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


Jaguar vector database has the following characteristics:

  1. It is a distributed vector database
  2. The β€œZeroMove” 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 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

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