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.

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Metadata

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Database

API Data

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.
pip install vectifyai
Documents
SDK

    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

Use Case: Query that contains multiple objectives.

Example: What is the weather difference between Boston and Seattle?

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

Use Case: Query that contains exact conditions like time or location.

Example: What are the latest climate change policies in European countries after 2020?

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.

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.

Vectify/mafin-with-full-labels

Vectify/mafin-with-10%-labels

Vectify/mafin-with-no-labels

thenlper/gte-large

BAAI/bge-large-en

OpenAI/text-embedding-ada-002

thenlper/gte-base

BAAI/bge-base-en

72.5%

71.7%

71.3%

69.7%

69.7%

68.9%

66.3%

65.6%

Demo: Recall Rates Comparison on Customer's Financial Data.