https://github.com/alpha-131/llamaindex-slack-bot
This project showcases a Slackbot leveraging LlamaIndex for NLP, OpenAI LLM for context-aware responses, and Qdrant for efficient data storage. Explore how this bot listens, learns, and interacts intelligently in Slack channels.
https://github.com/alpha-131/llamaindex-slack-bot
llamaindex llm openai qdrant vector-database
Last synced: 7 months ago
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This project showcases a Slackbot leveraging LlamaIndex for NLP, OpenAI LLM for context-aware responses, and Qdrant for efficient data storage. Explore how this bot listens, learns, and interacts intelligently in Slack channels.
- Host: GitHub
- URL: https://github.com/alpha-131/llamaindex-slack-bot
- Owner: Alpha-131
- Created: 2024-02-19T16:07:34.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-02-22T13:19:58.000Z (over 1 year ago)
- Last Synced: 2025-01-18T23:53:31.957Z (9 months ago)
- Topics: llamaindex, llm, openai, qdrant, vector-database
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Context-Aware Slackbot
Welcome to the Context-Aware Slackbot project! This bot leverages advanced technologies to provide intelligent responses within Slack channels.
## Overview
The Context-Aware Slackbot combines state-of-the-art components to deliver enhanced functionality:
- **LlamaIndex**: A powerful tool for natural language processing (NLP), enabling the bot to understand and analyze messages within Slack channels.
- **OpenAI LLM**: Empowers the bot with context-awareness, allowing it to generate meaningful responses based on the conversation history.
- **Qdrant**: Provides efficient data storage capabilities, ensuring seamless retrieval and management of chat messages and associated metadata.
## Features
- **Intelligent Responses**: The bot can understand user queries and generate relevant responses by analyzing the context of the conversation.
- **Real-time Learning**: Continuously learns from new messages to improve its understanding and response accuracy over time.
- **Efficient Data Storage**: Utilizes Qdrant for efficient storage and retrieval of chat messages, enabling fast and reliable access to historical conversations.
- **Speaker Metadata**: Metadata about the speaker is attached to each message, allowing the bot to answer questions like "What did Logan say about the project?"
- **Threaded Conversation Support**: The bot can recognize and respond to follow-up questions within threads, mimicking human conversation dynamics.
## Usage
To use the Context-Aware Slackbot:
1. **Deploy the Bot**: Deploy the bot using the provided instructions, ensuring all necessary dependencies and environment variables are set up correctly.
2. **Join Channels**: Add the bot to Slack channels where you want it to operate, allowing it to listen to and respond to messages.
3. **Interact**: Users can interact with the bot by sending messages or asking questions. The bot will analyze the incoming messages, generate context-aware responses, and provide assistance or information as needed.