> ## Documentation Index > Fetch the complete documentation index at: https://docs.orbit.devotel.io/llms.txt > Use this file to discover all available pages before exploring further. # AI API > LLM-backed analysis, translation, suggestion, classification, and content generation. The same primitives that power the Orbit dashboard, exposed for your own apps. # AI API A focused set of LLM-backed primitives that the Orbit platform uses internally — message analysis, reply suggestion, intent classification, summarization, content generation, and a small knowledge-base utility. All LLM requests route through Orbit's partner-vetted Anthropic gateway — you don't manage model-provider keys directly. **Base path:** `/api/v1/ai` **Authentication:** API key (`X-API-Key`) or session JWT. Service-to-service callers (with `X-Internal-Token`) get the `developer` role to cap blast radius. ## Analysis & insight | Method | Path | Purpose | | ------ | ----------------------------------- | ---------------------------------- | | `POST` | `/api/v1/ai/analyze` | General message / content analysis | | `POST` | `/api/v1/ai/analyze-message/{id}` | Analyze a specific message by ID | | `POST` | `/api/v1/ai/classify-intent` | Classify a message's intent | | `POST` | `/api/v1/ai/summarize-conversation` | Summarize a long conversation | | `POST` | `/api/v1/ai/dashboard-insights` | Generate dashboard insight prose | ## Generation & rewriting | Method | Path | Purpose | | ------ | ----------------------------- | ----------------------------------------- | | `POST` | `/api/v1/ai/suggest-reply` | Draft a reply to an inbound message | | `POST` | `/api/v1/ai/generate-content` | Generate marketing content from a brief | | `POST` | `/api/v1/ai/improve-text` | Rewrite for tone / clarity / length | | `POST` | `/api/v1/ai/translate` | Translate to a target language | | `POST` | `/api/v1/ai/compose-campaign` | Compose a multi-step campaign from a goal | | `POST` | `/api/v1/ai/optimize-channel` | Recommend best channel/time for a contact | ## Inference building blocks Two general-purpose endpoints let you run embeddings and chat inference on the same managed fleet — and the same wallet — as the rest of your Orbit comms. Both are OpenAI-compatible, so existing SDKs drop in by pointing their base URL at `/api/v1/ai`. | Method | Path | Purpose | | ------ | ----------------------------- | ------------------------------------------------ | | `POST` | `/api/v1/ai/embeddings` | Generate embedding vectors for one or more texts | | `POST` | `/api/v1/ai/chat/completions` | Run a chat completion (general inference) | Both endpoints call a model provider on every request and are billed per token, metered to your workspace wallet. They share the same rate limit as the other AI routes. ### Embeddings `POST /api/v1/ai/embeddings` returns one vector per input. Send `input` as a single string, or as an array of up to 96 strings (each 1–10,000 characters) to embed a batch in one request. ```bash theme={null} curl -X POST https://api.orbit.devotel.io/api/v1/ai/embeddings \ -H "X-API-Key: dv_live_sk_your_key_here" \ -H "Content-Type: application/json" \ -d '{ "input": ["order status", "refund policy"] }' ``` | Field | Type | Required | Notes | | ------- | ---------------------- | -------- | -------------------------------------------------- | | `input` | `string` or `string[]` | yes | One text, or up to 96 texts (each 1–10,000 chars). | The response carries the vectors and per-request token usage under `data`. A malformed body (missing `input`, an empty string, or more than 96 items) returns `400`. ### Chat completions `POST /api/v1/ai/chat/completions` is OpenAI-compatible. Point any OpenAI SDK at your `/api/v1/ai` base URL — the SDK appends `/chat/completions` — and send a `messages` array. OpenAI model ids are remapped to the platform's managed model, so you don't manage provider keys. ```bash theme={null} curl -X POST https://api.orbit.devotel.io/api/v1/ai/chat/completions \ -H "X-API-Key: dv_live_sk_your_key_here" \ -H "Content-Type: application/json" \ -d '{ "messages": [ { "role": "system", "content": "You are a concise support assistant." }, { "role": "user", "content": "Summarize our refund policy in one line." } ] }' ``` | Field | Type | Required | Notes | | ----------------- | ---------- | -------- | ------------------------------------------------------------------------------------------ | | `messages` | `object[]` | yes | 1–50 turns; each has `role` (`system`/`user`/`assistant`) and `content` (1–100,000 chars). | | `model` | `string` | no | Optional model id; OpenAI ids are remapped to the platform's fast tier. | | `temperature` | `number` | no | Sampling temperature, `0`–`2`. | | `max_tokens` | `integer` | no | Output token cap, `1`–`8192`. | | `response_format` | `object` | no | Set `{ "type": "json_object" }` to force JSON output (default `text`). | The completion and token usage are returned under `data`. A body missing `messages` (or with an out-of-range field) returns `400`. ## Knowledge base An embedded, org-wide knowledge base: ingest documents (upload, URL, markdown, or a bulk help-center import), keep them fresh with a staleness scorecard and per-document verification, and query them with raw semantic search or a grounded answer with citations. For a separately provisioned, per-knowledge-base store, use the [Knowledge Bases API](/api-reference/knowledge-bases) instead. | Method | Path | Purpose | Request | | -------- | -------------------------------------- | ----------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | | `GET` | `/api/v1/ai/kb/documents` | List embedded KB documents (keyset-paged) | Query: `cursor`, `limit` (1–100, default 100) | | `POST` | `/api/v1/ai/kb/documents` | Upload or import a document | Multipart file, **or** JSON `{ "source_type": "url", "source_url", "name"?, "metadata"? }` / `{ "source_type": "markdown", "name", "content" }` | | `POST` | `/api/v1/ai/kb/import/help-center` | Bulk-import a public Zendesk Guide help center — one document per published article | JSON `{ "subdomain", "provider"?, "locale"?, "name_prefix"?, "max_articles"? (1–1000, default 250) }` | | `GET` | `/api/v1/ai/kb/documents/{id}` | Get a document | Path: `id` (UUID) | | `DELETE` | `/api/v1/ai/kb/documents/{id}` | Delete a document and its vectors | Path: `id` (UUID) | | `POST` | `/api/v1/ai/kb/documents/{id}/reindex` | Force reindex | Path: `id` (UUID) | | `POST` | `/api/v1/ai/kb/documents/{id}/verify` | Stamp a document as reviewed and still accurate (`last_verified_at`) | Path: `id` (UUID) | | `GET` | `/api/v1/ai/kb/staleness` | Org-wide freshness scorecard — stale-document counts by reason | Query: `limit` (1–1000, default 25) | | `GET` | `/api/v1/ai/kb/connectors` | Browse the knowledge-connector catalog | Query: `category`?, `status`?, `search`? | | `GET` | `/api/v1/ai/kb/connectors/{id}` | Get one connector plus its activation detail | Path: `id` (connector slug) | | `POST` | `/api/v1/ai/kb/search` | Semantic search across documents (raw chunks + scores) | JSON `{ "query", "limit"? (1–20, default 5), "threshold"? }` | | `POST` | `/api/v1/ai/kb/answers` | Grounded answer with citations and a confidence score | JSON `{ "question", "knowledge_base_ids"?, "limit"? (1–10, default 8), "threshold"?, "min_confidence"?, "language"? }` | ## Example — suggest a reply ```bash theme={null} curl -X POST https://api.orbit.devotel.io/api/v1/ai/suggest-reply \ -H "X-API-Key: dv_live_sk_your_key_here" \ -H "Content-Type: application/json" \ -d '{ "conversation_id": "conv_abc" }' ``` ## See also * [AI Agents API](/api-reference/agents) — full conversational agents with memory, tools, and deployment * [Knowledge Bases API](/api-reference/knowledge-bases) — multi-doc RAG store