PraisonAI Code

PraisonAI Code helps you to interact with your whole codebase using the power of AI.

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Different User Interfaces:

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Table of Contents

• Install PraisonAI Code
• Other Models
• To Use Gemini 1.5
• Ignore Files
  • Using .praisonignore
  • Using settings.yaml
  • Using .env File
  • Using Environment Variables in the Terminal
• Include Files
• Set Max Tokens

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Install PraisonAI Code

pip install "praisonai[code]"

export OPENAI_API_KEY=xxxxxxxx

praisonai code

4. Username and Password will be asked for the first time. admin is the default username and password.

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

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Other Models

• Use 100+ LLMs - Litellm
• Includes Gemini 1.5 for 2 Million Context Length

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To Use Gemini 1.5

• export GEMINI_API_KEY=xxxxxxxxx
• praisonai code
• Set Model name to be gemini/gemini-1.5-flash in the settings

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Ignore Files

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Using .praisonignore

• Create a .praisonignore file in the root folder of the project
• Add files to ignore

.*
*.pyc
pycache
.git
.gitignore
.vscode
.idea
.DS_Store
.lock
.pyc
.env

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Using settings.yaml

(.praisonignore is preferred)

• Create a settings.yaml file in the root folder of the project
• Add below Variables and required Ignore Files

code:
  ignore_files:
  - ".*"
  - "*.pyc"
  - "pycache"
  - ".git"
  - ".gitignore"
  - ".vscode"
  - ".idea"
  - ".DS_Store"
  - ".lock"
  - ".pyc"
  - ".env"

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Using .env File

• Create a .env file in the root folder of the project
• Add below Variables and required Ignore Files

PRAISONAI_IGNORE_FILES=".*,*.pyc,__pycache__,.git,.gitignore,.vscode"

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Using Environment Variables in the Terminal

export PRAISONAI_IGNORE_FILES=".*,*.pyc,__pycache__,.git,.gitignore,.vscode"

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Include Files .praisoninclude

• Add files you wish to Include files in the context
• This will include the files/folders mentioned in .praisoninclude to the original context (files in the folder - .gitignore - .praisonignore)

• Create a .praisoninclude file in the root folder of the project
• Add files to Include

projectfiles
docs

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Include ONLY these Files .praisoncontext (Context)

• Add files you wish to Include files in the context
• This will include ONLY the files/folders mentioned in .praisoncontext to the context

• Create a .praisoncontext file in the root folder of the project
• Add files to Include

projectfiles
docs

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Set Max Tokens

Note: By Default Max Tokens set is 900,000

export PRAISONAI_MAX_TOKENS=1000000

or

• Create a .env file in the root folder of the project
• Add below Variables and required Max Tokens
• Copy

  PRAISONAI_MAX_TOKENS=1000000
  

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Default DB Location

~/.praison/database.sqlite

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Key Features

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Internet Search

PraisonAI Code now includes internet search capabilities using Crawl4AI and Tavily. This feature allows you to retrieve up-to-date information and code snippets during your coding sessions, enhancing your ability to find relevant programming information and examples.

To use this feature:

1. Ask a question or request information about a specific coding topic
2. The AI will use internet search to find the most relevant and current information
3. You’ll receive code snippets, documentation references, or explanations based on the latest available resources

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Vision Language Model (VLM) Support

While primarily designed for code interactions, PraisonAI Code also supports Vision Language Model capabilities. This feature can be particularly useful when dealing with visual aspects of programming, such as UI design, data visualization, or understanding code structure through diagrams.

To use this feature:

1. Upload an image related to your coding query (e.g., a screenshot of a UI, a flowchart, or a code snippet image)
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, helping you understand or implement the visual concepts in your code

These new features significantly expand the capabilities of PraisonAI Code, allowing for more comprehensive and up-to-date coding assistance.

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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:

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   FROM python:3.11-slim
   
   WORKDIR /app
   
   COPY . .
   
   RUN pip install flask praisonai==2.2.25 watchdog
   
   EXPOSE 5555
   
   ENV FLASK_ENV=development
   
   CMD ["flask", "run", "--host=0.0.0.0"]
   

2. Create a docker-compose.yml:

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   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:

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   docker-compose up
   

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