Building an Open Source Chatbot Using LangChain and Milvus in Under 5 Minutes

This blog post demonstrates how to build an open source chatbot using LangChain and Milvus in under 5 minutes. The process involves creating a retrieval augmented generation (RAG) stack with LangChain, which allows for answering questions about custom data while reducing hallucinations. The text is grounded on factual, custom data such as product documentation to ensure accuracy. The source code for the live chatbot is available on GitHub. The blog post also explains how to use Milvus, a high-performance vector database optimized for fast storage, indexing, and searching of embeddings or vectors. OpenAI's language models like GPT series are used in this process. Overall, the RAG retrieval and question-answering chatbot on custom documents is shown to be efficient and cost-effective as it allows free calls to data almost all the time for retrieval, evaluation, and development iterations, with only a paid call to OpenAI once for the final chat generation step.