syntra-ai

name	syntra-ai
description	AI features and vector search with Syntra. Use when configuring AI models, running chat completions, generating images, creating embeddings, building semantic search, setting up RAG pipelines, or managing vector collections.

Syntra AI

All AI features route through OpenRouter, supporting 200+ models from OpenAI, Anthropic, Google, Meta, Mistral, and more.

Setup

1. Set OPENROUTER_API_KEY in environment
2. ai_create_config — define a model configuration
3. Use the config ID for inference calls

Create a config

{
  "input_modality": "text",
  "output_modality": "text",
  "provider": "openrouter",
  "model_id": "anthropic/claude-sonnet-4-5-20250929",
  "system_prompt": "You are a helpful assistant."
}

AI model configs

• ai_list_configs — list all configs
• ai_create_config — create a new config (model, provider, modality, system prompt)
• ai_get_config / ai_update_config / ai_delete_config — CRUD operations

Inference

Chat completion

{
  "config_id": "uuid-of-text-config",
  "messages": [
    { "role": "system", "content": "You are a coding assistant." },
    { "role": "user", "content": "Explain closures in JavaScript." }
  ],
  "temperature": 0.7,
  "max_tokens": 1000
}

Image generation

{
  "config_id": "uuid-of-image-config",
  "prompt": "A futuristic city at sunset, cyberpunk style",
  "n": 1,
  "size": "1024x1024"
}

Embeddings

{
  "config_id": "uuid-of-embedding-config",
  "input": "The quick brown fox jumps over the lazy dog"
}

Or batch: "input": ["text one", "text two", "text three"]

Vector search (RAG)

Syntra includes built-in vector storage powered by pgvector.

Workflow

1. ai_create_collection — create a vector collection
2. ai_upsert_documents — add documents (embeddings auto-generated)
3. ai_vector_search — semantic search by text query

Collection management

• ai_list_collections / ai_get_collection / ai_delete_collection
• Each collection has: name, embedding_model, dimensions, distance_metric

Distance metrics

Metric	Use case
cosine	General-purpose similarity (default)
l2	Euclidean distance
inner_product	When vectors are normalized

Document operations

• ai_upsert_documents — insert or update documents with auto-embedding
• ai_delete_documents — remove by IDs
• ai_vector_search — search by text query with optional metadata filter
• ai_vector_search_by_vector — search by raw embedding vector

Reference docs

• For the complete RAG workflow: see vector-workflow.md