The Platform for Retrieval Augmented Generation
Everything you need for a better RAG pipeline
- Retrieval-as-a-Service (RaaS)
Production-ready managed RAG service and hassle-free retrieval data platform.
- Intelligent Retrieval Agents
Specialized retrieval agents equipped for your complex LLM tasks.
- Evaluation & Fine-Tuning
Embedding model evaluation, selection and fine-tuning service for your data.
Easy-to-Manage Data Sources
Managed Retrieval Platform
Unlock managed RAG service and retrieval data platform. Stop worrying about managing retrieval data infrastructure and focus on building your RAG applications that are ready to scale.
Hassle-free Deployment
Retrieval API & SDK
Our API, coupled with a versatile Python SDK, empowers users to retrieve data effortlessly and integrate it seamlessly into your RAG workflows and AI applications.
- Retrieval API
- Experience fast and reliable data retrieval with our API. Ideal for seamless integrations and retrieve data on-demand.
- Python SDK
- Enhance your Python AI projects with our SDK. Simplified data retrieval, optimized for a seamless development experience.
import vectifyai
vc = vectifyai.Client(api_key='YOUR_API_KEY')
results = vc.retrieve(query='What is RAG?', top_k=5, sources=['my_source'])
Tackle Complex Queries with Enhanced Retrieval Performance
Intelligent Retrieval Agents
In addition to the classic semantic search, our RAG platform integrates innovative state-of-the-art retrieval agents, offering superior accuracy and efficiency.
- Multi-Strategy Retrieval Agent
- Our Multi-Strategy Retrieval Agent excels in handling complex queries, effectively re-ranking and merging multiple retrieved lists to deliver high-recall retrieval results with fewer context tokens.
- Metadata Retrieval Agent
- By integrating detailed document-level metadata, our Metadata Retrieval Agent is capable of processing natural language queries with a sophisticated blend of semantic understanding and exact metadata filtering.
Use Case: Query that contains multiple objectives.
Example: What is the weather difference between Boston and Seattle?
Use Case: Query that contains exact conditions like time or location.
Example: What are the latest climate change policies in European countries after 2020?
Select the Best Embedding Model for Your Data
Embedding Model Evaluation and Selection
Unsure which embedding model to use for your data? We provide a systematic and scientific solution for embedding models evaluation. Select the best embedding model for your data with one click.
Continuous Improvement through Embedding Model Fine-Tuning
Embedding Model Fine-Tuning
Our unique fine-tuning technique for embedding models, named Mafin, significantly improves the retrieval accuracy of your data and use case at a minimal cost.
65.6%
66.3%
68.9%
69.7%
69.7%
71.3%
71.7%
72.5%