Download the PHP package undkonsorten/easychat without Composer

EasyChat Chatbot (TYPO3 Extension)

EasyChat is a lightweight open source chatbot for TYPO3 websites without third party chat tools.

All you need is TYPO3 and an LLM endpoint.

EasyChat is focused on privacy and data protection, since all chat conversations are only stored in your TYPO3 database.

🚀 Features

• 🗨 Chatbot frontend

  • based on the open source chat framework Deep Chat (Supports: Vanilla JS, Vue, React, Angular etc.)

• 🤖 LLM Endpoints for

  • (self-hosted) open source LLMs (e.g. gpt‑oss) and
  • standard LLMs (ChatGPT, Claude Opus, Mistral etc.)
  • Chat memory (Chatbot can remember old questions)

• 🔒 Data protection:

  • Chat session data is saved only within TYPO3 (on your own server)
  • Optional privacy consent before the chat starts
  • Automated cleaning of user data (scheduler task)
• ⛁ TYPO3 content as knowledge base (RAG, Vector Database)
  • TYPO3 Content to RAG via ext:index
  • Connector (to Qdrant vector database)

🛠️ Setup guide (4 Steps)

1. Install the TYPO3 Extension

Install the TYPO3 ChatBot Extension EasyChat via composer:

After installation a database compare is necessary (via Install Tool or TYPO3 Console) to create new tables.

2. Configure the LLM provider

Now you need to connect TYPO3 to your LLM provider via API. Create a new database record "Configuration" in TYPO3.

Then fill out the fields of the Configuration record.

The LLM Settings (URL, API key, model, system prompt) are always required. The Vector DB settings (shown here with Qdrant and EXT:index configurations) are only needed if the chatbot should answer from your own content (RAG); leave Vector db on None otherwise. See How reactions, configurations and index configurations connect.

Sample configurations for your LLM

• Mistral (Free plan available | How to get an API key?)
  • Name: Mistral (mistral-tiny)
  • Model (of the LLM): mistral-tiny
  • URL (of the API endpoint): https://api.mistral.ai
  • API Key: your-api-key-abc123xyz-...
  • System Message (Prompt): You are a support chatbot ...

More samples (OpenAI, mittwald, Groq, Ollama …) → Sample configurations

3. Setup the TYPO3 Reaction

A TYPO3 Reaction needs to be created. The reaction serves as a connector (aka endpoint) between the chat frontend and TYPO3.

• Create a new reaction with the Reaction Type Reaction for easychat.
• Be sure to copy the generated secret before saving
• Choose one of the previously created EasyChat configuration records
  • This choice decides everything the chatbot uses: the LLM, and — if the configuration has a vector database — which vector database/collection it searches and therefore which Index configurations (EXT:index) it knows about. See How reactions, configurations and index configurations connect.

After successfully creating the reaction you will see the following interface.

Now also copy the reaction URL (like https://my-domain.com/typo3/reaction/afce5efb-861e-4e0e-8a8b-d159f194670d). You will need it in step 4.

4. Setup the Content Element

Last but not least you need to setup a content element for the chatbot.

• Be sure to have your Reaction URL and secret available.

• Open a TYPO3 page
• Add/create a new content element "Chatbot".
• Connect the content Element to the reaction.

Hint: Use /typo3/reaction/XXXXXXXX-XXXXX instead of https://mydomain.dev/XXXXXXXX-XXXXX in order to be domain independent (on Local, Staging, Live)

✨ YOU ARE DONE!!! 👊 CONGRATULATIONS 🎉

Session storage in TYPO3

How are chat sessions stored?

Each browser session (the easychat_session_id cookie) maps to exactly one row in tx_easychat_domain_model_session. The whole conversation — the system prompt plus every question and answer — is stored in that row.

With the EasyChat backend module you can

• Watch, review, delete and export chat sessions
• Export as CSV: Click Export as CSV on the session list or Export this session as CSV on a single session's detail view

Cleaner task: Delete old chat sessions

For data protection we recommend setting up the 🗑 cleaner task in order to delete old chat sessions.

Steps:

• Choose the task Execute console commands (scheduler)
• Schedulable command: easychat:delete-sessions: Deletes sessions older than given date interval.
• Set the scheduler interval
• Save!
• Then define the keepDateInterval in the ISO 8601 durations format: 1 Day = P1D, 2 Weeks = P2W, 3 Months = P3M, 1 Year = P1Y, 1 Year and 2 Months = P1Y2M

Extension Configuration

EasyChat has a small set of global options in the Extension Configuration (Admin Tools → Settings → Extension Configuration → easychat, or in config/system/settings.php):

We recommend pointing storagePid at a dedicated SysFolder rather than the root page.

Knowledge base (RAG)

EasyChat can answer questions using your own TYPO3 content and files as a knowledge base instead of (or in addition to) the LLM's general knowledge, by embedding your pages/files into a vector store (like Qdrant).

The knowledge indexing itself is delegated to and configured via the TYPO3 Extension Index, a generic TYPO3 content-crawling framework. EasyChat listens to the indexer and pushes the crawled content into the vector store(s) of any matching EasyChat Configuration record.

Index can be configured to read

• content elements,
• plugin content (e.g. FAQs, News) but also
• Files (manuals, documentation in PDF, XLS etc.)

Quick setup

1. composer require symfony/ai-qdrant-store
2. Create an EXT:index configuration on your root page and the two scheduler tasks index:queue and messenger:consume.
3. On your EasyChat configuration set Vector db to Qdrant, fill in the connection fields and select the index configuration(s).
4. Make sure the reaction uses exactly this EasyChat configuration.

More indexer setup hints here →

How reactions, configurations and index configurations connect

Everything hangs off the EasyChat configuration record that a reaction points to:

• At chat time the reaction loads its EasyChat configuration, and the similarity search queries exactly the vector database/collection configured there.
• At index time EasyChat looks up, for every crawled page or file, all EasyChat configurations whose Index configurations field contains the index configuration that is running, and writes the content into each of their collections. Configurations with Vector db none are ignored.

So the Index configurations field on the EasyChat configuration is the one place that decides what a chatbot knows, and the reaction decides which configuration (and thus which knowledge base) a chatbot uses.

Multiple Chatbots

You can run multiple chatbots with different system prompts, LLMs or knowledge bases simultaneously in one TYPO3 installation.

This is especially useful for testing.

Theming & Templates

Chat Frontend: Deep Chat

EasyChat comes along with Deep Chat - an open source chat web component in the frontend.

For simplicity we integrated Deep Chat as a plain Vanilla JS web component, but it can be used with many other frameworks (e.g. React, Vue, Svelte, Angular). EasyChat is able to communicate with popular AI providers, but can also connect to your own servers - in our example with TYPO3.

DeepChat is an example implementation. Feel free to use another chatbot frontend. The default dummy template is located at

• /Resources/Private/Templates/ChatFrontend.html.

Styled version

If you want to use our suggested default styles for ChatBot & Cookie Consent you need to include the TypoScript templates in /Configuration/Styling/.

How to add your own styles 💐

You can either overlay the default template or the styled template, by setting your own template paths.

Be aware,

• there are many ways to inject styles into a web component like <deep-chat>
• keep also in mind the styles for the chatbot trigger button and the consent module

How to Change the texts

You can edit some of the content directly in the frontend.

You can override texts used in the template via locallang.xml or via TypoScript.

Development & testing

Tests and code checks run in containers via Build/Scripts/runTests.sh (docker or podman), no local PHP needed.

→ See Documentation/Development-and-Testing.md for all commands, Composer scripts and CI details.

Credits

🙏 This TYPO3 Extension was built by the Berlin-based digital agency undkonsorten.

• Eike Starkmann (Product Owner & Inspirator, TYPO3 Development)
• Lars Hayer (Frontend, Theming)
• Thomas Alboth (Product Owner & Documentation)
• Jule Nott (UI Design)
• Felix Althaus & J. (Critical Thinking)

License

GNU General Public License, version 2

Upgrading

From 0.1.x to 0.2.0

• Database compare required: new columns on tx_easychat_configuration and the new table tx_easychat_index_point.
• Vector dimensions are configurable: Qdrant collections used to be created with a hardcoded size of 4096. The new field Embedding dimensions (vector_db_dimensions) defaults to 1536. If you already use a vector store, set it to the size of your existing collection (4096 for collections created by 0.1.x), or drop the collection and re-index.
• New dependency: typo3/cms-install is now required.
• API change: StoreFactory::create() takes a new required int $dimensions argument and throws an exception for unsupported store types.
• Sessions: new sessions are stored on the configured storage PID. The session table is now visible in the list module, and its fields are read-only.

Planned Features

To Do

• Add Redis as a vector store
• Connect EasyChat configuration and reaction URL directly
• More LLM settings (like temperature)
• Voting for good/bad answers
• Pre suggested questions

Implemented

• ✅ Version 0.2.0 Website scraping/indexing via TYPO3 for the knowledge base (via vector database) — done, see Knowledge base (RAG)

Contact

Any more ideas, questions, suggestions? Feel free to 📧 contact us.

Contact us via our website, GitHub or TYPO3 Slack.