@khaveeai/providers-pgvector

@khaveeai/providers-pgvector

PostgreSQL + pgvector provider for the Khavee AI SDK. Bring your own Postgres database and get document storage, CSV import, and vector similarity search out of the box.

Installation

npm install @khaveeai/providers-pgvector
# requires: pg, openai (peer)

Quick start

import { PgVectorProvider } from "@khaveeai/providers-pgvector";

const db = new PgVectorProvider({
  connectionString: process.env.DATABASE_URL!,
  openaiApiKey:     process.env.OPENAI_API_KEY!,
});

// 1. Create schema on first run
await db.migrate();

// 2. Insert a document
await db.insertDocument("Hello my name is Non", { category: "greeting" });

// 3. Import a CSV
await db.importCSV(csvString); // must have a "content" column

// 4. Vector search
const results = await db.search("what is your name?", 5, 0.3);
// → [{ id, content, metadata, createdAt, similarity }]

// 5. Browse all
const docs = await db.listDocuments(20);

CSV format

The CSV must have a content column (or set contentColumn in options).
All other columns become metadata.

content,category,author
"Hello my name is Non",greeting,Non
"The Eiffel Tower is in Paris",geography,system

await db.importCSV(csvString, { contentColumn: "content", concurrency: 5 });

Config

Option	Type	Default	Description
connectionString	string	required	PostgreSQL connection URL
openaiApiKey	string	required	OpenAI key for embeddings
embeddingModel	string	text-embedding-3-small	Any OpenAI embedding model
embeddingDimensions	number	1536	Must match the model
tableName	string	documents	Table to read/write
defaultTopK	number	5	Default search results
defaultThreshold	number	0.3	Default min similarity
defaultConcurrency	number	5	Parallel embed calls

Use with OpenAI Realtime (RAG)

import { OpenAIRealtimeProvider } from "@khaveeai/providers-openai-realtime";
import { PgVectorProvider } from "@khaveeai/providers-pgvector";

const vectorDB = new PgVectorProvider({ connectionString, openaiApiKey });

const provider = new OpenAIRealtimeProvider({
  useProxy: true,
  rag: {
    provider: vectorDB,
    topK: 5,
    threshold: 0.3,
    systemPromptPrefix: "Use the following context to answer questions:",
  },
});