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 search, cosine similarity, euclidean distance, dot product, wrangler vectorize, metadata index, create vectorize index, vectorize dimensions, vectorize metric, vectorize binding
Comprehensive guide for building semantic search, RAG, and AI-powered applications with Cloudflare Vectorize
This skill automatically activates when you mention:
Vector Database Operations: vectorize, vector database, vector index, vector search, similarity search, semantic search, nearest neighbor, knn search, ann search, vector embeddings, embedding database
RAG & AI Patterns: RAG, retrieval augmented generation, chat with data, document search, semantic Q&A, context retrieval, document retrieval, knowledge base search, AI search, conversational search
Embedding Models: bge-base, @cf/baai/bge-base-en-v1.5, Workers AI embeddings, text-embedding-3-small, text-embedding-3-large, openai embeddings, embedding generation, vector generation
Operations: insert vectors, upsert vectors, query vectors, delete vectors, list vectors, vector metadata, metadata filtering, namespace filtering, topK search, cosine similarity, euclidean distance, dot product similarity
Setup & Configuration: create vectorize index, vectorize dimensions, vectorize metric, vectorize binding, wrangler vectorize, metadata index, metadata filtering, vector namespace
Use Cases: semantic document search, product recommendations, image similarity, content recommendations, duplicate detection, anomaly detection, classification, clustering
Integration: vectorize + workers ai, vectorize + d1, vectorize + r2, vectorize + kv, vectorize + openai, cloudflare vector database
Instead of repeatedly explaining:
You get complete, tested code templates ready to use.
Provides:
# CRITICAL: Dimensions and metric CANNOT be changed later!
npx wrangler vectorize create my-index \
--dimensions=768 \
--metric=cosine
npx wrangler vectorize create-metadata-index my-index \
--property-name=category \
--type=string
npx wrangler vectorize create-metadata-index my-index \
--property-name=timestamp \
--type=number
wrangler.jsonc:
{
"vectorize": [
{
"binding": "VECTORIZE_INDEX",
"index_name": "my-index"
}
],
"ai": {
"binding": "AI"
}
}
export default {
async fetch(request: Request, env: Env): Promise<Response> {
// Generate embedding
const embedding = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
text: "Search query"
});
// Search vectors
const results = await env.VECTORIZE_INDEX.query(
embedding.data[0],
{
topK: 5,
filter: { category: "docs" },
returnMetadata: 'all'
}
);
return Response.json(results);
}
};
Simple semantic search with Workers AI embeddings. Perfect for documentation search, FAQ lookup, or product search.
Complete RAG chatbot that retrieves relevant context and generates answers using LLM. Includes conversation history and source citations.
Document processing pipeline with text chunking, batch embedding generation, and metadata tagging. Handles large documents efficiently.
Advanced filtering examples: range queries, nested metadata, multi-condition filters, namespace isolation for multi-tenant apps.
Complete CLI reference for all vectorize commands with flags and examples.
Index creation, configuration, management. Covers dimensions, metrics, and when to use each.
Detailed guide for insert, upsert, query, delete, list, and get operations with examples.
Metadata indexes, filtering operators, cardinality considerations, and performance optimization.
Configuration for Workers AI and OpenAI models, dimension requirements, and batch processing.
Complete integration guide for Workers AI's @cf/baai/bge-base-en-v1.5 model (768 dimensions, cosine metric).
OpenAI text-embedding-3-small/large integration with API key management and error handling.
✅ Semantic search over documents, products, or content ✅ RAG chatbots with context retrieval ✅ Recommendation engines based on similarity ✅ Multi-tenant applications (namespaces) ✅ Classification and clustering ✅ Anomaly detection ✅ Duplicate content detection ✅ Image/audio similarity (with embeddings)
Author: Jezweb License: MIT Category: Cloudflare Infrastructure
npx skills add jackspace/cloudflare-vectorize下载完整 Skill 目录，包含 SKILL.md 及所有相关文件
Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Start voice calls via the OpenClaw voice-call plugin.
Notion API for creating and managing pages, databases, and blocks.
Gemini CLI for one-shot Q&A, summaries, and generation.
Category:developer