Guide to PraisonAI’s chat interface with support for 100+ LLMs, internet search, vision models, and custom database configurations
Interface | Description | URL |
---|---|---|
UI | Multi Agents such as CrewAI or AG2 | https://docs.praison.ai/ui/ui |
Chat | Chat with 100+ LLMs, single AI Agent | https://docs.praison.ai/ui/chat |
Code | Chat with entire Codebase, single AI Agent | https://docs.praison.ai/ui/code |
URL : http://localhost:8084/
Username: admin
Password: admin
Set Model name to be gpt-4o-mini in the settings
PraisonAI Chat now includes internet search capabilities using Crawl4AI and Tavily. This feature allows you to retrieve up-to-date information during your conversations, enhancing the AI’s ability to provide current and relevant information.
You can now upload images and ask questions based on them using Vision Language Models. This multimodal support enables visual understanding and analysis within your chat sessions, allowing for a more comprehensive interaction with the AI.
To use this feature:
These new features significantly expand the capabilities of PraisonAI Chat, allowing for more diverse and informative interactions.
PraisonAI Chat supports custom database configurations, allowing you to use PostgreSQL or other databases instead of the default SQLite database. This is particularly useful for production environments or when you need more advanced database features.
To use PostgreSQL as your database backend:
Install Required Dependencies
For local development:
For Replit:
python3-dev
libpq-dev
Set Environment Variables
Add these variables to your .env
file or Replit Secrets:
For Replit:
DATABASE_URL
Database Tables The application will automatically:
Cloud Database Services For Replit, we recommend using cloud database services that provide free tiers:
These services provide:
If no DATABASE_URL
is provided, PraisonAI Chat will automatically use SQLite with the following default configuration:
PraisonAI Chat supports various database backends through SQLAlchemy:
For other database types, refer to the SQLAlchemy documentation for the correct connection string format.
To facilitate local development with live reload, you can use Docker. Follow the steps below:
Create a Dockerfile.dev
:
Create a docker-compose.yml
:
Run Docker Compose:
This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.