LlamaIndex.TS Quickstart

npm install @visiq/harness llamaindex @llamaindex/workflow @llamaindex/openai zod

import { visiq } from "@visiq/harness";
import { tool } from "llamaindex";
import { agent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";
import { z } from "zod";

// Any function returning document-shaped results works as a RAG source.
const searchDocs = async (query: string) => [
  { pageContent: `Q3 revenue was $4.2M. (matched: ${query})`, metadata: { classification: "internal" } },
];

// Your tools — unchanged.
const tools = [
  tool({
    name: "issue_refund",
    description: "Issue a refund to a customer",
    parameters: z.object({ customerId: z.string(), amount: z.number() }),
    execute: async ({ customerId, amount }) => `Refunded $${amount} to ${customerId}`,
  }),
  tool({
    name: "search_knowledge",
    description: "Search the company knowledge base",
    parameters: z.object({ query: z.string() }),
    execute: async ({ query }) => searchDocs(query),
  }),
];

// ── One call — governance + per-run session activate here ──
const supportAgent = visiq(
  agent({ name: "support", llm: openai({ model: "gpt-4o" }), tools, systemPrompt: "You are a helpful assistant." }),
  { agentId: "support-bot" },
);

const result = await supportAgent.run("What was Q3 revenue?");
console.log(result.data.result);

pip install "visiq>=0.5.0" llama-index-core llama-index-llms-openai "llama-index-workflows<2.24"

import asyncio

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

from visiq import govern  # pip install visiq


def issue_refund(customer_id: str, amount: int) -> str:
    """Issue a refund to a customer."""
    return f"Refunded ${amount} to {customer_id}"


def search_knowledge(query: str) -> list:
    """Search the company knowledge base."""
    # Your real RAG store returns the same shape: a list of
    # {"page_content", "metadata"} dicts (or LlamaIndex nodes) the
    # retrieval facet can filter.
    return [
        {"page_content": f"Q3 revenue was $4.2M. (matched: {query})",
         "metadata": {"classification": "internal"}},
    ]


agent = FunctionAgent(
    tools=[issue_refund, search_knowledge],
    llm=OpenAI(model="gpt-4o-mini"),
    system_prompt="You are a helpful assistant.",
)

# ── One call: every tool this agent runs is now governed ──
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})


async def main():
    print(await agent.run(user_msg="What was Q3 revenue?"))


asyncio.run(main())

pip install llama-index-llms-bedrock-converse

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.bedrock_converse import BedrockConverse

from visiq import govern

agent = FunctionAgent(
    tools=[issue_refund, search_knowledge],
    llm=BedrockConverse(model="amazon.nova-micro-v1:0", region_name="us-east-1", temperature=0),
    system_prompt="You are a helpful assistant.",
)
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})