Building Intelligent Document Chatbots with Laravel AI SDK

Overview of Document-Based Chatbots

Integrating artificial intelligence into

applications has become significantly more streamlined with the introduction of the

Laravel AI SDK

. This tutorial focuses on creating a Q&A chatbot that interacts with uploaded documents—such as help files, internal knowledge bases, or technical documentation—to provide contextually accurate answers. By utilizing vector stores and embeddings, the system transforms static files into searchable, intelligent data sources that an AI agent can query.

Prerequisites

Before beginning, ensure you have a solid grasp of the

framework, specifically

Eloquent

models and

Livewire

components. You should also be comfortable with asynchronous processing using

Laravel Queues

. An

OpenAI

API key is required if you plan to use their managed vector storage and embedding services.

Key Libraries & Tools

• Laravel AI SDK: The core package providing high-level abstractions for AI interactions.
• Livewire: Used for creating the dynamic, real-time chatbot interface.
• OpenAI Vector Store: A managed service for storing and searching document embeddings.
• PostgreSQL (Optional): An alternative to
  OpenAI
  for local vector storage via pgvector.

Code Walkthrough: Document Processing

When a user uploads a document, the system must first prepare it for the AI. This involves creating a local record and then syncing it with a vector store.

// DocumentController.php
public function store(Request $request)
{
    $document = Document::create([
        'file_path' => $request->file('doc')->store('documents'),
    ]);

    ProcessDocument::dispatch($document);
    return redirect()->back();
}

The background job then handles the

logic to create the remote store and upload the document content.

// ProcessDocument Job
$store = Stores::create('openai');
$aiDocument = AIDocument::fromStorage($this->document->file_path);
$store->add($aiDocument);

// Save the remote IDs for later retrieval
$this->document->update([
    'store_id' => $store->id,
    'file_id' => $aiDocument->id,
]);

Implementing the QA Agent

Agents are the "brains" of the operation. You generate them using php artisan make:agent DocumentQA. Within the agent, you define the tools and models required to perform the search.

namespace App\AI\Agents;

use Laravel\AI\Agent;
use Laravel\AI\Tools\FileSearch;

class DocumentQA extends Agent
{
    public function tools(): array
    {
        return [new FileSearch()];
    }

    public function instructions(): string
    {
        return "You are a helpful assistant. Use the provided document store to answer questions accurately.";
    }
}

Syntax Notes & Best Practices

The

uses a declarative approach. By using the #[Provider('openai')] attribute on your Agent classes, you can swap between different AI backends without changing your implementation logic. This follows the standard

Laravel

"driver" pattern, keeping your code decoupled from specific API implementations.

Practical Examples

This technique is ideal for building internal support bots. For instance, uploading a 1,000-line markdown file of technical documentation allows a developer to ask, "How do I configure the codec support?" and receive a human-readable answer derived specifically from that text, rather than the AI's general training data.

Tips & Gotchas

• Cost Management: Using high-end models like GPT-4o for document retrieval can be expensive. Each question can cost several cents depending on document size and token usage.
• Performance: AI responses over document stores aren't instant; they often take 10-15 seconds. Always use
  Livewire
  's loading states or
  Laravel Queues
  to manage user expectations.
• Local Storage: For production environments with high volume, consider
  PostgreSQL
  with vector extensions to avoid the recurring costs of third-party vector storage.