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AI-Powered SlackBot

Important Notes

1. Security: Never commit the .env file to version control. Ensure it's listed in your .gitignore file to prevent accidental exposure of sensitive information.

2. Slack Bot Configuration: This SlackBot operates in Socket Mode. Make sure your Slack app is configured to use Socket Mode in the Slack API dashboard. When generating the SLACK_APP_TOKEN, select the Socket Mode option.

Features

This SlackBot leverages OpenAI API and LangChain to provide intelligent responses within Slack. Key features include:

• Intelligent Thread Management:

  • Automatically preserves conversation context within Slack threads
  • Understands the full thread history for more contextual responses
  • Seamlessly handles multiple concurrent conversations

• Advanced AI Integration:

  • Powered by OpenAI's language models
  • Multiple specialized tools for different tasks:
    • Web Search: Access to current information
    • Vector Search: Query internal documents and guidelines

• Performance Optimized:

  • Efficient thread history management using Slack API
  • Configurable timeouts and retry mechanisms
  • Detailed logging for monitoring and debugging

• Easy Setup:

  • Simple configuration through environment variables
  • Docker support for easy deployment
  • Flexible tool enabling/disabling

Slack Configuration

OAuth Scopes

app_mentions:read
channels:history
chat:write
groups:history
im:history
im:read
im:write

Event Subscriptions

app_mention
message.groups
message.im

Getting Started

You can run the project using either pipenv or Docker (recommended).

Create a .env file in the project root directory and add the following environment variables: Refer to .env.dev for guidance.

# Environment
ENVIRONMENT=dev
DEBUG=false           # Enable detailed error messages

# OpenAI API
MODEL_NAME=gpt-4     # Or gpt-3.5-turbo
OPENAI_API_KEY=your_openai_api_key
MODEL_TEMPERATURE=0   # 0-1, lower for more focused responses
MODEL_TIMEOUT=30     # Seconds
MAX_AGENT_ITERATIONS=3

# Slack
SLACK_APP_TOKEN=your_slack_app_token
SLACK_BOT_TOKEN=your_slack_bot_token
SLACK_BOT_ID=your_slack_bot_id
API_TIMEOUT=60       # Slack API timeout

# Vector Store Configuration
VECTOR_STORE_PATH=./vector_store
VECTOR_CHUNK_SIZE=100
VECTOR_CHUNK_OVERLAP=20
VECTOR_DOC_PATH=./vector_file/sample.txt

# Tools Configuration
ENABLE_SEARCH=true
ENABLE_VECTOR=true

# Langsmith (Optional)
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your_langsmith_api_key
LANGCHAIN_PROJECT=your_project_name
LANGCHAIN_ENDPOINT=https://api.smith.langchain.com

Using pipenv

1. Install pipenv: pip install pipenv

2. Clone the repository:

git clone https://github.com/02tYasui/slackbot.git
cd slackbot

3. Install project dependencies using pipenv:

pipenv install

4. Activate pipenv environment:

pipenv shell

5. Run the SlackBot:

python src/app.py

Using Docker (Recommended)

1. Clone the repository:

git clone https://github.com/02tYasui/slackbot.git
cd slackbot

2. Build and run Docker container:

docker compose up --build

System Architecture

The bot uses the following components:

1. Slack Integration:

   • Socket Mode for real-time communication
   • Thread history tracking for context awareness
   • Automatic mention detection and response

2. Language Model:

   • OpenAI's GPT models for natural language processing
   • Configurable temperature and timeout settings
   • Error handling and retry mechanisms

3. Tools:

   • Web Search: DuckDuckGo integration for current information
   • Vector Search: Document retrieval using ChromaDB
   • Customizable tool configuration via environment variables

4. Logging and Monitoring:

   • Detailed logging with file and line information
   • Performance metrics tracking
   • Debug mode for development

Error Handling

The bot includes comprehensive error handling:

• API timeouts and retry logic
• Invalid input detection
• Detailed error logging
• User-friendly error messages
• Debug mode for development

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.