README.md

Cohere Examples

Welcome! This repository provides a collection of examples to help you build LLM-powered applications with the Cohere API. They contain step-by-step guides, with code examples and explanations, to help you understand and use the API effectively.

The examples are grouped into 3 categories:

1. Getting started: A Cohere 101 guide. Build your first Cohere application – an onboarding assistant for new hires.
2. LLM University: The code companion to the LLM University course containing a comprehensive list of modules.
3. Cookbook: Deep dive into various techniques in the following topics: RAG, Agents, Search & embeddings, Summarization, and Others

Interested to contribute? Read the contributing guide.

Getting Started

This is a Cohere 101 guide. Build your first Cohere application: An onboarding assistant for new hires. Duration: ~15 mins.

Title	Colab
Part 1: Installation and setup
Part 2: Text generation
Part 3: Chatbots
Part 4: Semantic search
Part 5: Reranking
Part 6: Retrieval-augmented generation (RAG)
Part 7: Agents with tool use

LLM University

This section contains the code companion to the LLM University course containing a comprehensive list of modules.

Module	Title	Colab
What are Large Language Models?	Similarity Between Words and Sentences
Text Representation	Introduction to Text Embeddings, Semantic Search, and Clustering
	Topic Modeling
	Few-Shot Classification
	Fine-Tuning for Classification
Text Generation	Building a Chatbot
	Parameters for Controlling Outputs
	Prompt Engineering Basics
	Fine-Tuning for Chat
Deployment	Deploying with Streamlit	N/A
	Deploying with FastAPI	N/A
	Deploying on Google Sheets with Google Apps Script	N/A
	Deploying as a Chrome Extension	N/A
Semantic Search	What is Semantic Search?
	Keyword Search, Dense Retrieval, Reranking, and Generating Answers
Prompt Engineering	Constructing Prompts
	Use Case Patterns
	Validating Outputs
Retrieval-Augmented Generation (RAG)	Getting Started with RAG
	RAG with Chat, Embed, and Rerank
	RAG with Connectors
	RAG with Quickstart Connectors
	RAG over Large-Scale Data
Tool Use	Tool Use Anatomy
	Single-Step Tool Use
	Multi-Step Tool Use
	Tool Use with LangChain
Cohere on AWS	Text generation on Bedrock
	Semantic search on Bedrock
	Reranking on SageMaker
	RAG on Bedrock and SageMaker
	Tool use on Bedrock
	Fine-tuning on Bedrock/SageMaker

Cookbook

This section provides a deep dive into various techniques in the following topics:

• RAG
• Agents
• Search & embeddings
• Summarization
• Others

RAG

Title	Components	Colab
Basic RAG	Chat, Embed, Rerank
End-to-end RAG using Elasticsearch and Cohere	Chat, Embed, Rerank, Elasticsearch
Chunking Strategies	Chat, Embed, Rerank, LlamaIndex, LangChain
Migrating Monolithic Prompts to Command-R with RAG	Chat
RAG With Chat Embed and Rerank via Pinecone	Chat, Embed, Rerank, Pinecone
Creating a QA Bot From Technical Documentation	Chat, Embed, Rerank, LlamaIndex
Analysis of Form 10-K/10-Q Using Cohere and RAG	Cohere, Embed, Rerank, LlamaIndex, Langchain
Adaptive RAG	Chat, LangChain

Agents

Title	Components	Colab
Basic Tool Use	Chat
Multi-Step Tool Use	Chat, Embed, LangChain
Calendar Agent with Native Multi Step Tool	Chat
A Data Analyst Agent Built with Cohere and Langchain	Chat, LangChain
Short-Term Memory Handling for Agents	Chat, LangChain
Agent API Calls	Chat, LangChain
Financial CSV Agent with Langchain	Chat, LangChain
Agentic RAG for PDFs with mixed data	Chat, Embed, Rerank, LangChain
SQL Agent	Chat, LangChain
Financial CSV Agent with Native Multi-Step Cohere API	Chat, LangChain
PDF Extractor with Native Multi Step Tool Use	Chat, Unstructured
Agentic Multi-Stage RAG with Cohere Tools API	Chat, Embed
Agentic RAG with an Evaluator, Web Search, Human Input, and Python Tool	Chat, Embed, LangChain

Search and Embeddings

Title	Components	Colab
Basic Semantic Search	Embed
Basic Reranking	Rerank
Wikipedia Semantic Search with Cohere Embedding Archives	Embed
Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution	Embed, Pinecone
Semantic Search with Cohere Embed Jobs	Embed, Rerank
Wikipedia Semantic Search with Cohere + Weaviate	Embed, Weaviate

Summarization

Title	Components	Colab
Long Form General Strategies	Chat, Embed, Rerank
Summarization Evals	Chat
Grounded Summarization Using Command R	Chat, Embed

Others

Title	Components	Colab
Advanced Document Parsing For Enterprises	Chat, Embed, Rerank
Analyzing Hacker News with Six Language Understanding Methods	Embed
Text Classification Using Embeddings	Embed
Article Recommender with Text Embedding Classification Extraction	Chat, Embed, Classify
Fueling Generative Content with Keyword Research	Chat, Embed
Topic Modeling AI Papers	Embed

Contributing

Thank you for your interest in contributing! We appreciate your input and encourage you to share your ideas and improvements. Here are some ways you can contribute:

• New Examples and Guides: If you have an idea for a new example or guide, please share it with us! Create an issue to discuss your proposal, gather feedback, and get started. This ensures your contribution aligns with the project's scope and avoids duplication.

• Improvements and Updates: You can contribute by enhancing existing examples and guides. This could involve adding more detailed explanations, code snippets, error handling, or exploring advanced usage. Your insights and expertise will make our examples more useful.

• Feedback and Suggestions: Your feedback is invaluable. If you have suggestions for improving the structure, content, or overall user experience of this repository, please create an issue or contact us directly. We want to ensure this resource is as helpful as possible.

Please review existing issues and pull requests before starting your contribution to avoid duplication of efforts. We value your unique insights and contributions, and we want to ensure they are well-aligned with the project's goals.

Thank you for your contributions and for helping to make this repository a valuable resource for the developer community!