📄 中文摘要
AI 智能体在实时数据搜索方面面临挑战，需要处理不断变化的互联网信息和API数据。传统的索引方法难以满足对新鲜数据和流式更新的需求，导致搜索结果滞后。SochDB 引擎提供了一种解决方案，通过整合 SQL 数据库、向量嵌入和上下文记忆，实现对实时数据的有效管理和检索。该引擎将实时数据摄取（如HTTP、WebSocket、Kafka）与 SQL 表存储相结合，用于保存原始数据和元数据。同时，向量嵌入技术被应用于数据行中，以支持语义搜索和相似性匹配。此外，上下文记忆机制用于追踪已处理或已使用的信息，确保搜索结果的准确性和相关性。这种一体化架构使得 AI 智能体能够直接查询并利用最新的互联网信息和API数据，从而提供随互联网变化而更新的实时答案，满足对动态信息源的搜索需求。
📄 English Summary
Real-Time Search for AI Agents: SQL, Vectors, and Memory in One Engine
AI agents require real-time search capabilities to effectively interact with dynamic internet and API data. Traditional indexing approaches often fall short in handling fresh data and streaming updates, leading to stale search results. The SochDB engine addresses this by integrating SQL databases, vector embeddings, and context memory into a unified system. This architecture facilitates the ingestion of live data from various sources like HTTP, WebSocket, and Kafka, storing raw data and metadata within SQL tables. Crucially, vector embeddings are stored alongside data rows, enabling advanced semantic search and similarity matching. Furthermore, a context memory component tracks previously processed or utilized information, ensuring the relevance and accuracy of search outcomes. This integrated approach allows AI agents to directly query and leverage the most current internet information and API data. Consequently, the system delivers real-time answers that evolve with changes on the internet, fulfilling the critical demand for up-to-date information from dynamic sources.