chat.mdx

title	PraisonAI Chat
description	Guide to PraisonAI's chat interface with support for 100+ LLMs, internet search, vision models, and custom database configurations
icon	comments

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Different User Interfaces:

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

Quick Start

1. Install PraisonAI Chat:

pip install "praisonai[chat]"

2. Set up your OpenAI API key:

export OPENAI_API_KEY=xxxxxxxx

3. Set up your Database URL:

export DATABASE_URL=postgresql+asyncpg://<username>:<password>@<your-db-instance-url>/<database-name>

4. Launch PraisonAI Chat:

praisonai chat

5. URL : http://localhost:8084/

6. Username: admin

7. Password: admin

8. Set Model name to be gpt-4o-mini in the settings

Key Features

Internet Search

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.

Vision Language Model (VLM) Support

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:

1. Upload an image to the chat interface
2. Ask questions or request analysis based on the uploaded image
3. The VLM will process the image and provide insights or answers based on its visual content

These new features significantly expand the capabilities of PraisonAI Chat, allowing for more diverse and informative interactions.

Custom Database

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.

PostgreSQL Configuration

To use PostgreSQL as your database backend:

1. Install Required Dependencies

   For local development:

   pip install asyncpg

   For Replit:

   • Open the "Packages" tab in the Tools section
   • Search for and install:
     • python3-dev
     • libpq-dev
   • Then install Python packages:

   pip install asyncpg

2. Set Environment Variables Add these variables to your .env file or Replit Secrets:

   DATABASE_URL=postgresql+asyncpg://<username>:<password>@<your-db-instance-url>/<database-name>
   DATABASE_SSL=true  # Required for most cloud PostgreSQL services

   For Replit:

   • Click on "Tools" in the left sidebar
   • Select "Secrets"
   • Add your database configuration as DATABASE_URL

3. Database Tables The application will automatically:

   • Detect PostgreSQL connections
   • Create all necessary tables if they don't exist
   • Set up proper indexes and constraints
   • Handle table creation errors

4. Cloud Database Services For Replit, we recommend using cloud database services that provide free tiers:

   • Neon (Recommended)
   • Supabase
   • ElephantSQL

   These services provide:

   • Free PostgreSQL hosting
   • Automatic SSL configuration
   • Connection string ready to use

Default Configuration

If no DATABASE_URL is provided, PraisonAI Chat will automatically use SQLite with the following default configuration:

DATABASE_URL=sqlite+aiosqlite:///{HOME}/.praison/database.sqlite

Supported Database Types

PraisonAI Chat supports various database backends through SQLAlchemy:

• PostgreSQL (recommended for production)
• MySQL/MariaDB
• SQLite (default)
• Oracle
• Microsoft SQL Server

For other database types, refer to the SQLAlchemy documentation for the correct connection string format.

Local Docker Development with Live Reload

To facilitate local development with live reload, you can use Docker. Follow the steps below:

1. Create a Dockerfile.dev:

   FROM python:3.11-slim
   
   WORKDIR /app
   
   COPY . .
   
   RUN pip install flask praisonai==2.2.2 watchdog
   
   EXPOSE 5555
   
   ENV FLASK_ENV=development
   
   CMD ["flask", "run", "--host=0.0.0.0"]

2. Create a docker-compose.yml:

   version: '3.8'
   
   services:
     app:
       build:
         context: .
         dockerfile: Dockerfile.dev
       volumes:
         - .:/app
       ports:
         - "5555:5555"
       environment:
         FLASK_ENV: development
       command: flask run --host=0.0.0.0
   
     watch:
       image: alpine:latest
       volumes:
         - .:/app
       command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"

3. Run Docker Compose:

   docker-compose up

This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.