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

# Quickstart

> From a fresh clone to your first grounded, cited answer in about five minutes.

This walks you from a clone to a grounded answer. For the full prerequisite
matrix and production hardening, see [Installation](/installation).

<Note>
  AskMyDocs is a Laravel 13 application. You need **PHP 8.3+**, **Composer**,
  **Node 20+**, and a **PostgreSQL 15+** database with the **pgvector** extension.
  The Quickstart uses OpenAI for chat + embeddings; any supported provider works
  (see [AI providers](/installation#3-choose-ai-providers)).
</Note>

<Steps>
  <Step title="Clone and install">
    ```bash theme={null}
    git clone https://github.com/lopadova/AskMyDocs.git
    cd AskMyDocs
    composer install
    npm install && npm run build
    ```
  </Step>

  <Step title="Configure the environment">
    Copy the example env and set your database + one AI provider key.

    ```bash theme={null}
    cp .env.example .env
    php artisan key:generate
    ```

    Then edit `.env`:

    ```env theme={null}
    DB_CONNECTION=pgsql
    DB_HOST=127.0.0.1
    DB_PORT=5432
    DB_DATABASE=askmydocs
    DB_USERNAME=postgres
    DB_PASSWORD=secret

    AI_PROVIDER=openai
    AI_EMBEDDINGS_PROVIDER=openai
    OPENAI_API_KEY=sk-...
    ```

    <Warning>
      Embedding dimensions are part of the contract. The stock schema is sized
      for a **1536-dim** model (`openai/text-embedding-3-small`). Switching to a
      different-dimension model means migrating the `vector(N)` columns, flushing
      the cache, and re-indexing — see [Installation](/installation#embedding-dimensions).
    </Warning>
  </Step>

  <Step title="Migrate the database">
    pgvector must be installed in PostgreSQL first (`CREATE EXTENSION vector;`).
    Then run the migrations:

    ```bash theme={null}
    php artisan migrate
    ```
  </Step>

  <Step title="Ingest some documents">
    Point the folder ingester at a directory of markdown and assign a project key.
    Ingestion is idempotent — re-running on identical bytes is a no-op.

    ```bash theme={null}
    php artisan kb:ingest-folder docs/ --project=handbook
    ```

    The same execution path is available over HTTP (`POST /api/kb/ingest`,
    Sanctum-protected, batch ≤ 100) — see [Architecture overview](/architecture/overview).
  </Step>

  <Step title="Ask a grounded question">
    Use the stateless JSON API:

    ```bash theme={null}
    curl -X POST https://your-host/api/kb/chat \
      -H "Authorization: Bearer <token>" \
      -H "Content-Type: application/json" \
      -d '{"question": "What is our remote-work policy?", "project_key": "handbook"}'
    ```

    The response carries the `answer`, the `citations` that grounded it, and a
    `meta` block (provider, model, latency, retrieved-chunk count). Or open the
    React chat UI at **`/app/chat`** for the streaming experience with inline
    citations and suggested follow-ups.
  </Step>
</Steps>

## What just happened

1. Your markdown was chunked (section-aware), embedded, and upserted into
   PostgreSQL on the idempotency tuple `(project_key, source_path, version_hash)`.
2. Your question was embedded and run through **hybrid retrieval** — vector +
   full-text — then fused by the [reranker](/core-concepts).
3. The grounded context was composed into a prompt and sent to your provider via
   `AiManager`, and the answer came back **with citations** to the exact chunks.

## Next steps

<CardGroup cols={3}>
  <Card title="Core concepts" icon="diagram-project" href="/core-concepts">
    Understand the canonical layer and the knowledge graph.
  </Card>

  <Card title="Connectors" icon="plug" href="https://github.com/lopadova/AskMyDocs">
    Pull from Google Drive, Notion, Confluence, Jira and more.
  </Card>

  <Card title="Admin panel" icon="gauge" href="https://github.com/lopadova/AskMyDocs">
    Dashboards, RBAC, the KB explorer, logs, and maintenance.
  </Card>
</CardGroup>