Simple RAG

import { Agentset } from "agentset";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const agentset = new Agentset({
  apiKey: process.env.AGENTSET_API_KEY,
});

const ns = agentset.namespace("YOUR_NAMESPACE_ID");

const query = "What are the key findings?";

// Search for relevant context
const results = await ns.search(query);
const context = results.map((r) => r.text).join("\n\n");

// Generate a response
const { text } = await generateText({
  model: openai("gpt-5.1"),
  system: `Answer questions based on the following context:\n\n${context}`,
  prompt: query,
});

console.log(text);

import os
from agentset import Agentset
from openai import OpenAI as OpenAIClient

client = Agentset(
    namespace_id="YOUR_NAMESPACE_ID",
    token=os.environ["AGENTSET_API_KEY"],
)

openai = OpenAIClient()

query = "What are the key findings?"

# Search for relevant context
results = client.search.execute(query=query)
context = "\n\n".join([r.text for r in results.data])

# Generate a response
response = openai.responses.create(
    model="gpt-5.1",
    input=[
        {
            "role": "system",
            "content": f"Answer questions based on the following context:\n\n{context}",
        },
        {
            "role": "user",
            "content": query,
        },
    ],
)

print(response.output_text)

const results = await ns.search(query, {
  filter: {
    category: "technical",
    year: { $gte: 2024 },
  },
});

results = client.search.execute(
    query=query,
    filter={
        "category": "technical",
        "year": {"$gte": 2024},
    },
)