> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.cohere.com/v2/page/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.cohere.com/_mcp/server.

# Cookbooks

> Cohere's API documentation helps developers easily integrate natural language processing and generation into their products.

## Docs

- [Cookbooks](https://docs.cohere.com/page/cookbooks.md): Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized.
- [Building an LLM Agent with the Cohere API](https://docs.cohere.com/page/agent-api-calls.md): This page how to use Cohere's API to build an LLM-based agent.
- [Short-Term Memory Handling for Agents](https://docs.cohere.com/page/agent-short-term-memory.md): This page describes how to manage short-term memory in an agent built with Cohere models.
- [Agentic Multi-Stage RAG with Cohere Tools API](https://docs.cohere.com/page/agentic-multi-stage-rag.md): This page describes how to build a powerful, multi-stage agent with the Cohere platform.
- [Agentic RAG for PDFs with mixed data](https://docs.cohere.com/page/agentic-rag-mixed-data.md): This page describes building a powerful, multi-step chatbot with Cohere's models.
- [Analysis of Form 10-K/10-Q Using Cohere and RAG](https://docs.cohere.com/page/analysis-of-financial-forms.md): This page describes how to use Cohere's large language models to build an agent able to analyze financial forms like a 10-K or a 10-Q.
- [Analyzing Hacker News with Cohere](https://docs.cohere.com/page/analyzing-hacker-news.md): This page describes building a generative-AI powered tool to analyze headlines with Cohere.
- [Article Recommender via Embedding & Classification](https://docs.cohere.com/page/article-recommender-with-text-embeddings.md): This page describes how to build a generative-AI tool to recommend articles with Cohere.
- [Multi-Step Tool Use with Cohere](https://docs.cohere.com/page/basic-multi-step.md): This page describes how to create a multi-step, tool-using AI agent with Cohere's tool use functionality.
- [Basic RAG: Retrieval-Augmented Generation with Cohere](https://docs.cohere.com/page/basic-rag.md): This page describes how to work with Cohere's basic retrieval-augmented generation functionality.
- [Basic Semantic Search with Cohere Models](https://docs.cohere.com/page/basic-semantic-search.md): This page describes how to do basic semantic search with Cohere's models.
- [Getting Started with Basic Tool Use](https://docs.cohere.com/page/basic-tool-use.md): This page describes how to work with Cohere's basic tool use functionality.
- [Calendar Agent with Native Multi Step Tool](https://docs.cohere.com/page/calendar-agent.md): This page describes how to use cohere Chat API with list_calendar_events and create_calendar_event tools to book appointments.
- [Effective Chunking Strategies for RAG](https://docs.cohere.com/page/chunking-strategies.md): This page describes various chunking strategies you can use to get better RAG performance.
- [Creating a QA Bot From Technical Documentation](https://docs.cohere.com/page/creating-a-qa-bot.md): This page describes how to use Cohere to build a simple question-answering system.
- [Financial CSV Agent with Native Multi-Step Cohere API](https://docs.cohere.com/page/csv-agent-native-api.md): This page describes how to use Cohere's models and its native API to build an agent able to work with CSV data.
- [Financial CSV Agent with Langchain](https://docs.cohere.com/page/csv-agent.md): This page describes how to use Cohere's models to build an agent able to work with CSV data.
- [Migrating away from create_csv_agent in langchain-cohere](https://docs.cohere.com/page/migrate-csv-agent.md): This page contains a tutorial on how to build a CSV agent without the deprecated `create_csv_agent` abstraction in langchain-cohere v0.3.5 and beyond.
- [A Data Analyst Agent Built with Cohere and Langchain](https://docs.cohere.com/page/data-analyst-agent.md): This page describes how to build a data-analysis system out of Cohere's models.
- [Advanced Document Parsing For Enterprises](https://docs.cohere.com/page/document-parsing-for-enterprises.md): This page describes how to use Cohere's models to build a document-parsing agent.
- [End-to-end RAG using Elasticsearch and Cohere](https://docs.cohere.com/page/elasticsearch-and-cohere.md): This page contains a basic tutorial on how to get Cohere and ElasticSearch to work well together.
- [Serverless Semantic Search with Cohere and Pinecone](https://docs.cohere.com/page/embed-jobs-serverless-pinecone.md): This page contains a basic tutorial on how to get Cohere and the Pinecone vector database to work well together.
- [Semantic Search with Cohere Embed Jobs](https://docs.cohere.com/page/embed-jobs.md): This page contains a basic tutorial on how to use Cohere's Embed Jobs functionality.
- [Fueling Generative Content with Keyword Research](https://docs.cohere.com/page/fueling-generative-content.md): This page contains a basic workflow for using Cohere's models to come up with keyword content ideas.
- [Grounded Summarization Using Command R](https://docs.cohere.com/page/grounded-summarization.md): This page contains a basic tutorial on how to do grounded summarization with Cohere's models.
- [Hello World! Explore Language AI with Cohere](https://docs.cohere.com/page/hello-world-meet-ai.md): This page contains a breakdown of some of what can be achieved with Cohere's LLM platform.
- [Long-Form Text Strategies with Cohere](https://docs.cohere.com/page/long-form-general-strategies.md): This discusses ways of getting Cohere's LLM platform to perform well in generating long-form text.
- [Migrating Monolithic Prompts to Command A with RAG](https://docs.cohere.com/page/migrating-prompts.md): This page contains a discussion of how to automatically migrating monolothic prompts.
- [Multilingual Search with Cohere and Langchain](https://docs.cohere.com/page/multilingual-search.md): This page contains a basic tutorial on how to do search across different languages with Cohere's LLM platform.
- [PDF Extractor with Native Multi Step Tool Use](https://docs.cohere.com/page/pdf-extractor.md): This page describes how to create an AI agent able to extract information from PDFs.
- [Pondr, Fostering Connection through Good Conversation](https://docs.cohere.com/page/pondr.md): This page contains a basic tutorial on how tplay an AI-powered version of the icebreaking game 'Pondr'.
- [Deep Dive Into Evaluating RAG Outputs](https://docs.cohere.com/page/rag-evaluation-deep-dive.md): This page contains information on evaluating the output of RAG systems.
- [RAG With Chat Embed and Rerank via Pinecone](https://docs.cohere.com/page/rag-with-chat-embed.md): This page contains a basic tutorial on how to build a RAG-powered chatbot.
- [Learn How Cohere's Rerank Models Work](https://docs.cohere.com/page/rerank-demo.md): This page contains a basic tutorial on how Cohere's ReRank models work and how to use them.
- [Build a SQL Agent with Cohere's LLM Platform](https://docs.cohere.com/page/sql-agent.md): This page contains a tutorial on how to build a SQL agent with Cohere's LLM platform.
- [Evaluating Text Summarization Models](https://docs.cohere.com/page/summarization-evals.md): This page discusses how to evaluate a model's text summarization.
- [Text Classification Using Embeddings](https://docs.cohere.com/page/text-classification-using-embeddings.md): This page discusses the creation of a text classification model using word vector embeddings.
- [Topic Modeling System for AI Papers](https://docs.cohere.com/page/topic-modeling-ai-papers.md): This page discusses how to create a topic-modeling system for papers focused on AI papers.
- [Wikipedia Semantic Search with Cohere + Weaviate](https://docs.cohere.com/page/wikipedia-search-with-weaviate.md): This page contains a description of building a Wikipedia-focused search engine with Cohere's LLM platform and the Weaviate vector database.
- [Wikipedia Semantic Search with Cohere Embedding Archives](https://docs.cohere.com/page/wikipedia-semantic-search.md): This page contains a description of building a Wikipedia-focused semantic search engine with Cohere's LLM platform and the Weaviate vector database.
- [Build Chatbots with MongoDB and Cohere](https://docs.cohere.com/page/rag-cohere-mongodb.md): This page describes how to build a chatbot that provides actionable insights on technology company market reports.
- [Finetuning on Cohere's Platform](https://docs.cohere.com/page/convfinqa-finetuning-wandb.md): An example of finetuning using Cohere's platform and a financial dataset.
- [Deploy your finetuned model on AWS Marketplace](https://docs.cohere.com/page/deploy-finetuned-model-aws-marketplace.md): Learn how to deploy your finetuned model on AWS Marketplace.
- [Finetuning Cohere Models on AWS Sagemaker](https://docs.cohere.com/page/finetune-on-sagemaker.md): Learn how to finetune one of Cohere's models on AWS Sagemaker.
- [SQL Agent with Cohere and LangChain (i-5O Case Study)](https://docs.cohere.com/page/sql-agent-cohere-langchain.md): This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry.
- [Introduction to Aya Vision](https://docs.cohere.com/page/aya-vision-intro.md): In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses.
- [Retrieval evaluation using LLM-as-a-judge via Pydantic AI](https://docs.cohere.com/page/retrieval-eval-pydantic-ai.md): This page contains a tutorial on how to evaluate retrieval systems using LLMs as judges via Pydantic AI.
- [Document Translation with Command A Translate](https://docs.cohere.com/page/command-a-translate.md): This page describes how to use Command A Translate for automated translation across 23 languages with industry-leading performance.