QyverLabs Docs
  • 👋Welcome
  • Getting Started
    • 👀Why Qyver?
    • 💻Setup Qyver
    • 🏗️Basic Building Blocks
  • Run In Production
    • 🎯Overview
    • 💻Setup Qyver Server
      • 🧮Configuring your app
      • 📔Interacting with app via API
    • ⚙️Supported Vector Databases
      • Redis
      • Mongo DB
      • Qdrant
  • Concepts
    • 🗓️Combining Multiple Embeddings for Better Retrieval Outcomes
    • 🏹Dynamic Parameters/Query Time weights
  • Reference
    • 🎯Overview
    • ⏰Changelog
  • Help & FAQ
    • 📒Logging
    • ❔Support
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Welcome

NextWhy Qyver?

Last updated 3 months ago

Qyver is a Python framework designed for AI Engineers developing high-performance search and recommendation applications that integrate both structured and unstructured data.

Why Qyver?

Enhance vector search relevance by encoding metadata alongside unstructured data into vectors. Learn more at Why Qyver.

What is Qyver?

A framework and self-hostable REST API server that seamlessly connects your data, vector database, and backend services.

How does it work?

Build custom data and query embedding models using pre-trained encoders from sentence-transformers, open-clip, and custom encoders for numbers, timestamps, and categorical data. Explore concepts and use cases for examples.

If you find Qyver useful, give us a !

Getting Started

Run In Production

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Why Qyver?

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Setup Qyver

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Basic Building Blocks

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Overview

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Setup a Server

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Configuring your app

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Interacting via API

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Supported Vector DBs

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Multiple Embeddings

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Dynamic Parameters

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Changelog

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Framework Overview

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