RAG API | Vector Database for Retrieval Augmented Generation
Build RAG Enhanced Chatbots → Upload Documents → Auto-Chunk & Embed → Vector Search → Retrieval Augmented Generation Pipeline
RAG as a Service • HNSW Vector Search • RAG Enhanced Chatbot Ready
What is RAG in AI? Retrieval Augmented Generation Explained
RAG (Retrieval Augmented Generation) combines vector database search with AI generation. Our RAG API lets you build RAG enhanced chatbots that retrieve relevant information from your documents and generate accurate, contextual responses.
1. Document Retrieval
RAG systems search your vector database to find relevant documents matching the user query using semantic similarity.
2. Context Augmentation
Retrieved documents augment the AI prompt with relevant context, ensuring responses are grounded in your actual data.
3. Enhanced Generation
AI generates responses using both the user query and retrieved context, creating accurate RAG enhanced chatbot interactions.
From Documents to RAG API in Minutes
Upload your documents through our dashboard, then query them via our RAG API. Perfect for building RAG enhanced chatbots and AI applications.
1. Create a Knowledge Base
Each Knowledge Base is a secure, isolated container for your documents. Create one for each customer or project.
curl -X POST \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{"name": "My First Project"}' \
"https://app.yardee.ai/api/v1/knowledgebases/"2. Upload Documents
Upload documents to a specific Knowledge Base via API or our dashboard. We handle the processing and embedding.
✅ Ready for search • 8.2MB stored
3. Query via API
Perform semantic search scoped to a specific Knowledge Base for fast, secure, and relevant results.
curl -X POST \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"query": "vacation policy remote work",
"top_k": 5
}' \
"https://app.yardee.ai/api/v1/knowledgebases/123/search/"Complete RAG Pipeline for AI Applications
Everything needed to build RAG enhanced chatbots and retrieval augmented generation applications at scale.
Smart Document Processing
Automatic parsing of structured and unstructured document types including PDF, DOCX, TXT, MD, RTF, CSV, XLSX, and PPT files with metadata extraction.
Automated Embeddings
State-of-the-art text embeddings for accurate semantic search and content understanding.
HNSW Search Engine
Enterprise-grade vector search with sub-100ms query times and dynamic performance optimization.
RAG Vector Database
Optimized vector database for RAG applications with advanced metadata filtering and Knowledge Base isolation.
REST API
A simple, well-documented REST API for both management and search that integrates with any platform.
Secure & Isolated
True multi-tenancy. Each Knowledge Base is a dedicated, isolated container for your data.
Multi-Tenant Ready Out of the Box
Scale from one customer to thousands with perfect data isolation. No complex architecture required.
Perfect for SaaS Applications
Isolated Knowledge Bases
Each customer gets their own secure, isolated container for documents and data.
Zero Configuration
No database partitioning, no complex setup. Just create a new Knowledge Base per customer.
Scale Seamlessly
From 1 to 10,000+ customers with the same simple API calls.
Multi-Tenant API Pattern
// Customer A's documents
POST /v1/knowledgebases/123/search/
{
"query": "pricing policy",
"top_k": 5
}
// Customer B's documents
POST /v1/knowledgebases/456/search/
{
"query": "pricing policy",
"top_k": 5
}
// Perfect isolation - no data mixingCommon Use Cases
SaaS Platforms
One Knowledge Base per customer account
Agency Work
Separate client data with complete isolation
Enterprise
Department or project-based separation
Documentation
Version or product-specific knowledge bases
Simple, Transparent Pricing
Pay only for what you store. No per-query fees, no hidden costs. Everything included.
Free Tier
Perfect for testing and small projects
- Up to 10MB storage
- 1,000 total API calls
- Document processing included
- REST API access
- No credit card required
Scale Plan
75-90% less than leading competitors!
- Unlimited storage at $10/GB
- Unlimited queries
- OpenAI Embedding at-cost
- No markup on processing
- Enterprise performance
Why Developers Choose Our RAG API:
Traditional Vector Databases:
- • $100-500/month minimums
- • $0.02-0.05 per page processing
- • Complex setup for RAG applications
- • Limited multi-tenancy support
Yardee RAG API:
- • $10/GB storage only
- • No per-query or processing fees
- • RAG-ready out of the box
- • Perfect multi-tenant Knowledge Bases
Frequently Asked Questions
What is RAG in AI and how does your RAG API work?
RAG (Retrieval Augmented Generation) combines vector search with AI generation. Our RAG API lets you upload documents, automatically processes them into searchable vectors, then retrieve relevant context for AI responses. Perfect for building RAG enhanced chatbots.
How does your RAG vector database compare to other solutions?
Unlike traditional vector databases with $100-500/month minimums and complex setup, our RAG API charges only $10/GB storage with no per-query fees. We're built specifically for RAG applications with multi-tenant Knowledge Bases and instant setup.
How does the pricing work?
It's simple. Storage is $10 per gigabyte per month. Embeddings cost is $.01 per 1,000,000 tokens
When do I need to add a credit card?
You can start building for free without a credit card. We only ask for payment information when you exceed 10MB of storage or 1,000 total API calls.
How does this compare to the competition?
Other platforms charge $100-500/month for processing limits, plus $0.02-0.05 per page in overages, plus connector fees. We only charge $10/GB/month plus embeddings, no monthly minimums, no query fees.
Is this for me if I'm not an AI expert?
Absolutely. Yardee is designed for developers who want to add powerful semantic search to their applications without needing to become experts in vector databases or RAG pipelines. If you can use a REST API, you can use Yardee.
Can I build RAG enhanced chatbots with your API?
Absolutely! Our RAG API is perfect for building RAG enhanced chatbots. Upload your knowledge base documents, use our search endpoints to retrieve relevant context, then feed that to your AI model for accurate, grounded responses.
What file formats do you support for RAG applications?
We support all major document types for RAG pipelines including PDF, DOCX, TXT, MD, RTF, CSV, XLSX, and PPT files. Documents are automatically processed, chunked, and converted to searchable vector embeddings optimized for retrieval augmented generation.
How fast is the search performance?
Our HNSW (Hierarchical Navigable Small World) implementation delivers enterprise-grade performance with sub-100ms query times, even for large datasets. We use dynamic optimization for both speed and accuracy.
How secure is my data?
Your data is protected by network-isolated infrastructure, AES-256 encryption for storage, and TLS encryption for transmission. Unlike standard cloud providers, your data never touches public networks.
Ready to Build RAG Enhanced Applications?
Join developers building RAG enhanced chatbots with predictable pricing. Upload your documents and get a production-ready RAG API in minutes - no monthly minimums, no complexity.
Get Your API Key