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Semantic Memory

Infinite Knowledge Retrieval

Auralius uses pgvector to turn unstructured documents into highly searchable semantic embeddings. Agents instantly recall specific details from your business knowledge base during live conversations.

sql
CREATE TABLE knowledge_base (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    tenant_id UUID NOT NULL,
    content TEXT NOT NULL,
    embedding VECTOR(1536) NOT NULL
);

-- HNSW index for ultra-fast ANN search
CREATE INDEX ON knowledge_base USING hnsw (embedding vector_cosine_ops);

Native pgvector Architecture

We built our RAG (Retrieval-Augmented Generation) pipeline directly on PostgreSQL with pgvector, ensuring ACID compliance and strict tenant data isolation via Row-Level Security (RLS).

OpenAI Embeddings

Powered by text-embedding-3-small for highly accurate, dense vector representations of your data.

Context-Aware Search

Context-Aware Search

Agents dynamically execute similarity searches against the KB based on the caller's spoken questions in real-time.

Instant Sync

When you update a document in the Canvas dashboard, the embeddings are instantly recalculated and available for the next call.

Multi-Document Support

Upload FAQs, PDF manuals, or company policies. Auralius chunks and indexes them automatically.

Strict Tenant Isolation

Vectors are scoped strictly to your tenant ID using Postgres RLS policies. Your data never leaks across organizations.