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

# Quick Start: Ingest and Search

> Connect a data source, ingest documents, and search them from an agent in about 10 minutes.

This guide walks you through connecting a data source, ingesting documents, and searching them from an agent — in about 10 minutes.

## Prerequisites

* An Airia account with access to a project
* At least one document to upload (PDF, DOCX, or TXT)

## Step 1: Create a Data Source

1. Open your project and navigate to **Data Sources**
2. Click **Add Data Source**
3. Choose **File Upload** as the connector type (simplest for getting started)
4. Name your data source — use a descriptive name like "Product Documentation" or "HR Policies" (this name is visible to the LLM when using agentic retrieval, so make it meaningful)
5. Upload one or more files

## Step 2: Configure Ingestion Settings

Before ingesting, review the key settings:

1. **PDF Parser** — For standard documents, **Basic** works fine. If your documents contain tables, images, or complex layouts, choose **Advanced** or **Universal**. See [Ingestion Settings](./data-ingestion-settings.md) for details on each parser.
2. **Scan Document for Images** — Enabled by default. Leave it on if your documents contain relevant images or diagrams.
3. **Vector Database** — Leave as **Airia DB** (default) unless you're bringing your own vector store.
4. **Knowledge Graph Extraction** — Leave off for this quick start. See the [Graph RAG guide](./guide-graph-rag.md) when you're ready to try it.

Click **Save** to start ingestion. You can monitor progress in the data source detail view — files will show their processing status.

## Step 3: Add the Data Source to an Agent

Once ingestion is complete:

1. Open or create an agent in the **Agent Builder**
2. You have two options:

**Option A: Data Search Step (simple)**

* Drag a **Data Search Step** into your agent flow, before the AI Model step
* Select your data source
* Configure search settings:
  * **Max Results:** 5 (default, good starting point)
  * **Relevance Threshold:** 70 (default)
  * **Neighboring Chunks:** 1 (includes surrounding context)
* Connect the Data Search Step output to your AI Model step's input

**Option B: MCP Multi-Hop Retrieval (agentic)**

* Open your **AI Model Step** settings
* Toggle on **Datasources**
* Select your data source
* The Airia Datasource MCP Server is automatically deployed — the LLM will dynamically search your data as needed

> 💡 **Which should I choose?** Start with **Option B** (MCP Multi-Hop Retrieval) for the most natural experience. The LLM decides when and how to search. Use **Option A** if you need deterministic, single-pass retrieval every time.

## Step 4: Test Your Agent

1. Click **Test** in the agent builder
2. Ask a question about the content in your uploaded documents
3. The agent should respond with information grounded in your data, with source citations

If results are not relevant enough:

* Try adjusting the **Relevance Threshold** lower (e.g., 50) to return more results
* Try enabling **Hybrid Search** at 0.5 alpha for a blend of semantic and keyword matching
* Check that your documents were fully ingested (file status should show as "Processed")

## Next Steps

* [Set up Graph RAG](./guide-graph-rag.md) to extract structured entities and improve retrieval for complex queries
* [Configure hybrid search and reranking](./hybrid-search-reranking.md) to fine-tune retrieval quality
* [Connect external agents via MCP](./guide-mcp.md) to use your Airia data sources from Claude, GPT, or other LLM clients