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Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
  • Get Started
    • Introduction
    • Installation
    • Creating a client
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    • An Overview of Cohere's Models
    • Aya
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    • Introduction to Text Generation at Cohere
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    • Image Inputs
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    • Advanced Generation Parameters
    • Tool Use
    • Tokens and Tokenizers
    • Summarizing Text
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    • Introduction to Embeddings at Cohere
    • Semantic Search with Embeddings
    • Multimodal Embeddings
    • Batch Embedding Jobs
  • Going to Production
    • API Keys and Rate Limits
    • Going Live
    • Deprecations
    • How Does Cohere's Pricing Work?
  • Integrations
    • Integrating Embedding Models with Other Tools
    • Cohere and LangChain
    • LlamaIndex and Cohere
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    • SDK Compatibility
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      • Model Deployment - AWS
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On this page
  • Installation
  • Getting Started
  • Integrations
Deployment OptionsPrivate Deployment

Private Deployment Usage

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Cohere on Amazon Web Services (AWS)

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Built with

You can use Cohere’s SDK to access privately deployed Cohere models.

Installation

To install the Cohere SDK, choose from the following 4 languages:

Python
TypeScript
Java
Go
$pip install -U cohere

Source

Getting Started

The only difference between using Cohere’s models on private deployments and the Cohere platform is how you set up the client. With private deployments, you need to pass the following parameters:

  • api_key - Pass a blank value
  • base_url - Pass the URL of your private deployment
PYTHON
1import cohere
2
3co = cohere.ClientV2(
4 api_key="", # Leave this blank
5 base_url="<YOUR_DEPLOYMENT_URL>",
6)

To get started with example use cases, refer to the following quickstart examples:

  • Text Generation (Command model)
  • RAG (Command model)
  • Tool Use (Command model)
  • Semantic Search (Embed model)
  • Reranking (Rerank model)

Integrations

You can use the LangChain library with privately deployed Cohere models. Refer to the LangChain section for more information on setting up LangChain for private deployments.