VectorAI 2.0 Documentation

import { VectorAIClient } from "@vectorai-sdk/sdk";

// Initialize client with your profile API key
const client = new VectorAIClient({
  apiKey: process.env.VECTORAI_API_KEY || "vec_live_YOUR_API_KEY",
  baseUrl: "https://vector.acadmyai.com",
});

async function main() {
  // 1. Ingest Knowledge Chunk with PII masking & firewall
  const ingestResult = await client.ingest({
    text: "Enterprise SSO supports Okta, Google Workspace, and Azure AD with SCIM provisioning.",
    collection_name: "launch_sales_vault",
    metadata: {
      category: "security_compliance",
      doc_type: "sales_battlecard",
      agent_id: "launch_sdr_01",
    },
    pii_redaction: true,
    threat_action: "sanitize",
  });
  console.log(`Stored ${ingestResult.chunks_stored} chunk(s).`);

  // 2. Query Knowledge Base with sub-10ms similarity match
  const searchRes = await client.search({
    query: "How do we configure SAML SSO with Okta?",
    collection_name: "launch_sales_vault",
    top_k: 3,
    threat_action: "sanitize",
  });

  for (const hit of searchRes.results) {
    console.log(`[Score: ${hit.score.toFixed(4)}] ID: ${hit.id}`);
    console.log("Chunk text:", hit.payload.text);
  }
}

main().catch(console.error);

from vectorai import VectorAI

client = VectorAI(api_key="vec_live_YOUR_API_KEY")

def ground_sales_objection(prospect_question: str) -> str:
    hits = client.search(
        query=prospect_question,
        collection_name="launch_sales_vault",
        top_k=2,
        threat_action="sanitize" # Blocks malicious prompt injections inside inbound prospect queries
    )
    context_chunks = [hit.payload.get("text", "") for hit in hits.results]
    return "\n\n".join(context_chunks)