AI

VentureKit provides AI capabilities through @venturekit-pro/ai with embeddings, vector stores, RAG pipelines, and agents with tool use.

Setup

npm install @venturekit-pro/ai@dev

# Install provider(s) you need
npm install openai                          # OpenAI embeddings + agents
npm install @aws-sdk/client-bedrock-runtime # AWS Bedrock embeddings
npm install @pinecone-database/pinecone     # Pinecone vector store

Embeddings

Generate vector embeddings from text:

import { createEmbedder } from '@venturekit-pro/ai';

const embedder = createEmbedder({
  provider: 'openai',
  model: 'text-embedding-3-small',
  apiKey: process.env.OPENAI_API_KEY,
});

const vector = await embedder.embed('What is VentureKit?');
const vectors = await embedder.embedBatch(['Question 1', 'Question 2']);

Providers

Provider	Models	Setup
OpenAI	`text-embedding-3-small`, `text-embedding-3-large`	API key
AWS Bedrock	Titan, Cohere	AWS credentials

Vector Stores

Store and query vector embeddings:

import { createVectorStore } from '@venturekit-pro/ai';

const store = createVectorStore({
  provider: 'pinecone',
  indexName: 'my-index',
  apiKey: process.env.PINECONE_API_KEY,
});

// Upsert vectors
await store.upsert([
  { id: 'doc-1', vector, metadata: { title: 'Getting Started' } },
]);

// Query similar vectors
const results = await store.query(queryVector, { topK: 5 });

Providers

Provider	Description
Pinecone	Managed vector database
pgvector	PostgreSQL extension (use with `@venturekit/data`)
In-memory	Development and testing

RAG Pipeline

Build retrieval-augmented generation pipelines:

import { createRagPipeline, chunkText } from '@venturekit-pro/ai';

const rag = createRagPipeline({
  embedder,
  vectorStore: store,
  chunkSize: 500,
  chunkOverlap: 50,
});

Ingesting Documents

// Chunk a document
const chunks = chunkText(documentText, { size: 500, overlap: 50 });

// Ingest into the pipeline
await rag.ingest(chunks);

Retrieving Context

const context = await rag.retrieve('How do I deploy?', { topK: 3 });
// Returns the most relevant chunks for your query

Agents

Create AI agents with tool use:

import { createAgent, defineTool } from '@venturekit-pro/ai';

const searchTool = defineTool({
  name: 'search_docs',
  description: 'Search the documentation',
  parameters: {
    query: { type: 'string', description: 'Search query' },
  },
  handler: async ({ query }) => {
    const results = await rag.retrieve(query, { topK: 3 });
    return results.map(r => r.text).join('\n\n');
  },
});

const agent = createAgent({
  model: 'gpt-4',
  apiKey: process.env.OPENAI_API_KEY,
  tools: [searchTool],
  systemPrompt: 'You are a helpful assistant that answers questions about VentureKit.',
});

const response = await agent.run('How do I set up authentication?');

Using AI in Handlers

import { handler } from '@venturekit/runtime';
import { createEmbedder, createVectorStore, createRagPipeline } from '@venturekit-pro/ai';

export const main = handler(async (body, ctx, logger) => {
  const rag = createRagPipeline({ embedder, vectorStore: store });
  const context = await rag.retrieve(body.question, { topK: 3 });

  logger.info('RAG retrieval', { question: body.question, resultCount: context.length });
  return { answer: context };
}, { scopes: ['api.read'] });