📄 中文摘要
该项目展示了如何结合 Ollama 和 PostgreSQL 向量数据库构建一个本地的 RAG（Retrieval-Augmented Generation）系统。通过详细的步骤和技术实现，作者分享了在构建过程中遇到的挑战与解决方案，包括数据存储、检索机制和生成模型的集成。使用 PostgreSQL 的向量存储功能，系统能够高效地处理和检索信息，从而提升生成内容的准确性和相关性。该经验为后续的 AI 系统开发提供了宝贵的参考。
如何使用 Ollama 和 PostgreSQL 向量构建我的第一个本地 RAG 系统
📄 English Summary
How I Built My First Local RAG System Using Ollama and PostgreSQL Vector
The project demonstrates the construction of a local Retrieval-Augmented Generation (RAG) system using Ollama and PostgreSQL vector database. Through detailed steps and technical implementations, the author shares challenges faced during the build process and their solutions, including data storage, retrieval mechanisms, and the integration of generation models. By leveraging PostgreSQL's vector storage capabilities, the system efficiently processes and retrieves information, enhancing the accuracy and relevance of generated content. This experience provides valuable insights for future AI system development.