Skip to content

@venturekit-pro/ai

Terminal window
npm install @venturekit-pro/ai@dev
# Install providers you need
npm install openai # OpenAI
npm install @aws-sdk/client-bedrock-runtime # AWS Bedrock
npm install @pinecone-database/pinecone # Pinecone
import { createEmbedder, createEmbeddingConfig, DEFAULT_EMBEDDING_CONFIG } from '@venturekit-pro/ai';
const embedder = createEmbedder({ provider: 'openai', model: 'text-embedding-3-small', apiKey });
const vector = await embedder.embed('Hello world');
import { createVectorStore, createVectorStoreConfig, DEFAULT_VECTOR_STORE_CONFIG } from '@venturekit-pro/ai';
const store = createVectorStore({ provider: 'pinecone', indexName: 'my-index', apiKey });
await store.upsert([{ id: 'doc-1', vector, metadata: {} }]);
const results = await store.query(queryVector, { topK: 5 });
import { createRagPipeline, createRagConfig, DEFAULT_RAG_CONFIG, chunkText } from '@venturekit-pro/ai';
const rag = createRagPipeline({ embedder, vectorStore: store, chunkSize: 500 });
const chunks = chunkText(text, { size: 500, overlap: 50 });
await rag.ingest(chunks);
const context = await rag.retrieve('query', { topK: 3 });
import { createAgent, createAgentConfig, DEFAULT_AGENT_CONFIG, defineTool } from '@venturekit-pro/ai';
const tool = defineTool({ name: 'search', description: '...', parameters: {}, handler: async () => {} });
const agent = createAgent({ model: 'gpt-4', apiKey, tools: [tool], systemPrompt: '...' });
const response = await agent.run('question');
  • @venturekit/core — required
  • openai — optional peer (embeddings, agents)
  • @aws-sdk/client-bedrock-runtime — optional peer (Bedrock embeddings)
  • @pinecone-database/pinecone — optional peer (Pinecone vector store)