Build a Production-Grade Chatbot with Kimi K2 and Milvus

This post details the implementation of a production-grade chatbot using Kimi K2 and Milvus. It provides a practical example of integrating a large language model (Kimi K2) with a vector database (Milvus) for RAG applications. The core contribution is a Python implementation demonstrating automatic file processing, chunking, semantic search using OpenAI embeddings, and intelligent decision-making orchestrated by the LLM. It includes code for a `VectorDatabase` class handling Milvus connections, collection creation, document insertion, and similarity search, and a `SmartAssistant` class orchestrating tool usage. The post highlights the benefits of Kimi K2's agent capabilities and API compatibility, and the ease of use of Milvus for storing and querying vector embeddings.