> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentset.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Get started with Agentset in minutes

In this guide, you'll upload a document to Agentset, search it, and generate a response using the retrieved context.

## Step 1: Get your API key

1. [Sign up](https://app.agentset.ai) and create an organization
2. Create a namespace from the dashboard
3. Navigate to **Settings → API Keys → New API Key**

<Frame>
  <img src="https://mintcdn.com/agentset/142yXB_-ZjnrQ4s4/images/get-started/api-key.png?fit=max&auto=format&n=142yXB_-ZjnrQ4s4&q=85&s=c642ffa80251ab6dbf813fcd80ebd3dd" alt="Creating an API key in the Agentset dashboard" width="2672" height="1696" data-path="images/get-started/api-key.png" />
</Frame>

Copy your API key and namespace ID—you'll need them in the next steps.

## Step 2: Install the SDK

Install the Agentset SDK using your preferred package manager.

<CodeGroup>
  ```bash npm theme={null}
  npm install agentset
  ```

  ```bash yarn theme={null}
  yarn add agentset
  ```

  ```bash pnpm theme={null}
  pnpm add agentset
  ```

  ```bash bun theme={null}
  bun add agentset
  ```

  ```bash pip theme={null}
  pip install agentset
  ```
</CodeGroup>

## Step 3: Upload a document

Initialize the client and upload a file to your namespace.

<CodeGroup>
  ```typescript TypeScript theme={null}
  import { Agentset } from "agentset";

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

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

  const job = await ns.ingestion.create({
    name: "Attention Is All You Need",
    payload: {
      type: "FILE",
      fileUrl: "https://arxiv.org/pdf/1706.03762.pdf",
    },
  });

  console.log(`Uploaded: ${job.id}`);
  ```

  ```python Python theme={null}
  import os
  from agentset import Agentset

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

  job = client.ingest_jobs.create(
      name="Attention Is All You Need",
      payload={
          "type": "FILE",
          "fileUrl": "https://arxiv.org/pdf/1706.03762.pdf",
      }
  )

  print(f"Uploaded: {job.data.id}")
  ```
</CodeGroup>

<Info>
  Documents are processed asynchronously. Processing time depends on the file size. Learn how to [check upload status](/data-ingestion/upload-status).
</Info>

## Step 4: Search your document

Query your namespace to retrieve relevant chunks from your uploaded document.

<CodeGroup>
  ```typescript TypeScript theme={null}
  const results = await ns.search("What is multi-head attention?");

  for (const result of results) {
    console.log(result.text);
  }
  ```

  ```python Python theme={null}
  results = client.search.execute(query="What is multi-head attention?")

  for result in results.data:
      print(result.text)
  ```
</CodeGroup>

## Step 5: Generate a response

Use the search results as context for an LLM to generate answers grounded in your documents.

<CodeGroup>
  ```typescript TypeScript theme={null}
  import { generateText } from "ai";
  import { openai } from "@ai-sdk/openai";

  const results = await ns.search("What is multi-head attention?");
  const context = results.map((r) => r.text).join("\n\n");

  const { text } = await generateText({
    model: openai("gpt-5.1"),
    system: `Answer questions based on the following context:\n\n${context}`,
    prompt: "What is multi-head attention?",
  });

  console.log(text);
  ```

  ```python Python theme={null}
  from openai import OpenAI as OpenAIClient

  openai = OpenAIClient()

  results = client.search.execute(query="What is multi-head attention?")
  context = "\n\n".join([r.text for r in results.data])

  response = openai.responses.create(
      model="gpt-4.1",
      input=[
          {
              "role": "system",
              "content": f"Answer questions based on the following context:\n\n{context}",
          },
          {
              "role": "user",
              "content": "What is multi-head attention?",
          },
      ],
  )

  print(response.output_text)
  ```
</CodeGroup>

That's it. In a few minutes, you've built an end-to-end RAG pipeline that rivals systems built by dedicated ML teams.

## Next steps

* [Data Ingestion](/data-ingestion/file-uploads) — Learn about supported file types and ingestion options
* [Search and Retrieval](/search-and-retrieval/search) — Explore advanced search features like filtering and ranking
* [AI SDK Integration](/search-and-retrieval/ai-sdk-integration) — Use Agentset with Vercel AI SDK