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Documentation Index

Fetch the complete documentation index at: https://docs.insforge.dev/llms.txt

Use this file to discover all available pages before exploring further.

pgvector ships on every InsForge project. Use it for semantic search, recommendations, and RAG.

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Prompt your agent

Add pgvector to my project. Create a documents table with content and a 1536-dim embedding column, plus an HNSW cosine index. When I insert content, embed it with OpenRouter’s text-embedding-3-small from a server-side route. Expose a match_documents(query, count, threshold) RPC that returns top similarity matches.

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Concepts

A vector is a list of numbers representing an item. Two vectors are similar if they sit close in vector space. Store the vector next to its row, embed the user query the same way, and pgvector ranks by distance.

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Usage

Enable the extension and create a vector column. Match the dimension to your model (text-embedding-3-small is 1536). 
create extension if not exists vector;

create table documents (
  id bigserial primary key,
  content text,
  embedding vector(1536)
);
Generate an embedding server-side and insert it. 
import OpenAI from 'openai';
import { createClient } from '@insforge/sdk';

const openai = new OpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: process.env.OPENROUTER_API_KEY,
});
const insforge = createClient({ projectId: process.env.INSFORGE_PROJECT_ID });

const { data } = await openai.embeddings.create({
  model: 'openai/text-embedding-3-small',
  input: 'hello world',
});

await insforge.database.from('documents').insert({
  content: 'hello world',
  embedding: data[0].embedding,
});
Query by cosine distance (<=>). L2 (<->) and inner product (<#>) are also available. 
select id, content
from documents
order by embedding <=> $1
limit 5;

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Specific usage cases

Wrap search in a Postgres function and call it via rpc() to keep the math server-side: 
create or replace function match_documents(
  query_embedding vector(1536),
  match_count int default 5,
  match_threshold float default 0
)
returns table (id bigint, content text, similarity float)
language sql stable
as $$
  select id, content, 1 - (embedding <=> query_embedding) as similarity
  from documents
  where 1 - (embedding <=> query_embedding) > match_threshold
  order by embedding <=> query_embedding
  limit match_count;
$$;
Past ~10k rows, add an HNSW index: 
create index on documents using hnsw (embedding vector_cosine_ops);

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More resources