Embedding & Model Providers

Each team brings its own AI endpoints. Two provider types are configured under Settings → Integration:

• Embedding Providers power semantic search across prompts, artifacts, blueprints, and memories.
• Model Providers register OpenAI-compatible LLM endpoints for the team.

Both are team-scoped, managed entirely in the app, and store API keys encrypted. Nothing is configured through server environment variables.

Embedding providers

An embedding provider is any OpenAI-compatible /v1/embeddings endpoint: OpenAI, Ollama, LocalAI, vLLM, HuggingFace TEI, and similar.

Adding a provider

1. Open Settings → Integration → Embedding Providers
2. Click to add a provider and fill in:

Field	Default	Purpose
Base URL, API key, provider type	(required)	The endpoint to call. The key is stored encrypted.
Model	(required)	The embedding model id. Tagged on every vector and used as the search filter.
Chunk size	1000	How much text goes into each embedding chunk.
Chunk overlap	200	Overlap between chunks (must be smaller than the chunk size).
Concurrency	1	Max concurrent embedding requests sent to this provider.
Query / document prefix	(empty)	Optional instruction prefixes for asymmetric models (mxbai, BGE, E5).

3. Save. The provider is validated on save: VibeXP calls the endpoint and requires 1024-dimension vectors. The vector width is fixed and not configurable, so pick a model that outputs (or can be asked for) 1024 dimensions.

Once saved, embedding starts automatically for the team’s existing content.

Coverage, reprocess, and clear

The Embedding Providers page shows coverage cards: how much of the team’s content is embedded, pending, or failed. Two actions are available:

• Reprocess pending: re-enqueue anything not yet embedded.
• Clear all embeddings: delete every stored vector for the team. Content is untouched; embeddings are rebuilt on the next processing pass.

Changing a provider re-embeds everything

Changing a provider’s identity (endpoint or model) wipes that team’s vectors and re-embeds all content, so the search index stays consistent. Expect a re-embedding pass after such a change. Editing only chunk sizing, concurrency, or prefixes fills gaps instead of wiping.

Without a provider

Everything still works: content saves normally and search falls back to keyword (full-text) mode, which includes typo tolerance. Only semantic search is unavailable.

Model providers

Model providers let a team bring its own OpenAI-compatible LLM endpoint.

1. Open Settings → Integration → Model Providers
2. Add the endpoint URL, API key, and model details
3. Save. The provider’s connectivity is validated on save.

API keys are stored encrypted and can be updated or removed anytime.

API access

Both provider types have team-scoped REST endpoints:

# Embedding providers
GET|POST   /api/v1/{team_id}/settings/embedding-providers
GET|PUT|DELETE /api/v1/{team_id}/settings/embedding-providers/{id}
POST       /api/v1/{team_id}/settings/embedding-providers/validate
GET        /api/v1/{team_id}/settings/embedding-providers/coverage
POST       /api/v1/{team_id}/settings/embedding-providers/{id}/reprocess
DELETE     /api/v1/{team_id}/settings/embedding-providers/embeddings

# Model providers
GET|POST   /api/v1/{team_id}/settings/model-providers
GET|PUT|DELETE /api/v1/{team_id}/settings/model-providers/{id}
POST       /api/v1/{team_id}/settings/model-providers/validate

See API Keys for authentication.

Related

• Memory and Artifacts explain how semantic search is used day to day.
• Self-hosters: see Self-Hosting → Search and embeddings.