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cloudflare-vectorize

Complete knowledge domain for Cloudflare Vectorize - globally distributed vector database for building semantic search, RAG (Retrieval Augmented Generation), and AI-powered applications. Use when: creating vector indexes, inserting embeddings, querying vectors, implementing semantic search, building RAG systems, configuring metadata filtering, working with Workers AI embeddings, integrating with OpenAI embeddings, or encountering metadata index timing errors, dimension mismatches, filter syntax issues, or insert vs upsert confusion. Keywords: vectorize, vector database, vector index, vector search, similarity search, semantic search, nearest neighbor, knn search, ann search, RAG, retrieval augmented generation, chat with data, document search, semantic Q&A, context retrieval, bge-base, @cf/baai/bge-base-en-v1.5, text-embedding-3-small, text-embedding-3-large, Workers AI embeddings, openai embeddings, insert vectors, upsert vectors, query vectors, delete vectors, metadata filtering, namespace filtering, topK

jackspace☆ 9 仓库 Stars3 仓库 Forks

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

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技能说明

功能与用途

Complete knowledge domain for Cloudflare Vectorize - globally distributed vector database for building semantic search, RAG (Retrieval Augmented Generation), and AI-powered applications. Use when: creating vector indexes, inserting embeddings, querying vectors, implementing semantic search, building RAG systems, configuring metadata filtering, working with Workers AI embeddings, integrating with OpenAI embeddings, or encountering metadata index timing errors, dimension mismatches, filter syntax issues, or insert vs upsert confusion. Keywords: vectorize, vector database, vector index, vector search, similarity search, semantic search, nearest neighbor, knn search, ann search, RAG, retrieval augmented generation, chat with data, document search, semantic Q&A, context retrieval, bge-base, @cf/baai/bge-base-en-v1.5, text-embedding-3-small, text-embedding-3-large, Workers AI embeddings, openai embeddings, insert vectors, upsert vectors, query vectors, delete vectors, metadata filtering, namespace filtering, topK

快速开始

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

使用指南 ↗

使用示例

查看来源仓库中的使用示例与配置说明。

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来源历史

收录时间
2026-01-20
索引更新时间
2026-02-28
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

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