agentic-chatbot


Nameagentic-chatbot JSON
Version 2.0.1 PyPI version JSON
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home_pagehttps://github.com/deuex-solutions/Agentic-Boilerplate
SummaryEnterprise-grade AI agents for enhanced chatbot capabilities with RAG, security, and multi-user support
upload_time2025-09-15 17:18:02
maintainerNone
docs_urlNone
authorDhruv Yadav
requires_python>=3.8
licenseMIT
keywords ai agents chatbot security context model-selection openai gpt llm machine-learning nlp conversational-ai rag retrieval-augmented-generation enterprise multi-user conversation-memory vector-store chromadb redis postgresql anthropic claude google-gemini ollama langchain streaming async tools function-calling monitoring analytics
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            # 🤖 AI Agents for Enhanced Chatbots

**Enterprise-grade AI agents that work together to create smarter, safer, and more efficient chatbots with conversation memory and advanced features.**

## 🎯 Quick Start

```python
from agentic_chatbot import create_openai_agent

# Basic agent with tools
agent = create_openai_agent(enable_tools=True)
response = agent.invoke("user_123", "What's the latest news about AI?")

# Full-featured agent with RAG
agent = create_openai_agent(enable_rag=True, enable_tools=True)
response = agent.invoke("user_123", "Tell me about quantum computing")
```

## ✨ Key Features

- **🛡️ Security Agent** - Detects malicious content and security threats
- **🧠 Context Agent** - Analyzes query relevance and conversation flow  
- **🎯 Model Selection Agent** - Intelligently selects optimal LLM models
- **💬 Advanced Conversation Agent** - Multi-user memory with RAG, tools, and monitoring

## 🚀 Core Capabilities

- **Multi-LLM Support**: OpenAI, Anthropic, Google, Ollama
- **RAG Integration**: Document retrieval with ChromaDB and FAISS
- **Tool Integration**: Google Search, Wikipedia, custom tools
- **Multi-User Sessions**: Separate conversation history per user
- **Enterprise Storage**: Redis, MongoDB, PostgreSQL backends
- **Comprehensive Monitoring**: Token tracking, cost estimation, analytics

## 📦 Installation

```bash
pip install agentic-chatbot
```

## 🔧 Optional Features

```bash
# RAG capabilities
pip install agentic-chatbot[rag]

# Tool integration  
pip install agentic-chatbot[tools]

# Enterprise features
pip install agentic-chatbot[enterprise]

# Development tools
pip install agentic-chatbot[dev]
```

## 📚 Documentation

- **Full Documentation**: [GitHub Repository](https://github.com/deuex-solutions/Agentic-Boilerplate)
- **Examples**: See `examples/` directory
- **Advanced Guide**: `COMPREHENSIVE_AGENT_GUIDE.md`

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guide](https://github.com/deuex-solutions/Agentic-Boilerplate) for details.

## 📄 License

MIT License - see [LICENSE](https://github.com/deuex-solutions/Agentic-Boilerplate/blob/main/LICENSE) file for details.

---

**Ready to build enterprise-grade AI chatbots?** 🚀

Made with ❤️ by the AI Agents Team

            

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    "description": "# \ud83e\udd16 AI Agents for Enhanced Chatbots\n\n**Enterprise-grade AI agents that work together to create smarter, safer, and more efficient chatbots with conversation memory and advanced features.**\n\n## \ud83c\udfaf Quick Start\n\n```python\nfrom agentic_chatbot import create_openai_agent\n\n# Basic agent with tools\nagent = create_openai_agent(enable_tools=True)\nresponse = agent.invoke(\"user_123\", \"What's the latest news about AI?\")\n\n# Full-featured agent with RAG\nagent = create_openai_agent(enable_rag=True, enable_tools=True)\nresponse = agent.invoke(\"user_123\", \"Tell me about quantum computing\")\n```\n\n## \u2728 Key Features\n\n- **\ud83d\udee1\ufe0f Security Agent** - Detects malicious content and security threats\n- **\ud83e\udde0 Context Agent** - Analyzes query relevance and conversation flow  \n- **\ud83c\udfaf Model Selection Agent** - Intelligently selects optimal LLM models\n- **\ud83d\udcac Advanced Conversation Agent** - Multi-user memory with RAG, tools, and monitoring\n\n## \ud83d\ude80 Core Capabilities\n\n- **Multi-LLM Support**: OpenAI, Anthropic, Google, Ollama\n- **RAG Integration**: Document retrieval with ChromaDB and FAISS\n- **Tool Integration**: Google Search, Wikipedia, custom tools\n- **Multi-User Sessions**: Separate conversation history per user\n- **Enterprise Storage**: Redis, MongoDB, PostgreSQL backends\n- **Comprehensive Monitoring**: Token tracking, cost estimation, analytics\n\n## \ud83d\udce6 Installation\n\n```bash\npip install agentic-chatbot\n```\n\n## \ud83d\udd27 Optional Features\n\n```bash\n# RAG capabilities\npip install agentic-chatbot[rag]\n\n# Tool integration  \npip install agentic-chatbot[tools]\n\n# Enterprise features\npip install agentic-chatbot[enterprise]\n\n# Development tools\npip install agentic-chatbot[dev]\n```\n\n## \ud83d\udcda Documentation\n\n- **Full Documentation**: [GitHub Repository](https://github.com/deuex-solutions/Agentic-Boilerplate)\n- **Examples**: See `examples/` directory\n- **Advanced Guide**: `COMPREHENSIVE_AGENT_GUIDE.md`\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](https://github.com/deuex-solutions/Agentic-Boilerplate) for details.\n\n## \ud83d\udcc4 License\n\nMIT License - see [LICENSE](https://github.com/deuex-solutions/Agentic-Boilerplate/blob/main/LICENSE) file for details.\n\n---\n\n**Ready to build enterprise-grade AI chatbots?** \ud83d\ude80\n\nMade with \u2764\ufe0f by the AI Agents Team\n",
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