エージェントを強化
公開元からインストールします。PlugMCP のエンドポイントではありません。
リモート URL か、手元のマシンのコマンドか 2 つのインストール方法の違い
PlugMCP で実行
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
起動公開元より
Production-grade RAG on Cloudflare Workers. ~$5/month. No servers. No Pinecone bill.
Hybrid search, knowledge reflection, multimodal ingestion, metadata filtering, multi-tenancy, rate limiting, and a native MCP server — all in one deployable Worker.
Hybrid RAG Worker on Cloudflare Edge — Vector + BM25 search, metadata filtering, multimodal vision, MCP server, and intelligent query routing. ~400ms p99.
Andrej Karpathy made a compelling argument recently: LLMs are becoming the new Wikipedia. Ask the model, get the answer. For general knowledge questions, he's right — if you're asking "what is gradient descent," you don't need a retrieval pipeline.
But that framing has a blind spot:
Your data isn't in any LLM's training set. It never will be.
Your internal docs, customer contracts, support tickets, financial reports, product changelog — none of it lives in any model. The moment you need answers grounded in your specific knowledge base, you need retrieval. The moment you need results that are cited, up-to-date, and not hallucinated, you need RAG.
This project is for that use case. It's not trying to beat LLMs at general knowledge. It's making them useful on your data.
The second reason this exists: most RAG tutorials hand you a 50-line Python script that calls OpenAI and Pinecone and calls it a day. That's fine for a weekend demo. It falls apart the moment you need multi-tenancy, metadata filtering, sub-second cached responses, a proper MCP server, and something you'd actually trust in production. This is what happens when you build the thing properly from the start.
Hybrid Search — Vector similarity + BM25 keyword search, fused with Reciprocal Rank Fusion. Pure vector search misses exact matches. Pure keyword search misses synonyms. You need both.
Cross-Encoder Reranking — A cross-encoder rescores the top results against your query after initial retrieval. In my benchmarks: +9.3 percentage point improvement in MRR@5.
Knowledge Reflection (new) — After every ingest, the system finds semantically related documents and asks Llama to synthesize what's new, how it connects to existing knowledge, and what gap remains. These reflections are stored, embedded, and boosted in search results. The knowledge base gets smarter as you add more documents — not just bigger.
Intelligent Query Routing (V4) — Not every query needs vector search. Entity lookups go to SQL (11ms), exact keyword queries go to BM25 (18ms), semantic questions go to the full pipeline. In testing, this cuts average embedding cost by 71% compared to running everything through vectors.
Metadata Filtering — Filter by source_type, category, doc_type, tags, date_created, tenant_id, mime_type, file_name — using $eq, $ne, $in, $gt/$gte/$lt/$lte. Filters run at the Vectorize layer, not in memory.
Multimodal — Ingest images by upload or URL. Llama 4 Scout 17B handles vision + OCR. Text queries can surface image results. Everything lives in one unified index.
Native MCP Server — Streamable HTTP at /mcp, backed by Cloudflare Durable Objects per session. Six tools: search, ingest, ingest_image_url, find_similar_by_url, delete, stats. Two lines of JSON to connect Claude Desktop. That's it.
Multi-Tenancy — One deployment, multiple isolated tenants. Map API keys to tenant IDs via a JSON secret. Every operation is automatically scoped. Tenants can't escape their namespace. You don't need a separate deployment per customer.
Two-Layer Caching — Cloudflare Cache API (global, shared, 60s SWR) + in-memory Map cache. Repeat queries cost zero.
Rate Limiting — Sliding-window limiter per tenant or IP. Configurable. Returns proper Retry-After headers.
Query Analytics — Every query recorded: latency per stage, cache hit/miss, filters used. Surfaces through /stats and the built-in dashboard. No separate logging service.
Three Embedding Models — qwen3-0.6b (1024d, best retrieval quality 2026, default), bge-m3 (1024d, multilingual), bge-small (384d, legacy). One env var to switch: EMBEDDING_MODEL.
Standard RAG retrieves documents. It doesn't learn. Every query goes in cold — no memory of what it found before, no synthesis across documents, no growing understanding of your knowledge base.
The reflection layer changes that. Every time you ingest a document:
@cf/google/gemma-4-26b-a4b-it) synthesises a three-sentence insight: what the new document adds, how it connects to existing knowledge, and what gap remainsdoc_type=reflection, and given a 1.5× ranking boost in search resultsdoc_type=summary — a compressed view of what the knowledge base has learnedYour knowledge base builds a second layer of synthesised knowledge on top of raw documents. Related concepts get explicitly linked. Contradictions get surfaced. Search results include both raw chunks and distilled insights.
You can filter to see only reflections (doc_type: { "$eq": "reflection" }), or exclude them ("$ne": "reflection"), or let them naturally surface alongside raw results — which is the default.
Internal knowledge base — Ingest your Notion exports, Confluence pages, internal docs. Give your team a search interface that understands context, not just keywords. Connect Claude Desktop via MCP and every team member has AI-assisted search over your entire knowledge base without you building anything custom.
SaaS with per-customer document search — Enable multi-tenancy. Each customer gets their own isolated namespace in the same deployment. One Worker, one Vectorize index, true data isolation. No separate search service per customer.
Legal / compliance retrieval — Ingest contracts, filings, policy docs. Filter by date_created, category, source_type. The reflection layer surfaces connections between documents across time — useful for spotting contradictions in evolving policy.
Developer tool that understands your codebase — Ingest your docs, changelogs, runbooks, incident postmortems. Hook it to Claude Desktop. Ask questions about your own architecture decisions and incident history.
Receipt and invoice processing — Upload receipt images, Llama 4 Scout extracts the text, both semantic description and raw OCR land in the index. Search "dinner expense over $100 in March" and it works.
Once deployed, your dashboard lives at:
https://your-worker.workers.dev/dashboard
The OpenAPI spec is available at /openapi.json — import it directly into Postman, Insomnia, Bruno, or any API client that accepts OpenAPI 3.0.
It's a full playground: search with filters and intent classification debug, ingest documents (drag-and-drop file upload, advanced metadata), upload images, find visually similar images, delete documents, manage licenses, monitor cache hit rate, check which embedding model is active, browse query analytics. A built-in Guide tab walks through every setup step with copyable commands and a live status checker. Not a toy.
You need: a Cloudflare account (free tier works), Node.js v18+, Wrangler CLI.
npm install -g wrangler
wrangler login
git clone https://github.com/dannwaneri/vectorize-mcp-worker.git
cd vectorize-mcp-worker
npm install
# Default (recommended): qwen3-0.6b, 1024 dimensions
wrangler vectorize create mcp-knowledge-base --dimensions=1024 --metric=cosine
# Legacy (existing 384d deployments): bge-small
# wrangler vectorize create mcp-knowledge-base --dimensions=384 --metric=cosine
# Then set EMBEDDING_MODEL=bge-small in wrangler.toml [vars]
wrangler d1 create mcp-knowledge-db
Copy the database_id from the output.
cp wrangler.toml.example wrangler.toml
Paste your database_id into wrangler.toml. That's the only required edit.
[[d1_databases]]
binding = "DB"
database_name = "mcp-knowledge-db"
database_id = "paste-your-id-here"
wrangler d1 execute mcp-knowledge-db --remote --file=./schema.sql
wrangler d1 execute mcp-knowledge-db --remote --file=./migrations/001_add_tenant_id.sql
wrangler d1 execute mcp-knowledge-db --remote --file=./migrations/002_add_reflection_fields.sql
wrangler deploy
wrangler secret put API_KEY
Done. Visit https://your-worker.workers.dev/test to confirm everything is connected.
Prefer one click? The deploy button at the top scaffolds everything automatically. You'll still need to set API_KEY as a secret afterward.
The MCP server runs at /mcp using Streamable HTTP transport (the 2026 standard). Each session gets a dedicated Durable Object instance for isolated state. Six tools cover the full lifecycle.
Add this to your config file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"vectorize": {
"command": "npx",
"args": [
"mcp-remote",
"https://your-worker.workers.dev/mcp",
"--header",
"Authorization: Bearer YOUR_API_KEY"
]
}
}
}
Restart Claude Desktop. The six tools appear in the tool picker. Then:
"Search my knowledge base for anything about our Q1 pricing decisions" "Ingest this document — tag it category: finance, source_type: pdf" "What synthesised insights do we have on infrastructure costs?" "Find images similar to this URL and show me the top 5"
{
"mcpServers": {
"vectorize": {
"url": "https://your-worker.workers.dev/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
All tools are automatically scoped to your tenant from the API key. You can't accidentally read another tenant's data.
Everything runs on Cloudflare. No external services. No third-party billing. No data leaving your account.
Switching embedding models is one env var (EMBEDDING_MODEL). Switching reflection quality vs cost is one env var (REFLECTION_MODEL=llama-3.2-3b for lower cost). Switching from bge-small (384d) to qwen3-0.6b (1024d) requires a new Vectorize index and re-ingest — no code changes.
Straight numbers, no vague "cheaper than Pinecone" hand-waving.
At 1,000 queries/day with V4 intelligent routing:
At 10,000 queries/day: roughly $1–5/month depending on query mix and cache hit rate.
The Cloudflare Workers free tier handles 100,000 requests/day before you pay anything for compute. Vectorize, D1, and Workers AI each have their own free tiers too. At the volumes most teams actually run, the total bill is boring in the best way.
If your data is public knowledge and freshness doesn't matter, the LLM alone is often the right call. If your data is private, proprietary, or time-sensitive — you need retrieval. This is the fastest path to production-grade retrieval I've found, and the only one in this list where the knowledge base gets smarter over time.
POST /ingest/batch, up to 100 docs, concurrency-controlled, per-doc resultsPOST /analyze-image, fetch any public image URL and return an AI description (Llama 4 Scout) without ingesting — useful for enriching external content before indexingPOST /reflect/batch, sample N un-reflected raw documents and generate knowledge reflections on demand — run periodically to build up synthesis without blocking ingestGET /openapi.json, OpenAPI 3.0.3, importable into Postman / Insomnia / BrunoPRs welcome on any of these. If you're running this in production and hitting a specific wall, open an issue and I'll prioritise it.
If this saved you time or money, a GitHub star helps other people find it.
If something's broken or confusing, open an issue. I read every one.
The codebase is intentionally straightforward — no frameworks on top of frameworks. The highest-value contributions right now are real-world usage reports, the batch ingestion endpoint, and test coverage for the middleware and reflection layer.
Need this deployed, customised, or integrated into an existing product?
MIT — see LICENSE.
Daniel Nwaneri — building useful things on Cloudflare Workers
Hybrid RAG Worker on Cloudflare Edge — Vector + BM25 search, metadata filtering, multimodal vision, MCP server, and intelligent query routing. ~400ms p99.
ツール
ツール一覧はまだありません。リモートサーバーは、最初のヘルスチェック後に取得されます。
よくある質問
このページで Cursor、Claude Desktop、Claude Code、VS Code 用の stdio 設定をコピーできます。PlugMCP のプロキシではなく、公開元のインストール方法を使います。
このレコードに必須の環境変数は記載されていません。
Vectorize MCP Worker は stdio 経由でローカル実行されます。
最終チェックは 2026-10-28 です。 Hybrid RAG Worker on Cloudflare Edge — Vector + BM25 search, metadata filtering, multimodal vision, MCP server, and intelligent query routing. ~400ms p99.
