apps/sim/tools/embeddings/mistral.ts @@ -0,0 +1,9 @@ +import { createEmbeddingTool } from '@/tools/embeddings/factory' + +export const embeddingsMistralTool = createEmbeddingTool({ + id: 'embeddings_mistral', + name: 'Mistral Embeddings', + provider: 'mistral', + description: "Generate embeddings from text using Mistral's embedding models", + envKeyPrefix: 'MISTRAL_API_KEY', +}) diff --git a/apps/sim/tools/embeddings/openai.ts b/apps/sim/tools/embeddings/openai.ts new file mode 100644 index 00000000000..89408afc10b --- /dev/null +++ b/apps/sim/tools/embeddings/openai.ts @@ -0,0 +1,9 @@ +import { createEmbeddingTool } from '@/tools/embeddings/factory' + +export const embeddingsOpenAITool = createEmbeddingTool({ + id: 'embeddings_openai', + name: 'OpenAI Embeddings', + provider: 'openai', + description: "Generate embeddings from text using OpenAI's embedding models", + envKeyPrefix: 'OPENAI_API_KEY', +}) diff --git a/apps/sim/tools/embeddings/types.ts b/apps/sim/tools/embeddings/types.ts new file mode 100644 index 00000000000..fc57f23d8d3 --- /dev/null +++ b/apps/sim/tools/embeddings/types.ts @@ -0,0 +1,25 @@ +import type { EmbeddingTaskTypeName } from '@/lib/api/contracts/tools/embeddings' +import type { ToolResponse } from '@/tools/types' + +export interface EmbeddingsParams { + apiKey: string + input: string | string[] + model?: string + taskType?: EmbeddingTaskTypeName + dimensions?: number +} + +export interface EmbeddingsResponse extends ToolResponse { + output: { + embeddings: number[][] + model: string + provider: string + dimensions: number + usage: { + prompt_tokens: number + total_tokens: number + } + /** Token count used by the hosted-key pricing hook. Internal. */ + __embeddingTokens?: number + } +} diff --git a/apps/sim/tools/generated/tool-ids.ts b/apps/sim/tools/generated/tool-ids.ts index 6c6425c38ca..387ce193f9c 100644 --- a/apps/sim/tools/generated/tool-ids.ts +++ b/apps/sim/tools/generated/tool-ids.ts @@ -3,7 +3,7 @@ /** Every registered tool id, including versioned variants. */ const toolIds: string[] = JSON.parse( - 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"grafana_delete_folder","grafana_get_alert_rule","grafana_get_dashboard","grafana_get_data_source","grafana_get_folder","grafana_get_health","grafana_list_alert_rules","grafana_list_annotations","grafana_list_contact_points","grafana_list_dashboards","grafana_list_data_sources","grafana_list_folders","grafana_update_alert_rule","grafana_update_annotation","grafana_update_dashboard","grafana_update_folder","grain_create_hook","grain_create_hook_v2","grain_delete_hook","grain_delete_hook_v2","grain_get_recording","grain_get_transcript","grain_list_hooks","grain_list_hooks_v2","grain_list_meeting_types","grain_list_recordings","grain_list_teams","grain_list_views","granola_get_note","granola_list_folders","granola_list_notes","greenhouse_get_application","greenhouse_get_candidate","greenhouse_get_job","greenhouse_get_user","greenhouse_list_applications","greenhouse_list_candidates","greenhouse_list_departments","greenhouse_list_job_stages","greenhouse_list_jobs","greenhouse_list_offices","greenhouse_list_users","greptile_index_repo","greptile_query","greptile_search","greptile_status","guardrails_validate","hex_cancel_run","hex_create_collection","hex_create_group","hex_deactivate_user","hex_delete_group","hex_get_collection","hex_get_data_connection","hex_get_group","hex_get_project","hex_get_project_runs","hex_get_queried_tables","hex_get_run_status","hex_list_collections","hex_list_data_connections","hex_list_groups","hex_list_projects","hex_list_users","hex_run_project","hex_update_collection","hex_update_group","hex_update_project","http_request","hubspot_add_list_memberships","hubspot_create_appointment","hubspot_create_association","hubspot_create_company","hubspot_create_contact","hubspot_create_deal","hubspot_create_email","hubspot_create_line_item","hubspot_create_list","hubspot_create_note","hubspot_create_ticket","hubspot_delete_association","hubspot_delete_company","hubspot_delete_contact","hubspot_delete_deal","hubspot_delete_line_item","hubspot_delete_ticket","hubspot_get_appointment","hubspot_get_association_labels","hubspot_get_cart","hubspot_get_company","hubspot_get_contact","hubspot_get_deal","hubspot_get_email","hubspot_get_line_item","hubspot_get_list","hubspot_get_list_memberships","hubspot_get_marketing_event","hubspot_get_note","hubspot_get_properties","hubspot_get_quote","hubspot_get_ticket","hubspot_get_users","hubspot_list_appointments","hubspot_list_associations","hubspot_list_carts","hubspot_list_companies","hubspot_list_contacts","hubspot_list_deals","hubspot_list_emails","hubspot_list_line_items","hubspot_list_lists","hubspot_list_marketing_events","hubspot_list_notes","hubspot_list_owners","hubspot_list_quotes","hubspot_list_tickets","hubspot_remove_list_memberships","hubspot_search_companies","hubspot_search_contacts","hubspot_search_deals","hubspot_search_emails","hubspot_search_line_items","hubspot_search_notes","hubspot_search_quotes","hubspot_search_tickets","hubspot_update_appointment","hubspot_update_company","hubspot_update_contact","hubspot_update_deal","hubspot_update_line_item","hubspot_update_ticket","huggingface_chat","hunter_companies_find","hunter_discover","hunter_domain_search","hunter_email_count","hunter_email_finder","hunter_email_verifier","iam_add_user_to_group","iam_attach_role_policy","iam_attach_user_policy","iam_create_access_key","iam_create_role","iam_create_user","iam_delete_access_key","iam_delete_role","iam_delete_user","iam_detach_role_policy","iam_detach_user_policy","iam_get_role","iam_get_user","iam_list_attached_role_policies","iam_list_attached_user_policies","iam_list_groups","iam_list_policies","iam_list_roles","iam_list_users","iam_remove_user_from_group","iam_simulate_principal_policy","icypeas_find_email","icypeas_verify_email","identity_center_check_assignment_deletion_status","identity_center_check_assignment_status","identity_center_create_account_assignment","identity_center_delete_account_assignment","identity_center_describe_account","identity_center_get_group","identity_center_get_user","identity_center_list_account_assignments","identity_center_list_accounts","identity_center_list_groups","identity_center_list_instances","identity_center_list_permission_sets","image_generate","incidentio_actions_list","incidentio_actions_show","incidentio_custom_fields_create","incidentio_custom_fields_delete","incidentio_custom_fields_list","incidentio_custom_fields_show","incidentio_custom_fields_update","incidentio_escalation_paths_create","incidentio_escalation_paths_delete","incidentio_escalation_paths_list","incidentio_escalation_paths_show","incidentio_escalation_paths_update","incidentio_escalations_create","incidentio_escalations_list","incidentio_escalations_show","incidentio_follow_ups_list","incidentio_follow_ups_show","incidentio_incident_roles_create","incidentio_incident_roles_delete","incidentio_incident_roles_list","incidentio_incident_roles_show","incidentio_incident_roles_update","incidentio_incident_statuses_list","incidentio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_kernel","jupyter_list_contents","jupyter_list_kernels","jupyter_list_kernelspecs","jupyter_list_sessions","jupyter_rename_content","jupyter_restart_kernel","jupyter_start_kernel","jupyter_stop_kernel","jupyter_upload_file","kalshi_amend_order","kalshi_amend_order_v2","kalshi_cancel_order","kalshi_cancel_order_v2","kalshi_create_order","kalshi_create_order_v2","kalshi_get_balance","kalshi_get_balance_v2","kalshi_get_candlesticks","kalshi_get_candlesticks_v2","kalshi_get_event","kalshi_get_event_candlesticks","kalshi_get_event_candlesticks_v2","kalshi_get_event_v2","kalshi_get_events","kalshi_get_events_v2","kalshi_get_exchange_announcements","kalshi_get_exchange_announcements_v2","kalshi_get_exchange_schedule","kalshi_get_exchange_schedule_v2","kalshi_get_exchange_status","kalshi_get_exchange_status_v2","kalshi_get_fills","kalshi_get_fills_v2","kalshi_get_market","kalshi_get_market_v2","kalshi_get_markets","kalshi_get_markets_v2","kalshi_get_order","kalshi_get_order_v2","kalshi_get_orderbook","kalshi_get_orderbook_v2","kalshi_get_orders","kalshi_get_orders_v2","kalshi_get_positions","kalshi_get_positions_v2","kalshi_get_series_by_ticker","kalshi_get_series_by_ticker_v2","kalshi_get_series_list","kalshi_get_series_list_v2","kalshi_get_settlements","kalshi_get_settlements_v2","kalshi_get_trades","kalshi_get_trades_v2","ketch_get_consent","ketch_get_subscriptions","ketch_invoke_right","ketch_set_consent","ketch_set_subscriptions","knowledge_create_document","knowledge_delete_chunk","knowledge_delete_document","knowledge_get_connector","knowledge_get_document","knowledge_list_chunks","knowledge_list_connectors","knowledge_list_documents","knowledge_list_tags","knowledge_search","knowledge_trigger_sync","knowledge_update_chunk","knowledge_upload_chunk","knowledge_upsert_document","langsmith_create_feedback","langsmith_create_run","langsmith_create_runs_batch","langsmith_get_run","langsmith_update_run","latex_compile","latex_get_package","latex_list_fonts","latex_search_packages","launchdarkly_create_flag","launchdarkly_delete_flag","launchdarkly_get_audit_log","launchdarkly_get_flag","launchdarkly_get_flag_status","launchdarkly_list_environments","launchdarkly_list_flags","launchdarkly_list_members","launchdarkly_list_projects","launchdarkly_list_segments","launchdarkly_toggle_flag","launchdarkly_update_flag","leadmagic_company_search","leadmagic_email_to_profile","leadmagic_find_email","leadmagic_find_mobile","leadmagic_get_credits","leadmagic_profile_search","leadmagic_profile_to_email","leadmagic_role_finder","leadmagic_validate_email","lemlist_get_activities","lemlist_get_lead","lemlist_send_email","linear_add_label_to_issue","linear_add_label_to_project","linear_archive_issue","linear_archive_label","linear_archive_project","linear_create_attachment","linear_create_comment","linear_create_customer","linear_create_customer_request","linear_create_customer_status","linear_create_customer_tier","linear_create_cycle","linear_create_favorite","linear_create_issue","linear_create_issue_relation","linear_create_label","linear_create_project","linear_create_project_label","linear_create_project_milestone","linear_create_project_status","linear_create_project_update","linear_create_workflow_state","linear_delete_attachment","linear_delete_comment","linear_delete_customer","linear_delete_customer_status","linear_delete_customer_tier","linear_delete_issue","linear_delete_issue_relation","linear_delete_project","linear_delete_project_label","linear_delete_project_milestone","linear_delete_project_status","linear_get_active_cycle","linear_get_customer","linear_get_cycle","linear_get_issue","linear_get_project","linear_get_viewer","linear_list_attachments","linear_list_comments","linear_list_customer_requests","linear_list_customer_statuses","linear_list_customer_tiers","linear_list_customers","linear_list_cycles","linear_list_favorites","linear_list_issue_relations","linear_list_labels","linear_list_notifications","linear_list_project_labels","linear_list_project_milestones","linear_list_project_statuses","linear_list_project_updates","linear_list_projects","linear_list_teams","linear_list_users","linear_list_workflow_states","linear_merge_customers","linear_read_issues","linear_remove_label_from_issue","linear_remove_label_from_project","linear_search_issues","linear_unarchive_issue","linear_update_attachment","linear_update_comment","linear_update_customer","linear_update_customer_request","linear_update_customer_status","linear_update_customer_tier","linear_update_issue","linear_update_label","linear_update_notification","linear_update_project","linear_update_project_label","linear_update_project_milestone","linear_update_project_status","linear_update_workflow_state","linkedin_get_profile","linkedin_share_post","linkup_search","linq_add_participant","linq_check_imessage","linq_check_rcs","linq_create_attachment","linq_create_chat","linq_create_contact_card","linq_create_webhook_subscription","linq_delete_attachment"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ate_psp","vanta_download_document_file","vanta_get_control","vanta_get_document","vanta_get_framework","vanta_get_person","vanta_get_policy","vanta_get_risk_scenario","vanta_get_test","vanta_get_vendor","vanta_get_vulnerable_asset","vanta_list_control_documents","vanta_list_control_tests","vanta_list_controls","vanta_list_document_uploads","vanta_list_documents","vanta_list_framework_controls","vanta_list_frameworks","vanta_list_monitored_computers","vanta_list_people","vanta_list_policies","vanta_list_risk_scenarios","vanta_list_test_entities","vanta_list_tests","vanta_list_vendors","vanta_list_vulnerabilities","vanta_list_vulnerability_remediations","vanta_list_vulnerable_assets","vanta_submit_document","vanta_upload_document_file","vercel_add_domain","vercel_add_project_domain","vercel_cancel_deployment","vercel_create_alias","vercel_create_check","vercel_create_deployment","vercel_create_dns_record","vercel_create_edge_config","vercel_create_env_var","vercel_create_project","vercel_create_webhook","vercel_delete_alias","vercel_delete_deployment","vercel_delete_dns_record","vercel_delete_domain","vercel_delete_edge_config","vercel_delete_env_var","vercel_delete_project","vercel_delete_webhook","vercel_get_alias","vercel_get_check","vercel_get_deployment","vercel_get_deployment_events","vercel_get_domain","vercel_get_domain_config","vercel_get_edge_config","vercel_get_edge_config_items","vercel_get_env_vars","vercel_get_project","vercel_get_team","vercel_get_user","vercel_get_webhook","vercel_list_aliases","vercel_list_checks","vercel_list_deployment_files","vercel_list_deployments","vercel_list_dns_records","vercel_list_domains","vercel_list_edge_configs","vercel_list_project_domains","vercel_list_projects","vercel_list_team_members","vercel_list_teams","vercel_list_webhooks","vercel_pause_project","vercel_promote_deployment","vercel_remove_project_domain","vercel_rerequest_check","vercel_unpause_project","vercel_update_check","vercel_update_dns_record","vercel_update_edge_config_items","vercel_update_env_var","vercel_update_project","vercel_update_project_domain","vercel_verify_project_domain","video_falai","video_luma","video_minimax","video_runway","video_veo","vision_tool","vision_tool_v2","wealthbox_read_contact","wealthbox_read_note","wealthbox_read_task","wealthbox_write_contact","wealthbox_write_note","wealthbox_write_task","webflow_create_item","webflow_delete_item","webflow_get_item","webflow_list_items","webflow_update_item","webhook_request","whatsapp_get_media","whatsapp_mark_read","whatsapp_send_interactive","whatsapp_send_media","whatsapp_send_message","whatsapp_send_reaction","whatsapp_send_template","whatsapp_upload_media","wikipedia_content","wikipedia_random","wikipedia_search","wikipedia_summary","wiza_company_enrichment","wiza_get_credits","wiza_individual_reveal","wiza_prospect_search","wordpress_create_category","wordpress_create_comment","wordpress_create_page","wordpress_create_post","wordpress_create_tag","wordpress_delete_category","wordpress_delete_comment","wordpress_delete_media","wordpress_delete_page","wordpress_delete_post","wordpress_delete_tag","wordpress_get_category","wordpress_get_current_user","wordpress_get_media","wordpress_get_page","wordpress_get_post","wordpress_get_tag","wordpress_get_user","wordpress_list_categories","wordpress_list_comments","wordpress_list_media","wordpress_list_pages","wordpress_list_posts","wordpress_list_tags","wordpress_list_users","wordpress_search_content","wordpress_update_category","wordpress_update_comment","wordpress_update_page","wordpress_update_post","wordpress_update_tag","wordpress_upload_media","workday_assign_onboarding","workday_change_job","workday_create_prehire","workday_get_compensation","workday_get_organizations","workday_get_worker","workday_hire_employee","workday_list_workers","workday_terminate_worker","workday_update_worker","workflow_executor","x_create_bookmark","x_create_tweet","x_delete_bookmark","x_delete_tweet","x_get_blocking","x_get_bookmarks","x_get_followers","x_get_following","x_get_liked_tweets","x_get_liking_users","x_get_me","x_get_personalized_trends","x_get_quote_tweets","x_get_retweeted_by","x_get_trends_by_woeid","x_get_tweets_by_ids","x_get_usage","x_get_user_mentions","x_get_user_timeline","x_get_user_tweets","x_hide_reply","x_manage_block","x_manage_follow","x_manage_like","x_manage_mute","x_manage_retweet","x_read","x_search","x_search_tweets","x_search_users","x_user","x_write","youtube_channel_info","youtube_channel_playlists","youtube_channel_videos","youtube_comments","youtube_playlist_items","youtube_search","youtube_trending","youtube_video_categories","youtube_video_details","zendesk_autocomplete_organizations","zendesk_create_organization","zendesk_create_organizations_bulk","zendesk_create_ticket","zendesk_create_tickets_bulk","zendesk_create_user","zendesk_create_users_bulk","zendesk_delete_organization","zendesk_delete_ticket","zendesk_delete_user","zendesk_get_current_user","zendesk_get_organization","zendesk_get_organizations","zendesk_get_ticket","zendesk_get_tickets","zendesk_get_user","zendesk_get_users","zendesk_merge_tickets","zendesk_search","zendesk_search_count","zendesk_search_users","zendesk_update_organization","zendesk_update_ticket","zendesk_update_tickets_bulk","zendesk_update_user","zendesk_update_users_bulk","zep_add_messages","zep_add_user","zep_create_thread","zep_delete_thread","zep_get_context","zep_get_messages","zep_get_threads","zep_get_user","zep_get_user_threads","zerobounce_get_credits","zerobounce_verify_email","zoho_desk_add_comment","zoho_desk_get_attachment","zoho_desk_get_contact","zoho_desk_get_thread","zoho_desk_get_ticket","zoho_desk_list_comments","zoho_desk_list_organizations","zoho_desk_list_threads","zoho_desk_list_tickets","zoho_desk_update_ticket","zoom_create_meeting","zoom_delete_meeting","zoom_delete_recording","zoom_get_meeting","zoom_get_meeting_invitation","zoom_get_meeting_recordings","zoom_list_meetings","zoom_list_past_participants","zoom_list_recordings","zoom_update_meeting","zoominfo_enrich_companies","zoominfo_enrich_contacts","zoominfo_search_companies","zoominfo_search_contacts","zoominfo_search_intent","zoominfo_search_news"]' 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e","square_create_order","square_create_payment","square_delete_catalog_object","square_delete_customer","square_delete_invoice","square_get_catalog_object","square_get_customer","square_get_invoice","square_get_location","square_get_order","square_get_payment","square_get_refund","square_list_catalog","square_list_customers","square_list_invoices","square_list_locations","square_list_payments","square_list_refunds","square_pay_order","square_publish_invoice","square_refund_payment","square_search_catalog_objects","square_search_customers","square_search_invoices","square_search_orders","square_update_customer","square_upsert_catalog_object","ssh_check_command_exists","ssh_check_file_exists","ssh_create_directory","ssh_delete_file","ssh_download_file","ssh_execute_command","ssh_execute_script","ssh_get_system_info","ssh_list_directory","ssh_move_rename","ssh_read_file_content","ssh_upload_file","ssh_write_file_content","stagehand_agent","stagehand_extract","stripe_cancel_payment_intent","stripe_cancel_subscription","stripe_capture_charge","stripe_capture_payment_intent","stripe_confirm_payment_intent","stripe_create_charge","stripe_create_customer","stripe_create_invoice","stripe_create_payment_intent","stripe_create_price","stripe_create_product","stripe_create_subscription","stripe_delete_customer","stripe_delete_invoice","stripe_delete_product","stripe_finalize_invoice","stripe_list_charges","stripe_list_customers","stripe_list_events","stripe_list_invoices","stripe_list_payment_intents","stripe_list_prices","stripe_list_products","stripe_list_subscriptions","stripe_pay_invoice","stripe_resume_subscription","stripe_retrieve_charge","stripe_retrieve_customer","stripe_retrieve_event","stripe_retrieve_invoice","stripe_retrieve_payment_intent","stripe_retrieve_price","stripe_retrieve_product","stripe_retrieve_subscription","stripe_search_charges","stripe_search_customers","stripe_search_invoices","stripe_search_payment_intents","stripe_search_prices","stripe_search_products","stripe_search_subscriptions","stripe_send_invoice","stripe_update_charge","stripe_update_customer","stripe_update_invoice","stripe_update_payment_intent","stripe_update_price","stripe_update_product","stripe_update_subscription","stripe_void_invoice","sts_assume_role","sts_assume_role_with_saml","sts_assume_role_with_web_identity","sts_get_access_key_info","sts_get_caller_identity","sts_get_session_token","stt_assemblyai","stt_assemblyai_v2","stt_deepgram","stt_deepgram_v2","stt_elevenlabs","stt_elevenlabs_v2","stt_gemini","stt_gemini_v2","stt_whisper","stt_whisper_v2","supabase_count","supabase_delete","supabase_get_row","supabase_insert","supabase_introspect","supabase_invoke_function","supabase_query","supabase_rpc","supabase_storage_copy","supabase_storage_create_bucket","supabase_storage_create_signed_upload_url","supabase_storage_create_signed_url","supabase_storage_delete","supabase_storage_delete_bucket","supabase_storage_download","supabase_storage_empty_bucket","supabase_storage_get_public_url","supabase_storage_list","supabase_storage_list_buckets","supabase_storage_move","supabase_storage_update_bucket","supabase_storage_upload","supabase_text_search","supabase_update","supabase_upsert","supabase_vector_search","table_batch_insert_rows","table_create","table_delete_row","table_delete_rows_by_filter","table_get_row","table_get_schema","table_insert_row","table_list","table_query_rows","table_query_rows_v2","table_update_row","table_update_rows_by_filter","table_upsert_row","tailscale_authorize_device","tailscale_create_auth_key","tailscale_delete_auth_key","tailscale_delete_device","tailscale_delete_user","tailscale_expire_device_key","tailscale_get_acl","tailscale_get_auth_key","tailscale_get_device","tailscale_get_device_routes","tailscale_get_dns_preferences","tailscale_get_dns_searchpaths","tailscale_list_auth_keys","tailscale_list_devices","tailscale_list_dns_nameservers","tailscale_list_users","tailscale_set_acl","tailscale_set_device_routes","tailscale_set_device_tags","tailscale_set_dns_nameservers","tailscale_set_dns_preferences","tailscale_set_dns_searchpaths","tailscale_suspend_user","tailscale_update_device_key","tavily_crawl","tavily_extract","tavily_map","tavily_search","telegram_copy_message","telegram_delete_message","telegram_edit_message_text","telegram_forward_message","telegram_get_chat","telegram_get_chat_member","telegram_message","telegram_pin_message","telegram_send_animation","telegram_send_audio","telegram_send_chat_action","telegram_send_contact","telegram_send_document","telegram_send_location","telegram_send_photo","telegram_send_poll","telegram_send_video","telegram_set_message_reaction","telegram_unpin_message","temporal_cancel_workflow","temporal_count_workflows","temporal_create_schedule","temporal_delete_schedule","temporal_describe_schedule","temporal_describe_task_queue","temporal_describe_workflow","temporal_get_workflow_history","temporal_list_schedules","temporal_list_workflows","temporal_pause_schedule","temporal_query_workflow","temporal_reset_workflow","temporal_signal_with_start","temporal_signal_workflow","temporal_start_workflow","temporal_terminate_workflow","temporal_trigger_schedule","temporal_unpause_schedule","temporal_update_workflow","textract_analyze_expense","textract_analyze_id","textract_parser","textract_parser_v2","thinking_tool","thrive_add_audience_managers","thrive_add_audience_members","thrive_add_user_tags","thrive_create_assignment","thrive_create_audience","thrive_create_completion","thrive_create_user","thrive_delete_assignment","thrive_delete_audience","thrive_delete_user","thrive_get_activity","thrive_get_assignment","thrive_get_audience","thrive_get_completion","thrive_get_content","thrive_get_cpd_category","thrive_get_cpd_entry","thrive_get_cpd_requirement","thrive_get_enrolment","thrive_get_skill_levels","thrive_get_tag","thrive_get_user_by_id","thrive_get_user_by_ref","thrive_list_assignments","thrive_list_audience_managers","thrive_list_audience_members","thrive_list_audiences","thrive_list_completions","thrive_list_enrolments","thrive_list_tags","thrive_query_activities","thrive_query_content","thrive_query_cpd_categories","thrive_query_cpd_entries","thrive_query_cpd_requirements","thrive_query_cpd_user_summaries","thrive_remove_audience_manager","thrive_remove_audience_member","thrive_remove_user_tags","thrive_replace_audience_managers","thrive_replace_audience_members","thrive_search_users","thrive_suspend_user","thrive_update_assignment","thrive_update_audience","thrive_update_user","thrive_update_user_skills","tiktok_get_post_status","tiktok_get_user","tiktok_list_videos","tiktok_query_videos","tiktok_upload_video_draft","tinybird_append_datasource","tinybird_delete_datasource_rows","tinybird_events","tinybird_get_job","tinybird_query","tinybird_query_pipe","tinybird_truncate_datasource","trello_add_checklist","trello_add_checklist_item","trello_add_comment","trello_add_label","trello_add_member","trello_create_board","trello_create_card","trello_create_list","trello_delete_card","trello_get_actions","trello_get_board","trello_get_card","trello_list_cards","trello_list_lists","trello_list_members","trello_remove_label","trello_remove_member","trello_search","trello_update_card","trello_update_checklist_item","trello_update_list","trigger_dev_activate_schedule","trigger_dev_add_run_tags","trigger_dev_batch_trigger_task","trigger_dev_cancel_run","trigger_dev_complete_waitpoint_token","trigger_dev_create_env_var","trigger_dev_create_schedule","trigger_dev_create_waitpoint_token","trigger_dev_deactivate_schedule","trigger_dev_delete_env_var","trigger_dev_delete_schedule","trigger_dev_execute_query","trigger_dev_get_batch","trigger_dev_get_batch_results","trigger_dev_get_deployment","trigger_dev_get_env_var","trigger_dev_get_latest_deployment","trigger_dev_get_query_schema","trigger_dev_get_queue","trigger_dev_get_run","trigger_dev_get_run_events","trigger_dev_get_run_result","trigger_dev_get_run_trace","trigger_dev_get_schedule","trigger_dev_get_waitpoint_token","trigger_dev_import_env_vars","trigger_dev_list_deployments","trigger_dev_list_env_vars","trigger_dev_list_queues","trigger_dev_list_runs","trigger_dev_list_schedules","trigger_dev_list_timezones","trigger_dev_list_waitpoint_tokens","trigger_dev_override_queue_concurrency","trigger_dev_pause_queue","trigger_dev_promote_deployment","trigger_dev_replay_run","trigger_dev_reschedule_run","trigger_dev_reset_queue_concurrency","trigger_dev_resume_queue","trigger_dev_trigger_task","trigger_dev_update_env_var","trigger_dev_update_run_metadata","trigger_dev_update_schedule","tts_azure","tts_cartesia","tts_deepgram","tts_elevenlabs","tts_google","tts_openai","tts_playht","twilio_send_sms","twilio_voice_get_recording","twilio_voice_list_calls","twilio_voice_make_call","typeform_create_form","typeform_delete_form","typeform_files","typeform_get_form","typeform_insights","typeform_list_forms","typeform_responses","typeform_update_form","upstash_redis_command","upstash_redis_delete","upstash_redis_exists","upstash_redis_expire","upstash_redis_get","upstash_redis_hget","upstash_redis_hgetall","upstash_redis_hset","upstash_redis_incr","upstash_redis_incrby","upstash_redis_keys","upstash_redis_lpush","upstash_redis_lrange","upstash_redis_set","upstash_redis_setnx","upstash_redis_ttl","uptimerobot_create_alert_contact","uptimerobot_create_maintenance_window","uptimerobot_create_monitor","uptimerobot_create_psp","uptimerobot_delete_alert_contact","uptimerobot_delete_maintenance_window","uptimerobot_delete_monitor","uptimerobot_delete_psp","uptimerobot_get_account","uptimerobot_get_alert_contact","uptimerobot_get_incident","uptimerobot_get_maintenance_window","uptimerobot_get_monitor","uptimerobot_get_psp","uptimerobot_list_alert_contacts","uptimerobot_list_incidents","uptimerobot_list_maintenance_windows","uptimerobot_list_monitors","uptimerobot_list_psps","uptimerobot_pause_monitor","uptimerobot_start_monitor","uptimerobot_update_maintenance_window","uptimerobot_update_monitor","uptimerobot_update_psp","vanta_download_document_file","vanta_get_control","vanta_get_document","vanta_get_framework","vanta_get_person","vanta_get_policy","vanta_get_risk_scenario","vanta_get_test","vanta_get_vendor","vanta_get_vulnerable_asset","vanta_list_control_documents","vanta_list_control_tests","vanta_list_controls","vanta_list_document_uploads","vanta_list_documents","vanta_list_framework_controls","vanta_list_frameworks","vanta_list_monitored_computers","vanta_list_people","vanta_list_policies","vanta_list_risk_scenarios","vanta_list_test_entities","vanta_list_tests","vanta_list_vendors","vanta_list_vulnerabilities","vanta_list_vulnerability_remediations","vanta_list_vulnerable_assets","vanta_submit_document","vanta_upload_document_file","vercel_add_domain","vercel_add_project_domain","vercel_cancel_deployment","vercel_create_alias","vercel_create_check","vercel_create_deployment","vercel_create_dns_record","vercel_create_edge_config","vercel_create_env_var","vercel_create_project","vercel_create_webhook","vercel_delete_alias","vercel_delete_deployment","vercel_delete_dns_record","vercel_delete_domain","vercel_delete_edge_config","vercel_delete_env_var","vercel_delete_project","vercel_delete_webhook","vercel_get_alias","vercel_get_check","vercel_get_deployment","vercel_get_deployment_events","vercel_get_domain","vercel_get_domain_config","vercel_get_edge_config","vercel_get_edge_config_items","vercel_get_env_vars","vercel_get_project","vercel_get_team","vercel_get_user","vercel_get_webhook","vercel_list_aliases","vercel_list_checks","vercel_list_deployment_files","vercel_list_deployments","vercel_list_dns_records","vercel_list_domains","vercel_list_edge_configs","vercel_list_project_domains","vercel_list_projects","vercel_list_team_members","vercel_list_teams","vercel_list_webhooks","vercel_pause_project","vercel_promote_deployment","vercel_remove_project_domain","vercel_rerequest_check","vercel_unpause_project","vercel_update_check","vercel_update_dns_record","vercel_update_edge_config_items","vercel_update_env_var","vercel_update_project","vercel_update_project_domain","vercel_verify_project_domain","video_falai","video_luma","video_minimax","video_runway","video_veo","vision_tool","vision_tool_v2","wealthbox_read_contact","wealthbox_read_note","wealthbox_read_task","wealthbox_write_contact","wealthbox_write_note","wealthbox_write_task","webflow_create_item","webflow_delete_item","webflow_get_item","webflow_list_items","webflow_update_item","webhook_request","whatsapp_get_media","whatsapp_mark_read","whatsapp_send_interactive","whatsapp_send_media","whatsapp_send_message","whatsapp_send_reaction","whatsapp_send_template","whatsapp_upload_media","wikipedia_content","wikipedia_random","wikipedia_search","wikipedia_summary","wiza_company_enrichment","wiza_get_credits","wiza_individual_reveal","wiza_prospect_search","wordpress_create_category","wordpress_create_comment","wordpress_create_page","wordpress_create_post","wordpress_create_tag","wordpress_delete_category","wordpress_delete_comment","wordpress_delete_media","wordpress_delete_page","wordpress_delete_post","wordpress_delete_tag","wordpress_get_category","wordpress_get_current_user","wordpress_get_media","wordpress_get_page","wordpress_get_post","wordpress_get_tag","wordpress_get_user","wordpress_list_categories","wordpress_list_comments","wordpress_list_media","wordpress_list_pages","wordpress_list_posts","wordpress_list_tags","wordpress_list_users","wordpress_search_content","wordpress_update_category","wordpress_update_comment","wordpress_update_page","wordpress_update_post","wordpress_update_tag","wordpress_upload_media","workday_assign_onboarding","workday_change_job","workday_create_prehire","workday_get_compensation","workday_get_organizations","workday_get_worker","workday_hire_employee","workday_list_workers","workday_terminate_worker","workday_update_worker","workflow_executor","x_create_bookmark","x_create_tweet","x_delete_bookmark","x_delete_tweet","x_get_blocking","x_get_bookmarks","x_get_followers","x_get_following","x_get_liked_tweets","x_get_liking_users","x_get_me","x_get_personalized_trends","x_get_quote_tweets","x_get_retweeted_by","x_get_trends_by_woeid","x_get_tweets_by_ids","x_get_usage","x_get_user_mentions","x_get_user_timeline","x_get_user_tweets","x_hide_reply","x_manage_block","x_manage_follow","x_manage_like","x_manage_mute","x_manage_retweet","x_read","x_search","x_search_tweets","x_search_users","x_user","x_write","youtube_channel_info","youtube_channel_playlists","youtube_channel_videos","youtube_comments","youtube_playlist_items","youtube_search","youtube_trending","youtube_video_categories","youtube_video_details","zendesk_autocomplete_organizations","zendesk_create_organization","zendesk_create_organizations_bulk","zendesk_create_ticket","zendesk_create_tickets_bulk","zendesk_create_user","zendesk_create_users_bulk","zendesk_delete_organization","zendesk_delete_ticket","zendesk_delete_user","zendesk_get_current_user","zendesk_get_organization","zendesk_get_organizations","zendesk_get_ticket","zendesk_get_tickets","zendesk_get_user","zendesk_get_users","zendesk_merge_tickets","zendesk_search","zendesk_search_count","zendesk_search_users","zendesk_update_organization","zendesk_update_ticket","zendesk_update_tickets_bulk","zendesk_update_user","zendesk_update_users_bulk","zep_add_messages","zep_add_user","zep_create_thread","zep_delete_thread","zep_get_context","zep_get_messages","zep_get_threads","zep_get_user","zep_get_user_threads","zerobounce_get_credits","zerobounce_verify_email","zoho_desk_add_comment","zoho_desk_get_attachment","zoho_desk_get_contact","zoho_desk_get_thread","zoho_desk_get_ticket","zoho_desk_list_comments","zoho_desk_list_organizations","zoho_desk_list_threads","zoho_desk_list_tickets","zoho_desk_update_ticket","zoom_create_meeting","zoom_delete_meeting","zoom_delete_recording","zoom_get_meeting","zoom_get_meeting_invitation","zoom_get_meeting_recordings","zoom_list_meetings","zoom_list_past_participants","zoom_list_recordings","zoom_update_meeting","zoominfo_enrich_companies","zoominfo_enrich_contacts","zoominfo_search_companies","zoominfo_search_contacts","zoominfo_search_intent","zoominfo_search_news"]' ) export default toolIds diff --git a/apps/sim/tools/generated/tool-metadata.ts b/apps/sim/tools/generated/tool-metadata.ts index ccc90be7baf..d4f4116104f 100644 --- a/apps/sim/tools/generated/tool-metadata.ts +++ b/apps/sim/tools/generated/tool-metadata.ts @@ -3,7 +3,7 @@ /** Serializable metadata for every built-in tool, keyed by tool id. */ const toolMetadata: Record = JSON.parse( - '{"a2a_cancel_task":{"id":"a2a_cancel_task","name":"A2A Cancel Task","description":"Request cancellation of an in-progress A2A task.","version":"1.0.0","params":{"agentUrl":{"type":"string","required":true,"visibility":"user-only","description":"The A2A agent endpoint URL"},"taskId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The task ID to cancel"},"apiKey":{"type":"string","required":false,"visibility":"user-only","description":"API key for authentication (if required)"}}},"a2a_get_agent_card":{"id":"a2a_get_agent_card","name":"A2A Get Agent Card","description":"Fetch the Agent Card (discovery document) for an external A2A agent.","version":"1.0.0","params":{"agentUrl":{"type":"string","required":true,"visibility":"user-only","description":"The A2A agent endpoint URL"},"apiKey":{"type":"string","required":false,"visibility":"user-only","description":"API key for authentication (if required)"}}},"a2a_get_task":{"id":"a2a_get_task","name":"A2A Get Task","description":"Retrieve the current state and result of an A2A task.","version":"1.0.0","params":{"agentUrl":{"type":"string","required":true,"visibility":"user-only","description":"The A2A agent endpoint URL"},"taskId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The task ID to retrieve"},"historyLength":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of history messages to include"},"apiKey":{"type":"string","required":false,"visibility":"user-only","description":"API key for authentication (if required)"}}},"a2a_send_message":{"id":"a2a_send_message","name":"A2A Send Message","description":"Send a message to an external A2A agent and return its response.","version":"1.0.0","params":{"agentUrl":{"type":"string","required":true,"visibility":"user-only","description":"The A2A agent endpoint URL"},"message":{"type":"string","required":true,"visibility":"user-or-llm","description":"The message text to send"},"data":{"type":"json","required":false,"visibility":"user-or-llm","description":"Optional structured JSON data to attach"},"files":{"type":"json","required":false,"visibility":"user-or-llm","description":"Optional files to attach"},"taskId":{"type":"string","required":false,"visibility":"user-or-llm","description":"Existing task ID to continue"},"contextId":{"type":"string","required":false,"visibility":"user-or-llm","description":"Conversation context ID to continue"},"apiKey":{"type":"string","required":false,"visibility":"user-only","description":"API key for authentication (if required)"}}},"agentmail_create_draft":{"id":"agentmail_create_draft","name":"Create Draft","description":"Create a new email draft in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox to create the draft in"},"to":{"type":"string","required":false,"visibility":"user-or-llm","description":"Recipient email addresses (comma-separated)"},"subject":{"type":"string","required":false,"visibility":"user-or-llm","description":"Draft subject line"},"text":{"type":"string","required":false,"visibility":"user-or-llm","description":"Plain text draft body"},"html":{"type":"string","required":false,"visibility":"user-or-llm","description":"HTML draft body"},"cc":{"type":"string","required":false,"visibility":"user-or-llm","description":"CC recipient email addresses (comma-separated)"},"bcc":{"type":"string","required":false,"visibility":"user-or-llm","description":"BCC recipient email addresses (comma-separated)"},"inReplyTo":{"type":"string","required":false,"visibility":"user-or-llm","description":"ID of message being replied to"},"sendAt":{"type":"string","required":false,"visibility":"user-or-llm","description":"ISO 8601 timestamp to schedule sending"}}},"agentmail_create_inbox":{"id":"agentmail_create_inbox","name":"Create Inbox","description":"Create a new email inbox with AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"username":{"type":"string","required":false,"visibility":"user-or-llm","description":"Username for the inbox email address"},"domain":{"type":"string","required":false,"visibility":"user-or-llm","description":"Domain for the inbox email address"},"displayName":{"type":"string","required":false,"visibility":"user-or-llm","description":"Display name for the inbox"}}},"agentmail_delete_draft":{"id":"agentmail_delete_draft","name":"Delete Draft","description":"Delete an email draft in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox containing the draft"},"draftId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the draft to delete"}}},"agentmail_delete_inbox":{"id":"agentmail_delete_inbox","name":"Delete Inbox","description":"Delete an email inbox in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox to delete"}}},"agentmail_delete_thread":{"id":"agentmail_delete_thread","name":"Delete Thread","description":"Delete an email thread in AgentMail (moves to trash, or permanently deletes if already in trash)","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox containing the thread"},"threadId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the thread to delete"},"permanent":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Force permanent deletion instead of moving to trash"}}},"agentmail_forward_message":{"id":"agentmail_forward_message","name":"Forward Message","description":"Forward an email message to new recipients in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox containing the message"},"messageId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the message to forward"},"to":{"type":"string","required":true,"visibility":"user-or-llm","description":"Recipient email addresses (comma-separated)"},"subject":{"type":"string","required":false,"visibility":"user-or-llm","description":"Override subject line"},"text":{"type":"string","required":false,"visibility":"user-or-llm","description":"Additional plain text to prepend"},"html":{"type":"string","required":false,"visibility":"user-or-llm","description":"Additional HTML to prepend"},"cc":{"type":"string","required":false,"visibility":"user-or-llm","description":"CC recipient email addresses (comma-separated)"},"bcc":{"type":"string","required":false,"visibility":"user-or-llm","description":"BCC recipient email addresses (comma-separated)"}}},"agentmail_get_draft":{"id":"agentmail_get_draft","name":"Get Draft","description":"Get details of a specific email draft in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox the draft belongs to"},"draftId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the draft to retrieve"}}},"agentmail_get_inbox":{"id":"agentmail_get_inbox","name":"Get Inbox","description":"Get details of a specific email inbox in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox to retrieve"}}},"agentmail_get_message":{"id":"agentmail_get_message","name":"Get Message","description":"Get details of a specific email message in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox containing the message"},"messageId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the message to retrieve"}}},"agentmail_get_thread":{"id":"agentmail_get_thread","name":"Get Thread","description":"Get details of a specific email thread including messages in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox containing the thread"},"threadId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the thread to retrieve"}}},"agentmail_list_drafts":{"id":"agentmail_list_drafts","name":"List Drafts","description":"List email drafts in an inbox in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API key"},"inboxId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the inbox to list drafts from"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of drafts to return"},"pageToken":{"type":"string","required":false,"visibility":"user-or-llm","description":"Pagination token for next page of results"}}},"agentmail_list_inboxes":{"id":"agentmail_list_inboxes","name":"List Inboxes","description":"List all email inboxes in AgentMail","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"AgentMail API 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Attachment","description":"Remove an attached file from a field in an Agiloft record.","version":"1.0.0","params":{"instanceUrl":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft instance URL (e.g., https://mycompany.agiloft.com)"},"knowledgeBase":{"type":"string","required":true,"visibility":"user-only","description":"Knowledge base name"},"login":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft username"},"password":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft password"},"table":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table name (e.g., \\"contracts\\")"},"recordId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the record containing the attachment"},"fieldName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the attachment field"},"position":{"type":"string","required":true,"visibility":"user-or-llm","description":"Position index of the file to remove (starting from 0)"}}},"agiloft_retrieve_attachment":{"id":"agiloft_retrieve_attachment","name":"Agiloft Retrieve Attachment","description":"Download an attached file from an Agiloft record field.","version":"1.0.0","params":{"instanceUrl":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft instance URL (e.g., https://mycompany.agiloft.com)"},"knowledgeBase":{"type":"string","required":true,"visibility":"user-only","description":"Knowledge base name"},"login":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft username"},"password":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft password"},"table":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table name (e.g., \\"contracts\\")"},"recordId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the record containing the attachment"},"fieldName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the attachment field"},"position":{"type":"string","required":true,"visibility":"user-or-llm","description":"Position index of the file in the field (starting from 0)"}}},"agiloft_saved_search":{"id":"agiloft_saved_search","name":"Agiloft Saved Search","description":"List saved searches defined for an Agiloft table.","version":"1.0.0","params":{"instanceUrl":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft instance URL (e.g., https://mycompany.agiloft.com)"},"knowledgeBase":{"type":"string","required":true,"visibility":"user-only","description":"Knowledge base name"},"login":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft username"},"password":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft password"},"table":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table name to list saved searches for (e.g., \\"contracts\\")"}}},"agiloft_search_records":{"id":"agiloft_search_records","name":"Agiloft Search Records","description":"Search for records in an Agiloft table using a query.","version":"1.0.0","params":{"instanceUrl":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft instance URL (e.g., https://mycompany.agiloft.com)"},"knowledgeBase":{"type":"string","required":true,"visibility":"user-only","description":"Knowledge base name"},"login":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft username"},"password":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft password"},"table":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table name to search in (e.g., \\"contracts\\", \\"contacts.employees\\")"},"query":{"type":"string","required":true,"visibility":"user-or-llm","description":"Search query using Agiloft query syntax (e.g., \\"status=\'Active\'\\" or \\"company_name~=\'Acme\'\\")"},"fields":{"type":"string","required":false,"visibility":"user-or-llm","description":"Comma-separated list of field names to include in the results"},"page":{"type":"string","required":false,"visibility":"user-or-llm","description":"Page number for paginated results (starting from 0)"},"limit":{"type":"string","required":false,"visibility":"user-or-llm","description":"Maximum number of records to return per page"}}},"agiloft_select_records":{"id":"agiloft_select_records","name":"Agiloft Select Records","description":"Select record IDs matching a SQL WHERE clause from an Agiloft table.","version":"1.0.0","params":{"instanceUrl":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft instance URL (e.g., https://mycompany.agiloft.com)"},"knowledgeBase":{"type":"string","required":true,"visibility":"user-only","description":"Knowledge base name"},"login":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft username"},"password":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft password"},"table":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table name (e.g., \\"contracts\\", \\"contacts.employees\\")"},"where":{"type":"string","required":true,"visibility":"user-or-llm","description":"SQL WHERE clause using database column names (e.g., \\"summary like \'%new%\'\\" or \\"assigned_person=\'John Doe\'\\")"}}},"agiloft_update_record":{"id":"agiloft_update_record","name":"Agiloft Update Record","description":"Update an existing record in an Agiloft table.","version":"1.0.0","params":{"instanceUrl":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft instance URL (e.g., https://mycompany.agiloft.com)"},"knowledgeBase":{"type":"string","required":true,"visibility":"user-only","description":"Knowledge base name"},"login":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft username"},"password":{"type":"string","required":true,"visibility":"user-only","description":"Agiloft password"},"table":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table name (e.g., \\"contracts\\", \\"contacts.employees\\")"},"recordId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the record to update"},"data":{"type":"string","required":true,"visibility":"user-or-llm","description":"Updated field values as a JSON object (e.g., {\\"status\\": \\"Active\\", \\"priority\\": \\"High\\"})"}}},"ahrefs_anchors":{"id":"ahrefs_anchors","name":"Ahrefs Anchors","description":"Get the anchor text distribution for a target domain or URL\'s backlinks, showing how many links and referring domains use each anchor text.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\" or \\"https://example.com/page\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match)"},"history":{"type":"string","required":false,"visibility":"user-or-llm","description":"Historical scope: \\"live\\" (currently live), \\"all_time\\" (default, includes lost backlinks), or \\"since:YYYY-MM-DD\\" (backlinks found since a date)"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_backlinks":{"id":"ahrefs_backlinks","name":"Ahrefs Backlinks","description":"Get a list of backlinks pointing to a target domain or URL. Returns details about each backlink including source URL, anchor text, and domain rating.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\" or \\"https://example.com/page\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"history":{"type":"string","required":false,"visibility":"user-or-llm","description":"Historical scope: \\"live\\" (currently live backlinks), \\"all_time\\" (default, includes lost backlinks), or \\"since:YYYY-MM-DD\\" (backlinks found since a date)."},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_backlinks_stats":{"id":"ahrefs_backlinks_stats","name":"Ahrefs Backlinks Stats","description":"Get backlink and referring domain totals for a target domain or URL, both currently live and across all time.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\" or \\"https://example.com/page\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date to report metrics on, in YYYY-MM-DD format (defaults to today)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_batch_analysis":{"id":"ahrefs_batch_analysis","name":"Ahrefs Batch Analysis","description":"Get bulk SEO metrics (Domain Rating, backlinks, referring domains, organic traffic, and more) for multiple domains or URLs in a single request. Useful for comparing many competitors at once.","version":"1.0.0","params":{"targets":{"type":"string","required":true,"visibility":"user-or-llm","description":"Comma-separated list of domains or URLs to analyze. Example: \\"example.com,competitor.com\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode applied to every target: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match)"},"protocol":{"type":"string","required":false,"visibility":"user-or-llm","description":"Protocol applied to every target: \\"both\\" (default), \\"http\\", or \\"https\\""},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for traffic data. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"volumeMode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Search volume calculation: \\"monthly\\" or \\"average\\" (default: \\"monthly\\")"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_broken_backlinks":{"id":"ahrefs_broken_backlinks","name":"Ahrefs Broken Backlinks","description":"Get a list of broken backlinks pointing to a target domain or URL. Useful for identifying link reclamation opportunities.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\" or \\"https://example.com/page\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_domain_rating":{"id":"ahrefs_domain_rating","name":"Ahrefs Domain Rating","description":"Get the Domain Rating (DR) and Ahrefs Rank for a target domain. Domain Rating shows the strength of a website\'s backlink profile on a scale from 0 to 100.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain to analyze (e.g., example.com)"},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date for historical data in YYYY-MM-DD format (defaults to today)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_domain_rating_history":{"id":"ahrefs_domain_rating_history","name":"Ahrefs Domain Rating History","description":"Get the historical Domain Rating (DR) trend for a target domain or URL over a date range, grouped daily, weekly, or monthly.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"dateFrom":{"type":"string","required":true,"visibility":"user-only","description":"Start date of the historical period, in YYYY-MM-DD format"},"dateTo":{"type":"string","required":false,"visibility":"user-only","description":"End date of the historical period, in YYYY-MM-DD format (defaults to today)"},"historyGrouping":{"type":"string","required":false,"visibility":"user-or-llm","description":"Time interval for grouping data points: \\"daily\\", \\"weekly\\", or \\"monthly\\" (default: \\"monthly\\")"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_keyword_overview":{"id":"ahrefs_keyword_overview","name":"Ahrefs Keyword Overview","description":"Get detailed metrics for a keyword including search volume, keyword difficulty, CPC, clicks, and traffic potential.","version":"1.0.0","params":{"keyword":{"type":"string","required":true,"visibility":"user-or-llm","description":"The keyword to analyze"},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for keyword data. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_keywords_history":{"id":"ahrefs_keywords_history","name":"Ahrefs Keywords History","description":"Get the historical organic keyword ranking distribution for a target domain or URL over a date range: how many keywords rank in each position bucket at each point in time.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"dateFrom":{"type":"string","required":true,"visibility":"user-only","description":"Start date of the historical period, in YYYY-MM-DD format"},"dateTo":{"type":"string","required":false,"visibility":"user-only","description":"End date of the historical period, in YYYY-MM-DD format (defaults to today)"},"historyGrouping":{"type":"string","required":false,"visibility":"user-or-llm","description":"Time interval for grouping data points: \\"daily\\", \\"weekly\\", or \\"monthly\\" (default: \\"monthly\\")"},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for search results. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_metrics":{"id":"ahrefs_metrics","name":"Ahrefs Metrics","description":"Get a one-call organic and paid search overview for a target domain or URL: organic traffic, organic keywords, paid traffic, paid keywords, and estimated traffic cost.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for traffic data. Example: \\"us\\", \\"gb\\", \\"de\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date to report metrics on, in YYYY-MM-DD format (defaults to today)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_metrics_history":{"id":"ahrefs_metrics_history","name":"Ahrefs Metrics History","description":"Get the historical organic and paid traffic trend for a target domain or URL over a date range: organic traffic/cost and paid traffic/cost at each point in time.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"dateFrom":{"type":"string","required":true,"visibility":"user-only","description":"Start date of the historical period, in YYYY-MM-DD format"},"dateTo":{"type":"string","required":false,"visibility":"user-only","description":"End date of the historical period, in YYYY-MM-DD format (defaults to today)"},"volumeMode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Search volume calculation: \\"monthly\\" or \\"average\\" (default: \\"monthly\\")"},"historyGrouping":{"type":"string","required":false,"visibility":"user-or-llm","description":"Time interval for grouping data points: \\"daily\\", \\"weekly\\", or \\"monthly\\" (default: \\"monthly\\")"},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for traffic data. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_organic_competitors":{"id":"ahrefs_organic_competitors","name":"Ahrefs Organic Competitors","description":"Get domains that compete with a target domain or URL for the same organic keywords, ranked by keyword overlap.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for search results. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date to report metrics on, in YYYY-MM-DD format (defaults to today)"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_organic_keywords":{"id":"ahrefs_organic_keywords","name":"Ahrefs Organic Keywords","description":"Get organic keywords that a target domain or URL ranks for in Google search results. Returns keyword details including search volume, ranking position, and estimated traffic.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\" or \\"https://example.com/page\\""},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for search results. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date to report metrics on, in YYYY-MM-DD format (defaults to today)"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_paid_pages":{"id":"ahrefs_paid_pages","name":"Ahrefs Paid Pages","description":"Get a target domain\'s pages that receive paid search traffic, sorted by estimated paid traffic. Returns page URLs with their paid traffic, keyword counts, and estimated spend.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for traffic data. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match)"},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date to report metrics on, in YYYY-MM-DD format (defaults to today)"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_rank_tracker_competitors_overview":{"id":"ahrefs_rank_tracker_competitors_overview","name":"Ahrefs Rank Tracker Competitors Overview","description":"Get competitor rankings for the keywords tracked in an Ahrefs Rank Tracker project: each tracked keyword\'s volume and difficulty alongside every competitor\'s position, traffic, and traffic value. This endpoint is free and does not consume API units.","version":"1.0.0","params":{"projectId":{"type":"number","required":true,"visibility":"user-or-llm","description":"The Rank Tracker project ID (found in the project URL in Ahrefs)"},"date":{"type":"string","required":true,"visibility":"user-only","description":"Date to report rankings for, in YYYY-MM-DD format"},"device":{"type":"string","required":true,"visibility":"user-or-llm","description":"Rankings device type: \\"desktop\\" or \\"mobile\\""},"dateCompared":{"type":"string","required":false,"visibility":"user-only","description":"Comparison date in YYYY-MM-DD format, to compute position/traffic deltas"},"volumeMode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Search volume calculation: \\"monthly\\" or \\"average\\" (default: \\"monthly\\")"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_rank_tracker_competitors_stats":{"id":"ahrefs_rank_tracker_competitors_stats","name":"Ahrefs Rank Tracker Competitors Stats","description":"Get aggregate competitor stats for an Ahrefs Rank Tracker project: each competitor\'s traffic, traffic value, average position, and share of voice across all tracked keywords. This endpoint is free and does not consume API units.","version":"1.0.0","params":{"projectId":{"type":"number","required":true,"visibility":"user-or-llm","description":"The Rank Tracker project ID (found in the project URL in Ahrefs)"},"date":{"type":"string","required":true,"visibility":"user-only","description":"Date to report metrics for, in YYYY-MM-DD format"},"device":{"type":"string","required":true,"visibility":"user-or-llm","description":"Rankings device type: \\"desktop\\" or \\"mobile\\""},"volumeMode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Search volume calculation: \\"monthly\\" or \\"average\\" (default: \\"monthly\\")"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_rank_tracker_overview":{"id":"ahrefs_rank_tracker_overview","name":"Ahrefs Rank Tracker Overview","description":"Get ranking overview metrics for the keywords tracked in an Ahrefs Rank Tracker project: position, search volume, keyword difficulty, and estimated traffic. This endpoint is free and does not consume API units.","version":"1.0.0","params":{"projectId":{"type":"number","required":true,"visibility":"user-or-llm","description":"The Rank Tracker project ID (found in the project URL in Ahrefs)"},"date":{"type":"string","required":true,"visibility":"user-only","description":"Date to report rankings for, in YYYY-MM-DD format"},"device":{"type":"string","required":true,"visibility":"user-or-llm","description":"Rankings device type: \\"desktop\\" or \\"mobile\\""},"dateCompared":{"type":"string","required":false,"visibility":"user-only","description":"Comparison date in YYYY-MM-DD format, to compute position/traffic deltas"},"volumeMode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Search volume calculation: \\"monthly\\" or \\"average\\" (default: \\"monthly\\")"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_rank_tracker_serp_overview":{"id":"ahrefs_rank_tracker_serp_overview","name":"Ahrefs Rank Tracker SERP Overview","description":"Get the full SERP (search engine results page) for a keyword tracked in an Ahrefs Rank Tracker project, including every ranking URL with its position, title, and authority metrics. This endpoint is free and does not consume API units.","version":"1.0.0","params":{"projectId":{"type":"number","required":true,"visibility":"user-or-llm","description":"The Rank Tracker project ID (found in the project URL in Ahrefs)"},"keyword":{"type":"string","required":true,"visibility":"user-or-llm","description":"The tracked keyword to retrieve SERP data for"},"country":{"type":"string","required":true,"visibility":"user-or-llm","description":"Country code for the tracked keyword. Example: \\"us\\", \\"gb\\", \\"de\\""},"device":{"type":"string","required":true,"visibility":"user-or-llm","description":"Rankings device type: \\"desktop\\" or \\"mobile\\""},"topPositions":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of top organic positions to return (defaults to all available)"},"date":{"type":"string","required":false,"visibility":"user-only","description":"Timestamp to return the last available SERP Overview at, in YYYY-MM-DDThh:mm:ss format"},"locationId":{"type":"number","required":false,"visibility":"user-or-llm","description":"Location ID of the tracked keyword, if tracked at a specific location"},"languageCode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Language code of the tracked keyword"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_refdomains_history":{"id":"ahrefs_refdomains_history","name":"Ahrefs Referring Domains History","description":"Get the historical referring domains trend for a target domain or URL over a date range, grouped daily, weekly, or monthly.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\""},"dateFrom":{"type":"string","required":true,"visibility":"user-only","description":"Start date of the historical period, in YYYY-MM-DD format"},"dateTo":{"type":"string","required":false,"visibility":"user-only","description":"End date of the historical period, in YYYY-MM-DD format (defaults to today)"},"historyGrouping":{"type":"string","required":false,"visibility":"user-or-llm","description":"Time interval for grouping data points: \\"daily\\", \\"weekly\\", or \\"monthly\\" (default: \\"monthly\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_referring_domains":{"id":"ahrefs_referring_domains","name":"Ahrefs Referring Domains","description":"Get a list of domains that link to a target domain or URL. Returns unique referring domains with their domain rating, backlink counts, and discovery dates.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain or URL to analyze. Example: \\"example.com\\" or \\"https://example.com/page\\""},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"history":{"type":"string","required":false,"visibility":"user-or-llm","description":"Historical scope: \\"live\\" (currently live), \\"all_time\\" (default, includes lost domains), or \\"since:YYYY-MM-DD\\" (domains found since a date)."},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_related_terms":{"id":"ahrefs_related_terms","name":"Ahrefs Related Terms","description":"Get keyword ideas related to a seed keyword: terms the same top-ranking pages also rank for (\\"also rank for\\") or also discuss (\\"also talk about\\"), with volume, difficulty, and CPC.","version":"1.0.0","params":{"keyword":{"type":"string","required":true,"visibility":"user-or-llm","description":"The seed keyword to find related terms for"},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for keyword data. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"terms":{"type":"string","required":false,"visibility":"user-or-llm","description":"Type of related keywords to return: \\"also_rank_for\\", \\"also_talk_about\\", or \\"all\\" (default: \\"all\\")"},"viewFor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Whether to derive related terms from the top 10 or top 100 ranking pages (default: \\"top_10\\")"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_site_audit_page_explorer":{"id":"ahrefs_site_audit_page_explorer","name":"Ahrefs Site Audit Page Explorer","description":"Get crawled pages from an Ahrefs Site Audit project with health and SEO metrics: HTTP status, title, link counts, backlinks, indexability, and traffic. 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Example: 50 (default: 1000)"},"offset":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of results to skip, for pagination"},"issueId":{"type":"string","required":false,"visibility":"user-or-llm","description":"Only return pages affected by this issue ID"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"ahrefs_top_pages":{"id":"ahrefs_top_pages","name":"Ahrefs Top Pages","description":"Get the top pages of a target domain sorted by organic traffic. Returns page URLs with their traffic, keyword counts, and estimated traffic value.","version":"1.0.0","params":{"target":{"type":"string","required":true,"visibility":"user-or-llm","description":"The target domain to analyze. Example: \\"example.com\\""},"country":{"type":"string","required":false,"visibility":"user-or-llm","description":"Country code for traffic data. Example: \\"us\\", \\"gb\\", \\"de\\" (default: \\"us\\")"},"mode":{"type":"string","required":false,"visibility":"user-or-llm","description":"Analysis mode: domain (entire domain), prefix (URL prefix), subdomains (include all subdomains, default), exact (exact URL match). Example: \\"domain\\""},"date":{"type":"string","required":false,"visibility":"user-only","description":"Date to report metrics on, in YYYY-MM-DD format (defaults to today)"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of results to return. Example: 50 (default: 1000)"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Ahrefs API Key"}}},"airtable_create_records":{"id":"airtable_create_records","name":"Airtable Create Records","description":"Write new records to an Airtable table","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable base ID (starts with \\"app\\", e.g., \\"appXXXXXXXXXXXXXX\\")"},"tableId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table ID (starts with \\"tbl\\") or table name"},"records":{"type":"json","required":true,"visibility":"user-or-llm","description":"Array of records to create, each with a `fields` object"},"typecast":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"When true, Airtable automatically converts string values to the field type"}},"oauth":{"required":true,"provider":"airtable"}},"airtable_delete_records":{"id":"airtable_delete_records","name":"Airtable Delete Records","description":"Delete one or more records from an Airtable table by ID","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable base ID (starts with \\"app\\", e.g., \\"appXXXXXXXXXXXXXX\\")"},"tableId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table ID (starts with \\"tbl\\") or table name"},"recordIds":{"type":"json","required":true,"visibility":"user-or-llm","description":"Array of record IDs to delete (each starts with \\"rec\\", e.g., [\\"recXXXXXXXXXXXXXX\\"]). Pass a single-element array to delete one record."}},"oauth":{"required":true,"provider":"airtable"}},"airtable_get_base_schema":{"id":"airtable_get_base_schema","name":"Airtable Get Base Schema","description":"Get the schema of all tables, fields, and views in an Airtable base","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable base ID (starts with \\"app\\", e.g., \\"appXXXXXXXXXXXXXX\\")"}},"oauth":{"required":true,"provider":"airtable"}},"airtable_get_record":{"id":"airtable_get_record","name":"Airtable Get Record","description":"Retrieve a single record from an Airtable table by its ID","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable 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an Airtable table","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable base ID (starts with \\"app\\", e.g., \\"appXXXXXXXXXXXXXX\\")"},"tableId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table ID (starts with \\"tbl\\") or table name"},"maxRecords":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of records to return (default: all records)"},"filterFormula":{"type":"string","required":false,"visibility":"user-or-llm","description":"Formula to filter records (e.g., \\"({Field Name} = \'Value\')\\")"}},"oauth":{"required":true,"provider":"airtable"}},"airtable_list_tables":{"id":"airtable_list_tables","name":"Airtable List Tables","description":"List all tables and their schema in an Airtable base","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable base ID (starts with \\"app\\", e.g., \\"appXXXXXXXXXXXXXX\\")"}},"oauth":{"required":true,"provider":"airtable"}},"airtable_update_multiple_records":{"id":"airtable_update_multiple_records","name":"Airtable Update Multiple Records","description":"Update multiple existing records in an Airtable table","version":"1.0.0","params":{"accessToken":{"type":"string","required":true,"visibility":"hidden","description":"OAuth access token"},"baseId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Airtable base ID (starts with \\"app\\", e.g., \\"appXXXXXXXXXXXXXX\\")"},"tableId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table ID (starts with \\"tbl\\") or table 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e.g., \\"appXXXXXXXXXXXXXX\\")"},"tableId":{"type":"string","required":true,"visibility":"user-or-llm","description":"Table ID (starts with \\"tbl\\") or table name"},"records":{"type":"json","required":true,"visibility":"user-or-llm","description":"Array of records to upsert, each with a `fields` object"},"fieldsToMergeOn":{"type":"json","required":true,"visibility":"user-or-llm","description":"Array of field names used to match existing records (max 3). A record is updated when all merge fields match, otherwise it is created. Example: [\\"Name\\"]"},"typecast":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"When true, Airtable automatically converts string values to the field type"}},"oauth":{"required":true,"provider":"airtable"}},"airweave_search":{"id":"airweave_search","name":"Airweave Search","description":"Search your synced data collections using Airweave. Supports semantic search with hybrid, neural, or keyword retrieval strategies. Optionally generate AI-powered answers from search results.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Airweave API Key for authentication"},"collectionId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The readable ID of the collection to search"},"query":{"type":"string","required":true,"visibility":"user-or-llm","description":"The search query text"},"limit":{"type":"number","required":false,"visibility":"user-only","description":"Maximum number of results to return (default: 100)"},"retrievalStrategy":{"type":"string","required":false,"visibility":"user-or-llm","description":"Retrieval strategy: hybrid (default), neural, or keyword"},"expandQuery":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Generate query variations to improve recall"},"rerank":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Reorder results for improved relevance using LLM"},"generateAnswer":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Generate a natural-language answer to the query"}}},"algolia_add_record":{"id":"algolia_add_record","name":"Algolia Add Record","description":"Add or replace a record in an Algolia index","version":"1.0","params":{"applicationId":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Application ID"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Admin API Key"},"indexName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the Algolia index"},"objectID":{"type":"string","required":false,"visibility":"user-or-llm","description":"Object ID for the record (auto-generated if not provided)"},"record":{"type":"json","required":true,"visibility":"user-or-llm","description":"JSON object representing the record to add"}}},"algolia_batch_operations":{"id":"algolia_batch_operations","name":"Algolia Batch Operations","description":"Perform batch add, update, partial update, or delete operations on records in an Algolia index","version":"1.0","params":{"applicationId":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Application ID"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Admin API Key"},"indexName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the Algolia index"},"requests":{"type":"json","required":true,"visibility":"user-or-llm","description":"Array of batch operations. Each item has \\"action\\" (addObject, updateObject, partialUpdateObject, partialUpdateObjectNoCreate, deleteObject, delete, clear) and \\"body\\" (the record data; must include objectID for update/delete; use an empty object {} for the index-level delete/clear actions)"}}},"algolia_browse_records":{"id":"algolia_browse_records","name":"Algolia Browse Records","description":"Browse and iterate over all records in an Algolia index using cursor pagination","version":"1.0","params":{"applicationId":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Application ID"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Algolia API Key (must have browse ACL)"},"indexName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the Algolia index to browse"},"query":{"type":"string","required":false,"visibility":"user-or-llm","description":"Search query to filter browsed records"},"filters":{"type":"string","required":false,"visibility":"user-or-llm","description":"Filter string to narrow down results"},"attributesToRetrieve":{"type":"string","required":false,"visibility":"user-or-llm","description":"Comma-separated list of attributes to retrieve"},"hitsPerPage":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of hits per page (default: 1000, max: 1000)"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous browse response for pagination"},"aroundLatLng":{"type":"string","required":false,"visibility":"user-or-llm","description":"Coordinates for geo-search (e.g., \\"40.71,-74.01\\")"},"aroundRadius":{"type":"string","required":false,"visibility":"user-or-llm","description":"Maximum radius in meters for geo-search, or \\"all\\" for unlimited"},"insideBoundingBox":{"type":"json","required":false,"visibility":"user-or-llm","description":"Bounding box coordinates as [[lat1, lng1, lat2, lng2]] for geo-search"},"insidePolygon":{"type":"json","required":false,"visibility":"user-or-llm","description":"Polygon coordinates as [[lat1, lng1, lat2, lng2, lat3, lng3, ...]] for geo-search"}}},"algolia_clear_records":{"id":"algolia_clear_records","name":"Algolia Clear Records","description":"Clear all records from an Algolia index while keeping settings, synonyms, and rules","version":"1.0","params":{"applicationId":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Application ID"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Admin API Key (must have deleteIndex ACL)"},"indexName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the Algolia index to clear"}}},"algolia_copy_move_index":{"id":"algolia_copy_move_index","name":"Algolia Copy/Move Index","description":"Copy or move an Algolia index to a new destination","version":"1.0","params":{"applicationId":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Application ID"},"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Algolia Admin API Key"},"indexName":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the source index"},"operation":{"type":"string","required":true,"visibility":"user-or-llm","description":"Operation to perform: \\"copy\\" or \\"move\\""},"destination":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the destination index"},"scope":{"type":"json","required":false,"visibility":"user-or-llm","description":"Array of scopes to copy (only for \\"copy\\" operation): [\\"settings\\", \\"synonyms\\", \\"rules\\"]. 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Automatically enabled for >100 contacts"}}},"apollo_contact_create":{"id":"apollo_contact_create","name":"Apollo Create Contact","description":"Create a new contact in your Apollo database","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Apollo API key"},"first_name":{"type":"string","required":true,"visibility":"user-or-llm","description":"First name of the contact"},"last_name":{"type":"string","required":true,"visibility":"user-or-llm","description":"Last name of the contact"},"email":{"type":"string","required":false,"visibility":"user-or-llm","description":"Email address of the contact"},"title":{"type":"string","required":false,"visibility":"user-or-llm","description":"Job title (e.g., \\"VP of Sales\\", \\"Software Engineer\\")"},"account_id":{"type":"string","required":false,"visibility":"user-or-llm","description":"Apollo account ID to associate with (e.g., \\"acc_abc123\\")"},"owner_id":{"type":"string","required":false,"visibility":"user-only","description":"User ID of the contact owner (accepted by Apollo but not officially documented for POST /contacts)"},"organization_name":{"type":"string","required":false,"visibility":"user-or-llm","description":"Name of the contact\'s employer (e.g., \\"Apollo\\")"},"website_url":{"type":"string","required":false,"visibility":"user-or-llm","description":"Corporate website URL (e.g., \\"https://www.apollo.io/\\")"},"label_names":{"type":"array","required":false,"visibility":"user-or-llm","description":"Lists/labels to add the contact to (e.g., [\\"Prospects\\"])"},"contact_stage_id":{"type":"string","required":false,"visibility":"user-or-llm","description":"Apollo ID for the contact stage"},"present_raw_address":{"type":"string","required":false,"visibility":"user-or-llm","description":"Personal location for the contact (e.g., \\"Atlanta, United States\\")"},"direct_phone":{"type":"string","required":false,"visibility":"user-or-llm","description":"Primary phone number"},"corporate_phone":{"type":"string","required":false,"visibility":"user-or-llm","description":"Work/office phone number"},"mobile_phone":{"type":"string","required":false,"visibility":"user-or-llm","description":"Mobile phone number"},"home_phone":{"type":"string","required":false,"visibility":"user-or-llm","description":"Home phone number"},"other_phone":{"type":"string","required":false,"visibility":"user-or-llm","description":"Alternative phone number"},"typed_custom_fields":{"type":"json","required":false,"visibility":"user-or-llm","description":"Custom field values keyed by custom field ID"},"run_dedupe":{"type":"boolean","required":false,"visibility":"user-only","description":"When true, Apollo deduplicates against existing contacts"}}},"apollo_contact_search":{"id":"apollo_contact_search","name":"Apollo Search Contacts","description":"Search your team\'s contacts in Apollo","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Apollo API key"},"q_keywords":{"type":"string","required":false,"visibility":"user-or-llm","description":"Keywords to search for"},"contact_stage_ids":{"type":"array","required":false,"visibility":"user-only","description":"Filter by contact stage IDs"},"contact_label_ids":{"type":"array","required":false,"visibility":"user-only","description":"Filter by Apollo label IDs (lists)"},"sort_by_field":{"type":"string","required":false,"visibility":"user-only","description":"Sort field: contact_last_activity_date, contact_email_last_opened_at, contact_email_last_clicked_at, contact_created_at, or contact_updated_at"},"sort_ascending":{"type":"boolean","required":false,"visibility":"user-only","description":"When true, sort ascending. 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