Jaguar

This page describes how to use Jaguar vector database within LangChain. It contains three sections: introduction, installation and setup, and Jaguar API.

Introduction

Jaguar vector database has the following characteristics:

1. It is a distributed vector database
2. The “ZeroMove” feature of JaguarDB enables instant horizontal scalability
3. Multimodal: embeddings, text, images, videos, PDFs, audio, time series, and geospatial
4. All-masters: allows both parallel reads and writes
5. Anomaly detection capabilities
6. RAG support: combines LLM with proprietary and real-time data
7. Shared metadata: sharing of metadata across multiple vector indexes
8. Distance metrics: Euclidean, Cosine, InnerProduct, Manhatten, Chebyshev, Hamming, Jeccard, Minkowski

Overview of Jaguar scalable vector database

You can run JaguarDB in docker container; or download the software and run on-cloud or off-cloud.

Installation and Setup

• Install the JaguarDB on one host or multiple hosts
• Install the Jaguar HTTP Gateway server on one host
• Install the JaguarDB HTTP Client package

The steps are described in Jaguar Documents

Environment Variables in client programs:

export OPENAI_API_KEY="......"
export JAGUAR_API_KEY="......"

Jaguar API

Together with LangChain, a Jaguar client class is provided by importing it in Python:

from langchain_community.vectorstores.jaguar import Jaguar

API Reference:

• Jaguar

Supported API functions of the Jaguar class are:

• add_texts
• add_documents
• from_texts
• from_documents
• similarity_search
• is_anomalous
• create
• delete
• clear
• drop
• login
• logout

For more details of the Jaguar API, please refer to this notebook