RAG as a Service

Semantic search for AI agents. Index your documents, search with natural language. Simple API, powerful results.

                    
# Index a document
curl -X POST https://vectorpass-api.automators.work/v1/index \
  -H "X-API-Key: vp_live_xxx" \
  -d '{"id": "doc1", "text": "Your document text"}'

# Search semantically
curl -X POST https://vectorpass-api.automators.work/v1/search \
  -d '{"query": "find relevant content", "k": 10}'
                    
                

Everything you need for semantic search

Lightning Fast

Sub-millisecond search latency. Scales to millions of vectors effortlessly.

Multilingual

Search in 100+ languages out of the box. No configuration needed.

Secure

API key authentication, rate limiting, and data isolation per account.

Hybrid Search

Combine semantic vectors with BM25 keyword search for best results.

No Infrastructure

Runs on Cloudflare edge. No servers to manage, scales automatically.

Simple API

RESTful API with just 5 endpoints. Integrate in minutes, not days.

Simple, transparent pricing

Start free, scale as you grow

Free

$0 /month

1,000 vectors
100 searches/day
Batch size: 10

Starter

$9 /month

50,000 vectors
10K searches/day
Batch size: 100
Email support

Popular

Pro

$29 /month

500,000 vectors
100K searches/day
Batch size: 500
Priority support

Business

$79 /month

5M vectors
1M searches/day
Batch size: 1,000
Dedicated support

Referral Program

Earn up to 50% off your subscription

Share your referral code with friends. For each friend with an active subscription, you get 10% off. Stack up to 5 active referrals for 50% off.

10%

per active referral

50%

max discount

Ongoing

while they pay

Documentation

Everything you need to integrate semantic search into your AI agents

1 Get your API key

2 Index documents

                        
curl -X POST https://vectorpass-api.automators.work/v1/index \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "id": "doc1",
    "text": "Your document content here. Can be any length!",
    "metadata": {"category": "docs", "author": "John"}
  }'
                        
                    

Automatic Chunking: Long documents are automatically split into ~1500 character chunks with 200 char overlap. Each chunk is indexed separately for better retrieval.

3 Search semantically

                        
curl -X POST https://vectorpass-api.automators.work/v1/search \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "find relevant documents", "k": 10}'

Response:

                        
{
  "success": true,
  "data": {
    "results": [
      {
        "id": "doc1",
        "distance": 0.234,
        "metadata": {"category": "docs", "_snippet": "Your document..."}
      }
    ],
    "query": "find relevant documents",
    "k": 10
  }
}
                        
                    

API Reference

Endpoint	Description	Body
POST /v1/index	Index a document	{id, text, metadata?}
POST /v1/batch	Batch index (up to tier limit)	{items: [{id, text, metadata?}]}
POST /v1/search	Semantic vector search	{query, k?}
POST /v1/keyword	BM25 keyword search	{query, k?}
DELETE /v1/vectors/:id	Delete document + chunks	-
GET /v1/stats	Usage statistics	-

Automatic Chunking

Long documents split into ~1500 char chunks
200 char overlap for context continuity
Intelligent sentence boundary detection
Each chunk indexed as id:chunk_N

Semantic Search

100+ languages supported
Understands meaning, not just keywords
Sub-100ms search latency
BM25 keyword search included

Authentication

Header: X-API-Key: vp_live_xxx
Or: Authorization: Bearer vp_live_xxx
Email verification required for writes
Rate limits per tier (see Pricing)

Response Format

All responses: {success, data?, error?}
Search results include distance (0-2)
Lower distance = more similar
Metadata preserved from indexing