From d557840b64a030f01eaf3ced289ac400714d4cb3 Mon Sep 17 00:00:00 2001 From: Vikhyath Mondreti Date: Thu, 3 Sep 2026 19:21:35 -0700 Subject: [PATCH 1/5] feat(embeddings): store knowledge bases at five vector widths and add Ollama MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Knowledge bases were pinned to one 1536-dimension pgvector column, so only models that emit exactly that width could index one. The embedding table now carries a column per storable width — 384, 768, 1024, 1536, 3072 — and a base records which one it uses at creation, chosen with EMBEDDING_OUTPUT_DIMS. Ollama becomes an embedding provider on both paths: KB_EMBEDDING_MODEL accepts ollama/ for knowledge bases, and the Embeddings block offers the models installed on the configured server, with no API key and nothing billed. --- .../content/docs/integrations/embeddings.mdx | 29 +- .../docs/content/docs/knowledgebase/index.mdx | 2 + .../self-hosting/environment-variables.mdx | 29 +- .../docs/platform/self-hosting/index.mdx | 2 +- .../platform/self-hosting/troubleshooting.mdx | 4 +- .../docs/platform/self-hosting/verify.mdx | 4 +- .../app/api/knowledge/search/utils.test.ts | 33 +- apps/sim/app/api/knowledge/utils.test.ts | 18 +- apps/sim/app/api/knowledge/utils.ts | 3 +- .../app/api/v1/knowledge/search/route.test.ts | 27 +- apps/sim/app/api/v1/knowledge/search/route.ts | 34 +- apps/sim/blocks/blocks.test.ts | 6 +- apps/sim/blocks/blocks/embeddings.test.ts | 59 + apps/sim/blocks/blocks/embeddings.ts | 53 +- apps/sim/lib/api/contracts/knowledge/base.ts | 4 - apps/sim/lib/api/contracts/providers.ts | 17 + apps/sim/lib/chunkers/constants.ts | 9 + apps/sim/lib/chunkers/docs-chunker.test.ts | 1 - apps/sim/lib/chunkers/docs-chunker.ts | 21 +- apps/sim/lib/copilot/docs/docs-search.ts | 7 +- apps/sim/lib/core/config/env.ts | 3 +- apps/sim/lib/embeddings/catalog.test.ts | 23 +- apps/sim/lib/embeddings/catalog.ts | 144 +- apps/sim/lib/embeddings/client.test.ts | 70 + apps/sim/lib/embeddings/client.ts | 57 +- apps/sim/lib/embeddings/index.ts | 1 + apps/sim/lib/embeddings/keys.ts | 6 +- .../knowledge-embedding-family.test.ts | 43 + .../ollama-model-catalog.server.test.ts | 152 + .../embeddings/ollama-model-catalog.server.ts | 160 + apps/sim/lib/embeddings/providers/index.ts | 14 +- apps/sim/lib/embeddings/providers/ollama.ts | 52 + .../embeddings/providers/providers.test.ts | 41 + apps/sim/lib/embeddings/types.ts | 34 +- .../internal/embeddings/execute-tool.test.ts | 37 +- .../lib/internal/embeddings/execute-tool.ts | 10 + .../sim/lib/internal/embeddings/operations.ts | 52 +- apps/sim/lib/internal/embeddings/schema.ts | 13 + .../tool-operations/registry.server.ts | 1 + .../application/knowledge-bases.test.ts | 3 +- .../knowledge/application/knowledge-bases.ts | 7 +- .../lib/knowledge/application/search.test.ts | 1 + apps/sim/lib/knowledge/application/search.ts | 31 +- apps/sim/lib/knowledge/chunks/service.ts | 50 +- .../documents/document-indexing-usage.test.ts | 4 +- .../document-processing-source.test.ts | 4 +- apps/sim/lib/knowledge/documents/service.ts | 39 +- apps/sim/lib/knowledge/embedding-models.ts | 133 +- apps/sim/lib/knowledge/embeddings.test.ts | 149 +- apps/sim/lib/knowledge/embeddings.ts | 93 +- .../orchestration/knowledge-bases.test.ts | 3 +- .../orchestration/knowledge-bases.ts | 8 +- apps/sim/lib/knowledge/search/queries.ts | 50 +- apps/sim/lib/knowledge/types.ts | 3 +- apps/sim/lib/knowledge/vector-columns.test.ts | 54 + apps/sim/lib/knowledge/vector-columns.ts | 71 + apps/sim/lib/selectors/manifest.test.ts | 4 +- apps/sim/lib/selectors/manifest.ts | 1 + apps/sim/lib/selectors/server/internal.ts | 22 + apps/sim/tools/embeddings/factory.ts | 146 +- apps/sim/tools/embeddings/index.ts | 1 + apps/sim/tools/embeddings/ollama.ts | 8 + apps/sim/tools/generated/tool-ids.ts | 2 +- apps/sim/tools/generated/tool-metadata.ts | 2 +- apps/sim/tools/generated/tool-outputs.ts | 2 +- apps/sim/tools/registry.ts | 2 + .../0321_multi_width_embeddings.sql | 72 + .../db/migrations/meta/0321_snapshot.json | 21741 ++++++++++++++++ packages/db/migrations/meta/_journal.json | 7 + packages/db/schema.ts | 74 +- .../deployment-config/src/env-capabilities.ts | 132 +- .../deployment-config/src/integrations.json | 4 +- .../sim-setup/src/capability-config.test.ts | 4 +- packages/sim-setup/src/capability-config.ts | 119 +- .../sim-setup/src/capability-status.test.ts | 82 + packages/testing/src/mocks/schema.mock.ts | 4 + packages/testing/src/mocks/urls.mock.ts | 7 + 77 files changed, 24061 insertions(+), 353 deletions(-) create mode 100644 apps/sim/lib/embeddings/knowledge-embedding-family.test.ts create mode 100644 apps/sim/lib/embeddings/ollama-model-catalog.server.test.ts create mode 100644 apps/sim/lib/embeddings/ollama-model-catalog.server.ts create mode 100644 apps/sim/lib/embeddings/providers/ollama.ts create mode 100644 apps/sim/lib/knowledge/vector-columns.test.ts create mode 100644 apps/sim/lib/knowledge/vector-columns.ts create mode 100644 apps/sim/tools/embeddings/ollama.ts create mode 100644 packages/db/migrations/0321_multi_width_embeddings.sql create mode 100644 packages/db/migrations/meta/0321_snapshot.json diff --git a/apps/docs/content/docs/integrations/embeddings.mdx b/apps/docs/content/docs/integrations/embeddings.mdx index d20a6c47628..57bfe791069 100644 --- a/apps/docs/content/docs/integrations/embeddings.mdx +++ b/apps/docs/content/docs/integrations/embeddings.mdx @@ -13,19 +13,21 @@ 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. +The Embeddings block generates those numbers using OpenAI, Google Gemini, Cohere, Mistral, OpenRouter, or a model on your own Ollama server. 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. +Ollama is the exception to most of the above. It runs on your own deployment, so it needs no API key and costs nothing, and the model list is whatever you have pulled onto that server rather than a catalog Sim maintains — the block reads it live, filters it to models that can actually embed, and shows each one's vector width next to its name. Ollama accepts neither a task type nor a size reduction, so the block does not offer those controls for it. Self-hosted deployments configure the server with `OLLAMA_URL`; on Sim Cloud there is no Ollama to reach, so the list comes back empty. + +Sim's knowledge bases embed separately: a base fixes one model and one vector width when it is created, 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, OpenRouter, Google Gemini, Cohere, and Mistral embedding models. +Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, OpenRouter, Google Gemini, Cohere, and Mistral embedding models, plus any model on a self-hosted Ollama. @@ -151,4 +153,25 @@ Generate embeddings from text using Mistral's embedding models | `dimensions` | number | Dimensionality of each vector | | `usage` | json | Token usage | +### Ollama Embeddings + +Generate embeddings on a self-hosted Ollama server + +#### Input + +| Parameter | Type | Required | Description | +| --------- | ---- | -------- | ----------- | +| `input` | string | Yes | Text to embed, or an array of texts to embed in one call | +| `model` | string | Yes | Embedding model pulled on the configured Ollama server | + +#### 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/knowledgebase/index.mdx b/apps/docs/content/docs/knowledgebase/index.mdx index a2d79a0d58a..3b7940516b5 100644 --- a/apps/docs/content/docs/knowledgebase/index.mdx +++ b/apps/docs/content/docs/knowledgebase/index.mdx @@ -23,6 +23,8 @@ When you upload a document, Sim processes it in the background: A document is searchable once its status reads `completed`. Open any document to view, edit, merge, or split its chunks. +Every knowledge base records the embedding model and the vector width it was built with, and keeps them for its lifetime. Two bases built differently cannot be searched in one request — vectors are only comparable when they come from the same model at the same size — so search them separately, or rebuild one to match. On Sim Cloud every base uses `text-embedding-3-small` at 1,536 dimensions; self-hosted deployments choose both with [`KB_EMBEDDING_MODEL` and `EMBEDDING_OUTPUT_DIMS`](/platform/self-hosting/environment-variables), including models on their own Ollama. + ## What you can upload Sim accepts PDF, Word, text, Markdown, HTML, Excel, PowerPoint, CSV, JSON, and YAML files, up to 100 MB each (best under 50 MB). Scanned PDFs work too: with Azure or [Mistral OCR](https://docs.mistral.ai/ocr/) configured, Sim extracts text from image-based pages. diff --git a/apps/docs/content/docs/platform/self-hosting/environment-variables.mdx b/apps/docs/content/docs/platform/self-hosting/environment-variables.mdx index 53a241d7d41..f66868917e6 100644 --- a/apps/docs/content/docs/platform/self-hosting/environment-variables.mdx +++ b/apps/docs/content/docs/platform/self-hosting/environment-variables.mdx @@ -55,7 +55,7 @@ import { Callout } from 'fumadocs-ui/components/callout' | `VERTEX_PROJECT` / `VERTEX_LOCATION` | Google Vertex AI — GCP project ID and region (region defaults to `us-central1`) | - **Knowledge bases require a hosted embedding provider.** Three are supported, selected with `KB_EMBEDDING_MODEL`: `text-embedding-3-small` (default) and `text-embedding-3-large` on OpenAI or Azure OpenAI, and `gemini-embedding-001` on Gemini. There is no local embedding backend — configuring Ollama or vLLM does not substitute, because embeddings do not route through the configured chat model. + **Knowledge bases need an embedding model, selected with `KB_EMBEDDING_MODEL`.** Hosted: `text-embedding-3-small` (default) and `text-embedding-3-large` on OpenAI or Azure OpenAI, and `gemini-embedding-001` on Gemini. Local: any embedding model on your own Ollama, named `ollama/` (for example `ollama/nomic-embed-text`) with `OLLAMA_URL` set. Ollama serves embeddings only through this variable — embeddings never route through the configured chat model, so vLLM and LM Studio do not substitute. @@ -190,10 +190,35 @@ See [Observability](/platform/self-hosting/observability). | Variable | Description | |----------|-------------| -| `KB_EMBEDDING_MODEL` | Embedding model for new knowledge bases. Defaults to `text-embedding-3-small`; an unsupported value falls back to the default | +| `KB_EMBEDDING_MODEL` | Embedding model for new knowledge bases. Defaults to `text-embedding-3-small`; use `ollama/` for a model on your own Ollama. An unsupported value falls back to the default | +| `EMBEDDING_OUTPUT_DIMS` | Vector width new knowledge bases are stored at: `384`, `768`, `1024`, `1536` (default), or `3072`. It must be a width the chosen model can emit; anything else falls back to `1536` with a warning | | `OPENROUTER_API_KEY` | Fallback route for the OpenAI embedding models — used when it is set and `OPENAI_API_KEY` is not the chosen path | | `COHERE_API_KEY` | Enables the Knowledge block reranker | +Both variables apply at creation time and are recorded on the knowledge base, so changing either +affects new knowledge bases only. Existing ones keep the model and width they were built with, and +knowledge bases with different settings cannot be searched together. + +Matching the width to the model is yours to get right. Sim only knows the widths a hosted provider +documents, and nothing at all about a model on your Ollama, so a mismatch surfaces as a failed +document rather than a rejected configuration. Common pairings: + +| `EMBEDDING_OUTPUT_DIMS` | Works with | +|----------|-------------| +| `384` | `ollama/all-minilm` | +| `768` | `ollama/nomic-embed-text`, `ollama/embeddinggemma`, `text-embedding-3-small`, `gemini-embedding-001` | +| `1024` | `ollama/mxbai-embed-large`, `ollama/bge-m3`, `text-embedding-3-small`, `text-embedding-3-large` | +| `1536` | `text-embedding-3-small`, `text-embedding-3-large`, `gemini-embedding-001` | +| `3072` | `text-embedding-3-large`, `gemini-embedding-001` | + +`sim-setup add knowledge-embeddings` walks through all of this — pick OpenAI, Azure OpenAI, Gemini, +or Ollama and it writes the variables that family needs. `sim-setup status` then reports the one +family your `KB_EMBEDDING_MODEL` actually selects, rather than every provider you happen to hold a +key for. + +The Embeddings block reads the same `OLLAMA_URL`. It lists the embedding-capable models installed on +that server with the width each one emits, so a workflow can embed locally without an API key. + ## Chat & PII | Variable | Description | diff --git a/apps/docs/content/docs/platform/self-hosting/index.mdx b/apps/docs/content/docs/platform/self-hosting/index.mdx index ce75aa68058..7cbfc77249b 100644 --- a/apps/docs/content/docs/platform/self-hosting/index.mdx +++ b/apps/docs/content/docs/platform/self-hosting/index.mdx @@ -109,7 +109,7 @@ Sim is self-contained for the core editor and execution engine. A few features r | Feature | Requires | Notes | |---|---|---| -| **Knowledge bases** | An OpenAI, Azure OpenAI, or Gemini API key | Embeddings are generated by a hosted provider, selected with `KB_EMBEDDING_MODEL` (`text-embedding-3-small` by default). There is no local embedding backend — knowledge bases are unavailable without one of these keys. | +| **Knowledge bases** | An OpenAI, Azure OpenAI, or Gemini API key, or an Ollama server | Embeddings are generated by the model named in `KB_EMBEDDING_MODEL` (`text-embedding-3-small` by default). Set it to `ollama/` with `OLLAMA_URL` to embed locally instead. `EMBEDDING_OUTPUT_DIMS` sets the vector width. | | **Agent blocks** | An API key for at least one model provider | Or a self-hosted OpenAI-compatible endpoint: Ollama, vLLM, LM Studio, or LiteLLM. | | **Chat module** | `COPILOT_API_KEY` from sim.ai | Set `NEXT_PUBLIC_CHAT_DISABLED=true` to hide the module instead. | | **Integrations** | Your own OAuth app per service | See [Integrations & OAuth](/platform/self-hosting/integrations-oauth). | diff --git a/apps/docs/content/docs/platform/self-hosting/troubleshooting.mdx b/apps/docs/content/docs/platform/self-hosting/troubleshooting.mdx index 0e07620595a..cedbafadff7 100644 --- a/apps/docs/content/docs/platform/self-hosting/troubleshooting.mdx +++ b/apps/docs/content/docs/platform/self-hosting/troubleshooting.mdx @@ -250,7 +250,9 @@ The load balancer's backend timeout is closing them. On GKE, attach a `BackendCo ## Knowledge base upload fails -Embeddings need a hosted provider — set `OPENAI_API_KEY`, configure Azure OpenAI, or set `KB_EMBEDDING_MODEL=gemini-embedding-001` with a Gemini key. There is no local embedding backend, so Ollama or vLLM does not substitute. If a key is set, verify pgvector is installed on the database. +Embeddings need a provider — set `OPENAI_API_KEY`, configure Azure OpenAI, set `KB_EMBEDDING_MODEL=gemini-embedding-001` with a Gemini key, or set `KB_EMBEDDING_MODEL=ollama/` with `OLLAMA_URL` to embed on your own Ollama. If one is configured, verify pgvector is installed on the database. + +A document that fails with `vector 0 has N unexpected dimensions` means `EMBEDDING_OUTPUT_DIMS` does not match what the model actually emits. Sim cannot check this for you — the message names both widths, so set the variable to the one the model returned and recreate the knowledge base. Existing knowledge bases keep the width they were created with. ## Credentials Unreadable After a Restore diff --git a/apps/docs/content/docs/platform/self-hosting/verify.mdx b/apps/docs/content/docs/platform/self-hosting/verify.mdx index 49d6c020b68..f902981ac2e 100644 --- a/apps/docs/content/docs/platform/self-hosting/verify.mdx +++ b/apps/docs/content/docs/platform/self-hosting/verify.mdx @@ -47,7 +47,7 @@ The full command list is in the [Docker guide](/platform/self-hosting/docker#the | 5 | Paste a model API key in settings and run a two-block workflow | Execution engine, credential encryption, outbound network | App logs; check `ENCRYPTION_KEY` is set and outbound egress is allowed | | 6 | Upload a small file in Files | File storage end to end | With object storage configured: presigned URL + bucket CORS. On local disk: the upload proxies through the app | | 7 | Upload a file larger than 50 MB | Multipart upload path (object storage only) | Check app logs for provider part-listing or completion errors | -| 8 | Create a knowledge base and upload a PDF | Document parsing, embeddings, pgvector | Needs a hosted embedding provider — see below | +| 8 | Create a knowledge base and upload a PDF | Document parsing, embeddings, pgvector | Needs an embedding provider — see below | | 9 | Invite a teammate from workspace settings | Email delivery | App logs for the mailer; see [Email](/platform/self-hosting/email) | | 10 | Connect an integration account | OAuth configuration | Redirect URI mismatch → see [Integrations & OAuth](/platform/self-hosting/integrations-oauth) | | 11 | Create a workflow with a Schedule trigger set to every minute, deploy it, wait 2 minutes | **Background jobs** | Check the scheduler's logs — see [Background Jobs](/platform/self-hosting/background-jobs) | @@ -102,7 +102,7 @@ All six should be present on Compose: `simstudio`, `realtime`, `db`, `redis`, `c **Step 6 or 7 fails.** With object storage configured, a CORS error in the browser console means the bucket policy does not allow your Sim origin or the signed upload headers. If step 7 fails only during completion, check the app logs and verify the server identity can list multipart parts (for S3, `s3:ListMultipartUploadParts`). On local-disk storage there is no CORS involved — uploads proxy through the app, so look at the app logs and the proxy body-size limit instead. -**Step 8 fails — knowledge base upload errors.** Knowledge bases need a hosted embedding provider — OpenAI, Azure OpenAI, or Gemini. There is no local embedding backend. If a key is set, check pgvector is installed on the database. +**Step 8 fails — knowledge base upload errors.** Knowledge bases need an embedding provider: OpenAI, Azure OpenAI, or Gemini with an API key, or a model on your own Ollama via `KB_EMBEDDING_MODEL=ollama/` and `OLLAMA_URL`. If one is configured, check pgvector is installed on the database. **Step 9 fails — no email arrives.** With no provider configured the mailer no-ops: it records the recipient, subject, and sender at `info` and reports success, never the message body. Raise `LOG_LEVEL` to `INFO` to see that line — the variable is uppercase-only, and at the production default of `ERROR` nothing is logged at all. diff --git a/apps/sim/app/api/knowledge/search/utils.test.ts b/apps/sim/app/api/knowledge/search/utils.test.ts index 169aac67c7d..fd09d744f64 100644 --- a/apps/sim/app/api/knowledge/search/utils.test.ts +++ b/apps/sim/app/api/knowledge/search/utils.test.ts @@ -48,7 +48,14 @@ afterEach(() => { }) import { WORKSPACE_ACCESS_SCOPE } from '@/lib/knowledge/access/scope' -import { generateSearchEmbedding } from '@/lib/knowledge/embeddings' +import { generateSearchEmbedding, type KbEmbeddingTarget } from '@/lib/knowledge/embeddings' + +/** The platform default model and vector width, as a knowledge base records them. */ +const DEFAULT_EMBEDDING_TARGET: KbEmbeddingTarget = { + model: 'text-embedding-3-small', + dimensions: 1536, +} + import { executeKeywordSearch, executeKnowledgeSearch, @@ -596,7 +603,7 @@ describe('Knowledge Search Utils', () => { mockNextEmbeddingResponse() - const result = await generateSearchEmbedding('test query') + const result = await generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET) expect(vi.mocked(fetch)).toHaveBeenCalledWith( 'https://test.openai.azure.com/openai/deployments/text-embedding-ada-002/embeddings?api-version=2024-12-01-preview', @@ -621,7 +628,7 @@ describe('Knowledge Search Utils', () => { mockNextEmbeddingResponse() - const result = await generateSearchEmbedding('test query') + const result = await generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET) expect(vi.mocked(fetch)).toHaveBeenCalledWith( 'https://api.openai.com/v1/embeddings', @@ -649,7 +656,7 @@ describe('Knowledge Search Utils', () => { mockNextEmbeddingResponse() - await generateSearchEmbedding('test query') + await generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET) expect(vi.mocked(fetch)).toHaveBeenCalledWith( 'https://api.openai.com/v1/embeddings', @@ -673,7 +680,7 @@ describe('Knowledge Search Utils', () => { mockNextEmbeddingResponse() - await generateSearchEmbedding('test query', 'text-embedding-3-small') + await generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET) expect(vi.mocked(fetch)).toHaveBeenCalledWith( 'https://test.openai.azure.com/openai/deployments/custom-embedding-model/embeddings?api-version=2024-12-01-preview', @@ -695,7 +702,7 @@ describe('Knowledge Search Utils', () => { OPENROUTER_API_KEY: undefined, }) - await expect(generateSearchEmbedding('test query')).rejects.toThrow( + await expect(generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET)).rejects.toThrow( 'OPENAI_API_KEY is not configured' ) }) @@ -717,7 +724,9 @@ describe('Knowledge Search Utils', () => { text: 'Deployment not found', }) - await expect(generateSearchEmbedding('test query')).rejects.toThrow('Embedding API failed') + await expect(generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET)).rejects.toThrow( + 'Embedding API failed' + ) // Clean up Object.keys(env).forEach((key) => delete (env as any)[key]) @@ -738,7 +747,9 @@ describe('Knowledge Search Utils', () => { text: 'Rate limit exceeded', }) - await expect(generateSearchEmbedding('test query')).rejects.toThrow('Embedding API failed') + await expect(generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET)).rejects.toThrow( + 'Embedding API failed' + ) // Clean up Object.keys(env).forEach((key) => delete (env as any)[key]) @@ -756,7 +767,7 @@ describe('Knowledge Search Utils', () => { mockNextEmbeddingResponse() - await generateSearchEmbedding('test query') + await generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET) expect(vi.mocked(fetch)).toHaveBeenCalledWith( expect.any(String), @@ -782,7 +793,7 @@ describe('Knowledge Search Utils', () => { mockNextEmbeddingResponse() - await generateSearchEmbedding('test query', 'text-embedding-3-small') + await generateSearchEmbedding('test query', DEFAULT_EMBEDDING_TARGET) expect(vi.mocked(fetch)).toHaveBeenCalledWith( expect.any(String), @@ -811,7 +822,7 @@ describe('Knowledge Search Utils', () => { registry.recordResolved('TOKEN', 'secret-value') await runWithKnowledgeModelInputProvenance(registry, () => - generateSearchEmbedding('prefix secret-value suffix', 'text-embedding-3-small') + generateSearchEmbedding('prefix secret-value suffix', DEFAULT_EMBEDDING_TARGET) ) expect(vi.mocked(fetch)).toHaveBeenCalledWith( diff --git a/apps/sim/app/api/knowledge/utils.test.ts b/apps/sim/app/api/knowledge/utils.test.ts index d20f5c8b4a2..2fca7aa4ecc 100644 --- a/apps/sim/app/api/knowledge/utils.test.ts +++ b/apps/sim/app/api/knowledge/utils.test.ts @@ -133,7 +133,14 @@ function createEmbeddingFetchMock() { vi.stubGlobal('fetch', createEmbeddingFetchMock()) import { processDocumentAsync } from '@/lib/knowledge/documents/service' -import { generateEmbeddings } from '@/lib/knowledge/embeddings' +import { generateEmbeddings, type KbEmbeddingTarget } from '@/lib/knowledge/embeddings' + +/** The platform default model and vector width, as a knowledge base records them. */ +const DEFAULT_EMBEDDING_TARGET: KbEmbeddingTarget = { + model: 'text-embedding-3-small', + dimensions: 1536, +} + import { checkKnowledgeBaseAccess } from '@/app/api/knowledge/utils' describe('Knowledge Utils', () => { @@ -167,6 +174,7 @@ describe('Knowledge Utils', () => { knowledgeBaseUserId: 'user1', chunkingConfig: { maxSize: 1024, minSize: 1, overlap: 200 }, embeddingModel: 'text-embedding-3-small', + embeddingDimension: 1536, billedAccountUserId: 'billing-user-1', uploadedBy: null, filename: 'file.txt', @@ -248,7 +256,7 @@ describe('Knowledge Utils', () => { describe('generateEmbeddings', () => { it('should return same length as input', async () => { - const result = await generateEmbeddings(['a', 'b']) + const result = await generateEmbeddings(['a', 'b'], DEFAULT_EMBEDDING_TARGET) expect(result.embeddings.length).toBe(2) }) @@ -267,7 +275,7 @@ describe('Knowledge Utils', () => { const fetchSpy = vi.mocked(fetch) fetchSpy.mockResolvedValueOnce(createEmbeddingResponse([0.1])) - await generateEmbeddings(['test text']) + await generateEmbeddings(['test text'], DEFAULT_EMBEDDING_TARGET) expect(fetchSpy).toHaveBeenCalledWith( 'https://test.openai.azure.com/openai/deployments/text-embedding-ada-002/embeddings?api-version=2024-12-01-preview', @@ -291,7 +299,7 @@ describe('Knowledge Utils', () => { const fetchSpy = vi.mocked(fetch) fetchSpy.mockResolvedValueOnce(createEmbeddingResponse([0.1])) - await generateEmbeddings(['test text']) + await generateEmbeddings(['test text'], DEFAULT_EMBEDDING_TARGET) expect(fetchSpy).toHaveBeenCalledWith( 'https://api.openai.com/v1/embeddings', @@ -318,7 +326,7 @@ describe('Knowledge Utils', () => { }) try { - await expect(generateEmbeddings(['test text'])).rejects.toThrow( + await expect(generateEmbeddings(['test text'], DEFAULT_EMBEDDING_TARGET)).rejects.toThrow( 'OPENAI_API_KEY is not configured' ) } finally { diff --git a/apps/sim/app/api/knowledge/utils.ts b/apps/sim/app/api/knowledge/utils.ts index 20a00d38cba..3a6a4aa98a2 100644 --- a/apps/sim/app/api/knowledge/utils.ts +++ b/apps/sim/app/api/knowledge/utils.ts @@ -22,7 +22,7 @@ export interface KnowledgeBaseAccessResult { hasAccess: true knowledgeBase: Pick< KnowledgeBaseData, - 'id' | 'userId' | 'workspaceId' | 'name' | 'embeddingModel' + 'id' | 'userId' | 'workspaceId' | 'name' | 'embeddingModel' | 'embeddingDimension' > } @@ -53,6 +53,7 @@ async function resolveKnowledgeBaseAccess( workspaceId: knowledgeBase.workspaceId, name: knowledgeBase.name, embeddingModel: knowledgeBase.embeddingModel, + embeddingDimension: knowledgeBase.embeddingDimension, }) .from(knowledgeBase) .where(and(eq(knowledgeBase.id, knowledgeBaseId), isNull(knowledgeBase.deletedAt))) diff --git a/apps/sim/app/api/v1/knowledge/search/route.test.ts b/apps/sim/app/api/v1/knowledge/search/route.test.ts index 40747a907a4..54c23374a28 100644 --- a/apps/sim/app/api/v1/knowledge/search/route.test.ts +++ b/apps/sim/app/api/v1/knowledge/search/route.test.ts @@ -98,12 +98,13 @@ import { POST } from '@/app/api/v1/knowledge/search/route' const mockCheckKnowledgeBaseAccess = knowledgeApiUtilsMockFns.mockCheckKnowledgeBaseAccess -const baseKb = (id: string, embeddingModel: string) => ({ +const baseKb = (id: string, embeddingModel: string, embeddingDimension = 1536) => ({ id, userId: 'user-1', name: `KB ${id}`, workspaceId: 'ws-1', embeddingModel, + embeddingDimension, deletedAt: null, }) @@ -150,7 +151,7 @@ describe('v1 knowledge search route — per-KB embedding model', () => { expect(res.status).toBe(200) expect(mockGenerateSearchEmbedding).toHaveBeenCalledWith( 'hello', - 'gemini-embedding-001', + { model: 'gemini-embedding-001', dimensions: 1536 }, 'ws-1' ) expect(mockResolveBillingAttribution).toHaveBeenCalledWith({ @@ -191,6 +192,28 @@ describe('v1 knowledge search route — per-KB embedding model', () => { ) }) + it('rejects cross-KB queries with mixed vector widths, which store in different columns', async () => { + mockCheckKnowledgeBaseAccess + .mockResolvedValueOnce({ + hasAccess: true, + knowledgeBase: baseKb('kb-1536', 'text-embedding-3-large', 1536), + }) + .mockResolvedValueOnce({ + hasAccess: true, + knowledgeBase: baseKb('kb-3072', 'text-embedding-3-large', 3072), + }) + + const req = createMockRequest('POST', { + workspaceId: 'ws-1', + knowledgeBaseIds: ['kb-1536', 'kb-3072'], + query: 'hello', + }) + const res = await POST(req) + + expect(res.status).toBe(400) + expect(mockGenerateSearchEmbedding).not.toHaveBeenCalled() + }) + it('rejects cross-KB queries with mixed embedding models', async () => { mockCheckKnowledgeBaseAccess .mockResolvedValueOnce({ diff --git a/apps/sim/app/api/v1/knowledge/search/route.ts b/apps/sim/app/api/v1/knowledge/search/route.ts index 05037fb52f0..b00de143e5c 100644 --- a/apps/sim/app/api/v1/knowledge/search/route.ts +++ b/apps/sim/app/api/v1/knowledge/search/route.ts @@ -8,7 +8,12 @@ import { } from '@/lib/billing/core/billing-attribution' import { withRouteHandler } from '@/lib/core/utils/with-route-handler' import { ALL_TAG_SLOTS } from '@/lib/knowledge/constants' -import { generateSearchEmbedding, recordSearchEmbeddingUsage } from '@/lib/knowledge/embeddings' +import { toKbEmbeddingDimensions } from '@/lib/knowledge/embedding-models' +import { + generateSearchEmbedding, + type KbEmbeddingTarget, + recordSearchEmbeddingUsage, +} from '@/lib/knowledge/embeddings' import { resolveKnowledgeSearchDefaults } from '@/lib/knowledge/search/defaults' import { executeKnowledgeSearch, @@ -183,17 +188,28 @@ export const POST = withRouteHandler(async (request: NextRequest) => { const hasQuery = query && query.trim().length > 0 const hasFilters = structuredFilters.length > 0 - const embeddingModels = Array.from(new Set(accessibleKbs.map((kb) => kb.embeddingModel))) - if (hasQuery && embeddingModels.length > 1) { + /** + * One query embedding serves every base in the request, so all of them must + * be indexed the same way — including the vector width, which selects the + * pgvector column each comparison reads. + */ + const embeddingTargets = new Map( + accessibleKbs.map((kb) => [ + `${kb.embeddingModel}:${kb.embeddingDimension}`, + { model: kb.embeddingModel, dimensions: toKbEmbeddingDimensions(kb.embeddingDimension) }, + ]) + ) + if (hasQuery && embeddingTargets.size > 1) { return NextResponse.json( { error: - 'Selected knowledge bases use different embedding models and cannot be searched together. Search them separately.', + 'Selected knowledge bases use different embedding models or vector widths and cannot be searched together. Search them separately.', }, { status: 400 } ) } - const queryEmbeddingModel = embeddingModels[0] + const queryEmbeddingTarget = [...embeddingTargets.values()][0] + const queryEmbeddingModel = queryEmbeddingTarget.model let results: SearchResult[] let queryEmbeddingIsBYOK: boolean | null = null @@ -219,11 +235,10 @@ export const POST = withRouteHandler(async (request: NextRequest) => { } else if (hasQuery) { const queryEmbeddingResult = await generateSearchEmbedding( query!, - queryEmbeddingModel, + queryEmbeddingTarget, workspaceId ) queryEmbeddingIsBYOK = queryEmbeddingResult.isBYOK - const queryVector = JSON.stringify(queryEmbeddingResult.embedding) results = await executeKnowledgeSearch({ knowledgeBaseIds: accessibleKbIds, topK, @@ -231,7 +246,10 @@ export const POST = withRouteHandler(async (request: NextRequest) => { searchMode, boostRecency, query, - queryVector, + queryVector: { + vector: JSON.stringify(queryEmbeddingResult.embedding), + dimensions: queryEmbeddingTarget.dimensions, + }, structuredFilters: hasFilters ? structuredFilters : undefined, }) } else { diff --git a/apps/sim/blocks/blocks.test.ts b/apps/sim/blocks/blocks.test.ts index 1f8aeb28cbd..693b2d00bdf 100644 --- a/apps/sim/blocks/blocks.test.ts +++ b/apps/sim/blocks/blocks.test.ts @@ -943,7 +943,7 @@ describe.concurrent('Blocks Module', () => { expect(providerSubBlock?.commandSearchable).toBe(true) expect(providerSubBlock?.value?.()).toBe('openai') - expect(providerIds).toEqual(['openai', 'gemini', 'cohere', 'mistral', 'openrouter']) + expect(providerIds).toEqual(['openai', 'gemini', 'cohere', 'mistral', 'openrouter', 'ollama']) for (const provider of providerIds) { // Each provider routes to its own registered tool... @@ -953,7 +953,9 @@ describe.concurrent('Blocks Module', () => { const modelSubBlock = block?.subBlocks.find( (sb) => sb.id === 'model' && sb.condition?.value === provider ) - if (provider === 'openrouter') { + // OpenRouter's catalog is remote and Ollama's is the deployment's own, + // so neither can be enumerated here. + if (provider === 'openrouter' || provider === 'ollama') { expect(modelSubBlock?.selectorKey).toBeTypeOf('string') } else { expect( diff --git a/apps/sim/blocks/blocks/embeddings.test.ts b/apps/sim/blocks/blocks/embeddings.test.ts index 370c0f21a73..fb341b78f73 100644 --- a/apps/sim/blocks/blocks/embeddings.test.ts +++ b/apps/sim/blocks/blocks/embeddings.test.ts @@ -351,4 +351,63 @@ describe('Embeddings block', () => { EmbeddingsBlock.tools.config?.params?.({ provider: 'openai', apiKey: 'k' }) ).toThrow('Input text is required') }) + + describe('Ollama', () => { + it('reads its models from the server rather than the catalog, with no default', () => { + const subBlock = subBlocksById('model').find( + (candidate) => conditionProvider(candidate) === 'ollama' + ) + expect(subBlock).toBeDefined() + expect(subBlock?.type).toBe('combobox') + expect(subBlock?.selectorKey).toBe('providers.ollamaEmbeddingModels') + /** Whatever the operator pulled; Sim cannot pre-select one it cannot enumerate. */ + expect(subBlock?.value).toBeUndefined() + expect(subBlock?.required).toBe(true) + }) + + it('offers neither task types nor dimensions, which the adapter cannot send', () => { + for (const id of ['taskType', 'dimensions']) { + expect( + subBlocksById(id).some((candidate) => conditionProvider(candidate) === 'ollama'), + `${id} must not be offered for ollama` + ).toBe(false) + } + }) + + it('hides the API key field, since Ollama authenticates with nothing', () => { + const condition = subBlocksById('apiKey')[0].condition as { + value: string[] + not?: boolean + } + expect(condition.not).toBe(true) + expect(condition.value).toContain('ollama') + expect(condition.value).toContain('openrouter') + }) + + it('forwards the bare model name and drops every credential-bearing field', () => { + expect( + EmbeddingsBlock.tools.config?.params?.({ + provider: 'ollama', + model: 'nomic-embed-text:latest', + input: 'hello', + apiKey: 'stale-key', + taskType: 'query', + dimensions: '768', + }) + ).toEqual({ + input: 'hello', + model: 'nomic-embed-text:latest', + taskType: undefined, + dimensions: undefined, + }) + }) + + it('routes to its own tool and requires a model', () => { + expect(TOOL_ID_BY_PROVIDER.ollama).toBe('embeddings_ollama') + expect(EmbeddingsBlock.tools.access).toContain('embeddings_ollama') + expect(() => + EmbeddingsBlock.tools.config?.params?.({ provider: 'ollama', input: 'hello' }) + ).toThrow('An Ollama embedding model is required') + }) + }) }) diff --git a/apps/sim/blocks/blocks/embeddings.ts b/apps/sim/blocks/blocks/embeddings.ts index 8ab60bbe859..870d24df41a 100644 --- a/apps/sim/blocks/blocks/embeddings.ts +++ b/apps/sim/blocks/blocks/embeddings.ts @@ -20,7 +20,11 @@ import type { BlockConfig, BlockMeta, SubBlockConfig } from '@/blocks/types' import { AuthMode, IntegrationType } from '@/blocks/types' import type { EmbeddingsResponse } from '@/tools/embeddings/types' -export const EMBEDDING_BLOCK_PROVIDERS = [...EMBEDDING_CATALOG_PROVIDERS, 'openrouter'] as const +export const EMBEDDING_BLOCK_PROVIDERS = [ + ...EMBEDDING_CATALOG_PROVIDERS, + 'openrouter', + 'ollama', +] as const type EmbeddingBlockProvider = (typeof EMBEDDING_BLOCK_PROVIDERS)[number] @@ -30,6 +34,7 @@ const TOOL_ID_BY_PROVIDER: Record = { gemini: 'embeddings_gemini', cohere: 'embeddings_cohere', mistral: 'embeddings_mistral', + ollama: 'embeddings_ollama', } const PROVIDER_LABELS: Record = { @@ -38,8 +43,12 @@ const PROVIDER_LABELS: Record = { gemini: 'Google Gemini', cohere: 'Cohere', mistral: 'Mistral', + ollama: 'Ollama', } +/** Providers whose models are the deployment's to install, not Sim's to catalogue. */ +const KEYLESS_PROVIDERS = ['ollama'] as const satisfies readonly EmbeddingBlockProvider[] + const TASK_TYPE_LABELS: Record = { document: 'Document', query: 'Query', @@ -78,6 +87,24 @@ MODEL_SUB_BLOCKS.push({ dependsOn: ['provider'], }) +/** + * Ollama's catalog is whatever the operator pulled onto their own server, so the + * list is read from it at open time rather than declared here, and there is no + * default to pre-select. Each option carries the width the model emits, because + * that is the one thing a user has to match when the same base is also indexed + * by a knowledge base. + */ +MODEL_SUB_BLOCKS.push({ + id: 'model', + title: 'Model', + type: 'combobox', + selectorKey: 'providers.ollamaEmbeddingModels', + placeholder: 'Select a model on your Ollama server', + required: true, + condition: { field: 'provider', value: 'ollama' }, + 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 @@ -130,7 +157,7 @@ export const EmbeddingsBlock: BlockConfig = { description: 'Generate embeddings', authMode: AuthMode.ApiKey, longDescription: - 'Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, OpenRouter, Google Gemini, Cohere, and Mistral embedding models.', + 'Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, OpenRouter, Google Gemini, Cohere, and Mistral embedding models, plus any model on a self-hosted Ollama.', category: 'tools', integrationType: IntegrationType.AI, docsLink: 'https://docs.sim.ai/integrations/embeddings', @@ -181,7 +208,7 @@ export const EmbeddingsBlock: BlockConfig = { placeholder: 'Enter your provider API key', password: true, required: true, - condition: { field: 'provider', value: 'openrouter', not: true }, + condition: { field: 'provider', value: [...KEYLESS_PROVIDERS, 'openrouter'], not: true }, connectionDroppable: false, hideWhenHosted: true, }, @@ -203,6 +230,7 @@ export const EmbeddingsBlock: BlockConfig = { 'embeddings_gemini', 'embeddings_cohere', 'embeddings_mistral', + 'embeddings_ollama', ], config: { /** @@ -234,6 +262,25 @@ export const EmbeddingsBlock: BlockConfig = { throw new Error('Input text is required') } + /** + * Ollama takes no credential and offers no task or dimension + * conditioning, so a value stored under a previous provider is cleared + * rather than forwarded. The model is passed as the bare name the + * server lists; the routing prefix is added server-side. + */ + if (provider === 'ollama') { + const model = typeof params.model === 'string' ? params.model.trim() : '' + if (!model) { + throw new Error('An Ollama embedding model is required') + } + return { + input: params.input, + model, + taskType: undefined, + dimensions: undefined, + } + } + if (provider === 'openrouter') { if (typeof params.openRouterApiKey !== 'string' || !params.openRouterApiKey.trim()) { throw new Error('OpenRouter API key is required') diff --git a/apps/sim/lib/api/contracts/knowledge/base.ts b/apps/sim/lib/api/contracts/knowledge/base.ts index 5e154df85ec..dab587ae1e5 100644 --- a/apps/sim/lib/api/contracts/knowledge/base.ts +++ b/apps/sim/lib/api/contracts/knowledge/base.ts @@ -154,8 +154,6 @@ export const createKnowledgeBaseBodySchema = z.object({ * `null` (or omitted) creates it at the workspace root. */ folderId: z.string().min(1, 'Folder ID cannot be empty').nullable().optional(), - embeddingModel: z.literal('text-embedding-3-small').default('text-embedding-3-small'), - embeddingDimension: z.literal(1536).default(1536), chunkingConfig: chunkingConfigSchema.default(DEFAULT_CHUNKING_CONFIG), }) @@ -173,8 +171,6 @@ export const updateKnowledgeBaseBodySchema = createKnowledgeBaseBodySchema */ folderId: z.string().min(1, 'Folder ID cannot be empty').nullable().optional(), workspaceId: z.string().nullable().optional(), - embeddingModel: z.literal('text-embedding-3-small').optional(), - embeddingDimension: z.literal(1536).optional(), }) const knowledgeChunkingConfigSchema = z diff --git a/apps/sim/lib/api/contracts/providers.ts b/apps/sim/lib/api/contracts/providers.ts index 3b7f1d5eb28..7dcab18ab31 100644 --- a/apps/sim/lib/api/contracts/providers.ts +++ b/apps/sim/lib/api/contracts/providers.ts @@ -143,6 +143,23 @@ export const ollamaUpstreamResponseSchema = z.object({ .default([]), }) +/** + * Ollama `/api/show`. `capabilities` is what separates an embedding model from a + * chat one — `/api/tags` lists both and says nothing about either. `model_info` + * is keyed by architecture (`nomic-bert.embedding_length`, `bert.embedding_length`, + * …), so the width is found by suffix rather than by a fixed key. + * + * Both fields are optional: an Ollama older than 0.5 reports no `capabilities`, + * and a model whose architecture publishes no embedding length simply has none + * to show. + */ +export const ollamaShowUpstreamResponseSchema = z + .object({ + capabilities: z.array(z.string()).optional(), + model_info: z.record(z.string(), z.unknown()).optional(), + }) + .passthrough() + export const getBaseProviderModelsContract = defineRouteContract({ method: 'GET', path: '/api/providers/base/models', diff --git a/apps/sim/lib/chunkers/constants.ts b/apps/sim/lib/chunkers/constants.ts index 0e67a927407..62cab1953e7 100644 --- a/apps/sim/lib/chunkers/constants.ts +++ b/apps/sim/lib/chunkers/constants.ts @@ -15,3 +15,12 @@ export const MAX_CHUNKING_SEPARATORS = 32 /** Max characters in a single chunking separator. Real delimiters are a few characters. */ export const MAX_CHUNKING_SEPARATOR_LENGTH = 100 + +/** + * Width of Sim's own documentation index (`docs_embeddings`), which is a single + * fixed-width pgvector column independent of the `EMBEDDING_OUTPUT_DIMS` a + * deployment chooses for its knowledge bases. Both the indexer and the docs + * search path pin this, so a deployment configured for another width cannot + * write vectors the docs column will not accept or query it at the wrong width. + */ +export const DOCS_EMBEDDING_DIMENSIONS = 1536 as const diff --git a/apps/sim/lib/chunkers/docs-chunker.test.ts b/apps/sim/lib/chunkers/docs-chunker.test.ts index cacd431c02f..58d99aa0031 100644 --- a/apps/sim/lib/chunkers/docs-chunker.test.ts +++ b/apps/sim/lib/chunkers/docs-chunker.test.ts @@ -5,7 +5,6 @@ import { describe, expect, it, vi } from 'vitest' vi.mock('@/lib/knowledge/embeddings', () => ({ generateEmbeddings: vi.fn(async () => ({ embeddings: [] })), - getConfiguredEmbeddingModel: vi.fn(() => 'test-model'), })) import { ChunkLimitExceededError } from '@/lib/chunkers/chunk-budget' diff --git a/apps/sim/lib/chunkers/docs-chunker.ts b/apps/sim/lib/chunkers/docs-chunker.ts index cbd0b581ee7..728ee155f4d 100644 --- a/apps/sim/lib/chunkers/docs-chunker.ts +++ b/apps/sim/lib/chunkers/docs-chunker.ts @@ -2,10 +2,12 @@ import fs from 'fs/promises' import path from 'path' import { createLogger } from '@sim/logger' import { ChunkBudget } from '@/lib/chunkers/chunk-budget' +import { DOCS_EMBEDDING_DIMENSIONS } from '@/lib/chunkers/constants' import { TextChunker } from '@/lib/chunkers/text-chunker' import type { DocChunk, DocsChunkerOptions } from '@/lib/chunkers/types' import { estimateTokens } from '@/lib/chunkers/utils' -import { generateEmbeddings, getConfiguredEmbeddingModel } from '@/lib/knowledge/embeddings' +import { DEFAULT_EMBEDDING_MODEL } from '@/lib/knowledge/embedding-models' +import { generateEmbeddings } from '@/lib/knowledge/embeddings' interface HeaderInfo { level: number @@ -116,9 +118,22 @@ export class DocsChunker { const headers = this.extractHeaders(cleanedContent) logger.info(`Generating embeddings for ${textChunks.length} chunks in ${relativePath}`) - const embeddingModel = getConfiguredEmbeddingModel() + /** + * Pinned to the platform default rather than the deployment's configured + * knowledge-base model: `docs_embeddings` is one fixed-width column that + * every Sim install queries, so a deployment-specific model or width would + * write vectors it cannot store. + */ + const embeddingModel = DEFAULT_EMBEDDING_MODEL const embeddings: number[][] = - textChunks.length > 0 ? (await generateEmbeddings(textChunks, embeddingModel)).embeddings : [] + textChunks.length > 0 + ? ( + await generateEmbeddings(textChunks, { + model: embeddingModel, + dimensions: DOCS_EMBEDDING_DIMENSIONS, + }) + ).embeddings + : [] const chunks: DocChunk[] = [] let currentPosition = 0 diff --git a/apps/sim/lib/copilot/docs/docs-search.ts b/apps/sim/lib/copilot/docs/docs-search.ts index e506dff48ef..66b36aa9316 100644 --- a/apps/sim/lib/copilot/docs/docs-search.ts +++ b/apps/sim/lib/copilot/docs/docs-search.ts @@ -3,6 +3,7 @@ import { docsEmbeddings } from '@sim/db/schema' import { createLogger } from '@sim/logger' import { and, eq, like, ne, notLike, or, sql } from 'drizzle-orm' import { escapeLikePattern } from '@/lib/api/list-query' +import { DOCS_EMBEDDING_DIMENSIONS } from '@/lib/chunkers/constants' import { docsPathForSourceDocument, isDocsDir, @@ -11,6 +12,7 @@ import { } from '@/lib/copilot/docs/docs-corpus' import { docsSourceCandidates, UNMOUNTED_DOCS_SECTIONS } from '@/lib/copilot/docs/docs-path' import { OrchestrationError } from '@/lib/core/orchestration/types' +import { DEFAULT_EMBEDDING_MODEL } from '@/lib/knowledge/embedding-models' import { generateSearchEmbedding } from '@/lib/knowledge/embeddings' const logger = createLogger('DocsSearch') @@ -152,7 +154,10 @@ export async function searchDocs( path: options?.path ?? null, }) - const { embedding: queryEmbedding } = await generateSearchEmbedding(query) + const { embedding: queryEmbedding } = await generateSearchEmbedding(query, { + model: DEFAULT_EMBEDDING_MODEL, + dimensions: DOCS_EMBEDDING_DIMENSIONS, + }) if (!queryEmbedding || queryEmbedding.length === 0) { return { results: [], candidatesConsidered: 0, droppedBelowThreshold: 0, droppedStale: 0 } } diff --git a/apps/sim/lib/core/config/env.ts b/apps/sim/lib/core/config/env.ts index 8a77dc285cd..5970bf44db1 100644 --- a/apps/sim/lib/core/config/env.ts +++ b/apps/sim/lib/core/config/env.ts @@ -293,7 +293,8 @@ export const env = createEnv({ AZURE_ANTHROPIC_API_KEY: z.string().min(1).optional(), // Azure Anthropic API key AZURE_ANTHROPIC_API_VERSION: z.string().min(1).optional(), // Azure Anthropic API version (e.g. 2023-06-01) KB_OPENAI_MODEL_NAME: z.string().optional(), // Azure deployment name serving the configured KB embedding model (used only when AZURE_OPENAI_* credentials are set). - KB_EMBEDDING_MODEL: z.string().optional(), // Embedding model used for all new knowledge bases. Must be one of the supported model ids; defaults to text-embedding-3-small. + KB_EMBEDDING_MODEL: z.string().optional(), // Embedding model used for all new knowledge bases. Must be one of the supported model ids, or `ollama/` for a model on OLLAMA_URL; defaults to text-embedding-3-small. + EMBEDDING_OUTPUT_DIMS: z.coerce.number().int().positive().optional(), // Vector width new knowledge bases are stored at. One of 384, 768, 1024, 1536, 3072, and the configured KB_EMBEDDING_MODEL must emit it; anything else falls back to 1536. WAND_OPENAI_MODEL_NAME: z.string().optional(), // Wand generation OpenAI model name (works with both regular OpenAI and Azure OpenAI) OCR_AZURE_ENDPOINT: z.string().url().optional(), // Azure Mistral OCR service endpoint OCR_AZURE_MODEL_NAME: z.string().optional(), // Azure Mistral OCR model name for document processing diff --git a/apps/sim/lib/embeddings/catalog.test.ts b/apps/sim/lib/embeddings/catalog.test.ts index 8f9e594aa1f..d4525371551 100644 --- a/apps/sim/lib/embeddings/catalog.test.ts +++ b/apps/sim/lib/embeddings/catalog.test.ts @@ -4,11 +4,13 @@ import { describe, expect, it } from 'vitest' import { EMBEDDING_MODELS, + findEmbeddingModelInfo, getEmbeddingModelInfo, getKbEligibleModels, + getKbEmbeddingDimensions, getModelsForProvider, hasApproximateTokenCount, - KB_EMBEDDING_DIMENSIONS, + KB_EMBEDDING_STORAGE_DIMENSIONS, resolveDimensions, } from '@/lib/embeddings/catalog' import { EMBEDDING_MODEL_PRICING } from '@/providers/models' @@ -58,16 +60,23 @@ describe('embedding catalog', () => { } }) - it('only marks a model KB-eligible when it can emit the fixed KB vector width', () => { + it('only marks a model KB-eligible when it can emit a storable 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 ${KB_EMBEDDING_DIMENSIONS} dimensions`).toBe(true) + const widths = getKbEmbeddingDimensions(EMBEDDING_MODELS[modelId]) + expect( + widths, + `${modelId} emits none of ${KB_EMBEDDING_STORAGE_DIMENSIONS.join(', ')}` + ).not.toHaveLength(0) } }) + it('resolves an ollama-prefixed model to every storable width', () => { + const info = getEmbeddingModelInfo('ollama/nomic-embed-text') + expect(info.provider).toBe('ollama') + expect(getKbEmbeddingDimensions(info)).toEqual([...KB_EMBEDDING_STORAGE_DIMENSIONS]) + expect(findEmbeddingModelInfo('ollama/')).toBeUndefined() + }) + 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. diff --git a/apps/sim/lib/embeddings/catalog.ts b/apps/sim/lib/embeddings/catalog.ts index 69f5f7cb11c..b31d8569a95 100644 --- a/apps/sim/lib/embeddings/catalog.ts +++ b/apps/sim/lib/embeddings/catalog.ts @@ -1,6 +1,7 @@ import type { EmbeddingCatalogProvider, EmbeddingTaskType, + KeyedEmbeddingProvider, TokenizerProviderId, } from '@/lib/embeddings/types' import type { BYOKProviderId } from '@/tools/types' @@ -14,11 +15,31 @@ import type { BYOKProviderId } from '@/tools/types' 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. + * Widths the `embedding` table has a pgvector column for, largest first. + * + * A knowledge base pins one of these at creation and every chunk in it is + * stored in the matching column, so a model used for KB indexing must emit + * vectors of exactly the width its base was created with. The set covers the + * sizes the popular embedding models actually emit: 384 (all-minilm and the + * small Sentence Transformers), 768 (nomic-embed-text, embeddinggemma, most + * open-weight models), 1024 (mxbai-embed-large, bge-m3, qwen3-embedding, + * Voyage), 1536 (OpenAI's small model), and 3072 (OpenAI's large model and + * gemini-embedding-001). + */ +export const KB_EMBEDDING_STORAGE_DIMENSIONS = [3072, 1536, 1024, 768, 384] as const + +export type KbEmbeddingDimensions = (typeof KB_EMBEDDING_STORAGE_DIMENSIONS)[number] + +/** + * Width a knowledge base is created at when the deployment names no other one. + * Matches the `embedding.embedding` column every base predating multi-width + * storage was written into, so an unconfigured deployment keeps its behavior. */ -export const KB_EMBEDDING_DIMENSIONS = 1536 as const +export const DEFAULT_KB_EMBEDDING_DIMENSIONS = 1536 as const + +export function isKbEmbeddingDimensions(value: number): value is KbEmbeddingDimensions { + return (KB_EMBEDDING_STORAGE_DIMENSIONS as readonly number[]).includes(value) +} /** * OpenAI caps a single `/v1/embeddings` call at 300,000 tokens summed across all @@ -60,8 +81,9 @@ export interface EmbeddingModelInfo { */ maxTokensPerRequest?: number /** - * Selectable for knowledge-base indexing. Requires the model to emit exactly - * KB_EMBEDDING_DIMENSIONS. + * Selectable for knowledge-base indexing. Requires the model to emit at least + * one width in {@link KB_EMBEDDING_STORAGE_DIMENSIONS}; a model that can is + * still opted out here when it is superseded and only kept for placed blocks. */ kbEligible: boolean } @@ -152,8 +174,13 @@ export const EMBEDDING_MODELS: Record = { }, } -/** Providers a user can pick, in the order the block offers them. */ -export const EMBEDDING_CATALOG_PROVIDERS: readonly EmbeddingCatalogProvider[] = [ +/** + * Providers a user can pick, in the order the block offers them. Ollama is + * absent because its models are whatever the operator has pulled onto their own + * server, which no static catalog can enumerate; it is reachable through the + * `ollama/` model prefix that {@link findEmbeddingModelInfo} resolves. + */ +export const EMBEDDING_CATALOG_PROVIDERS: readonly KeyedEmbeddingProvider[] = [ 'openai', 'gemini', 'cohere', @@ -164,7 +191,7 @@ export const EMBEDDING_CATALOG_PROVIDERS: readonly EmbeddingCatalogProvider[] = * 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 = { +export const DEFAULT_MODEL_BY_PROVIDER: Record = { openai: DEFAULT_EMBEDDING_MODEL, gemini: 'gemini-embedding-001', cohere: 'embed-v4.0', @@ -175,15 +202,82 @@ export const DEFAULT_MODEL_BY_PROVIDER: Record * 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 = { +export const BYOK_PROVIDER_IDS: Record = { openai: 'openai', gemini: 'google', cohere: 'cohere', mistral: 'mistral', } +/** + * Marks a model id as living on the deployment's own Ollama server. The + * remainder is passed to Ollama verbatim, so tags survive + * (`ollama/mxbai-embed-large:335m`), matching how the OpenRouter transport + * already namespaces ids it does not own. + */ +export const OLLAMA_EMBEDDING_MODEL_PREFIX = 'ollama/' + +/** + * Per-input token ceiling assumed for an Ollama model. + * + * Sim cannot know a local model's context length — it varies from 512 + * (mxbai-embed-large) to 8192 (nomic-embed-text, bge-m3) — so inputs are held + * to the common upper figure and the adapter asks Ollama to truncate anything + * the loaded model cannot fit. Ollama truncating is a silent shortening, but + * the alternative is an outright rejection of input every other provider in the + * catalog accepts. + */ +const OLLAMA_MAX_INPUT_TOKENS = 8192 + +export function isOllamaEmbeddingModel(model: string): boolean { + return model.startsWith(OLLAMA_EMBEDDING_MODEL_PREFIX) +} + +/** Model name as Ollama expects it on the wire, with the routing prefix removed. */ +export function ollamaEmbeddingModelName(model: string): string { + return model.slice(OLLAMA_EMBEDDING_MODEL_PREFIX.length) +} + +/** + * Routing id for a model on the deployment's Ollama. Tolerant of a value that + * already carries the prefix, so an operator who writes + * `KB_EMBEDDING_MODEL=ollama/nomic-embed-text` and a block that stores the bare + * name Ollama itself lists both resolve to the same id. + */ +export function toOllamaEmbeddingModelId(model: string): string { + const name = (isOllamaEmbeddingModel(model) ? ollamaEmbeddingModelName(model) : model).trim() + if (!name) throw new Error('Ollama embedding model name is required') + return `${OLLAMA_EMBEDDING_MODEL_PREFIX}${name}` +} + +/** + * Metadata for a model served by the deployment's own Ollama instance. + * + * Ollama publishes no catalog Sim can read at module load, so the entry is + * synthesized: every storage width is offered, and `nativeDimensions` is the + * platform default only so a caller that requests no width still names one. + * The model's real width is whatever it emits, and the client's response + * validation is what enforces the match — a base created at 768 whose model + * returns 1024 fails with both numbers rather than writing a wrong-width vector. + */ +function buildOllamaEmbeddingModelInfo(model: string): EmbeddingModelInfo | undefined { + const name = ollamaEmbeddingModelName(model) + if (!name) return undefined + return { + provider: 'ollama', + label: name, + /** No entry in EMBEDDING_MODEL_PRICING: local inference costs Sim nothing. */ + pricingId: model, + tokenizerProvider: 'ollama', + nativeDimensions: DEFAULT_KB_EMBEDDING_DIMENSIONS, + supportedDimensions: KB_EMBEDDING_STORAGE_DIMENSIONS, + maxInputTokens: OLLAMA_MAX_INPUT_TOKENS, + kbEligible: true, + } +} + export function getEmbeddingModelInfo(model: string): EmbeddingModelInfo { - const info = EMBEDDING_MODELS[model] + const info = findEmbeddingModelInfo(model) if (!info) { throw new Error(`Unsupported embedding model: ${model}`) } @@ -191,10 +285,11 @@ export function getEmbeddingModelInfo(model: string): EmbeddingModelInfo { } export function findEmbeddingModelInfo(model: string): EmbeddingModelInfo | undefined { + if (isOllamaEmbeddingModel(model)) return buildOllamaEmbeddingModelInfo(model) return EMBEDDING_MODELS[model] } -export function getModelsForProvider(provider: EmbeddingCatalogProvider): string[] { +export function getModelsForProvider(provider: KeyedEmbeddingProvider): string[] { return Object.keys(EMBEDDING_MODELS).filter((id) => EMBEDDING_MODELS[id].provider === provider) } @@ -203,6 +298,18 @@ export function getKbEligibleModels(): string[] { return Object.keys(EMBEDDING_MODELS).filter((id) => EMBEDDING_MODELS[id].kbEligible) } +/** + * Storage widths a model can be indexed at, largest first — the intersection of + * what it emits with what the `embedding` table has a column for. Empty when the + * model emits no width knowledge bases can store, which is what makes it + * unusable for indexing regardless of its {@link EmbeddingModelInfo.kbEligible} + * flag. + */ +export function getKbEmbeddingDimensions(info: EmbeddingModelInfo): KbEmbeddingDimensions[] { + const emitted = info.supportedDimensions ?? [info.nativeDimensions] + return KB_EMBEDDING_STORAGE_DIMENSIONS.filter((width) => emitted.includes(width)) +} + /** * True when a model's tokens cannot be counted exactly. * @@ -223,6 +330,19 @@ export function hasApproximateTokenCount(info: EmbeddingModelInfo): boolean { */ export function resolveDimensions(info: EmbeddingModelInfo, requested?: number): number { if (requested === undefined) return info.nativeDimensions + /** + * A local model's width is whatever the operator pulled, and Ollama publishes + * no catalog Sim can enumerate at module load, so any positive width is taken + * at face value here. It is not unchecked: the caller reads the real width off + * `/api/show` (or, for a knowledge base, off the row it stores into), and the + * client's response validation rejects a model that returns anything else. + */ + if (info.provider === 'ollama') { + if (!Number.isInteger(requested) || requested <= 0) { + throw new Error(`${info.label} cannot produce ${requested}-dimensional output`) + } + return requested + } if (!info.supportedDimensions?.includes(requested)) { throw new Error( `${info.label} does not support ${requested}-dimensional output. Supported: ${ diff --git a/apps/sim/lib/embeddings/client.test.ts b/apps/sim/lib/embeddings/client.test.ts index 054a83c8568..68a918c19f9 100644 --- a/apps/sim/lib/embeddings/client.test.ts +++ b/apps/sim/lib/embeddings/client.test.ts @@ -102,6 +102,7 @@ beforeEach(() => { OPENAI_API_KEY_2: undefined, OPENAI_API_KEY_3: undefined, OPENROUTER_API_KEY: undefined, + OLLAMA_URL: undefined, }) }) @@ -1383,3 +1384,72 @@ describe('knowledge embedding transport fallback', () => { expect(isBYOKEmbeddingCredentialRejection(error)).toBe(false) }) }) + +describe('ollama embeddings', () => { + function ollamaBody(vectors: number[][], dimensions: number, promptEvalCount = 3) { + return { + embeddings: vectors.map((vector) => sizedVector(vector, dimensions)), + prompt_eval_count: promptEvalCount, + } + } + + it('embeds against the configured server with no credential and bills nothing', async () => { + setEnv({ OLLAMA_URL: 'http://ollama.internal:11434/' }) + fetchMock.mockResolvedValue(jsonResponse(ollamaBody([[1, 2, 3]], 768))) + + const result = await embed(['hello'], { + model: 'ollama/nomic-embed-text', + dimensions: 768, + projectInputs: null, + }) + + const [url, init] = fetchMock.mock.calls[0] + expect(url).toBe('http://ollama.internal:11434/api/embed') + expect((init as RequestInit).headers).not.toHaveProperty('Authorization') + expect(JSON.parse((init as RequestInit).body as string)).toEqual({ + model: 'nomic-embed-text', + input: ['hello'], + truncate: true, + }) + expect(result.dimensions).toBe(768) + expect(result.modelName).toBe('nomic-embed-text') + expect(result.isBYOK).toBe(true) + expect(result.billableTokens).toBe(0) + expect(result.totalTokens).toBe(3) + }) + + /** + * The width is the operator's to get right, so the failure has to name both + * numbers rather than storing a vector the knowledge base cannot query. + */ + it('rejects a model that returns a different width than the base stores', async () => { + setEnv({ OLLAMA_URL: 'http://ollama.internal:11434' }) + fetchMock.mockResolvedValue(jsonResponse(ollamaBody([[1, 2, 3]], 1024))) + + await expect( + embed(['hello'], { model: 'ollama/mxbai-embed-large', dimensions: 768, projectInputs: null }) + ).rejects.toThrow('has 1024 unexpected dimensions; expected 768') + }) + + it('refuses to fall back to the loopback default when OLLAMA_URL names no server', async () => { + await expect( + embed(['hello'], { model: 'ollama/nomic-embed-text', dimensions: 768, projectInputs: null }) + ).rejects.toThrow('OLLAMA_URL must be configured for Ollama embeddings') + expect(fetchMock).not.toHaveBeenCalled() + }) + + it('ignores a caller-supplied key rather than sending one Ollama cannot use', async () => { + setEnv({ OLLAMA_URL: 'http://ollama.internal:11434' }) + fetchMock.mockResolvedValue(jsonResponse(ollamaBody([[1, 2, 3]], 384))) + + await embed(['hello'], { + model: 'ollama/all-minilm', + dimensions: 384, + apiKey: 'sk-not-applicable', + projectInputs: null, + }) + + const [, init] = fetchMock.mock.calls[0] + expect((init as RequestInit).headers).toEqual({ 'Content-Type': 'application/json' }) + }) +}) diff --git a/apps/sim/lib/embeddings/client.ts b/apps/sim/lib/embeddings/client.ts index c9e0a623345..8ff7076bc2e 100644 --- a/apps/sim/lib/embeddings/client.ts +++ b/apps/sim/lib/embeddings/client.ts @@ -15,11 +15,13 @@ import { readResponseJsonWithLimit, readResponseTextWithLimit, } from '@/lib/core/utils/stream-limits' +import { getOllamaUrl, isOllamaUrlConfigured } from '@/lib/core/utils/urls' import { DEFAULT_EMBEDDING_MODEL, type EmbeddingModelInfo, getEmbeddingModelInfo, hasApproximateTokenCount, + ollamaEmbeddingModelName, resolveDimensions, } from '@/lib/embeddings/catalog' import { resolveProviderKey } from '@/lib/embeddings/keys' @@ -359,6 +361,37 @@ async function resolveProvider(model: string, options: EmbedOptions): Promise { @@ -993,6 +1037,17 @@ export async function embedKnowledgeForDeployment( isBYOK: false, } }, + /** + * Gemini and Ollama are declared on the capability because they serve + * knowledge embeddings, but never through this chain: it is built only for + * OpenAI models (the guard above returns for everything else), and the + * capability's own family gating marks them inactive here for the same + * reason. `wireFallback` throws if a provider it resolved as ready returns + * null, so this stays a loud failure rather than a silent wrong provider if + * either assumption ever stops holding. + */ + gemini: () => null, + ollama: () => null, } satisfies FallbackFactories const fallback = wireFallback({ diff --git a/apps/sim/lib/embeddings/index.ts b/apps/sim/lib/embeddings/index.ts index 9bd367954fa..ddc7babbd12 100644 --- a/apps/sim/lib/embeddings/index.ts +++ b/apps/sim/lib/embeddings/index.ts @@ -8,6 +8,7 @@ export { DEFAULT_MODEL_BY_PROVIDER, findEmbeddingModelInfo, resolveDimensions, + toOllamaEmbeddingModelId, } from '@/lib/embeddings/catalog' export { BYOK_EMBEDDING_CREDENTIAL_REJECTION_MESSAGE, diff --git a/apps/sim/lib/embeddings/keys.ts b/apps/sim/lib/embeddings/keys.ts index 98f591484ee..0121363afa1 100644 --- a/apps/sim/lib/embeddings/keys.ts +++ b/apps/sim/lib/embeddings/keys.ts @@ -3,7 +3,7 @@ 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 { KeyedEmbeddingProvider } from '@/lib/embeddings/types' import type { BYOKProviderId } from '@/tools/types' const logger = createLogger('EmbeddingKeys') @@ -28,7 +28,7 @@ interface ProviderKeyConfig { * 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> = { +const PROVIDER_KEY_CONFIG: Record ProviderKeyConfig> = { openai: () => ({ byokProviderId: BYOK_PROVIDER_IDS.openai, envKey: env.OPENAI_API_KEY, @@ -57,7 +57,7 @@ const PROVIDER_KEY_CONFIG: Record ProviderKeyCon } export async function resolveProviderKey( - provider: EmbeddingCatalogProvider, + provider: KeyedEmbeddingProvider, workspaceId?: string | null ): Promise { const config = PROVIDER_KEY_CONFIG[provider]() diff --git a/apps/sim/lib/embeddings/knowledge-embedding-family.test.ts b/apps/sim/lib/embeddings/knowledge-embedding-family.test.ts new file mode 100644 index 00000000000..a3119d7dc74 --- /dev/null +++ b/apps/sim/lib/embeddings/knowledge-embedding-family.test.ts @@ -0,0 +1,43 @@ +/** + * @vitest-environment node + */ +import { describe, expect, it } from 'vitest' +import { knowledgeEmbeddingFamily } from '@/lib/core/config/env-capabilities' +import { + DEFAULT_EMBEDDING_MODEL, + getEmbeddingModelInfo, + getKbEligibleModels, +} from '@/lib/embeddings/catalog' + +/** + * `knowledgeEmbeddingFamily` decides which credential the setup CLI and the + * capability status treat as serving knowledge embeddings. It lives in + * `@sim/deployment-config`, which cannot import the app's model catalog, so it + * classifies model ids by shape instead. These pin the two together: a catalog + * model whose family the classifier gets wrong would make a correctly + * configured deployment report as unconfigured, and an incorrectly configured + * one report as ready. + */ +describe('knowledgeEmbeddingFamily', () => { + it('agrees with the catalog for every model a knowledge base can be created with', () => { + for (const model of getKbEligibleModels()) { + expect(knowledgeEmbeddingFamily({ KB_EMBEDDING_MODEL: model }), model).toBe( + getEmbeddingModelInfo(model).provider + ) + } + }) + + it('classifies any model on the deployment’s own Ollama by its routing prefix', () => { + for (const model of ['ollama/nomic-embed-text', 'ollama/mxbai-embed-large:335m', 'OLLAMA/x']) { + expect(knowledgeEmbeddingFamily({ KB_EMBEDDING_MODEL: model }), model).toBe('ollama') + } + }) + + it('falls back to the family of the model an unset variable defaults to', () => { + const defaultFamily = getEmbeddingModelInfo(DEFAULT_EMBEDDING_MODEL).provider + expect(knowledgeEmbeddingFamily({})).toBe(defaultFamily) + expect(knowledgeEmbeddingFamily({ KB_EMBEDDING_MODEL: ' ' })).toBe(defaultFamily) + /** An unrecognised id falls back to the default model, which is OpenAI's. */ + expect(knowledgeEmbeddingFamily({ KB_EMBEDDING_MODEL: 'not-a-model' })).toBe(defaultFamily) + }) +}) diff --git a/apps/sim/lib/embeddings/ollama-model-catalog.server.test.ts b/apps/sim/lib/embeddings/ollama-model-catalog.server.test.ts new file mode 100644 index 00000000000..421de29f497 --- /dev/null +++ b/apps/sim/lib/embeddings/ollama-model-catalog.server.test.ts @@ -0,0 +1,152 @@ +/** + * @vitest-environment node + */ +import { resetEnvMock, setEnv } from '@sim/testing' +import { afterAll, afterEach, beforeEach, describe, expect, it, vi } from 'vitest' +import { + fetchOllamaEmbeddingModelCatalog, + getOllamaEmbeddingModelMetadata, + OllamaEmbeddingModelNotFoundError, + OllamaEmbeddingWidthUnknownError, +} from '@/lib/embeddings/ollama-model-catalog.server' + +const fetchMock = vi.fn() + +/** Mirrors an Ollama server: `/api/tags` lists everything, `/api/show` classifies it. */ +function serve( + models: Record }> +) { + fetchMock.mockImplementation((url: string, init?: RequestInit) => { + if (url.endsWith('/api/tags')) { + return Promise.resolve( + Response.json({ models: Object.keys(models).map((name) => ({ name })) }) + ) + } + const { model } = JSON.parse(String(init?.body)) as { model: string } + const detail = models[model] + if (!detail) return Promise.resolve(new Response('not found', { status: 404 })) + return Promise.resolve( + Response.json({ + ...(detail.capabilities ? { capabilities: detail.capabilities } : {}), + ...(detail.modelInfo ? { model_info: detail.modelInfo } : {}), + }) + ) + }) +} + +describe('Ollama embedding model catalog', () => { + beforeEach(() => { + vi.clearAllMocks() + vi.stubGlobal('fetch', fetchMock) + setEnv({ OLLAMA_URL: 'http://ollama.internal:11434' }) + }) + + afterEach(() => { + resetEnvMock() + }) + + afterAll(() => { + vi.unstubAllGlobals() + }) + + it('keeps embedding models with the width their architecture publishes', async () => { + serve({ + 'nomic-embed-text:latest': { + capabilities: ['embedding'], + modelInfo: { 'nomic-bert.embedding_length': 768, 'nomic-bert.block_count': 12 }, + }, + }) + + await expect(fetchOllamaEmbeddingModelCatalog()).resolves.toEqual([ + { id: 'nomic-embed-text:latest', dimensions: 768 }, + ]) + }) + + /** `/api/tags` lists chat and embedding models together and distinguishes neither. */ + it('drops a chat model, which the tags listing does not separate out', async () => { + serve({ + 'smollm2:135m': { + capabilities: ['completion'], + modelInfo: { 'llama.embedding_length': 576 }, + }, + 'all-minilm:latest': { + capabilities: ['embedding'], + modelInfo: { 'bert.embedding_length': 384 }, + }, + }) + + await expect(fetchOllamaEmbeddingModelCatalog()).resolves.toEqual([ + { id: 'all-minilm:latest', dimensions: 384 }, + ]) + }) + + it('keeps a model from a server too old to report capabilities', async () => { + serve({ 'legacy-embed': { modelInfo: { 'bert.embedding_length': 512 } } }) + + await expect(fetchOllamaEmbeddingModelCatalog()).resolves.toEqual([ + { id: 'legacy-embed', dimensions: 512 }, + ]) + }) + + it('keeps the rest of the catalog when one model cannot be inspected', async () => { + serve({ + broken: { capabilities: ['embedding'] }, + 'all-minilm:latest': { + capabilities: ['embedding'], + modelInfo: { 'bert.embedding_length': 384 }, + }, + }) + fetchMock.mockImplementationOnce(() => + Promise.resolve( + Response.json({ models: [{ name: 'broken' }, { name: 'all-minilm:latest' }] }) + ) + ) + + const catalog = await fetchOllamaEmbeddingModelCatalog() + expect(catalog).toContainEqual({ id: 'all-minilm:latest', dimensions: 384 }) + }) + + it('offers nothing rather than throwing when no Ollama answers', async () => { + fetchMock.mockRejectedValue(new Error('ECONNREFUSED')) + + await expect(fetchOllamaEmbeddingModelCatalog()).resolves.toEqual([]) + }) + + it('resolves a bare name against the tag Ollama installed it under', async () => { + serve({ + 'all-minilm:latest': { + capabilities: ['embedding'], + modelInfo: { 'bert.embedding_length': 384 }, + }, + }) + + await expect(getOllamaEmbeddingModelMetadata('all-minilm')).resolves.toEqual({ + id: 'all-minilm:latest', + dimensions: 384, + }) + await expect(getOllamaEmbeddingModelMetadata('ollama/all-minilm')).resolves.toEqual({ + id: 'all-minilm:latest', + dimensions: 384, + }) + }) + + it('rejects a model that is not installed', async () => { + serve({}) + + await expect(getOllamaEmbeddingModelMetadata('missing')).rejects.toBeInstanceOf( + OllamaEmbeddingModelNotFoundError + ) + }) + + /** + * The width is what the client validates the response against, so a model + * whose width cannot be read is refused rather than embedded unchecked. + */ + it('refuses a model whose width Ollama does not report', async () => { + serve({ 'widthless:latest': { capabilities: ['embedding'] } }) + + await expect(getOllamaEmbeddingModelMetadata('widthless')).rejects.toBeInstanceOf( + OllamaEmbeddingWidthUnknownError + ) + }) +}) diff --git a/apps/sim/lib/embeddings/ollama-model-catalog.server.ts b/apps/sim/lib/embeddings/ollama-model-catalog.server.ts new file mode 100644 index 00000000000..cf706258514 --- /dev/null +++ b/apps/sim/lib/embeddings/ollama-model-catalog.server.ts @@ -0,0 +1,160 @@ +import { createLogger } from '@sim/logger' +import { getErrorMessage } from '@sim/utils/errors' +import { + ollamaShowUpstreamResponseSchema, + ollamaUpstreamResponseSchema, +} from '@/lib/api/contracts/providers' +import { isHosted } from '@/lib/core/config/env-flags' +import { mapWithConcurrency } from '@/lib/core/utils/concurrency' +import { readResponseJsonWithLimit } from '@/lib/core/utils/stream-limits' +import { getOllamaUrl, isOllamaUrlConfigured } from '@/lib/core/utils/urls' +import { isOllamaEmbeddingModel, ollamaEmbeddingModelName } from '@/lib/embeddings/catalog' + +const logger = createLogger('OllamaEmbeddingCatalog') + +const MAX_OLLAMA_CATALOG_BYTES = 4 * 1024 * 1024 + +/** `/api/show` is one request per installed model, so the fan-out is bounded. */ +const OLLAMA_SHOW_CONCURRENCY = 6 + +/** Suffix every architecture uses for the width it emits (`bert.embedding_length`). */ +const EMBEDDING_LENGTH_SUFFIX = '.embedding_length' + +export interface OllamaEmbeddingModel { + /** Name as Ollama lists it, tag included (`nomic-embed-text:latest`). */ + id: string + /** Width the model emits, when its architecture publishes one. */ + dimensions?: number +} + +export class OllamaEmbeddingModelNotFoundError extends Error { + constructor(model: string) { + super( + `Unsupported Ollama embedding model: ${model}. Pull it on the configured Ollama server first.` + ) + this.name = 'OllamaEmbeddingModelNotFoundError' + } +} + +export class OllamaEmbeddingWidthUnknownError extends Error { + constructor(model: string) { + super( + `Ollama did not report an embedding width for ${model}, so Sim cannot verify what it returns. Upgrade Ollama or choose another model.` + ) + this.name = 'OllamaEmbeddingWidthUnknownError' + } +} + +async function fetchOllamaJson(path: string, init: RequestInit, signal?: AbortSignal) { + const response = await fetch(`${getOllamaUrl().replace(/\/+$/, '')}${path}`, { + headers: { 'Content-Type': 'application/json' }, + signal, + ...init, + }) + if (!response.ok) { + throw new Error(`Ollama ${path} failed: ${response.status} ${response.statusText}`) + } + return readResponseJsonWithLimit(response, { + maxBytes: MAX_OLLAMA_CATALOG_BYTES, + label: `Ollama ${path} response`, + signal, + }) +} + +function readEmbeddingLength(modelInfo: Record | undefined): number | undefined { + if (!modelInfo) return undefined + for (const [key, value] of Object.entries(modelInfo)) { + if (!key.endsWith(EMBEDDING_LENGTH_SUFFIX)) continue + if (typeof value === 'number' && Number.isInteger(value) && value > 0) return value + } + return undefined +} + +/** + * Embedding-capable models installed on the deployment's Ollama, with the width + * each one emits. + * + * `/api/tags` lists chat and embedding models together and distinguishes + * neither, so every entry is resolved through `/api/show`, whose `capabilities` + * is the only thing that says which is which. A model that reports no + * capabilities at all (Ollama older than 0.5) is kept rather than dropped: + * hiding a usable model on an older server is worse than offering one the user + * has to recognise as a chat model. + * + * Returns an empty list rather than throwing when no Ollama is configured or + * reachable, mirroring `/api/providers/ollama/models` — an absent Ollama is a + * normal deployment shape, not a failure of this lookup. + */ +export async function fetchOllamaEmbeddingModelCatalog( + signal?: AbortSignal +): Promise { + /** + * Hosted Sim runs no Ollama, and the loopback default cannot answer there, so + * an unconfigured hosted deployment is not dialled at all. An explicit + * `OLLAMA_URL` states an intent to reach a real server and is still honoured. + */ + if (isHosted && !isOllamaUrlConfigured()) return [] + + let names: string[] + try { + const tags = ollamaUpstreamResponseSchema.parse( + await fetchOllamaJson('/api/tags', { method: 'GET' }, signal) + ) + names = tags.models.map((model) => model.name) + } catch (error) { + signal?.throwIfAborted() + logger.info('Ollama is not reachable; offering no embedding models', { + error: getErrorMessage(error, 'Unknown error'), + }) + return [] + } + + const resolved = await mapWithConcurrency(names, OLLAMA_SHOW_CONCURRENCY, async (name) => { + try { + const detail = ollamaShowUpstreamResponseSchema.parse( + await fetchOllamaJson( + '/api/show', + { method: 'POST', body: JSON.stringify({ model: name }) }, + signal + ) + ) + if (detail.capabilities && !detail.capabilities.includes('embedding')) return null + const dimensions = readEmbeddingLength(detail.model_info) + return { id: name, ...(dimensions !== undefined ? { dimensions } : {}) } + } catch (error) { + signal?.throwIfAborted() + /** One unreadable model must not hide the rest of the server's catalog. */ + logger.info('Skipping an Ollama model that could not be inspected', { + model: name, + error: getErrorMessage(error, 'Unknown error'), + }) + return null + } + }) + + return resolved.filter((model): model is OllamaEmbeddingModel => model !== null) +} + +/** + * Resolves one selected model against the server's live catalog, the way the + * OpenRouter tool resolves a model against OpenRouter's. + * + * The width is required rather than optional because it is what the client + * validates the response against. Without it a model that quietly returns a + * different size than the caller expects would be indistinguishable from a good + * one — the exact failure the knowledge-base path names both numbers for. + */ +export async function getOllamaEmbeddingModelMetadata( + model: string, + signal?: AbortSignal +): Promise> { + const name = isOllamaEmbeddingModel(model) ? ollamaEmbeddingModelName(model) : model + const catalog = await fetchOllamaEmbeddingModelCatalog(signal) + /** Ollama resolves a bare name to its `:latest` tag, so both spellings match. */ + const metadata = catalog.find( + (candidate) => candidate.id === name || candidate.id === `${name}:latest` + ) + if (!metadata) throw new OllamaEmbeddingModelNotFoundError(name) + if (metadata.dimensions === undefined) throw new OllamaEmbeddingWidthUnknownError(name) + return { id: metadata.id, dimensions: metadata.dimensions } +} diff --git a/apps/sim/lib/embeddings/providers/index.ts b/apps/sim/lib/embeddings/providers/index.ts index 17a46b1f6f8..46314ab7d81 100644 --- a/apps/sim/lib/embeddings/providers/index.ts +++ b/apps/sim/lib/embeddings/providers/index.ts @@ -2,18 +2,26 @@ import { createAzureOpenAIAdapter } from '@/lib/embeddings/providers/azure-opena import { createCohereAdapter } from '@/lib/embeddings/providers/cohere' import { createGeminiAdapter } from '@/lib/embeddings/providers/gemini' import { createMistralAdapter } from '@/lib/embeddings/providers/mistral' +import { createOllamaAdapter } from '@/lib/embeddings/providers/ollama' import { createOpenAIAdapter } from '@/lib/embeddings/providers/openai' import { createOpenRouterAdapter } from '@/lib/embeddings/providers/openrouter' import type { AzureEmbeddingAdapterContext, EmbeddingAdapterFactory, EmbeddingProviderKind, + OllamaEmbeddingAdapterContext, } from '@/lib/embeddings/types' -/** Azure's entry keeps its own context type so its routing fields stay required. */ +/** + * Azure and Ollama keep their own context types so their routing fields stay + * required and, for Ollama, so no caller can hand it a credential it has no way + * to send. + */ type AdapterFactoryFor = K extends 'azure-openai' ? EmbeddingAdapterFactory - : EmbeddingAdapterFactory + : K extends 'ollama' + ? EmbeddingAdapterFactory + : EmbeddingAdapterFactory const ADAPTER_FACTORIES: { [K in EmbeddingProviderKind]: AdapterFactoryFor } = { openai: createOpenAIAdapter, @@ -22,6 +30,7 @@ const ADAPTER_FACTORIES: { [K in EmbeddingProviderKind]: AdapterFactoryFor } gemini: createGeminiAdapter, cohere: createCohereAdapter, mistral: createMistralAdapter, + ollama: createOllamaAdapter, } export function getAdapterFactory( @@ -35,6 +44,7 @@ export { createCohereAdapter, createGeminiAdapter, createMistralAdapter, + createOllamaAdapter, createOpenAIAdapter, createOpenRouterAdapter, } diff --git a/apps/sim/lib/embeddings/providers/ollama.ts b/apps/sim/lib/embeddings/providers/ollama.ts new file mode 100644 index 00000000000..5aedaa082bc --- /dev/null +++ b/apps/sim/lib/embeddings/providers/ollama.ts @@ -0,0 +1,52 @@ +import type { EmbeddingAdapterFactory, OllamaEmbeddingAdapterContext } from '@/lib/embeddings/types' + +/** + * Ollama documents no cap on `/api/embed`, but it embeds a batch on one local + * model serially, so a large batch is a long single request against the + * client's 60s timeout. Capping items keeps a slow local model's failures cheap + * to retry — the same reason the shared batch token target sits below every + * provider's documented maximum. + */ +const OLLAMA_MAX_ITEMS_PER_REQUEST = 64 + +interface OllamaEmbeddingResponse { + embeddings: number[][] + /** Ollama reports the tokens it actually embedded, after any truncation. */ + prompt_eval_count?: number +} + +/** + * Ollama `/api/embed` on the deployment's own server. Takes no credential: + * Ollama exposes no authentication, so the server URL is the whole trust + * boundary. + * + * `dimensions` is deliberately not forwarded. Only recent Ollama builds accept + * it, and only for Matryoshka-capable models; an older server ignores unknown + * fields rather than rejecting them, so sending it would silently produce + * native-width vectors passing as reduced ones. The requested width is enforced + * by the client's response validation instead, which names both the width asked + * for and the width returned. + */ +export const createOllamaAdapter: EmbeddingAdapterFactory = ({ + modelName, + baseUrl, +}) => ({ + maxItemsPerRequest: OLLAMA_MAX_ITEMS_PER_REQUEST, + buildRequest: ({ inputs }) => ({ + apiUrl: `${baseUrl}/api/embed`, + headers: { 'Content-Type': 'application/json' }, + body: { + model: modelName, + input: inputs, + /** + * Sim cannot know a local model's context length, so it holds inputs to a + * common upper figure and lets Ollama shorten anything the loaded model + * still cannot fit. Without this, a model with a small context rejects + * the whole batch. + */ + truncate: true, + }, + parse: (json) => (json as OllamaEmbeddingResponse).embeddings, + parseTokens: (json) => (json as OllamaEmbeddingResponse).prompt_eval_count, + }), +}) diff --git a/apps/sim/lib/embeddings/providers/providers.test.ts b/apps/sim/lib/embeddings/providers/providers.test.ts index e2382560bb7..2713bc9fc22 100644 --- a/apps/sim/lib/embeddings/providers/providers.test.ts +++ b/apps/sim/lib/embeddings/providers/providers.test.ts @@ -7,6 +7,7 @@ import { createCohereAdapter, createGeminiAdapter, createMistralAdapter, + createOllamaAdapter, createOpenAIAdapter, createOpenRouterAdapter, } from '@/lib/embeddings/providers' @@ -223,3 +224,43 @@ describe('Mistral adapter', () => { expect(request.parseTokens?.(json)).toBe(4) }) }) + +describe('Ollama adapter', () => { + const adapter = createOllamaAdapter({ + modelName: 'nomic-embed-text', + baseUrl: 'http://ollama.internal:11434', + nativeDimensions: 1536, + }) + + it('posts the batch to the configured server with no credential header', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + expect(request.apiUrl).toBe('http://ollama.internal:11434/api/embed') + expect(request.headers).toEqual({ 'Content-Type': 'application/json' }) + expect(request.body).toEqual({ + model: 'nomic-embed-text', + input: INPUTS, + truncate: true, + }) + }) + + it('never forwards dimensions, which older servers ignore rather than reject', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document', dimensions: 768 }) + expect(request.body).not.toHaveProperty('dimensions') + }) + + it('parses vectors and the tokens Ollama reports embedding', () => { + const request = adapter.buildRequest({ inputs: INPUTS, taskType: 'document' }) + const json = { + embeddings: [ + [1, 2], + [3, 4], + ], + prompt_eval_count: 9, + } + expect(request.parse(json)).toEqual([ + [1, 2], + [3, 4], + ]) + expect(request.parseTokens?.(json)).toBe(9) + }) +}) diff --git a/apps/sim/lib/embeddings/types.ts b/apps/sim/lib/embeddings/types.ts index f7b6b647110..3efd6c20b74 100644 --- a/apps/sim/lib/embeddings/types.ts +++ b/apps/sim/lib/embeddings/types.ts @@ -11,6 +11,7 @@ export type EmbeddingProviderKind = | 'gemini' | 'cohere' | 'mistral' + | 'ollama' /** * Providers a catalog model can belong to. Azure OpenAI and OpenRouter are @@ -18,8 +19,15 @@ export type EmbeddingProviderKind = */ export type EmbeddingCatalogProvider = Exclude +/** + * Catalog providers reached with an API key. Ollama is a self-hosted server + * addressed by URL and takes no credential, so every key-resolution table is + * keyed by this narrower type rather than carrying a meaningless Ollama entry. + */ +export type KeyedEmbeddingProvider = Exclude + /** Provider id for `estimateTokenCount` so token counts match the embedding provider's tokenization. */ -export type TokenizerProviderId = 'openai' | 'google' | 'cohere' | 'mistral' +export type TokenizerProviderId = 'openai' | 'google' | 'cohere' | 'mistral' | 'ollama' /** * What the embedding will be used for. Providers that support task-conditioned @@ -55,14 +63,29 @@ export interface EmbeddingProviderAdapter { maxItemsPerRequest?: number } -export interface EmbeddingAdapterContext { +/** What every adapter needs regardless of how its provider is reached. */ +export interface EmbeddingAdapterIdentity { /** 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 } +export interface EmbeddingAdapterContext extends EmbeddingAdapterIdentity { + apiKey: string +} + +/** + * Ollama is addressed by server URL and authenticates with nothing, so it needs + * routing fields no other provider takes and holds no credential at all. + * Declared as its own context rather than as an optional `baseUrl` on the shared + * one, so the adapter cannot be constructed without a server to talk to. + */ +export interface OllamaEmbeddingAdapterContext extends EmbeddingAdapterIdentity { + /** Origin of the Ollama server, without a trailing slash. */ + baseUrl: string +} + /** * 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 @@ -74,8 +97,9 @@ export interface AzureEmbeddingAdapterContext extends EmbeddingAdapterContext { apiVersion: string } -export type EmbeddingAdapterFactory = - (context: Ctx) => EmbeddingProviderAdapter +export type EmbeddingAdapterFactory< + Ctx extends EmbeddingAdapterIdentity = EmbeddingAdapterContext, +> = (context: Ctx) => EmbeddingProviderAdapter export interface EmbedOptions { /** Cancels provider requests, retry waits, and remaining batches. */ diff --git a/apps/sim/lib/internal/embeddings/execute-tool.test.ts b/apps/sim/lib/internal/embeddings/execute-tool.test.ts index 66524f6d630..63d5e09e4ff 100644 --- a/apps/sim/lib/internal/embeddings/execute-tool.test.ts +++ b/apps/sim/lib/internal/embeddings/execute-tool.test.ts @@ -9,7 +9,11 @@ vi.mock('@/lib/internal/embeddings/operations', () => ({ executeEmbedding: mockExecuteEmbedding, })) -import { executeEmbeddingsTool } from '@/lib/internal/embeddings/execute-tool' +import { + EMBEDDINGS_TOOL_PROVIDERS, + executeEmbeddingsTool, +} from '@/lib/internal/embeddings/execute-tool' +import { getRegisteredInternalToolOperationIds } from '@/lib/internal/tool-operations/registry.server' function request(input: unknown, overrides: Record = {}) { return { @@ -75,4 +79,35 @@ describe('executeEmbeddingsTool', () => { expect(response.status).toBe(400) expect(mockExecuteEmbedding).not.toHaveBeenCalled() }) + + /** + * Registering a tool id with the operation registry is only half the wiring: + * without an entry here the handler rejects its own tool as unsupported, and + * nothing but a call would say so. + */ + it('maps every embeddings tool the operation registry routes to this handler', () => { + const registered = getRegisteredInternalToolOperationIds().filter((id) => + Object.hasOwn(EMBEDDINGS_TOOL_PROVIDERS, id) + ) + const routedHere = getRegisteredInternalToolOperationIds().filter( + (id) => id.startsWith('embeddings_') || id === 'openai_embeddings' + ) + + expect(registered.sort()).toEqual(routedHere.sort()) + }) + + it('runs Ollama without a credential the schema does not declare', async () => { + const response = await executeEmbeddingsTool( + request( + { provider: 'ollama', model: 'nomic-embed-text', input: 'hello' }, + { toolId: 'embeddings_ollama' } + ) + ) + + expect(response.status).toBe(200) + expect(mockExecuteEmbedding).toHaveBeenCalledWith( + expect.objectContaining({ provider: 'ollama', model: 'nomic-embed-text' }), + expect.any(Object) + ) + }) }) diff --git a/apps/sim/lib/internal/embeddings/execute-tool.ts b/apps/sim/lib/internal/embeddings/execute-tool.ts index ca400dbf556..f0cbcf05b9e 100644 --- a/apps/sim/lib/internal/embeddings/execute-tool.ts +++ b/apps/sim/lib/internal/embeddings/execute-tool.ts @@ -4,6 +4,11 @@ import { executeEmbedding } from '@/lib/internal/embeddings/operations' import { type EmbeddingProvider, embeddingsInputSchema } from '@/lib/internal/embeddings/schema' import type { InternalToolOperationHandler } from '@/lib/internal/tool-operations/types' +/** + * Every tool id the embeddings family registers must appear here, or its + * handler rejects the call as an unsupported tool. `execute-tool.test.ts` pins + * this map to the registry's family list so the two cannot drift. + */ const PROVIDERS_BY_TOOL_ID: Record = { openai_embeddings: 'openai', embeddings_openai: 'openai', @@ -11,8 +16,13 @@ const PROVIDERS_BY_TOOL_ID: Record = { embeddings_gemini: 'gemini', embeddings_cohere: 'cohere', embeddings_mistral: 'mistral', + embeddings_ollama: 'ollama', } +/** @internal Exported so the registry's family list can be pinned against it. */ +export const EMBEDDINGS_TOOL_PROVIDERS: Readonly> = + PROVIDERS_BY_TOOL_ID + export const executeEmbeddingsTool: InternalToolOperationHandler = async (request) => { request.signal?.throwIfAborted() if (!request.context.userId) { diff --git a/apps/sim/lib/internal/embeddings/operations.ts b/apps/sim/lib/internal/embeddings/operations.ts index 8154b1da469..4b21fb42f49 100644 --- a/apps/sim/lib/internal/embeddings/operations.ts +++ b/apps/sim/lib/internal/embeddings/operations.ts @@ -8,7 +8,13 @@ import { embedOpenRouter, findEmbeddingModelInfo, resolveDimensions, + toOllamaEmbeddingModelId, } from '@/lib/embeddings' +import { + getOllamaEmbeddingModelMetadata, + OllamaEmbeddingModelNotFoundError, + OllamaEmbeddingWidthUnknownError, +} from '@/lib/embeddings/ollama-model-catalog.server' import { getOpenRouterEmbeddingModelMetadata, type OpenRouterEmbeddingModelMetadata, @@ -48,7 +54,9 @@ export async function executeEmbedding( context: EmbeddingOperationContext ): Promise { context.signal?.throwIfAborted() - const { provider, apiKey, model, taskType, dimensions } = input + const { provider, model, taskType, dimensions } = input + /** Ollama takes no credential, so its input variant declares no `apiKey` at all. */ + const apiKey = provider === 'ollama' ? undefined : input.apiKey const texts = normalizeEmbeddingInput(input.input) if (texts.length === 0) return failureResponse('input must contain at least one text', 400) if (texts.length > MAX_EMBEDDING_INPUTS) { @@ -70,7 +78,33 @@ export async function executeEmbedding( let resolvedModel: string let openRouterModelMetadata: OpenRouterEmbeddingModelMetadata | undefined - if (provider === 'openrouter') { + /** + * Width Ollama reports for the selected model. Resolved from the server rather + * than requested by the caller: a local model's width is a property of what + * the operator pulled, and it is what the client validates the response + * against. + */ + let ollamaDimensions: number | undefined + if (provider === 'ollama') { + try { + resolvedModel = toOllamaEmbeddingModelId(model) + } catch (error) { + return failureResponse(getErrorMessage(error, 'Invalid Ollama embedding model'), 400) + } + try { + ollamaDimensions = (await getOllamaEmbeddingModelMetadata(resolvedModel, context.signal)) + .dimensions + } catch (error) { + context.signal?.throwIfAborted() + const userError = + error instanceof OllamaEmbeddingModelNotFoundError || + error instanceof OllamaEmbeddingWidthUnknownError + return failureResponse( + getErrorMessage(error, 'Failed to load Ollama embedding model metadata'), + userError ? 400 : 502 + ) + } + } else if (provider === 'openrouter') { try { resolvedModel = normalizeOpenRouterEmbeddingModelId( model || DEFAULT_OPENROUTER_EMBEDDING_MODEL @@ -110,13 +144,14 @@ export async function executeEmbedding( ) } try { - resolveDimensions(info, dimensions) + resolveDimensions(info, ollamaDimensions ?? dimensions) } catch (error) { return failureResponse(getErrorMessage(error, 'Invalid dimensions'), 400) } } - logger.info(`Embedding ${texts.length} input(s) with ${provider}/${resolvedModel}`) + /** `resolvedModel` already carries a routing prefix for the providers that use one. */ + logger.info(`Embedding ${texts.length} input(s)`, { provider, model: resolvedModel }) try { let result: EmbedResult if (provider === 'openrouter') { @@ -126,7 +161,7 @@ export async function executeEmbedding( result = await embedOpenRouter(texts, { model: resolvedModel, dimensions, - apiKey, + apiKey: input.apiKey, maxInputTokens: openRouterModelMetadata.maxInputTokens, projectInputs: null, signal: context.signal, @@ -135,7 +170,12 @@ export async function executeEmbedding( result = await embed(texts, { model: resolvedModel, taskType, - dimensions, + /** + * Ollama's width comes from the server, never from the caller: the + * adapter cannot ask for a reduction, so a requested size could only + * ever be an assertion, and the server already knows the answer. + */ + dimensions: ollamaDimensions ?? dimensions, apiKey, projectInputs: null, signal: context.signal, diff --git a/apps/sim/lib/internal/embeddings/schema.ts b/apps/sim/lib/internal/embeddings/schema.ts index ab37064c629..e5bbe7f6a8c 100644 --- a/apps/sim/lib/internal/embeddings/schema.ts +++ b/apps/sim/lib/internal/embeddings/schema.ts @@ -9,6 +9,7 @@ export const embeddingProviders = [ 'gemini', 'cohere', 'mistral', + 'ollama', ] as const satisfies readonly EmbeddingToolProvider[] export const embeddingTaskTypes = [ @@ -65,6 +66,18 @@ export const embeddingsInputSchema = z.discriminatedUnion('provider', [ provider: z.literal('openrouter'), apiKey: z.string({ error: 'apiKey is required' }).min(1, 'apiKey cannot be empty'), }), + /** + * Ollama is reached at `OLLAMA_URL` and authenticates with nothing, so this + * variant carries no `apiKey` at all rather than an optional one — there is no + * credential a caller could meaningfully supply. `model` is required because + * the server's catalog is whatever the operator pulled, so there is no default + * Sim could name. + */ + z.object({ + ...commonShape, + provider: z.literal('ollama'), + model: z.string().min(1, 'model cannot be empty'), + }), ]) export type EmbeddingsInput = z.output diff --git a/apps/sim/lib/internal/tool-operations/registry.server.ts b/apps/sim/lib/internal/tool-operations/registry.server.ts index 45bb6cb5cd4..765eb6678cb 100644 --- a/apps/sim/lib/internal/tool-operations/registry.server.ts +++ b/apps/sim/lib/internal/tool-operations/registry.server.ts @@ -1225,6 +1225,7 @@ const EMBEDDINGS_TOOL_IDS = [ 'embeddings_gemini', 'embeddings_cohere', 'embeddings_mistral', + 'embeddings_ollama', 'openai_embeddings', ] as const diff --git a/apps/sim/lib/knowledge/application/knowledge-bases.test.ts b/apps/sim/lib/knowledge/application/knowledge-bases.test.ts index 5f44cae43a1..bead72bf04f 100644 --- a/apps/sim/lib/knowledge/application/knowledge-bases.test.ts +++ b/apps/sim/lib/knowledge/application/knowledge-bases.test.ts @@ -76,8 +76,7 @@ vi.mock('@/lib/knowledge/application/folder-paths', () => ({ })) vi.mock('@/lib/knowledge/embeddings', () => ({ - EMBEDDING_DIMENSIONS: 1536, - getConfiguredEmbeddingModel: () => 'text-embedding-3-small', + getConfiguredKbEmbedding: () => ({ model: 'text-embedding-3-small', dimensions: 1536 }), })) vi.mock('@/lib/knowledge/service', () => ({ diff --git a/apps/sim/lib/knowledge/application/knowledge-bases.ts b/apps/sim/lib/knowledge/application/knowledge-bases.ts index 95d5f072452..53169fe1dbb 100644 --- a/apps/sim/lib/knowledge/application/knowledge-bases.ts +++ b/apps/sim/lib/knowledge/application/knowledge-bases.ts @@ -45,7 +45,7 @@ import { DEFAULT_CHUNKING_CONFIG, MAX_KNOWLEDGE_FOLDERS_PER_WORKSPACE, } from '@/lib/knowledge/constants' -import { EMBEDDING_DIMENSIONS, getConfiguredEmbeddingModel } from '@/lib/knowledge/embeddings' +import { getConfiguredKbEmbedding } from '@/lib/knowledge/embeddings' import { getRestorableKnowledgeBase, performDeleteKnowledgeBase, @@ -315,6 +315,7 @@ async function executeCreateKnowledgeBase(args: { ...DEFAULT_CHUNKING_CONFIG, ...args.input.chunkingConfig, } + const { model: embeddingModel, dimensions: embeddingDimension } = getConfiguredKbEmbedding() const knowledgeBase = await createAuthorizedKnowledgeBase( { name: args.input.name, @@ -322,8 +323,8 @@ async function executeCreateKnowledgeBase(args: { workspaceId: args.context.workspaceId, folderId, userId: resolveKnowledgeAttributedUserId(args.principal, args.context), - embeddingModel: getConfiguredEmbeddingModel(), - embeddingDimension: EMBEDDING_DIMENSIONS, + embeddingModel, + embeddingDimension, chunkingConfig, }, generateRequestId() diff --git a/apps/sim/lib/knowledge/application/search.test.ts b/apps/sim/lib/knowledge/application/search.test.ts index e5566f681bc..f5afce0d3fa 100644 --- a/apps/sim/lib/knowledge/application/search.test.ts +++ b/apps/sim/lib/knowledge/application/search.test.ts @@ -96,6 +96,7 @@ const knowledgeBase = { name: 'Docs', workspaceId: 'workspace-1', embeddingModel: 'text-embedding-3-small', + embeddingDimension: 1536, } describe('knowledge search application use case', () => { diff --git a/apps/sim/lib/knowledge/application/search.ts b/apps/sim/lib/knowledge/application/search.ts index ae87287303b..d35b0a2be41 100644 --- a/apps/sim/lib/knowledge/application/search.ts +++ b/apps/sim/lib/knowledge/application/search.ts @@ -34,8 +34,8 @@ import { } from '@/lib/knowledge/application/contexts' import { knowledgeOperations } from '@/lib/knowledge/application/operations' import { ALL_TAG_SLOTS } from '@/lib/knowledge/constants' -import { getEmbeddingModelInfo } from '@/lib/knowledge/embedding-models' -import { generateSearchEmbedding } from '@/lib/knowledge/embeddings' +import { getEmbeddingModelInfo, toKbEmbeddingDimensions } from '@/lib/knowledge/embedding-models' +import { generateSearchEmbedding, type KbEmbeddingTarget } from '@/lib/knowledge/embeddings' import { runWithKnowledgeModelInputProvenance } from '@/lib/knowledge/model-input-provenance' import { rerank } from '@/lib/knowledge/reranker' import type { RerankerStatus } from '@/lib/knowledge/reranker-models' @@ -274,21 +274,33 @@ export const searchKnowledge = defineAuthorizedKnowledgeUseCase({ definitionsByKnowledgeBase = built.definitionsByKnowledgeBase } - const embeddingModels = [...new Set(context.knowledgeBases.map((kb) => kb.embeddingModel))] - if (hasQuery && embeddingModels.length > 1) { + /** + * One query embedding serves every leg, so every base in the request has to + * be indexed the same way. The width is part of that: it selects the + * pgvector column each comparison reads, and two bases on the same model at + * different widths still live in different columns. + */ + const embeddingTargets = new Map( + context.knowledgeBases.map((kb) => [ + `${kb.embeddingModel}:${kb.embeddingDimension}`, + { model: kb.embeddingModel, dimensions: toKbEmbeddingDimensions(kb.embeddingDimension) }, + ]) + ) + if (hasQuery && embeddingTargets.size > 1) { throw new OrchestrationError( 'validation', - 'Selected knowledge bases use different embedding models and cannot be searched together. Search them separately.' + 'Selected knowledge bases use different embedding models or vector widths and cannot be searched together. Search them separately.' ) } - const embeddingModel = embeddingModels[0] + const embeddingTarget = [...embeddingTargets.values()][0] + const embeddingModel = embeddingTarget.model const preparedRegistry = input.prepareModelInputProvenance ? await input.prepareModelInputProvenance({ userId, workspaceId: context.workspaceId }) : undefined const resultSecretRegistry = preparedRegistry ?? input.resultSecretRegistry const queryEmbeddingPromise = hasQuery ? runWithKnowledgeModelInputProvenance(resultSecretRegistry, () => - generateSearchEmbedding(input.query!, embeddingModel, context.workspaceId) + generateSearchEmbedding(input.query!, embeddingTarget, context.workspaceId) ) : Promise.resolve(null) /** Resolved alongside the embedding call; both are needed before the first leg runs. */ @@ -317,7 +329,10 @@ export const searchKnowledge = defineAuthorizedKnowledgeUseCase({ boostRecency: searchDefaults.boostRecency, query: input.query, queryVector: hasQuery - ? JSON.stringify((await queryEmbeddingPromise)?.embedding ?? null) + ? { + vector: JSON.stringify((await queryEmbeddingPromise)?.embedding ?? null), + dimensions: embeddingTarget.dimensions, + } : undefined, structuredFilters: structuredFilters.length > 0 ? structuredFilters : undefined, }) diff --git a/apps/sim/lib/knowledge/chunks/service.ts b/apps/sim/lib/knowledge/chunks/service.ts index 6016be05265..fb19d830cbf 100644 --- a/apps/sim/lib/knowledge/chunks/service.ts +++ b/apps/sim/lib/knowledge/chunks/service.ts @@ -25,9 +25,10 @@ import type { ChunkSortBy, CreateChunkData, } from '@/lib/knowledge/chunks/types' -import { getEmbeddingModelInfo } from '@/lib/knowledge/embedding-models' -import { generateEmbeddings } from '@/lib/knowledge/embeddings' +import { getEmbeddingModelInfo, toKbEmbeddingDimensions } from '@/lib/knowledge/embedding-models' +import { generateEmbeddings, type KbEmbeddingTarget } from '@/lib/knowledge/embeddings' import { replaceKnowledgeEmbeddingSecretProvenanceInTx } from '@/lib/knowledge/secret-provenance' +import { embeddingVectorValues } from '@/lib/knowledge/vector-columns' import { estimateTokenCount } from '@/lib/tokenization/estimators' const logger = createLogger('ChunksService') @@ -196,19 +197,22 @@ export async function createChunk( ): Promise { logger.info(`[${requestId}] Generating embedding for manual chunk`) const kbRow = await db - .select({ embeddingModel: knowledgeBase.embeddingModel }) + .select({ + embeddingModel: knowledgeBase.embeddingModel, + embeddingDimension: knowledgeBase.embeddingDimension, + }) .from(knowledgeBase) .where(and(eq(knowledgeBase.id, knowledgeBaseId), isNull(knowledgeBase.deletedAt))) .limit(1) if (kbRow.length === 0) { throw new Error('Knowledge base not found') } - const kbEmbeddingModel = kbRow[0].embeddingModel - const { embeddings } = await generateEmbeddings( - [chunkData.content], - kbEmbeddingModel, - workspaceId - ) + const kbEmbedding: KbEmbeddingTarget = { + model: kbRow[0].embeddingModel, + dimensions: toKbEmbeddingDimensions(kbRow[0].embeddingDimension), + } + const kbEmbeddingModel = kbEmbedding.model + const { embeddings } = await generateEmbeddings([chunkData.content], kbEmbedding, workspaceId) const tokenCount = estimateTokenCount( chunkData.content, @@ -264,7 +268,7 @@ export async function createChunk( secretProvenanceVersion: secretProvenance ? 1 : null, contentLength: chunkData.content.length, tokenCount: tokenCount.count, - embedding: embeddings[0], + ...embeddingVectorValues(kbEmbedding.dimensions, embeddings[0]), embeddingModel: kbEmbeddingModel, startOffset: 0, // Manual chunks don't have document offsets endOffset: chunkData.content.length, @@ -458,26 +462,36 @@ export async function updateChunk( // The embedding is a function of the new content alone, so generating it // outside the transaction is always valid. - let regenerated: { embedding: number[]; tokenCount: number } | null = null + let regenerated: { + vectorValues: ReturnType + tokenCount: number + } | null = null if (content !== preRead.content) { const kbRow = await db - .select({ embeddingModel: knowledgeBase.embeddingModel }) + .select({ + embeddingModel: knowledgeBase.embeddingModel, + embeddingDimension: knowledgeBase.embeddingDimension, + }) .from(knowledgeBase) .innerJoin(document, eq(document.knowledgeBaseId, knowledgeBase.id)) .where(eq(document.id, preRead.documentId)) .limit(1) - const chunkEmbeddingModel = kbRow[0]?.embeddingModel - if (!chunkEmbeddingModel) { + const kbEmbeddingRow = kbRow[0] + if (!kbEmbeddingRow) { throw new Error('Knowledge base for chunk not found') } + const chunkEmbedding: KbEmbeddingTarget = { + model: kbEmbeddingRow.embeddingModel, + dimensions: toKbEmbeddingDimensions(kbEmbeddingRow.embeddingDimension), + } logger.info(`[${requestId}] Content changed, regenerating embedding for chunk ${chunkId}`) - const { embeddings } = await generateEmbeddings([content], chunkEmbeddingModel, workspaceId) + const { embeddings } = await generateEmbeddings([content], chunkEmbedding, workspaceId) regenerated = { - embedding: embeddings[0], + vectorValues: embeddingVectorValues(chunkEmbedding.dimensions, embeddings[0]), tokenCount: estimateTokenCount( content, - getEmbeddingModelInfo(chunkEmbeddingModel).tokenizerProvider + getEmbeddingModelInfo(chunkEmbedding.model).tokenizerProvider ).count, } } @@ -518,7 +532,7 @@ export async function updateChunk( chunkHash: sha256Hex(content), tokenCount: regenerated ? regenerated.tokenCount : oldTokenCount, secretProvenanceVersion: secretProvenance ? 1 : currentChunk[0].secretProvenanceVersion, - ...(regenerated ? { embedding: regenerated.embedding } : {}), + ...(regenerated ? regenerated.vectorValues : {}), ...(updateData.enabled !== undefined ? { enabled: updateData.enabled } : {}), } diff --git a/apps/sim/lib/knowledge/documents/document-indexing-usage.test.ts b/apps/sim/lib/knowledge/documents/document-indexing-usage.test.ts index ed540e42a0e..ba7e54b8e53 100644 --- a/apps/sim/lib/knowledge/documents/document-indexing-usage.test.ts +++ b/apps/sim/lib/knowledge/documents/document-indexing-usage.test.ts @@ -42,7 +42,8 @@ vi.mock('@/lib/knowledge/documents/document-processor', () => ({ })) vi.mock('@/lib/knowledge/embedding-models', () => ({ - EMBEDDING_DIMENSIONS: 1536, + DEFAULT_KB_EMBEDDING_DIMENSIONS: 1536, + toKbEmbeddingDimensions: (value: number) => value, getEmbeddingModelInfo: vi.fn(() => ({ tokenizerProvider: 'openai' })), })) @@ -81,6 +82,7 @@ const PERSISTED_CONTEXT = { knowledgeBaseUserId: 'knowledge-owner', chunkingConfig: null, embeddingModel: 'text-embedding-3-small', + embeddingDimension: 1536, billedAccountUserId: null, uploadedBy: 'uploader-1', filename: 'persisted.pdf', diff --git a/apps/sim/lib/knowledge/documents/document-processing-source.test.ts b/apps/sim/lib/knowledge/documents/document-processing-source.test.ts index 435c0c38116..375a320d0e0 100644 --- a/apps/sim/lib/knowledge/documents/document-processing-source.test.ts +++ b/apps/sim/lib/knowledge/documents/document-processing-source.test.ts @@ -46,7 +46,8 @@ vi.mock('@/lib/knowledge/documents/document-processor', () => ({ })) vi.mock('@/lib/knowledge/embedding-models', () => ({ - EMBEDDING_DIMENSIONS: 1536, + DEFAULT_KB_EMBEDDING_DIMENSIONS: 1536, + toKbEmbeddingDimensions: (value: number) => value, getEmbeddingModelInfo: mockGetEmbeddingModelInfo, })) @@ -95,6 +96,7 @@ const PERSISTED_CONTEXT = { knowledgeBaseUserId: 'knowledge-owner', chunkingConfig: null, embeddingModel: 'text-embedding-3-small', + embeddingDimension: 1536, billedAccountUserId: null, uploadedBy: 'uploader-1', filename: 'persisted.pdf', diff --git a/apps/sim/lib/knowledge/documents/service.ts b/apps/sim/lib/knowledge/documents/service.ts index cab2ea2f4f7..36c0c969ce7 100644 --- a/apps/sim/lib/knowledge/documents/service.ts +++ b/apps/sim/lib/knowledge/documents/service.ts @@ -121,8 +121,12 @@ import { QUEUED_DISPATCH_GRACE_MS, type SortOrder, } from '@/lib/knowledge/documents/types' -import { EMBEDDING_DIMENSIONS, getEmbeddingModelInfo } from '@/lib/knowledge/embedding-models' -import { generateEmbeddings } from '@/lib/knowledge/embeddings' +import { + DEFAULT_KB_EMBEDDING_DIMENSIONS, + getEmbeddingModelInfo, + toKbEmbeddingDimensions, +} from '@/lib/knowledge/embedding-models' +import { generateEmbeddings, type KbEmbeddingTarget } from '@/lib/knowledge/embeddings' import { runWithKnowledgeModelInputProvenance } from '@/lib/knowledge/model-input-provenance' import { bindKnowledgeDocumentFieldSecretProvenance, @@ -143,6 +147,7 @@ import { validateTagValue, } from '@/lib/knowledge/tags/utils' import type { ProcessedDocumentTags } from '@/lib/knowledge/types' +import { embeddingVectorValues } from '@/lib/knowledge/vector-columns' import { estimateTokenCount } from '@/lib/tokenization/estimators' import { getBoundWorkspaceFileSecretProvenanceByMetadata, @@ -318,9 +323,15 @@ const TIMEOUTS = { const LARGE_DOC_CONFIG = { MAX_CHUNKS_PER_BATCH: 500, + /** + * Sized against the platform default width rather than the base's own, which + * is not known at module load. The item limit only falls as the width grows, + * so a wider base is additionally bounded by the embedding client's own + * aggregate guard rather than by an under-tight ceiling here. + */ MAX_EMBEDDING_BATCH: Math.min( envNumber(env.KB_CONFIG_BATCH_SIZE, 2000, { min: 1, integer: true }), - getEmbeddingAggregateItemLimit(EMBEDDING_DIMENSIONS) + getEmbeddingAggregateItemLimit(DEFAULT_KB_EMBEDDING_DIMENSIONS) ), MAX_FILE_SIZE: 100 * 1024 * 1024, } @@ -1393,6 +1404,7 @@ export async function processDocumentAsync( knowledgeBaseUserId: knowledgeBase.userId, chunkingConfig: knowledgeBase.chunkingConfig, embeddingModel: knowledgeBase.embeddingModel, + embeddingDimension: knowledgeBase.embeddingDimension, billedAccountUserId: workspaceTable.billedAccountUserId, uploadedBy: document.uploadedBy, connectorId: document.connectorId, @@ -1530,7 +1542,11 @@ export async function processDocumentAsync( overlap: rawConfig?.overlap ?? 200, } - const kbEmbeddingModel = ctx.embeddingModel + const kbEmbedding: KbEmbeddingTarget = { + model: ctx.embeddingModel, + dimensions: toKbEmbeddingDimensions(ctx.embeddingDimension), + } + const kbEmbeddingModel = kbEmbedding.model const queuedBillingContext = hasDocumentProcessingBillingScope(providedBillingContext) ? assertDocumentProcessingBillingContext(providedBillingContext) : undefined @@ -1652,7 +1668,7 @@ export async function processDocumentAsync( billableTokens: batchBillableTokens, modelName, pricingId, - } = await generateEmbeddings(batch, kbEmbeddingModel, ctx.workspaceId) + } = await generateEmbeddings(batch, kbEmbedding, ctx.workspaceId) for (const emb of batchEmbeddings) { embeddings.push(emb) } @@ -1664,6 +1680,17 @@ export async function processDocumentAsync( } } + /** + * Every chunk must carry a vector. The row's width column would + * otherwise be NULL and `embedding_width_check` would reject the whole + * batch, naming a constraint rather than the chunk that went missing. + */ + if (embeddings.length !== processed.chunks.length) { + throw new Error( + `Embedding generation returned ${embeddings.length} vectors for ${processed.chunks.length} chunks` + ) + } + // Tag values prefetched above; reuse for the embedding rows. const documentTags = ctx @@ -1688,7 +1715,7 @@ export async function processDocumentAsync( secretProvenanceVersion: chunkProvenances[chunkIndex] ? 1 : null, contentLength: chunk.text.length, tokenCount: estimateTokenCount(chunk.text, tokenizerProvider).count, - embedding: embeddings[chunkIndex] || null, + ...embeddingVectorValues(kbEmbedding.dimensions, embeddings[chunkIndex]), embeddingModel: kbEmbeddingModel, startOffset: chunk.metadata.startIndex, endOffset: chunk.metadata.endIndex, diff --git a/apps/sim/lib/knowledge/embedding-models.ts b/apps/sim/lib/knowledge/embedding-models.ts index 83dee5e3063..ee2aa56cd94 100644 --- a/apps/sim/lib/knowledge/embedding-models.ts +++ b/apps/sim/lib/knowledge/embedding-models.ts @@ -1,21 +1,34 @@ /** * 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 - * module resolves provider, tokenizer, and pricing metadata at runtime for any - * model recorded on a knowledge base row. + * (`@/lib/embeddings/catalog`). A knowledge base stores every vector at one + * fixed width chosen at creation, so only catalog models that can emit one of + * the widths the `embedding` table has a column for are selectable here. + * Selection happens server-side via the `KB_EMBEDDING_MODEL` and + * `EMBEDDING_OUTPUT_DIMS` env vars; this module resolves provider, tokenizer, + * width, and pricing metadata at runtime for any model recorded on a knowledge + * base row. */ import { DEFAULT_EMBEDDING_MODEL as CATALOG_DEFAULT_EMBEDDING_MODEL, + DEFAULT_KB_EMBEDDING_DIMENSIONS, + findEmbeddingModelInfo, getEmbeddingModelInfo as getCatalogModelInfo, getKbEligibleModels, - KB_EMBEDDING_DIMENSIONS, + getKbEmbeddingDimensions, + isKbEmbeddingDimensions, + isOllamaEmbeddingModel, + KB_EMBEDDING_STORAGE_DIMENSIONS, + type KbEmbeddingDimensions, } from '@/lib/embeddings/catalog' import type { EmbeddingProviderKind, TokenizerProviderId } from '@/lib/embeddings/types' -export const EMBEDDING_DIMENSIONS = KB_EMBEDDING_DIMENSIONS +export type { KbEmbeddingDimensions } from '@/lib/embeddings/catalog' +export { + DEFAULT_KB_EMBEDDING_DIMENSIONS, + isKbEmbeddingDimensions, + KB_EMBEDDING_STORAGE_DIMENSIONS, +} from '@/lib/embeddings/catalog' export const DEFAULT_EMBEDDING_MODEL = CATALOG_DEFAULT_EMBEDDING_MODEL @@ -29,39 +42,101 @@ export interface EmbeddingModelInfo { tokenizerProvider: TokenizerProviderId /** Maximum tokens accepted for one embedding input by the selected model. */ maxInputTokens: number + /** Widths a knowledge base using this model can be created at, largest first. */ + dimensions: KbEmbeddingDimensions[] } +function toKbModelInfo(model: string): EmbeddingModelInfo { + const info = getCatalogModelInfo(model) + return { + provider: info.provider, + pricingId: info.pricingId, + tokenizerProvider: info.tokenizerProvider, + maxInputTokens: info.maxInputTokens, + dimensions: getKbEmbeddingDimensions(info), + } +} + +/** + * Statically catalogued models selectable for knowledge-base indexing. Ollama + * models are absent by construction — they are whatever the operator pulled + * onto their own server — and resolve through {@link getEmbeddingModelInfo} + * instead. + */ 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, - maxInputTokens: info.maxInputTokens, - }, - ] - }) + getKbEligibleModels() + .map((id) => [id, toKbModelInfo(id)] as const) + .filter(([, info]) => info.dimensions.length > 0) ) +/** True when `model` may be recorded on a knowledge base, whatever its width. */ +export function isKbEmbeddingModel(model: string): boolean { + if (isOllamaEmbeddingModel(model)) return findEmbeddingModelInfo(model) !== undefined + return SUPPORTED_EMBEDDING_MODELS[model] !== undefined +} + /** - * 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. + * Throws unless `model` is selectable for knowledge-base indexing at `dimensions`. + * Call before handing a model to `embed()` so an ineligible pairing fails here, + * naming the knowledge-base constraint, rather than deeper in the provider path. */ -export function assertKbEmbeddingModel(model: string): void { - getEmbeddingModelInfo(model) +export function assertKbEmbeddingModel(model: string, dimensions: number): void { + const info = getEmbeddingModelInfo(model) + if (!info.dimensions.includes(dimensions as KbEmbeddingDimensions)) { + throw new Error( + `${model} cannot produce ${dimensions}-dimensional embeddings. Supported: ${info.dimensions.join(', ')}` + ) + } } export function getEmbeddingModelInfo(model: string): EmbeddingModelInfo { const info = SUPPORTED_EMBEDDING_MODELS[model] - if (!info) { - /** 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}`) + if (info) return info + + /** + * An Ollama id is resolved rather than looked up, because Sim has no catalog + * of what a given server has pulled. Every other unknown id surfaces the + * catalog's error, and a catalogued but ineligible model gets a KB-specific + * one. + */ + if (isOllamaEmbeddingModel(model)) return toKbModelInfo(model) + getCatalogModelInfo(model) + throw new Error(`Embedding model is not available for knowledge bases: ${model}`) +} + +/** + * Narrows a persisted `knowledge_base.embedding_dimension` to a width the + * `embedding` table has a column for. A base recorded at any other width cannot + * be read or written, so this fails where the width is first used rather than + * letting a column lookup come back undefined. + */ +export function toKbEmbeddingDimensions(value: number): KbEmbeddingDimensions { + if (!isKbEmbeddingDimensions(value)) { + throw new Error( + `Knowledge base vector width ${value} has no storage column. Supported: ${KB_EMBEDDING_STORAGE_DIMENSIONS.join(', ')}` + ) + } + return value +} + +/** + * Width a knowledge base should be created at for `model` when the deployment + * configured none, or configured one the model cannot emit. Prefers the + * platform default so an unconfigured deployment keeps writing the column it + * always has, and otherwise takes the model's largest storable width rather + * than refusing to create a base at all. + */ +export function defaultKbEmbeddingDimensions(model: string): KbEmbeddingDimensions { + const { dimensions } = getEmbeddingModelInfo(model) + if (dimensions.includes(DEFAULT_KB_EMBEDDING_DIMENSIONS)) { + return DEFAULT_KB_EMBEDDING_DIMENSIONS + } + const widest = dimensions[0] + if (widest === undefined) { + throw new Error( + `${model} emits no width knowledge bases can store. Supported: ${KB_EMBEDDING_STORAGE_DIMENSIONS.join(', ')}` + ) } - return info + return widest } diff --git a/apps/sim/lib/knowledge/embeddings.test.ts b/apps/sim/lib/knowledge/embeddings.test.ts index 63ecaa3c7df..e21812532bb 100644 --- a/apps/sim/lib/knowledge/embeddings.test.ts +++ b/apps/sim/lib/knowledge/embeddings.test.ts @@ -1,95 +1,88 @@ /** * @vitest-environment node */ -import { afterAll, beforeEach, describe, expect, it, vi } from 'vitest' -import * as billingAttributionModule from '@/lib/billing/core/billing-attribution' -import * as usageLogModule from '@/lib/billing/core/usage-log' -import * as thresholdBillingModule from '@/lib/billing/threshold-billing' -import * as embeddingModelsModule from '@/lib/knowledge/embedding-models' -import { recordSearchEmbeddingUsage } from '@/lib/knowledge/embeddings' -import * as tokenizationModule from '@/lib/tokenization' -import * as providersUtilsModule from '@/providers/utils' +import { beforeEach, describe, expect, it, vi } from 'vitest' + +const { mockEnv } = vi.hoisted(() => ({ + mockEnv: {} as { KB_EMBEDDING_MODEL?: string; EMBEDDING_OUTPUT_DIMS?: string }, +})) /** - * Spy on the real module namespaces instead of vi.mock: under `isolate: false` - * `@/lib/knowledge/embeddings` is a shared consumer cached across test files, - * so vi.mock here would bind this file's fixtures into it for every later - * file. Patching the real namespaces (and restoring afterAll) is the only - * wiring that composes. + * `envNumber` is the real implementation, not a stub: the whole point of the + * cases below is that `createEnv` runs with `skipValidation`, so every value + * arrives as the raw string from the environment however its schema is declared. */ -const mockRecordUsage = vi - .spyOn(usageLogModule, 'recordUsage') - .mockResolvedValue(undefined as never) -const mockToBillingContext = vi.spyOn(billingAttributionModule, 'toBillingContext') -const mockCheckAndBillPayerOverageThreshold = vi - .spyOn(thresholdBillingModule, 'checkAndBillPayerOverageThreshold') - .mockResolvedValue(undefined as never) -const mockCalculateCost = vi.spyOn(providersUtilsModule, 'calculateCost') -const estimateTokenCountSpy = vi - .spyOn(tokenizationModule, 'estimateTokenCount') - .mockReturnValue({ count: 100 } as never) -const getEmbeddingModelInfoSpy = vi - .spyOn(embeddingModelsModule, 'getEmbeddingModelInfo') - .mockReturnValue({ tokenizerProvider: 'openai' } as never) +vi.mock('@/lib/core/config/env', async (importOriginal) => ({ + ...(await importOriginal()), + env: mockEnv, +})) -afterAll(() => { - mockRecordUsage.mockRestore() - mockToBillingContext.mockRestore() - mockCheckAndBillPayerOverageThreshold.mockRestore() - mockCalculateCost.mockRestore() - estimateTokenCountSpy.mockRestore() - getEmbeddingModelInfoSpy.mockRestore() -}) +import { getConfiguredKbEmbedding } from '@/lib/knowledge/embeddings' -describe('recordSearchEmbeddingUsage', () => { +describe('getConfiguredKbEmbedding', () => { beforeEach(() => { - vi.clearAllMocks() - mockRecordUsage.mockResolvedValue(undefined as never) - mockCheckAndBillPayerOverageThreshold.mockResolvedValue(undefined as never) - estimateTokenCountSpy.mockReturnValue({ count: 100 } as never) - getEmbeddingModelInfoSpy.mockReturnValue({ tokenizerProvider: 'openai' } as never) - mockCalculateCost.mockReturnValue({ total: 0.01 } as never) - mockToBillingContext.mockReturnValue({ - billingEntity: { type: 'organization', id: 'org-1' }, - billingPeriod: { - start: new Date('2026-07-01T00:00:00.000Z'), - end: new Date('2026-08-01T00:00:00.000Z'), - }, + mockEnv.KB_EMBEDDING_MODEL = undefined + mockEnv.EMBEDDING_OUTPUT_DIMS = undefined + }) + + it('defaults to the model and width knowledge bases were always created at', () => { + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'text-embedding-3-small', + dimensions: 1536, }) }) - it('records and bills against the attributed workspace payer', async () => { - await recordSearchEmbeddingUsage({ - userId: 'actor-1', - workspaceId: 'ws-1', - embeddingModel: 'text-embedding-3-small', - query: 'test query', - isBYOK: false, - sourceReference: 'search-1', - billingAttribution: { - actorUserId: 'actor-1', - workspaceId: 'ws-1', - organizationId: 'org-1', - billedAccountUserId: 'owner-1', - billingEntity: { type: 'organization', id: 'org-1' }, - billingPeriod: { - start: '2026-07-01T00:00:00.000Z', - end: '2026-08-01T00:00:00.000Z', - }, - payerSubscription: null, - }, + it('stores at the configured width when the model can emit it', () => { + mockEnv.KB_EMBEDDING_MODEL = 'text-embedding-3-large' + mockEnv.EMBEDDING_OUTPUT_DIMS = '3072' + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'text-embedding-3-large', + dimensions: 3072, }) + }) + + it('accepts any storable width from a model on the deployment’s own Ollama', () => { + mockEnv.KB_EMBEDDING_MODEL = 'ollama/nomic-embed-text' + mockEnv.EMBEDDING_OUTPUT_DIMS = '768' + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'ollama/nomic-embed-text', + dimensions: 768, + }) + }) + + it('falls back when the width is not a number at all', () => { + mockEnv.KB_EMBEDDING_MODEL = 'text-embedding-3-large' + mockEnv.EMBEDDING_OUTPUT_DIMS = 'wide' + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'text-embedding-3-large', + dimensions: 1536, + }) + }) + + it('falls back when the width has no storage column, keeping the chosen model', () => { + mockEnv.KB_EMBEDDING_MODEL = 'text-embedding-3-large' + mockEnv.EMBEDDING_OUTPUT_DIMS = '1000' + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'text-embedding-3-large', + dimensions: 1536, + }) + }) + + it('falls back when the model cannot emit the configured width', () => { + mockEnv.KB_EMBEDDING_MODEL = 'gemini-embedding-001' + mockEnv.EMBEDDING_OUTPUT_DIMS = '1024' + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'gemini-embedding-001', + dimensions: 1536, + }) + }) - expect(mockRecordUsage).toHaveBeenCalledWith( - expect.objectContaining({ - userId: 'actor-1', - workspaceId: 'ws-1', - billingEntity: { type: 'organization', id: 'org-1' }, - }) - ) - expect(mockCheckAndBillPayerOverageThreshold).toHaveBeenCalledWith({ - type: 'organization', - id: 'org-1', + it('falls back to the default model when the configured one cannot index a knowledge base', () => { + mockEnv.KB_EMBEDDING_MODEL = 'text-embedding-ada-002' + mockEnv.EMBEDDING_OUTPUT_DIMS = '768' + expect(getConfiguredKbEmbedding()).toEqual({ + model: 'text-embedding-3-small', + dimensions: 768, }) }) }) diff --git a/apps/sim/lib/knowledge/embeddings.ts b/apps/sim/lib/knowledge/embeddings.ts index 07616df83b7..6a5bfdcca51 100644 --- a/apps/sim/lib/knowledge/embeddings.ts +++ b/apps/sim/lib/knowledge/embeddings.ts @@ -6,14 +6,17 @@ import { } from '@/lib/billing/core/billing-attribution' import { recordUsage } from '@/lib/billing/core/usage-log' import { checkAndBillPayerOverageThreshold } from '@/lib/billing/threshold-billing' -import { env } from '@/lib/core/config/env' +import { env, envNumber } from '@/lib/core/config/env' import { embedKnowledge } from '@/lib/embeddings' import { assertKbEmbeddingModel, DEFAULT_EMBEDDING_MODEL, - EMBEDDING_DIMENSIONS, + defaultKbEmbeddingDimensions, getEmbeddingModelInfo, - SUPPORTED_EMBEDDING_MODELS, + isKbEmbeddingDimensions, + isKbEmbeddingModel, + KB_EMBEDDING_STORAGE_DIMENSIONS, + type KbEmbeddingDimensions, } from '@/lib/knowledge/embedding-models' import { projectKnowledgeModelInputs } from '@/lib/knowledge/model-input-provenance' import { estimateTokenCount } from '@/lib/tokenization' @@ -21,18 +24,27 @@ import { calculateCost } from '@/providers/utils' const logger = createLogger('EmbeddingUtils') -export { EMBEDDING_DIMENSIONS } from '@/lib/knowledge/embedding-models' - export type EmbeddingInputType = 'document' | 'query' +/** + * The model a knowledge base is indexed with and the vector width it stores. + * The two travel together everywhere: a width is only meaningful for the model + * that emits it, and a chunk written at the wrong width lands in the wrong + * pgvector column or none at all. + */ +export interface KbEmbeddingTarget { + model: string + dimensions: KbEmbeddingDimensions +} + /** * Returns the embedding model to use for new knowledge bases. * Sourced from the `KB_EMBEDDING_MODEL` env var; falls back to the default if - * unset or set to an unsupported model. + * unset or set to a model knowledge bases cannot use. */ -export function getConfiguredEmbeddingModel(): string { +function resolveConfiguredEmbeddingModel(): string { const configured = env.KB_EMBEDDING_MODEL - if (configured && SUPPORTED_EMBEDDING_MODELS[configured]) { + if (configured && isKbEmbeddingModel(configured)) { return configured } if (configured) { @@ -43,6 +55,51 @@ export function getConfiguredEmbeddingModel(): string { return DEFAULT_EMBEDDING_MODEL } +/** + * Vector width new knowledge bases are stored at, from `EMBEDDING_OUTPUT_DIMS`. + * + * Matching the width to what the configured model actually emits is the + * operator's job — Sim cannot verify it for a model on their own Ollama server, + * and for a catalogued model it can only check the widths the provider + * documents. Either way a value this deployment cannot store falls back rather + * than failing knowledge-base creation outright, because a base that exists at + * a working width is recoverable and one that could not be created is not. + */ +function resolveConfiguredEmbeddingDimensions(model: string): KbEmbeddingDimensions { + const raw = env.EMBEDDING_OUTPUT_DIMS + if (raw === undefined || String(raw).trim() === '') return defaultKbEmbeddingDimensions(model) + + const fallback = defaultKbEmbeddingDimensions(model) + /** + * Read through `envNumber` rather than trusted as the number its schema + * declares: `createEnv` runs with `skipValidation`, so the declared + * `z.coerce.number()` never executes and the value arrives as the raw string + * from the environment. Comparing that string against the storage widths + * matches nothing, which silently ignored every configured width. `0` is the + * sentinel for a value that is not a number at all; no storage width is 0. + */ + const configured = envNumber(raw, 0, { min: 1, integer: true }) + if (!isKbEmbeddingDimensions(configured)) { + logger.warn( + `EMBEDDING_OUTPUT_DIMS="${raw}" is not a storable vector width — falling back to ${fallback}. Supported: ${KB_EMBEDDING_STORAGE_DIMENSIONS.join(', ')}` + ) + return fallback + } + if (!getEmbeddingModelInfo(model).dimensions.includes(configured)) { + logger.warn( + `EMBEDDING_OUTPUT_DIMS="${raw}" is not a width ${model} can emit — falling back to ${fallback}` + ) + return fallback + } + return configured +} + +/** Model and vector width every knowledge base created on this deployment uses. */ +export function getConfiguredKbEmbedding(): KbEmbeddingTarget { + const model = resolveConfiguredEmbeddingModel() + return { model, dimensions: resolveConfiguredEmbeddingDimensions(model) } +} + export interface GenerateEmbeddingsResult { embeddings: number[][] totalTokens: number @@ -56,21 +113,21 @@ 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. + * Every vector is pinned to the width its knowledge base was created at, so it + * matches the pgvector column the base stores into. */ export async function generateEmbeddings( texts: string[], - embeddingModel: string = DEFAULT_EMBEDDING_MODEL, + target: KbEmbeddingTarget, workspaceId?: string | null ): Promise { - assertKbEmbeddingModel(embeddingModel) + assertKbEmbeddingModel(target.model, target.dimensions) const result = await embedKnowledge(texts, { - model: embeddingModel, + model: target.model, workspaceId, taskType: 'document', - dimensions: EMBEDDING_DIMENSIONS, + dimensions: target.dimensions, projectInputs: projectKnowledgeModelInputs, }) @@ -86,16 +143,16 @@ export async function generateEmbeddings( export async function generateSearchEmbedding( query: string, - embeddingModel: string = DEFAULT_EMBEDDING_MODEL, + target: KbEmbeddingTarget, workspaceId?: string | null ): Promise<{ embedding: number[]; isBYOK: boolean }> { - assertKbEmbeddingModel(embeddingModel) + assertKbEmbeddingModel(target.model, target.dimensions) const result = await embedKnowledge([query], { - model: embeddingModel, + model: target.model, workspaceId, taskType: 'query', - dimensions: EMBEDDING_DIMENSIONS, + dimensions: target.dimensions, projectInputs: projectKnowledgeModelInputs, }) diff --git a/apps/sim/lib/knowledge/orchestration/knowledge-bases.test.ts b/apps/sim/lib/knowledge/orchestration/knowledge-bases.test.ts index 74ed2b10951..4fb60593f2d 100644 --- a/apps/sim/lib/knowledge/orchestration/knowledge-bases.test.ts +++ b/apps/sim/lib/knowledge/orchestration/knowledge-bases.test.ts @@ -30,8 +30,7 @@ vi.mock('@/lib/core/telemetry', () => ({ PlatformEvents: { knowledgeBaseCreated: vi.fn(), knowledgeBaseDeleted: vi.fn() }, })) vi.mock('@/lib/knowledge/embeddings', () => ({ - EMBEDDING_DIMENSIONS: 1536, - getConfiguredEmbeddingModel: () => 'text-embedding-3-small', + getConfiguredKbEmbedding: () => ({ model: 'text-embedding-3-small', dimensions: 1536 }), })) vi.mock('@/lib/knowledge/service', () => ({ createKnowledgeBase: mockCreateKnowledgeBase, diff --git a/apps/sim/lib/knowledge/orchestration/knowledge-bases.ts b/apps/sim/lib/knowledge/orchestration/knowledge-bases.ts index b2f6d873764..7116757112f 100644 --- a/apps/sim/lib/knowledge/orchestration/knowledge-bases.ts +++ b/apps/sim/lib/knowledge/orchestration/knowledge-bases.ts @@ -3,7 +3,7 @@ import { createLogger } from '@sim/logger' import { PlatformEvents } from '@/lib/core/telemetry' import { generateRequestId } from '@/lib/core/utils/request' import { DEFAULT_CHUNKING_CONFIG } from '@/lib/knowledge/constants' -import { EMBEDDING_DIMENSIONS, getConfiguredEmbeddingModel } from '@/lib/knowledge/embeddings' +import { getConfiguredKbEmbedding } from '@/lib/knowledge/embeddings' import { auditActorFields, classifyKnowledgeFailure, @@ -55,7 +55,7 @@ export async function performCreateKnowledgeBase( const { workspaceId, name, description, folderId, request, source } = params const requestId = params.requestId ?? generateRequestId() const chunkingConfig: ChunkingConfig = { ...DEFAULT_CHUNKING_CONFIG, ...params.chunkingConfig } - const embeddingModel = getConfiguredEmbeddingModel() + const { model: embeddingModel, dimensions: embeddingDimension } = getConfiguredKbEmbedding() let created: KnowledgeBaseWithCounts try { @@ -67,7 +67,7 @@ export async function performCreateKnowledgeBase( folderId, userId: params.userId, embeddingModel, - embeddingDimension: EMBEDDING_DIMENSIONS, + embeddingDimension, chunkingConfig, }, requestId @@ -107,7 +107,7 @@ export async function performCreateKnowledgeBase( name: created.name, description: created.description, embeddingModel, - embeddingDimension: EMBEDDING_DIMENSIONS, + embeddingDimension, chunkingStrategy: chunkingConfig.strategy, chunkMaxSize: chunkingConfig.maxSize, chunkMinSize: chunkingConfig.minSize, diff --git a/apps/sim/lib/knowledge/search/queries.ts b/apps/sim/lib/knowledge/search/queries.ts index 57cece6f4c6..f5449ce625b 100644 --- a/apps/sim/lib/knowledge/search/queries.ts +++ b/apps/sim/lib/knowledge/search/queries.ts @@ -5,6 +5,7 @@ import { getErrorMessage, getPostgresErrorCode } from '@sim/utils/errors' import { and, eq, inArray, isNull, type SQL, sql } from 'drizzle-orm' import { knowledgeAccessCondition } from '@/lib/knowledge/access/predicate' import { type KnowledgeAccessScope, WORKSPACE_ACCESS_TOKENS } from '@/lib/knowledge/access/types' +import type { KbEmbeddingDimensions } from '@/lib/knowledge/embedding-models' import { applyRecencyBoost, RRF_K } from '@/lib/knowledge/search/recency' import { coerceTagFilterValue, @@ -12,6 +13,7 @@ import { uncompilableTagFilterError, } from '@/lib/knowledge/tags/utils' import type { StructuredFilter } from '@/lib/knowledge/types' +import { embeddingDistance } from '@/lib/knowledge/vector-columns' const logger = createLogger('KnowledgeSearchQueries') @@ -164,13 +166,25 @@ export interface SearchResult { sourceModifiedAt: Date | null } +/** + * A query embedding and the width it was produced at. The two travel together + * because the width selects both the pgvector column the comparison reads and + * the index form it has to be written in; a vector without it cannot be + * compared against anything. + */ +export interface KnowledgeQueryVector { + /** JSON array literal of the embedding, in pgvector's text input format. */ + vector: string + dimensions: KbEmbeddingDimensions +} + export interface SearchParams { knowledgeBaseIds: string[] topK: number /** What the caller may read; every leg applies it. Required so no leg can be written without it. */ access: KnowledgeAccessScope structuredFilters?: StructuredFilter[] - queryVector?: string + queryVector?: KnowledgeQueryVector distanceThreshold?: number } @@ -449,7 +463,7 @@ async function executeTagFilterQuery( async function executeVectorSearchOnIds( embeddingIds: string[], - queryVector: string, + queryVector: KnowledgeQueryVector, topK: number, distanceThreshold: number, access: KnowledgeAccessScope @@ -458,23 +472,20 @@ async function executeVectorSearchOnIds( return [] } + const distance = embeddingDistance(queryVector.dimensions, queryVector.vector) const rows = await withVectorScanSettings(access, topK, (executor) => executor - .select( - getSearchResultFields( - sql`${embedding.embedding} <=> ${queryVector}::vector`.as('distance') - ) - ) + .select(getSearchResultFields(distance.as('distance'))) .from(embedding) .innerJoin(document, eq(embedding.documentId, document.id)) .where( and( inArray(embedding.id, embeddingIds), ...getVisibilityConditions(access), - sql`${embedding.embedding} <=> ${queryVector}::vector < ${distanceThreshold}` + sql`${distance} < ${distanceThreshold}` ) ) - .orderBy(sql`${embedding.embedding} <=> ${queryVector}::vector`) + .orderBy(distance) .limit(topK) ) return rows.sort((a, b) => a.distance - b.distance) @@ -535,20 +546,17 @@ export async function handleVectorOnlySearch(params: SearchParams): Promise`${embedding.embedding} <=> ${queryVector}::vector`.as('distance') + const distance = embeddingDistance(queryVector.dimensions, queryVector.vector) + const distanceExpr = distance.as('distance') const vectorLeg = (executor: SearchExecutor, kbScope: SQL | undefined, limit: number) => executor .select(getSearchResultFields(distanceExpr)) .from(embedding) .innerJoin(document, eq(embedding.documentId, document.id)) .where( - and( - kbScope, - ...getVisibilityConditions(access), - sql`${embedding.embedding} <=> ${queryVector}::vector < ${distanceThreshold}` - ) + and(kbScope, ...getVisibilityConditions(access), sql`${distance} < ${distanceThreshold}`) ) - .orderBy(sql`${embedding.embedding} <=> ${queryVector}::vector`) + .orderBy(distance) .limit(limit) /** @@ -579,7 +587,7 @@ export interface KeywordSearchParams { access: KnowledgeAccessScope query: string /** Query embedding, so keyword-only hits still carry a real cosine distance. */ - queryVector: string + queryVector: KnowledgeQueryVector structuredFilters?: StructuredFilter[] } @@ -600,8 +608,8 @@ export interface KeywordSearchParams { * fusion is combining rankings taken over differently-shaped pools. * * Ranking and hydration are two steps on purpose. Projecting the cosine - * distance in the ranking query makes Postgres detoast the 1536-dimension - * vector and compute a distance for *every* full-text match before the `LIMIT` + * distance in the ranking query makes Postgres detoast the chunk's vector and + * compute a distance for *every* full-text match before the `LIMIT` * applies — work that scales with how common the query term is rather than * with `topK` (measured at ~59x the buffer reads on a 20k-chunk base for a term * matching every row). Ranking therefore touches no vectors, and only the rows @@ -660,7 +668,7 @@ export async function executeKeywordSearch(params: KeywordSearchParams): Promise const hydrated = await db .select( getSearchResultFields( - sql`${embedding.embedding} <=> ${queryVector}::vector`.as('distance') + embeddingDistance(queryVector.dimensions, queryVector.vector).as('distance') ) ) .from(embedding) @@ -796,7 +804,7 @@ export interface ExecuteKnowledgeSearchParams { boostRecency?: boolean query?: string /** Required whenever `query` is present. */ - queryVector?: string + queryVector?: KnowledgeQueryVector structuredFilters?: StructuredFilter[] } diff --git a/apps/sim/lib/knowledge/types.ts b/apps/sim/lib/knowledge/types.ts index 7a7d22666a4..f68268270cd 100644 --- a/apps/sim/lib/knowledge/types.ts +++ b/apps/sim/lib/knowledge/types.ts @@ -1,4 +1,5 @@ import type { ChunkingStrategy, StrategyOptions } from '@/lib/chunkers/types' +import type { KbEmbeddingDimensions } from '@/lib/knowledge/embedding-models' /** * Units: @@ -40,7 +41,7 @@ export interface CreateKnowledgeBaseData { workspaceId: string folderId?: string | null embeddingModel: string - embeddingDimension: 1536 + embeddingDimension: KbEmbeddingDimensions chunkingConfig: ChunkingConfig userId: string } diff --git a/apps/sim/lib/knowledge/vector-columns.test.ts b/apps/sim/lib/knowledge/vector-columns.test.ts new file mode 100644 index 00000000000..68898ad4c0a --- /dev/null +++ b/apps/sim/lib/knowledge/vector-columns.test.ts @@ -0,0 +1,54 @@ +/** + * @vitest-environment node + */ +import { describe, expect, it } from 'vitest' +import { KB_EMBEDDING_STORAGE_DIMENSIONS } from '@/lib/embeddings/catalog' +import { + embeddingDistance, + embeddingVectorColumn, + embeddingVectorValues, +} from '@/lib/knowledge/vector-columns' + +describe('embeddingVectorColumn', () => { + it('maps every storable width to a distinct column', () => { + const columns = KB_EMBEDDING_STORAGE_DIMENSIONS.map((width) => embeddingVectorColumn(width)) + expect(new Set(columns).size).toBe(KB_EMBEDDING_STORAGE_DIMENSIONS.length) + }) + + it('keeps 1536 on the original bare `embedding` column, where existing rows live', () => { + expect(embeddingVectorColumn(1536)).toBe('embedding.embedding') + }) +}) + +describe('embeddingVectorValues', () => { + const vector = [0.1, 0.2] + + it('writes the vector to the column its width belongs to', () => { + expect(embeddingVectorValues(768, vector)).toMatchObject({ embedding768: vector }) + }) + + it('nulls every other width, so a re-embed at a new width clears the old column', () => { + for (const width of KB_EMBEDDING_STORAGE_DIMENSIONS) { + const values = embeddingVectorValues(width, vector) + const populated = Object.entries(values).filter(([, value]) => value !== null) + expect(populated, `${width} populated ${populated.length} columns`).toHaveLength(1) + expect(Object.values(values).filter((value) => value === null)).toHaveLength( + KB_EMBEDDING_STORAGE_DIMENSIONS.length - 1 + ) + } + }) +}) + +describe('embeddingDistance', () => { + it('compares 3072 through the halfvec cast its expression index was built on', () => { + expect(embeddingDistance(3072, '[1,2]').toSQL().sql).toContain('::halfvec(3072)') + }) + + it('compares every indexable width against a plain vector', () => { + for (const width of KB_EMBEDDING_STORAGE_DIMENSIONS.filter((size) => size <= 2000)) { + const rendered = embeddingDistance(width, '[1,2]').toSQL().sql + expect(rendered, `${width} should not cast to halfvec`).not.toContain('halfvec') + expect(rendered).toContain('::vector') + } + }) +}) diff --git a/apps/sim/lib/knowledge/vector-columns.ts b/apps/sim/lib/knowledge/vector-columns.ts new file mode 100644 index 00000000000..3f6e084462c --- /dev/null +++ b/apps/sim/lib/knowledge/vector-columns.ts @@ -0,0 +1,71 @@ +/** + * Maps a knowledge base's stored vector width onto the one pgvector column that + * holds it. Every read and write of a chunk's vector goes through here, so the + * column choice, the distance expression, and the index each width is served by + * can never drift apart. + */ + +import { embedding } from '@sim/db/schema' +import { type SQL, sql } from 'drizzle-orm' +import type { KbEmbeddingDimensions } from '@/lib/knowledge/embedding-models' + +type VectorField = 'embedding' | 'embedding384' | 'embedding768' | 'embedding1024' | 'embedding3072' + +/** + * Column per stored width. `embedding` is the original 1536 column, kept under + * its bare name so rows written before the other widths existed stay put. + */ +const VECTOR_FIELD_BY_WIDTH = { + 384: 'embedding384', + 768: 'embedding768', + 1024: 'embedding1024', + 1536: 'embedding', + 3072: 'embedding3072', +} as const satisfies Record + +const VECTOR_FIELDS = Object.values(VECTOR_FIELD_BY_WIDTH) as readonly VectorField[] + +export function embeddingVectorColumn(dimensions: KbEmbeddingDimensions) { + return embedding[VECTOR_FIELD_BY_WIDTH[dimensions]] +} + +/** + * The vector slice of an `embedding` row: the column for this width carries the + * vector and every other width is explicitly NULL. + * + * Every width is named rather than just the one in use so an update that + * re-embeds a chunk at a different width clears the column it used to live in. + * Omitting the others satisfies an insert but leaves an update holding two + * vectors, which `embedding_width_check` rejects. + */ +export function embeddingVectorValues( + dimensions: KbEmbeddingDimensions, + vector: number[] +): Record { + const target = VECTOR_FIELD_BY_WIDTH[dimensions] + return Object.fromEntries( + VECTOR_FIELDS.map((field) => [field, field === target ? vector : null]) + ) as Record +} + +/** + * Cosine distance between a chunk's vector and the query vector, in the exact + * form the width's HNSW index was built on. + * + * The 3,072 column is compared through a `halfvec` cast because pgvector + * indexes `vector` only up to 2,000 dimensions, so its index is on that cast + * expression. Postgres matches an expression index by the expression, so a + * plain `<=>` against the column here would silently drop to a sequential scan + * — and the cast belongs here rather than at each call site precisely because + * getting it wrong is invisible in the results and only shows up as latency. + * `packages/db/schema.ts` records what the half-precision comparison costs. + */ +export function embeddingDistance( + dimensions: KbEmbeddingDimensions, + queryVector: string +): SQL { + if (dimensions === 3072) { + return sql`${embedding.embedding3072}::halfvec(3072) <=> ${queryVector}::halfvec(3072)` + } + return sql`${embeddingVectorColumn(dimensions)} <=> ${queryVector}::vector` +} diff --git a/apps/sim/lib/selectors/manifest.test.ts b/apps/sim/lib/selectors/manifest.test.ts index df9322d33e3..599b78e1866 100644 --- a/apps/sim/lib/selectors/manifest.test.ts +++ b/apps/sim/lib/selectors/manifest.test.ts @@ -9,9 +9,9 @@ describe('selector manifest', () => { const count = (classification: (typeof classifications)[number]) => classifications.filter((value) => value === classification).length - expect(Object.keys(selectorManifest)).toHaveLength(94) + expect(Object.keys(selectorManifest)).toHaveLength(95) expect(count('provider-server')).toBe(82) - expect(count('internal-server')).toBe(11) + expect(count('internal-server')).toBe(12) expect(count('local')).toBe(1) expect(classifications).not.toContain('provider-legacy') }) diff --git a/apps/sim/lib/selectors/manifest.ts b/apps/sim/lib/selectors/manifest.ts index 172fcfa0ac5..51ad39a29dd 100644 --- a/apps/sim/lib/selectors/manifest.ts +++ b/apps/sim/lib/selectors/manifest.ts @@ -375,6 +375,7 @@ export const selectorManifest = { 'workspace.secretNames': internalSelector(), 'workspace.rawSecretNames': internalSelector(), 'workspace.sandboxes': internalSelector(['language'], { detail: true }), + 'providers.ollamaEmbeddingModels': internalSelector(), 'providers.openrouterEmbeddingModels': internalSelector(), 'workspace.triggerTypes': { classification: 'local', diff --git a/apps/sim/lib/selectors/server/internal.ts b/apps/sim/lib/selectors/server/internal.ts index 4a5241cdf27..0c0819ba760 100644 --- a/apps/sim/lib/selectors/server/internal.ts +++ b/apps/sim/lib/selectors/server/internal.ts @@ -1,6 +1,7 @@ import { listCredentialGroupSettings } from '@/lib/credential-groups/application/manage-groups' import { getCredentialGroupProviderService } from '@/lib/credential-groups/providers' import { listInternalCredentials } from '@/lib/credentials/application/credential-crud' +import { fetchOllamaEmbeddingModelCatalog } from '@/lib/embeddings/ollama-model-catalog.server' import { fetchOpenRouterEmbeddingModelCatalog } from '@/lib/embeddings/openrouter-model-catalog.server' import { getEffectiveEnvironmentVariableNames } from '@/lib/environment/utils' import { listWorkspaceSandboxes } from '@/lib/execution/remote-sandbox/workspace-sandboxes' @@ -296,6 +297,27 @@ export const internalSelectorAttachments = { ) }, }, + 'providers.ollamaEmbeddingModels': { + destination: 'fixed', + async execute(args: ExecuteServerSelectorArgs) { + if (isProviderBlacklisted('ollama')) return listSelectorResult([]) + const models = await fetchOllamaEmbeddingModelCatalog(args.signal) + return listSelectorResult( + filterBlacklistedModels(models.map((model) => model.id)).map((id) => { + const dimensions = models.find((model) => model.id === id)?.dimensions + return { + id, + /** + * The width is in the label because matching it to + * `EMBEDDING_OUTPUT_DIMS` is the operator's job and Ollama is the one + * provider whose widths Sim cannot know ahead of time. + */ + label: dimensions === undefined ? id : `${id} (${dimensions})`, + } + }) + ) + }, + }, 'providers.openrouterEmbeddingModels': { destination: 'fixed', async execute(args: ExecuteServerSelectorArgs) { diff --git a/apps/sim/tools/embeddings/factory.ts b/apps/sim/tools/embeddings/factory.ts index 3aafb396677..015722d8583 100644 --- a/apps/sim/tools/embeddings/factory.ts +++ b/apps/sim/tools/embeddings/factory.ts @@ -1,5 +1,5 @@ import { BYOK_PROVIDER_IDS, DEFAULT_MODEL_BY_PROVIDER } from '@/lib/embeddings/catalog' -import type { EmbeddingCatalogProvider } from '@/lib/embeddings/types' +import type { KeyedEmbeddingProvider } from '@/lib/embeddings/types' import type { EmbeddingProvider } from '@/lib/internal/embeddings/schema' import { getEmbeddingModelPricing } from '@/providers/models' import type { EmbeddingsParams, EmbeddingsResponse } from '@/tools/embeddings/types' @@ -19,7 +19,7 @@ interface EmbeddingToolBaseOptions { } interface HostedEmbeddingToolOptions extends EmbeddingToolBaseOptions { - provider: EmbeddingCatalogProvider + provider: KeyedEmbeddingProvider envKeyPrefix: string defaultModel?: never } @@ -30,7 +30,21 @@ interface ExplicitKeyEmbeddingToolOptions extends EmbeddingToolBaseOptions { defaultModel: string } -type CreateEmbeddingToolOptions = HostedEmbeddingToolOptions | ExplicitKeyEmbeddingToolOptions +/** + * Ollama runs on the deployment's own server and authenticates with nothing, so + * it has neither a credential to collect nor a hosted key to meter. The model is + * whatever the operator pulled, so there is no default either. + */ +interface LocalEmbeddingToolOptions extends EmbeddingToolBaseOptions { + provider: Extract + envKeyPrefix?: never + defaultModel?: never +} + +type CreateEmbeddingToolOptions = + | HostedEmbeddingToolOptions + | ExplicitKeyEmbeddingToolOptions + | LocalEmbeddingToolOptions /** * Builds a provider-specific embeddings tool. Every provider shares the same @@ -41,39 +55,46 @@ export function createEmbeddingTool( options: CreateEmbeddingToolOptions ): InternalToolConfig { const { id, name, provider, description } = options + const isKeyless = provider === 'ollama' const defaultModel = - provider === 'openrouter' ? options.defaultModel : DEFAULT_MODEL_BY_PROVIDER[provider] + provider === 'openrouter' + ? options.defaultModel + : isKeyless + ? undefined + : DEFAULT_MODEL_BY_PROVIDER[provider] /** * Sim-hosted catalog providers are billed per input token with no markup. - * OpenRouter requires an explicit user key and therefore has no hosting config. + * OpenRouter requires an explicit user key and Ollama takes none at all, so + * neither has a hosting config — and neither has a Sim-funded key to meter. */ - const hostingConfig: InternalToolConfig['hosting'] = - provider === 'openrouter' - ? undefined - : { - envKeyPrefix: options.envKeyPrefix, - apiKeyParam: 'apiKey', - byokProviderId: BYOK_PROVIDER_IDS[provider], - 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, - } + let hostingConfig: InternalToolConfig['hosting'] + if (provider !== 'openrouter' && provider !== 'ollama') { + const hostedDefaultModel = DEFAULT_MODEL_BY_PROVIDER[provider] + hostingConfig = { + envKeyPrefix: options.envKeyPrefix, + apiKeyParam: 'apiKey', + byokProviderId: BYOK_PROVIDER_IDS[provider], + 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 : hostedDefaultModel + 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, + } + } return { id, @@ -90,30 +111,47 @@ export function createEmbeddingTool( }, model: { type: 'string', - required: false, + required: isKeyless, 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', + description: isKeyless + ? 'Embedding model pulled on the configured Ollama server' + : 'Embedding model to use', + ...(defaultModel !== undefined ? { default: defaultModel } : {}), }, + /** + * Ollama's API accepts neither a task type nor a reduction, and its width + * is read off the server rather than requested, so the keyless tool + * declares neither — a parameter a tool ignores is worse than one it does + * not offer. + */ + ...(isKeyless + ? {} + : { + taskType: { + type: 'string' as const, + required: false, + visibility: 'user-only' as const, + description: + 'What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering', + }, + dimensions: { + type: 'number' as const, + required: false, + visibility: 'user-only' as const, + description: + 'Output dimensions, when the model supports truncation. Defaults to native.', + }, + }), + ...(isKeyless + ? {} + : { + apiKey: { + type: 'string' as const, + required: true, + visibility: 'user-only' as const, + description: 'API key for the selected embedding provider', + }, + }), }, hosting: hostingConfig, diff --git a/apps/sim/tools/embeddings/index.ts b/apps/sim/tools/embeddings/index.ts index 81871fc1e60..8cd87d8bc77 100644 --- a/apps/sim/tools/embeddings/index.ts +++ b/apps/sim/tools/embeddings/index.ts @@ -2,6 +2,7 @@ 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 { embeddingsOllamaTool } from '@/tools/embeddings/ollama' export { embeddingsOpenAITool } from '@/tools/embeddings/openai' export { embeddingsOpenRouterTool } from '@/tools/embeddings/openrouter' export type { EmbeddingsParams, EmbeddingsResponse } from '@/tools/embeddings/types' diff --git a/apps/sim/tools/embeddings/ollama.ts b/apps/sim/tools/embeddings/ollama.ts new file mode 100644 index 00000000000..283d3a07e65 --- /dev/null +++ b/apps/sim/tools/embeddings/ollama.ts @@ -0,0 +1,8 @@ +import { createEmbeddingTool } from '@/tools/embeddings/factory' + +export const embeddingsOllamaTool = createEmbeddingTool({ + id: 'embeddings_ollama', + name: 'Ollama Embeddings', + provider: 'ollama', + description: 'Generate embeddings on a self-hosted Ollama server', +}) diff --git a/apps/sim/tools/generated/tool-ids.ts b/apps/sim/tools/generated/tool-ids.ts index 6d97df92a01..e798577a829 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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partial_update_record","algolia_search","algolia_update_settings","amplitude_event_segmentation","amplitude_funnels","amplitude_get_active_users","amplitude_get_revenue","amplitude_group_identify","amplitude_identify_user","amplitude_list_events","amplitude_realtime_active_users","amplitude_retention","amplitude_send_event","amplitude_user_activity","amplitude_user_profile","amplitude_user_search","apify_get_dataset_items","apify_get_run","apify_run_actor_async","apify_run_actor_sync","apify_run_task","apollo_account_bulk_create","apollo_account_bulk_update","apollo_account_create","apollo_account_search","apollo_account_update","apollo_contact_bulk_create","apollo_contact_bulk_update","apollo_contact_create","apollo_contact_search","apollo_contact_update","apollo_email_accounts","apollo_opportunity_create","apollo_opportunity_get","apollo_opportunity_search","apollo_opportunity_update","apollo_organization_bulk_enrich","apollo_organization_enrich","apollo_organization_search","apollo_people_bulk_enrich","apollo_people_enrich","apollo_people_search","apollo_sequence_add_contacts","apollo_sequence_search","apollo_task_create","apollo_task_search","appconfig_create_application","appconfig_create_configuration_profile","appconfig_create_environment","appconfig_create_hosted_configuration_version","appconfig_delete_application","appconfig_delete_configuration_profile","appconfig_delete_environment","appconfig_delete_hosted_configuration_version","appconfig_get_application","appconfig_get_configuration","appconfig_get_configuration_profile","appconfig_get_deployment","appconfig_get_environment","appconfig_get_hosted_configuration_version","appconfig_list_applications","appconfig_list_configuration_profiles","appconfig_list_deployment_strategies","appconfig_list_deployments","appconfig_list_environments","appconfig_list_hosted_configuration_versions","appconfig_start_deployment","appconfig_stop_deployment","appconfig_update_application","appconfig_update_configuration_profile","appconfig_update_environment","arxiv_get_author_papers","arxiv_get_paper","arxiv_search","asana_add_comment","asana_add_followers","asana_create_project","asana_create_section","asana_create_subtask","asana_create_task","asana_delete_task","asana_get_project","asana_get_projects","asana_get_task","asana_list_sections","asana_list_workspaces","asana_search_tasks","asana_update_task","ashby_add_candidate_tag","ashby_anonymize_candidate","ashby_change_application_source","ashby_change_application_stage","ashby_create_application","ashby_create_candidate","ashby_create_note","ashby_delete_application","ashby_get_application","ashby_get_candidate","ashby_get_job","ashby_get_job_posting","ashby_get_offer","ashby_get_opening","ashby_list_application_feedback","ashby_list_application_history","ashby_list_applications","ashby_list_archive_reasons","ashby_list_candidate_tags","ashby_list_candidates","ashby_list_custom_fields","ashby_list_departments","ashby_list_interview_plans","ashby_list_interview_stages","ashby_list_interviews","ashby_list_job_postings","ashby_list_jobs","ashby_list_locations","ashby_list_notes","ashby_list_offers","ashby_list_openings","ashby_list_sources","ashby_list_users","ashby_remove_candidate_tag","ashby_search_candidates","ashby_search_jobs","ashby_search_openings","ashby_search_users","ashby_set_custom_field_value","ashby_set_custom_field_values","ashby_transfer_application","ashby_update_candidate","ashby_upload_candidate_file","ashby_upload_resume","athena_batch_get_query_execution","athena_create_named_query","athena_delete_named_query","athena_get_named_query","athena_get_query_execution","athena_get_query_results","athena_list_databases","athena_list_named_queries","athena_list_query_executions","athena_list_table_metadata","athena_start_query","athena_stop_query","attio_assert_record","attio_create_attribute","attio_create_comment","attio_create_list","attio_create_list_entry","attio_create_note","attio_create_object","attio_create_record","attio_create_task","attio_create_webhook","attio_delete_comment","attio_delete_list_entry","attio_delete_note","attio_delete_record","attio_delete_task","attio_delete_webhook","attio_get_attribute","attio_get_comment","attio_get_list","attio_get_list_entry","attio_get_member","attio_get_note","attio_get_object","attio_get_record","attio_get_task","attio_get_thread","attio_get_webhook","attio_list_attributes","attio_list_lists","attio_list_members","attio_list_notes","attio_list_objects","attio_list_records","attio_list_tasks","attio_list_threads","attio_list_webhooks","attio_query_list_entries","attio_search_records","attio_update_attribute","attio_update_list","attio_update_list_entry","attio_update_object","attio_update_record","attio_update_task","attio_update_webhook","azure_data_explorer_create_table","azure_data_explorer_drop_table","azure_data_explorer_ingest_from_query","azure_data_explorer_ingest_inline","azure_data_explorer_list_databases","azure_data_explorer_list_fu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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","windchill_check_in_document","windchill_check_in_documents","windchill_check_out_document","windchill_check_out_documents","windchill_create_document","windchill_create_documents","windchill_delete_document","windchill_delete_documents","windchill_download_attachment","windchill_download_primary_content","windchill_get_document","windchill_get_document_structure","windchill_get_primary_content","windchill_get_valid_state_transitions","windchill_list_attachments","windchill_list_documents","windchill_revise_document","windchill_revise_documents","windchill_set_lifecycle_state","windchill_undo_check_out_document","windchill_undo_check_out_documents","windchill_update_common_properties","windchill_update_document","windchill_update_document_security_labels","windchill_update_documents","windchill_upload_attachments","windchill_upload_primary_content","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_sea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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","tinyfish_cancel_run","tinyfish_fetch","tinyfish_get_run","tinyfish_list_runs","tinyfish_list_vault_items","tinyfish_run","tinyfish_run_async","tinyfish_search","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","windchill_check_in_document","windchill_check_in_documents","windchill_check_out_document","windchill_check_out_documents","windchill_create_document","windchill_create_documents","windchill_delete_document","windchill_delete_documents","windchill_download_attachment","windchill_download_primary_content","windchill_get_document","windchill_get_document_structure","windchill_get_primary_content","windchill_get_valid_state_transitions","windchill_list_attachments","windchill_list_documents","windchill_revise_document","windchill_revise_documents","windchill_set_lifecycle_state","windchill_undo_check_out_document","windchill_undo_check_out_documents","windchill_update_common_properties","windchill_update_document","windchill_update_document_security_labels","windchill_update_documents","windchill_upload_attachments","windchill_upload_primary_content","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 a6952f954fe..6013b552f97 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)"}},"hostedApiKey":"none"},"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)"}},"hostedApiKey":"none"},"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)"}},"hostedApiKey":"none"},"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)"}},"hostedApiKey":"none"},"affinity_batch_update_entity_fields":{"id":"affinity_batch_update_entity_fields","name":"Affinity Batch Update Entity Fields","description":"Write up to 100 non-list field values on one company or person in a single request.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity to write the fields on: companies or persons"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company or person"},"updates":{"type":"json","required":true,"visibility":"user-or-llm","description":"Up to 100 field updates as [{\\"id\\":\\"\\",\\"value\\":{\\"type\\":\\"…\\",\\"data\\":…}}], using the same value shapes as a single field update"}},"hostedApiKey":"none"},"affinity_batch_update_list_entry_fields":{"id":"affinity_batch_update_list_entry_fields","name":"Affinity Batch Update List Entry Fields","description":"Write up to 100 field values on one list row in a single request. Requires the \\"Export data from Lists\\" permission.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"listEntryId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list entry ID"},"updates":{"type":"json","required":true,"visibility":"user-or-llm","description":"Up to 100 field updates as [{\\"id\\":\\"\\",\\"value\\":{\\"type\\":\\"…\\",\\"data\\":…}}], using the same value shapes as a single field update"}},"hostedApiKey":"none"},"affinity_create_list":{"id":"affinity_create_list","name":"Affinity Create List","description":"Create a list. Its type fixes which entities it can hold, and the API key holder becomes its creator and owner.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"name":{"type":"string","required":true,"visibility":"user-or-llm","description":"Name of the new list"},"type":{"type":"string","required":true,"visibility":"user-or-llm","description":"Entity kind the list holds: company, opportunity, or person"},"isPublic":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Whether everyone in the organization can see the list"}},"hostedApiKey":"none"},"affinity_create_list_field_dropdown_option":{"id":"affinity_create_list_field_dropdown_option","name":"Affinity Create List Field Dropdown Option","description":"Add a selectable option to a dropdown field on a list. A ranked or status option also needs a rank and a color.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown field ID on that list"},"type":{"type":"string","required":true,"visibility":"user-or-llm","description":"Kind of option to create, matching the field. dropdown takes only a label; ranked-dropdown also requires rank and color; status-dropdown additionally requires a status category. Sending a field the kind does not accept is rejected"},"text":{"type":"string","required":true,"visibility":"user-or-llm","description":"The option label"},"rank":{"type":"number","required":false,"visibility":"user-or-llm","description":"Sort order. Required on a ranked-dropdown or status-dropdown option"},"color":{"type":"string","required":false,"visibility":"user-or-llm","description":"Option color: white, gray, blue, green, purple, orange, or red. Required on a ranked-dropdown or status-dropdown option"},"statusCategory":{"type":"string","required":false,"visibility":"user-or-llm","description":"Pipeline meaning of the option: open, won, lost, or on-hold. Status-dropdown options only"},"winRate":{"type":"number","required":false,"visibility":"user-or-llm","description":"Expected win rate of the status. Status-dropdown options only"}},"hostedApiKey":"none"},"affinity_create_merge":{"id":"affinity_create_merge","name":"Affinity Create Merge","description":"Fold a duplicate company or person into the record you are keeping. The merge runs asynchronously — poll the returned task to see it finish. Requires the \\"Manage duplicates\\" permission and an admin role.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"What to merge: companies or persons"},"primaryId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the record to keep"},"duplicateId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of the duplicate record to fold in"}},"hostedApiKey":"none"},"affinity_create_note":{"id":"affinity_create_note","name":"Affinity Create Note","description":"Write a note — attached to companies, persons, and opportunities, anchored to a meeting, call, or chat message, or posted as a reply to an existing note.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"type":{"type":"string","required":true,"visibility":"user-or-llm","description":"Note shape: entities to attach it to records, interaction to anchor it to a meeting, call, or chat message, or user-reply to reply to a note"},"html":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note body as HTML"},"companyIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Companies to attach the note to, e.g. [1, 2]. Not used on a reply"},"personIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Persons to attach the note to, e.g. [1, 2]. Not used on a reply"},"opportunityIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Opportunities to attach the note to, e.g. [1, 2]. Not used on a reply"},"interactionId":{"type":"string","required":false,"visibility":"user-or-llm","description":"The interaction to anchor the note to. Required for an interaction note"},"interactionType":{"type":"string","required":false,"visibility":"user-or-llm","description":"Kind of the anchoring interaction: meeting, call, or chat-message. Required for an interaction note"},"parentId":{"type":"string","required":false,"visibility":"user-or-llm","description":"The note being replied to. Required for a user-reply note"},"creatorId":{"type":"string","required":false,"visibility":"user-or-llm","description":"Attribute the note to another internal person. Defaults to the API key holder"},"createdAt":{"type":"string","required":false,"visibility":"user-or-llm","description":"Backdate the note to this ISO 8601 timestamp"}},"hostedApiKey":"none"},"affinity_create_reminder":{"id":"affinity_create_reminder","name":"Affinity Create Reminder","description":"Create a reminder on one company, person, or opportunity. A recurring reminder resets whenever the chosen signal happens instead of firing once.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"type":{"type":"string","required":true,"visibility":"user-or-llm","description":"one-time to fire once, or recurring to reset on a signal"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"What the reminder is about: company, person, or opportunity"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company, person, or opportunity"},"dueDate":{"type":"string","required":false,"visibility":"user-or-llm","description":"When the reminder is due, as an ISO 8601 timestamp. Required for a one-time reminder; on a recurring one Affinity computes it from the period when omitted"},"content":{"type":"string","required":false,"visibility":"user-or-llm","description":"What the reminder says"},"ownerId":{"type":"string","required":true,"visibility":"user-or-llm","description":"User the reminder is assigned to. Must be an internal user. The API key holder is recorded as the creator, which is a separate field"},"resetTrigger":{"type":"string","required":false,"visibility":"user-or-llm","description":"What restarts a recurring reminder: interaction, email, or event. Required when the type is recurring"},"periodDays":{"type":"number","required":false,"visibility":"user-or-llm","description":"Days between firings of a recurring reminder. Required when the type is recurring"}},"hostedApiKey":"none"},"affinity_delete_list_field_dropdown_option":{"id":"affinity_delete_list_field_dropdown_option","name":"Affinity Delete List Field Dropdown Option","description":"Permanently delete a dropdown option on a list field. Every list entry currently set to it is cleared, and those values cannot be recovered.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown field ID on that list"},"dropdownOptionId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown option ID to delete"}},"hostedApiKey":"none"},"affinity_delete_note":{"id":"affinity_delete_note","name":"Affinity Delete Note","description":"Delete a note you created. Deleting a root note also deletes its replies; deleting a reply removes only that reply.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"noteId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note ID to delete"}},"hostedApiKey":"none"},"affinity_get_company":{"id":"affinity_get_company","name":"Affinity Get Company","description":"Look up one company by ID. Field data is returned only for the Field IDs or Field Types asked for.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"companyId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The company ID"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, or relationship-intelligence. Mutually exclusive with Field IDs"}},"hostedApiKey":"none"},"affinity_get_current_user":{"id":"affinity_get_current_user","name":"Affinity Get Current User","description":"Verify an Affinity API key and return the tenant, the user behind the key, and the scopes the grant carries.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"}},"hostedApiKey":"none"},"affinity_get_entity_field_value":{"id":"affinity_get_entity_field_value","name":"Affinity Get Entity Field Value","description":"Read one non-list field value from a company or person.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity to read the field from: companies or persons"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company or person"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The field ID to read"}},"hostedApiKey":"none"},"affinity_get_list":{"id":"affinity_get_list","name":"Affinity Get List","description":"Read one list — its name, type, owner, and privacy setting.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"}},"hostedApiKey":"none"},"affinity_get_list_entry":{"id":"affinity_get_list_entry","name":"Affinity Get List Entry","description":"Read one row of a list with its entity. Field data is returned only for the Field IDs or Field Types asked for.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"listEntryId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list entry ID"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, list, or relationship-intelligence. Mutually exclusive with Field IDs"}},"hostedApiKey":"none"},"affinity_get_list_entry_field":{"id":"affinity_get_list_entry_field","name":"Affinity Get List Entry Field","description":"Read one field value on a list row.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"listEntryId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list entry ID"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The field ID to read"}},"hostedApiKey":"none"},"affinity_get_list_field_dropdown_option":{"id":"affinity_get_list_field_dropdown_option","name":"Affinity Get List Field Dropdown Option","description":"Read one dropdown option on a list field.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown field ID on that list"},"dropdownOptionId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown option ID"}},"hostedApiKey":"none"},"affinity_get_merge":{"id":"affinity_get_merge","name":"Affinity Get Merge","description":"Read the status of one company or person merge, including why it failed if it did.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which merge to read: companies or persons"},"mergeId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The merge ID"}},"hostedApiKey":"none"},"affinity_get_merge_task":{"id":"affinity_get_merge_task","name":"Affinity Get Merge Task","description":"Read one merge task and how its merges are progressing. Poll this after starting a merge.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which merge task to read: companies or persons"},"taskId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The merge task ID"}},"hostedApiKey":"none"},"affinity_get_note":{"id":"affinity_get_note","name":"Affinity Get Note","description":"Read one note with its body, author, mentions, and attached records.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"noteId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note ID"},"includes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Extra properties to return, e.g. [\\"repliesCount\\",\\"personsPreview\\",\\"companiesPreview\\",\\"opportunitiesPreview\\"]. Those four fields are omitted unless requested here"}},"hostedApiKey":"none"},"affinity_get_opportunity":{"id":"affinity_get_opportunity","name":"Affinity Get Opportunity","description":"Read one opportunity and the list it belongs to. Its field data lives on the list entry.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"opportunityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The opportunity ID"}},"hostedApiKey":"none"},"affinity_get_person":{"id":"affinity_get_person","name":"Affinity Get Person","description":"Look up one person by ID. Field data is returned only for the Field IDs or Field Types asked for.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"personId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The person ID"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, or relationship-intelligence. Mutually exclusive with Field IDs"}},"hostedApiKey":"none"},"affinity_get_saved_view":{"id":"affinity_get_saved_view","name":"Affinity Get Saved View","description":"Read one saved view — its name, kind, and creation date.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"viewId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The saved view ID"}},"hostedApiKey":"none"},"affinity_get_transcript":{"id":"affinity_get_transcript","name":"Affinity Get Transcript","description":"Read one transcript with its first 100 fragments. Page the fragments endpoint for a longer meeting.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"transcriptId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The transcript ID"}},"hostedApiKey":"none"},"affinity_get_user":{"id":"affinity_get_user","name":"Affinity Get User","description":"Read one internal user. A user and their person record share the same numeric ID, so a person ID works here.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"userId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The user ID, which is also their person ID"}},"hostedApiKey":"none"},"affinity_list_calls":{"id":"affinity_list_calls","name":"Affinity List Calls","description":"Page through logged calls and their participants. Only calls the API key holder can see are returned.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_chat_messages":{"id":"affinity_list_chat_messages","name":"Affinity List Chat Messages","description":"Page through logged chat messages and their participants. Only messages the API key holder can see are returned.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_companies":{"id":"affinity_list_companies","name":"Affinity List Companies","description":"Page through companies. Companies come back without field data unless Field IDs or Field Types asks for it.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict the page to these company IDs, e.g. [1, 2, 3]"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, or relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_coworker_connections":{"id":"affinity_list_coworker_connections","name":"Affinity List Coworker Connections","description":"Find warm paths into a company through shared work history: who in your Affinity data once worked alongside the people you want to reach. Grouped by target, strongest first.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":true,"visibility":"user-or-llm","description":"Required scope. The only supported filter is target.currentCompany.id, e.g. \\"target.currentCompany.id=123\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of targets to return per page, 1-50. Defaults to 20"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_emails":{"id":"affinity_list_emails","name":"Affinity List Emails","description":"Page through email metadata — subject, participants, and timestamps. Affinity never exposes email bodies through the API.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_entity_field_values":{"id":"affinity_list_entity_field_values","name":"Affinity List Entity Field Values","description":"Page through a company\'s or person\'s non-list field values. List fields are not returned here — read those through the list entry.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity to read field values from: companies or persons"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company or person"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict to these field IDs. Mutually exclusive with Field Types"},"types":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict to these field categories: enriched, global, relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 20"}},"hostedApiKey":"none"},"affinity_list_entity_list_entries":{"id":"affinity_list_entity_list_entries","name":"Affinity List Entity List Entries","description":"Page through a company\'s or person\'s rows across every list, each carrying that list\'s field values and when the entity was added.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity to look up the rows of: companies or persons"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company or person"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_entity_lists":{"id":"affinity_list_entity_lists","name":"Affinity List Entity Lists","description":"List every list a company or person appears on that the caller can view.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity to look up the lists of: companies or persons"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company or person"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_entity_notes":{"id":"affinity_list_entity_notes","name":"Affinity List Entity Notes","description":"List the notes relevant to one company, person, or opportunity — directly attached notes plus notes reaching it through its people and meetings.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity the notes hang off: companies, persons, or opportunities"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company, person, or opportunity"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_entity_relationships":{"id":"affinity_list_entity_relationships","name":"Affinity List Entity Relationships","description":"List who knows a company or person, scored 0.0 to 1.0 by how much the two actually interact. Strongest first by default.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which entity to look up relationships for: companies or persons"},"entityId":{"type":"string","required":true,"visibility":"user-or-llm","description":"ID of that company or person"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression. This endpoint filters on interactionScore only, e.g. \\"interactionScore>=0.5\\""},"orderBy":{"type":"json","required":false,"visibility":"user-or-llm","description":"Sort order: [\\"interactionScore\\"] for weakest first, [\\"-interactionScore\\"] for strongest first (the default)"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_field_dropdown_options":{"id":"affinity_list_field_dropdown_options","name":"Affinity List Field Dropdown Options","description":"List the selectable options on a dropdown or ranked-dropdown company or person field. Writing such a field needs the option ID, not its text.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which field family the field belongs to: companies or persons"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown or ranked-dropdown field ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_field_metadata":{"id":"affinity_list_field_metadata","name":"Affinity List Field Metadata","description":"List the non-list company or person fields, with the value type, filter operators, and sort support of each. Start here to find the Field IDs the read and write tools take.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which fields to describe: companies or persons"},"includes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Extra properties to return: [\\"filterability\\",\\"sortability\\"]. Both are omitted unless requested here"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression. This endpoint filters on name only, e.g. \\"name=~Status\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_field_value_changes":{"id":"affinity_list_field_value_changes","name":"Affinity List Field Value Changes","description":"Page through field value changes across the whole workspace. Built for delta sync: follow nextCursor to the end of a run, then resume from the last cursor next time.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression over field.id, listEntry.id, changer.id, changedAt, or actionType. Resume a sync with e.g. \\"changedAt>2026-06-01T12:00:00Z\\""},"orderBy":{"type":"json","required":false,"visibility":"user-or-llm","description":"Sort order: [\\"changedAt\\"] for oldest first (the default), [\\"-changedAt\\"] for newest first"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_investor_executive_connections":{"id":"affinity_list_investor_executive_connections","name":"Affinity List Investor Executive Connections","description":"Find warm paths into a company through investment history: which investors in your Affinity data backed a company the people you want to reach once led. Grouped by target, strongest first.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":true,"visibility":"user-or-llm","description":"Required scope. The only supported filter is target.currentCompany.id, e.g. \\"target.currentCompany.id=123\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of targets to return per page, 1-50. Defaults to 20"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_list_entries":{"id":"affinity_list_list_entries","name":"Affinity List List Entries","description":"Page through the rows of a list. Rows come back without field data unless Field IDs or Field Types asks for it.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, list, or relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_list_entry_field_value_changes":{"id":"affinity_list_list_entry_field_value_changes","name":"Affinity List List Entry Field Value Changes","description":"Page through the history of one list row — who changed which field, when, and to what.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"listEntryId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list entry ID"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression over field.id, changer.id, changedAt, or actionType, e.g. \\"field.id=field-1234\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_list_entry_fields":{"id":"affinity_list_list_entry_fields","name":"Affinity List List Entry Fields","description":"Page through every field value on one list row, including the list-specific columns. All fields are returned unless narrowed.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"listEntryId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list entry ID"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict to these field IDs. Mutually exclusive with Field Types"},"types":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict to these field categories: enriched, global, list, relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 20"}},"hostedApiKey":"none"},"affinity_list_list_field_dropdown_options":{"id":"affinity_list_list_field_dropdown_options","name":"Affinity List List Field Dropdown Options","description":"List the selectable options on a dropdown, ranked-dropdown, or status-dropdown field of a list.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"fieldId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The dropdown field ID on that list"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_list_fields":{"id":"affinity_list_list_fields","name":"Affinity List List Fields","description":"List the fields available on one list, including its list-specific columns. Use these Field IDs when reading or writing list entries.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"includes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Extra properties to return: [\\"filterability\\",\\"sortability\\"]. Both are omitted unless requested here"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression. This endpoint filters on name only, e.g. \\"name=~Stage\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_lists":{"id":"affinity_list_lists","name":"Affinity List Lists","description":"Page through the lists in the organization that the caller can view.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"term":{"type":"string","required":false,"visibility":"user-or-llm","description":"Case-insensitive substring match on the list name"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_meetings":{"id":"affinity_list_meetings","name":"Affinity List Meetings","description":"Page through past and upcoming meetings with their organizer and attendees.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_merge_tasks":{"id":"affinity_list_merge_tasks","name":"Affinity List Merge Tasks","description":"Page through merge tasks, each summarizing how many of its merges are in progress, succeeded, or failed.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which merge tasks to list: companies or persons"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression. This endpoint filters on status only, e.g. \\"status=in-progress\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_merges":{"id":"affinity_list_merges","name":"Affinity List Merges","description":"Page through the company or person merges the organization has run, with the status and the records involved in each.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"entityType":{"type":"string","required":true,"visibility":"user-or-llm","description":"Which merges to list: companies or persons"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression over status or taskId, e.g. \\"status=failed\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_note_attached_companies":{"id":"affinity_list_note_attached_companies","name":"Affinity List Note Attached Companies","description":"List the companies directly attached to one note.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"noteId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_note_attached_opportunities":{"id":"affinity_list_note_attached_opportunities","name":"Affinity List Note Attached Opportunities","description":"List the opportunities directly attached to one note.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"noteId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_note_attached_persons":{"id":"affinity_list_note_attached_persons","name":"Affinity List Note Attached Persons","description":"List the persons directly attached to one note.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"noteId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_note_replies":{"id":"affinity_list_note_replies","name":"Affinity List Note Replies","description":"Page through the replies on one note, including AI Notetaker replies.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"noteId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The note ID whose replies to read"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_notes":{"id":"affinity_list_notes","name":"Affinity List Notes","description":"Page through every note the caller can see. Replies are excluded.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"includes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Extra properties to return, e.g. [\\"repliesCount\\",\\"personsPreview\\",\\"companiesPreview\\",\\"opportunitiesPreview\\"]. Those four fields are omitted unless requested here"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_opportunities":{"id":"affinity_list_opportunities","name":"Affinity List Opportunities","description":"Page through opportunities. Field data lives on the list entry, not here — read it through the list or saved view the opportunity belongs to.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict the page to these opportunity IDs, e.g. [1, 2, 3]"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_persons":{"id":"affinity_list_persons","name":"Affinity List Persons","description":"Page through persons. Persons come back without field data unless Field IDs or Field Types asks for it.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict the page to these person IDs, e.g. [1, 2, 3]"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, or relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_reminders":{"id":"affinity_list_reminders","name":"Affinity List Reminders","description":"Page through the reminders the caller can see. Filter by status to surface what is overdue.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_saved_view_entries":{"id":"affinity_list_saved_view_entries","name":"Affinity List Saved View Entries","description":"Page through the rows of a saved view. The view\'s own filters and columns decide which rows and which field data come back.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"viewId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The saved view ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_saved_views":{"id":"affinity_list_saved_views","name":"Affinity List Saved Views","description":"List the saved views on a list that the caller can view.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_list_transcript_fragments":{"id":"affinity_list_transcript_fragments","name":"Affinity List Transcript Fragments","description":"Page through everything said in a meeting, segment by segment with the speaker.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"transcriptId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The transcript ID"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_transcripts":{"id":"affinity_list_transcripts","name":"Affinity List Transcripts","description":"Page through meeting transcript metadata. Read one transcript to get what was actually said.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression, e.g. \\"createdAt>=2026-01-01\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_list_users":{"id":"affinity_list_users","name":"Affinity List Users","description":"Page through the internal users in the organization. Email addresses and roles are returned only to callers with the \\"Manage Users\\" permission.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"term":{"type":"string","required":false,"visibility":"user-or-llm","description":"Case-insensitive match across first name, last name, and primary email"},"filter":{"type":"string","required":false,"visibility":"user-or-llm","description":"Affinity Filtering Language expression over id or status, e.g. \\"status=active\\""},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"}},"hostedApiKey":"none"},"affinity_search_companies":{"id":"affinity_search_companies","name":"Affinity Search Companies","description":"Search companies by filters, sorts, and a free-text term. Requires the \\"Export All Organizations directory\\" permission.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filters":{"type":"json","required":false,"visibility":"user-or-llm","description":"Filter group as {operator: \\"and\\"|\\"or\\", filters: [...]}, at most 50 leaves. Each leaf is {valueType, fieldId, operator, value}, and a leaf may itself be a nested group"},"searchTerm":{"type":"string","required":false,"visibility":"user-or-llm","description":"Free-text term matched against the searchable fields. At least 3 characters"},"searchFieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs the search term is matched against. Defaults to the searchable fields"},"sorts":{"type":"json","required":false,"visibility":"user-or-llm","description":"Sort order as [{fieldId, direction: \\"asc\\"|\\"desc\\", attributeId?}], up to 5, applied in order"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, or relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_search_files":{"id":"affinity_search_files","name":"Affinity Search Files","description":"Search files by keyword, ordered by relevance. Narrow to specific files or to one company, or leave both unset to search the whole account.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"prompt":{"type":"string","required":true,"visibility":"user-or-llm","description":"What to search for. Between 3 and 500 characters"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict the search to these file IDs. Cannot be combined with Company ID"},"companyId":{"type":"string","required":false,"visibility":"user-or-llm","description":"Restrict the search to one company\'s files. Cannot be combined with file IDs"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of files to return, 1-100. Defaults to 20"}},"hostedApiKey":"none"},"affinity_search_list_entries":{"id":"affinity_search_list_entries","name":"Affinity Search List Entries","description":"Search the rows of one list by filters, sorts, and a free-text term. Requires the \\"Export data from Lists\\" permission.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"listId":{"type":"string","required":true,"visibility":"user-or-llm","description":"The list ID to search"},"filters":{"type":"json","required":false,"visibility":"user-or-llm","description":"Filter group as {operator: \\"and\\"|\\"or\\", filters: [...]}, at most 50 leaves. Each leaf is {valueType, fieldId, operator, value}, and a leaf may itself be a nested group"},"searchTerm":{"type":"string","required":false,"visibility":"user-or-llm","description":"Free-text term matched against the searchable fields. At least 3 characters"},"searchFieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs the search term is matched against. Defaults to the searchable fields"},"sorts":{"type":"json","required":false,"visibility":"user-or-llm","description":"Sort order as [{fieldId, direction: \\"asc\\"|\\"desc\\", attributeId?}], up to 5, applied in order"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, list, or relationship-intelligence. Mutually exclusive with Field IDs"},"cursor":{"type":"string","required":false,"visibility":"user-or-llm","description":"Cursor from a previous page, returned as nextCursor or prevCursor"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Number of items to return per page, 1-100. Defaults to 100"},"totalCount":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Include the total size of the collection. Costs an extra query"}},"hostedApiKey":"none"},"affinity_search_notes":{"id":"affinity_search_notes","name":"Affinity Search Notes","description":"Search notes by keyword, ordered by relevance. Narrow to specific notes or to one company, or leave both unset to search the whole account.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"prompt":{"type":"string","required":true,"visibility":"user-or-llm","description":"What to search for. Between 3 and 500 characters"},"ids":{"type":"json","required":false,"visibility":"user-or-llm","description":"Restrict the search to these note IDs. Cannot be combined with Company ID"},"companyId":{"type":"string","required":false,"visibility":"user-or-llm","description":"Restrict the search to one company\'s notes. Cannot be combined with note IDs"},"limit":{"type":"number","required":false,"visibility":"user-or-llm","description":"Maximum number of notes to return, 1-100. Defaults to 20"}},"hostedApiKey":"none"},"affinity_search_persons":{"id":"affinity_search_persons","name":"Affinity Search Persons","description":"Search persons by filters, sorts, and a free-text term. Requires the \\"Export All People directory\\" permission.","version":"1.0.0","params":{"apiKey":{"type":"string","required":true,"visibility":"user-only","description":"Affinity API key, sent as a bearer token"},"filters":{"type":"json","required":false,"visibility":"user-or-llm","description":"Filter group as {operator: \\"and\\"|\\"or\\", filters: [...]}, at most 50 leaves. Each leaf is {valueType, fieldId, operator, value}, and a leaf may itself be a nested group"},"searchTerm":{"type":"string","required":false,"visibility":"user-or-llm","description":"Free-text term matched against the searchable fields. At least 3 characters"},"searchFieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs the search term is matched against. Defaults to the searchable fields"},"sorts":{"type":"json","required":false,"visibility":"user-or-llm","description":"Sort order as [{fieldId, direction: \\"asc\\"|\\"desc\\", attributeId?}], up to 5, applied in order"},"fieldIds":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field IDs to return values for, e.g. [\\"affinity-data-location\\"]. Mutually exclusive with Field Types"},"fieldTypes":{"type":"json","required":false,"visibility":"user-or-llm","description":"Field categories to return values for: enriched, global, or relationship-intelligence. 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Pick something that identifies a record uniquely — if more than one record matches, Agiloft writes nothing and returns a conflict."},"async":{"type":"boolean","required":false,"visibility":"user-or-llm","description":"Queue the write instead of waiting for it. Returns a callback ID instead of a record ID; pass that to Async Status to poll the result."},"data":{"type":"string","required":true,"visibility":"user-or-llm","description":"Field values as a JSON object. On create these populate the new record; on update only the supplied fields change."}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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"}},"hostedApiKey":"none"},"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. 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