Cohere SDKs & Libraries: Official and Community Integrations

SDKs overview

Cohere offers Software Development Kits (SDKs) to enable developers to integrate its large language models into applications across various programming languages. These SDKs simplify interactions with the Cohere API, providing native language constructs for operations such as generating text, creating embeddings, and performing semantic reranking. The availability of official and community-supported libraries aims to reduce development overhead and accelerate the implementation of AI capabilities.

The official SDKs are maintained by Cohere and provide direct access to core products like Command-R+, Embed v3, and Rerank v3. Developers can find detailed documentation and examples on the Cohere documentation portal, which covers API authentication, model usage, and error handling. Beyond the official offerings, a broader ecosystem includes community-contributed libraries that extend functionality or provide integrations with specific frameworks.

Official SDKs by language

Cohere provides official SDKs for several popular programming languages, ensuring robust and supported integration paths for developers. Each SDK is designed to offer a idiomatic interface to the Cohere API, abstracting HTTP requests and JSON parsing. The table below outlines the officially supported SDKs, their respective package names, installation commands, and general maturity status.

Installation

Installing Cohere's official SDKs typically involves using the standard package manager for the respective programming language. Before installation, developers need to ensure they have the correct runtime environment set up. For example, Python installations require a compatible Python version (e.g., Python 3.8+), while JavaScript installations require Node.js and npm or yarn.

Python SDK Installation

To install the Python SDK, use pip:

pip install cohere

After installation, you can verify it by importing the library in a Python interpreter.

JavaScript/TypeScript SDK Installation

For JavaScript or TypeScript projects, use npm or yarn:

npm install cohere-ai
# or
yarn add cohere-ai

This will add the cohere-ai package to your project's dependencies.

Go SDK Installation

Install the Go SDK using the go get command:

go get github.com/cohere-ai/cohere-go/v2

Ensure your Go environment is correctly configured, typically with a GOPATH set.

Java SDK Installation

For Java projects, the SDK is typically managed via Maven or Gradle. Add the following dependency to your pom.xml (Maven) or build.gradle (Gradle) file. The latest version can be found on the Cohere Java SDK documentation.

Maven (pom.xml):

<dependency>
    <groupId>com.cohere</groupId>
    <artifactId>cohere-java</artifactId>
    <version>{latest_version}</version>
</dependency>

Gradle (build.gradle):

implementation 'com.cohere:cohere-java:{latest_version}'

Quickstart example

This Python quickstart demonstrates how to use the Cohere SDK to generate text. Before running, ensure you have installed the Python SDK and set your Cohere API key as an environment variable (COHERE_API_KEY).

import cohere
import os

# Initialize the Cohere client with your API key
# It's recommended to load the API key from an environment variable
co = cohere.Client(os.getenv("COHERE_API_KEY"))

# Example: Generate text using the Command R model
def generate_text_example():
    try:
        response = co.chat(
            model="command-r",
            message="What are the benefits of using large language models?",
            temperature=0.7,
        )
        print("Generated text:", response.text)
    except cohere.CohereError as e:
        print(f"An error occurred: {e}")

# Example: Create embeddings for a list of texts
def embed_texts_example():
    try:
        texts_to_embed = [
            "hello world",
            "goodbye world"
        ]
        response = co.embed(
            texts=texts_to_embed,
            model="embed-english-v3.0",
            input_type="search_query"
        )
        print("Embeddings for 'hello world':", response.embeddings[0][:5], "...") # Print first 5 dimensions
        print("Embeddings for 'goodbye world':", response.embeddings[1][:5], "...")
    except cohere.CohereError as e:
        print(f"An error occurred: {e}")

# Run the examples
if __name__ == "__main__":
    print("--- Generating Text Example ---")
    generate_text_example()
    print("\n--- Embedding Texts Example ---")
    embed_texts_example()

This quickstart illustrates two common operations: generating text using the chat endpoint and creating vector embeddings using the embed endpoint. The co.chat method is suitable for conversational AI and text generation tasks, while co.embed is used for semantic search, retrieval-augmented generation (RAG), and other applications requiring text similarity. For more detailed examples and advanced usage, refer to the Cohere Getting Started guide.

Community libraries

While Cohere provides official SDKs, the broader developer community contributes additional libraries and integrations that can enhance or extend Cohere's functionality. These community projects often offer:

• Framework Integrations: Libraries that integrate Cohere with popular web frameworks (e.g., Flask, Django, Next.js) or AI development frameworks (e.g., LangChain, LlamaIndex).
• Specialized Tools: Utilities for specific tasks not directly covered by the official SDKs, such as advanced prompt engineering tools or data preprocessing pipelines.
• Language Bindings: SDKs for languages not officially supported by Cohere, developed and maintained by community members.

Developers should exercise due diligence when using community-contributed libraries, as their maintenance, security, and compatibility may vary. It is advisable to check the project's GitHub repository for activity, issue tracking, and community support. Resources like Mozilla Developer Network's Web API documentation can provide general guidance on API integration patterns, which apply even when using community wrappers.

Some notable community integrations and frameworks that often feature Cohere support include:

• LangChain: A framework for developing applications powered by language models. LangChain provides Cohere integrations for various LLM operations.
• LlamaIndex: A data framework for LLM applications, offering tools to ingest, structure, and access private or domain-specific data. LlamaIndex includes Cohere connectors.

For a comprehensive list and the latest community contributions, developers can explore Cohere's GitHub repositories and community forums, which often highlight popular third-party tools and projects.