# Cohere Cookbooks: Build AI Agents and Solutions

> Get started with Cohere's cookbooks to build agents, QA bots, perform searches, and more, all organized by category.

In order to help developers get up and running on using Cohere's functionality, we've put together [some cookbooks](/page/cookbooks) that work through common use cases.

They're organized by categories like "Agents," "Cloud," and "Summarization" to allow you to quickly find what you're looking for. To jump to a particular use-case category, click one of the links below:

* [Agents](/page/cookbooks#agents)
* [Open Source Software Integrations](/page/cookbooks#oss)
* [Search and Embeddings](/page/cookbooks#search)
* [Cloud](/page/cookbooks#cloud)
* [RAG](/page/cookbooks#rag)
* [Summarization](/page/cookbooks#summarization)

<Info title="Note">
  The code examples in this section use the Cohere v1 API. The v2 API counterparts will be published at a later time.
</Info>

Here are some of the ones we think are most exciting!

* [A Data Analyst Agent Built with Cohere and Langchain](/page/data-analyst-agent) - Build a data analyst agent with Python and Cohere's Command R+ mode and Langchain.
* [Creating a QA Bot From Technical Documentation](/page/creating-a-qa-bot) - Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.
* [Multilingual Search with Cohere and Langchain](/page/multilingual-search) - Perform searches across a corpus of mixed-language documents with Cohere and Langchain.
* [Using Redis with Cohere](/docs/redis-and-cohere#building-a-retrieval-pipeline-with-cohere-and-redis) - Learn how to use Cohere's text vectorizer with Redis to create a semantic search index.
* [Wikipedia Semantic Search with Cohere + Weaviate](/page/wikipedia-search-with-weaviate) - Search 10 million Wikipedia vectors with Cohere's multilingual model and Weaviate's public dataset.
* [Long Form General Strategies](/page/long-form-general-strategies) - Techniques to address lengthy documents exceeding the context window of LLMs.