@semiont/vectors

Vector storage, embedding, and semantic search for Semiont.

Provides a pluggable abstraction over vector databases and embedding providers, with text chunking utilities. Used by the Smelter actor to index content and by Gatherer/Matcher to retrieve semantically similar resources and annotations.

Architecture

Two separate vector collections:

• resources — chunked full-text content from stored files
• annotations — W3C Web Annotation entities with motivation, entity types, and exact text

Both collections support filtered similarity search with configurable score thresholds.

Vector Stores

Qdrant (production)

import { createVectorStore } from '@semiont/vectors';

const store = await createVectorStore({
  type: 'qdrant',
  host: 'localhost',
  port: 6333,
  dimensions: 1024,
});

Requires a running Qdrant instance. The @qdrant/js-client-rest peer dependency is lazy-loaded on connect(). Collections are auto-created if they don't exist.

Memory (testing)

const store = await createVectorStore({
  type: 'memory',
  dimensions: 768,
});

Brute-force cosine similarity. No external dependencies.

Embedding Providers

Voyage AI (cloud)

import { createEmbeddingProvider } from '@semiont/vectors';

const provider = await createEmbeddingProvider({
  type: 'voyage',
  model: 'voyage-3',       // 1024 dimensions
  apiKey: '...',
});

Models: voyage-3 (1024), voyage-3-lite (512), voyage-code-3, voyage-finance-2, voyage-law-2.

Ollama (local)

const provider = await createEmbeddingProvider({
  type: 'ollama',
  model: 'nomic-embed-text',  // 768 dimensions
  baseURL: 'http://localhost:11434',
});

Models: nomic-embed-text (768), all-minilm (384), mxbai-embed-large (1024), snowflake-arctic-embed (1024).

Text Chunking

import { chunkText, DEFAULT_CHUNKING_CONFIG } from '@semiont/vectors';

const chunks = chunkText(longDocument, { chunkSize: 512, overlap: 50 });
// => string[]

Splits on paragraph boundaries, then sentence boundaries, then word boundaries. chunkSize and overlap are in tokens (~4 characters per token).

Search

const embedding = await provider.embed('quantum computing');

// Search resources
const resources = await store.searchResources(embedding, {
  limit: 10,
  scoreThreshold: 0.7,
  filter: { excludeResourceId: 'res-already-open' },
});

// Search annotations
const annotations = await store.searchAnnotations(embedding, {
  limit: 5,
  filter: { entityTypes: ['Person', 'Organization'], motivation: 'describing' },
});

Each result includes id, score, resourceId, text, and optionally annotationId and entityTypes.

Writing Vectors

// Index a resource's content
const chunks = chunkText(content, DEFAULT_CHUNKING_CONFIG);
const embeddings = await provider.embedBatch(chunks);
await store.upsertResourceVectors(resourceId, chunks.map((text, i) => ({
  chunkIndex: i,
  text,
  embedding: embeddings[i],
})));

// Index an annotation
const vec = await provider.embed(annotation.exactText);
await store.upsertAnnotationVector(annotationId, {
  annotationId,
  resourceId,
  motivation: 'describing',
  entityTypes: ['Person'],
  exactText: 'Marie Curie',
}, vec);

Configuration

In semiont.toml:

[environments.local.services.vectors]
type = "qdrant"
host = "localhost"
port = 6333

[environments.local.services.vectors.embedding]
type = "voyage"
model = "voyage-3"

[environments.local.services.vectors.chunking]
chunkSize = 512
overlap = 50

License

Apache-2.0