# Cookbooks

> Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized.

export const marco = {
  name: "Marco Del Tredici",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/f103c96-Marco.jpg",
};

export const shaan = {
  name: "Shaan Desai",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d17fc44-Shaan.jpg",
};

export const jason = {
  name: "Jason Jung",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/0803e3d-Jason_Jung.jpg",
};

export const ania = {
  name: "Ania Bialas",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/c5dc5a3-Ania.jpg",
};

export const aal = {
  name: "Aal Patankar",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d48e622-Aal.jpg",
};

export const alex = {
  name: "Alex Barbet",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bf2c763-Alex.jpg",
};

export const giannis = {
  name: "Giannis Chatziveroglou",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/73153cb-giannis.jpeg",
};

export const komal = {
  name: "Komal Teru",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/7026fcc-komal-headshot.jpg"
}

export const youran = {
  name: "Youran Qi",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/929cb1c-youran-headshot.jpg"
}

export const mike = {
  name: "Mike Mao",
  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d514b09-mike-headshot.jpg"
}

export const agentCards = [
  {
    title: "Calendar Agent with Native Multi Step Tool",
    description:
      "A minimal working example of how to use our chat API to call tools.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["agents"],
    href: "/page/calendar-agent",
  },
  {
    title: "Basic Tool Use",
    description:
      "Connect large language models to external tools, like search engines, APIs, and databases, to access and utilise a wider range of data.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["agents"],
    href: "/page/basic-tool-use",
  },
  {
    title: "Multi-Step Tool Use",
    description:
      "Multi-step tool use allows developers to connect Cohere's models to external tools like search engines, APIs, and databases.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
    tags: ["agents", "oss"],
    href: "/page/basic-multi-step",
  },
  {
    title: "A Data Analyst Agent Built with Cohere and Langchain",
    description:
      "Build a data analyst agent with Python and Cohere's Command R+ mode and Langchain.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: ["agents", "oss"],
    href: "/page/data-analyst-agent",
  },
  {
    title: "Short-Term Memory Handling for Agents",
    description:
      "A walkthrough of how to use Langchain cohere_react_agent to effectively manage short-term chat history that contains tool calls with Langchain.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
    tags: ["agents"],
    href: "/page/agent-short-term-memory",
    authors: [marco],
  },
  {
    title: "Agent API Calls",
    description:
      "A walkthrough of how to use Langchain cohere_react_agent to make API calls to external services that require regex.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
    tags: ["agents"],
    href: "/page/agent-api-calls",
    authors: [marco],
  },
  {
    title: "Financial CSV Agent with Langchain",
    description:
      "The notebook demonstrates how to setup a Langchain Cohere ReAct Agent to answer questions over the income statement and balance sheet from Apple's SEC10K 2020 form.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
    tags: ["agents", "oss"],
    href: "/page/csv-agent",
    authors: [shaan],
  },
  {
    title: "Agentic RAG for PDFs with mixed data",
    description:
      "A walkthrough of how to use Langchain cohere_react_agent to run RAG as an agent tool to handle PDFs with mixed table and text data.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/142e385-Community_Demo_9.png",
    tags: ["agents"],
    href: "/page/agentic-rag-mixed-data",
    authors: [shaan],
  },
  {
    title: "SQL Agent",
    description:
      "In this notebook we explore how to setup a Cohere ReAct Agent to answer questions over SQL Databases using Langchain's SQLDBToolkit.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["agents"],
    href: "/page/sql-agent",
    authors: [shaan],
  },
  {
    title: "SQL Agent with Cohere and LangChain (i-5O Case Study)",
    description:
      "Build a SQL agent with Cohere and LangChain in the manufacturing industry.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["agents"],
    href: "/page/sql-agent-cohere-langchain",
  },
  {
    title: "Financial CSV Agent with Native Multi-Step Cohere API",
    description:
      "This notebook demonstrates how to setup a Cohere Native API sequence of tool calls to answer questions over the income statement and balance sheet from Apple's SEC10K 2020 form.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["agents"],
    href: "/page/csv-agent-native-api",
    authors: [jason],
  },
  {
    title: "PDF Extractor with Native Multi Step Tool Use",
    description: "How we can leverage agents to extract information from PDFs?",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["agents"],
    href: "/page/pdf-extractor",
    authors: [jason],
  },
  {
    title: "Agentic Multi-Stage RAG with Cohere Tools API",
    description: "How to use Agents to improve RAG performance.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: ["agents"],
    href: "/page/agentic-multi-stage-rag",
    authors: [jason],
  },
];

export const ossCards = [
  {
    title: "Multi-Step Tool Use",
    description:
      "Multi-step tool use allows developers to connect Cohere's models to external tools like search engines, APIs, and databases.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
    tags: ["agents", "oss"],
    href: "/page/basic-multi-step",
  },
  {
    title: "A Data Analyst Agent Built with Cohere and Langchain",
    description:
      "Build a data analyst agent with Python and Cohere's Command R+ mode and Langchain.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: ["agents", "oss"],
    href: "/page/data-analyst-agent",
  },
  {
    title: "Financial CSV Agent with Langchain",
    description:
      "The notebook demonstrates how to setup a Langchain Cohere ReAct Agent to answer questions over the income statement and balance sheet from Apple's SEC10K 2020 form.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
    tags: ["agents", "oss"],
    href: "/page/csv-agent",
    authors: [shaan],
  },
  {
    title: "Multilingual Search with Cohere and Langchain",
    description: "Multilingual search with Cohere and Langchain.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
    tags: ["search", "oss"],
    href: "/page/multilingual-search",
  },
  {
    title: "Creating a QA Bot From Technical Documentation",
    description:
      "Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["oss", "search", "rag"],
    href: "/page/creating-a-qa-bot",
  },
  {
    title: "Migrating away from deprecated create_csv_agent in langchain-cohere",
    description:
      "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.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
    tags: ["agents", "oss", "csv"],
    href: "/page/migrate-csv-agent",
  },
];

export const searchCards = [
  {
    title: "Wikipedia Semantic Search with Cohere Embedding Archives",
    description:
      "Find relevant Wikipedia passages with semantic search and Cohere embeddings.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
    tags: ["search"],
    href: "/page/wikipedia-semantic-search",
  },
  {
    title:
      "Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution",
    description:
      "Learn how to use Cohere's Embed Jobs and Pinecone's serverless solution to perform semantic search.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
    tags: ["cloud", "search"],
    href: "/page/embed-jobs-serverless-pinecone",
  },
  {
    title: "End-to-end RAG using Elasticsearch and Cohere",
    description:
      "Learn how to use Cohere and Elastic for semantic search and retrieval-augmented generation.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["search", "rag", "cloud"],
    href: "/page/elasticsearch-and-cohere",
  },
  {
    title: "Semantic Search with Cohere Embed Jobs",
    description:
      "Learn how to use Cohere Embed Jobs to create semantic search functionality.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["search"],
    href: "/page/embed-jobs",
  },
  {
    title: "Basic Semantic Search",
    description:
      "Learn how to build a simple semantic search engine using sentence embeddings.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["search"],
    href: "/page/basic-semantic-search",
  },
  {
    title: "Multilingual Search with Cohere and Langchain",
    description: "Multilingual search with Cohere and Langchain.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
    tags: ["search", "oss"],
    href: "/page/multilingual-search",
  },
  {
    title: "Wikipedia Semantic Search with Cohere + Weaviate",
    description:
      "Search 10 million Wikipedia vectors with Cohere's multilingual model and Weaviate's public dataset.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["search"],
    href: "/page/wikipedia-search-with-weaviate",
  },
  {
    title: "Creating a QA Bot From Technical Documentation",
    description:
      "Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["oss", "search", "rag"],
    href: "/page/creating-a-qa-bot",
  },
  {
    title: "Demo of Rerank",
    description:
      "Improve search results with Cohere's Relevance Endpoint, which reranks documents for better accuracy.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
    tags: ["search"],
    href: "/page/rerank-demo",
  },
  {
    title: "RAG with MongoDB and Cohere",
    description:
      "Build a chatbot that provides actionable insights on technology company market reports.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["search"],
    href: "/page/rag-cohere-mongodb",
  },
  {
    title: "Retrieval Evaluation with LLM-as-a-Judge via Pydantic AI",
    description:
      "Evaluate retrieval systems using LLMs as judges via Pydantic AI.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["search"],
    href: "/page/retrieval-eval-pydantic-ai",
  },
];

export const cloudCards = [
  {
    title:
      "Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution",
    description:
      "Learn how to use Cohere's Embed Jobs and Pinecone's serverless solution to perform semantic search.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
    tags: ["cloud", "search"],
    href: "/page/embed-jobs-serverless-pinecone",
  },
  {
    title: "End-to-end RAG using Elasticsearch and Cohere",
    description:
      "Learn how to use Cohere and Elastic for semantic search and retrieval-augmented generation.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["search", "rag", "cloud"],
    href: "/page/elasticsearch-and-cohere",
  },
];

export const ragCards = [
  {
    title: "Basic RAG",
    description:
      "RAG boosts the accuracy of language models by combining them with a retrieval system.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/142e385-Community_Demo_9.png",
    tags: ["rag"],
    href: "/page/basic-rag",
  },
  {
    title: "End-to-end RAG using Elasticsearch and Cohere",
    description:
      "Learn how to use Cohere and Elastic for semantic search and retrieval-augmented generation.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["search", "rag", "cloud"],
    href: "/page/elasticsearch-and-cohere",
  },
  {
    title: "Chunking Strategies",
    description: "Explore chunking strategies for RAG systems.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["rag"],
    href: "/page/chunking-strategies",
    authors: [ania],
  },
  {
    title: "Migrating Monolithic Prompts to Command-R with RAG",
    description:
      "Command-R simplifies prompt migration to RAG, reducing hallucination and improving conciseness and grounding.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["rag"],
    href: "/page/migrating-prompts",
  },
  {
    title: "RAG With Chat Embed and Rerank via Pinecone",
    description:
      "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
    tags: ["rag"],
    href: "/page/rag-with-chat-embed",
  },
  {
    title: "Creating a QA Bot From Technical Documentation",
    description:
      "Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["oss", "search", "rag"],
    href: "/page/creating-a-qa-bot",
  },
  {
    title: "Deep Dive Into RAG Evaluation",
    description: "Learn how to evaluate RAG models.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
    tags: ["rag"],
    href: "/page/rag-evaluation-deep-dive",
    authors: [marco, aal],
  },
];

export const summarizationCards = [
  {
    title: "Analysis of Form 10-K/10-Q Using Cohere and RAG",
    description:
      "Jumpstart financial analysis of 10-Ks or 10-Qs with Cohere's Command model and LlamaIndex tooling.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
    tags: ["summarization"],
    href: "/page/analysis-of-financial-forms",
    authors: [alex],
  },
  {
    title: "Long Form General Strategies",
    description:
      "Techniques to address lengthy documents exceeding the context window of LLMs.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: ["summarization"],
    href: "/page/long-form-general-strategies",
    authors: [ania],
  },
  {
    title: "Summarization Evals",
    description:
      "This cookbook demonstrates an approach to evaluating summarization tasks using LLM evaluation.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: ["summarization"],
    href: "/page/summarization-evals",
  },
  {
    title: "Grounded Summarization Using Command R",
    description:
      "Learn how to summarise long documents with citations, reducing cost and improving latency.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["summarization"],
    href: "/page/grounded-summarization",
  },
];

export const finetuningCards = [
  {
    title: "Finetuning on Cohere's Platform",
    description:
      "An example of finetuning using Cohere's platform and a financial dataset.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: ["finetuning", "wandb"],
    href: "/page/convfinqa-finetuning-wandb",
    authors: [komal],
  },
  {
    title: "Deploy your finetuned model on AWS Marketplace",
    description:
      "Learn how to deploy your finetuned model on AWS Marketplace.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: ["finetuning", "sagemaker"],
    href: "/page/deploy-finetuned-model-aws-marketplace",
    authors: [youran],
  },
  {
    title: "Finetuning on AWS Sagemaker",
    description:
      "Learn how to finetune one of Cohere's models on AWS Sagemaker.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
    tags: ["finetuning", "sagemaker"],
    href: "/page/finetune-on-sagemaker",
    authors: [mike],
  }
]

export const otherCards = [
  {
    title: "Fueling Generative Content with Keyword Research",
    description:
      "Enhance content creation with keyword-based topic clusters, generating blog ideas with Cohere's Chat model.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
    tags: [],
    href: "/page/fueling-generative-content",
  },
  {
    title: "Text Classification Using Embeddings",
    description:
      "Build a text classifier with Cohere embeddings. This notebook shows you how to train a sentiment analysis model with a small dataset.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: [],
    href: "/page/text-classification-using-embeddings",
  },
  {
    title: "Article Recommender with Text Embedding Classification Extraction",
    description:
      "Improve news article recommendations with embeddings, text classification, and keyword extraction.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/142e385-Community_Demo_9.png",
    tags: [],
    href: "/page/article-recommender-with-text-embeddings",
  },
  {
    title: "Advanced Document Parsing For Enterprises",
    description: "Learn how to parse PDFs into text with a real-world example.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
    tags: [],
    href: "/page/document-parsing-for-enterprises",
    authors: [
      giannis,
      {
        name: "Justin Lee",
        imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/3678fac-Justin.jpg",
      },
    ],
  },
  {
    title: "Pondr, Fostering Connection through Good Conversation",
    description:
      "Learn how to create Pondr, a game that fosters connections and meaningful conversations with friends and strangers.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
    tags: [],
    href: "/page/pondr",
  },
  {
    title: "Hello World! Meet Language AI",
    description:
      "General quickstart with basic instructions on how to get started with generative AI.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
    tags: [],
    href: "/page/hello-world-meet-ai",
  },
  {
    title: "Topic Modeling AI Papers",
    description: "Learn how to build a topic modeling pipeline in Python.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
    tags: [],
    href: "/page/topic-modeling-ai-papers",
  },
  {
    title: "Analyzing Hacker News with Six Language Understanding Methods",
    description: "Learn how to analyze textual data using Cohere's tools.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: [],
    href: "/page/analyzing-hacker-news",
  },
  {
    title: "Introduction to Aya Vision: A state-of-the-art open-weights vision model",
    description: "Explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: [],
    href: "/page/aya-vision-intro",
  },
  {
    title: "Document Translation with Command A Translate",
    description: "Learn how to use Command A Translate for automated translation across 23 languages with industry-leading performance.",
    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
    tags: [],
    href: "/page/command-a-translate",
  },
];

export const sections = [
  {
    id: "agents",
    title: "Agents",
    description:
      "Learn how to build powerful agents that use tools to connect to external services, like search engines, APIs, and databases. Agents can be used to automate tasks, answer questions, and more.",
    cards: agentCards,
  },
  {
    id: "oss",
    title: "Open Source Software Integrations",
    description:
      "Cohere integrates natively with a variety of popular Open Source Software tools like LangChain and LlamaIndex. These guides will help you get started with these integrations.",
    cards: ossCards,
  },
  {
    id: "search",
    title: "Search and Embeddings",
    description:
      "Learn how to embed and search text with Cohere. These guides will help you build semantic search engines, search Wikipedia, and more.",
    cards: searchCards,
  },
  {
    id: "cloud",
    title: "Cloud",
    description:
      "Learn how to use Cohere's cloud-based services in your preferred environment. Cohere is integrated with most major cloud providers. These guides will help you get started wherever your code lives.",
    cards: cloudCards,
  },
  {
    id: "rag",
    title: "RAG",
    description:
      "Learn how to use Cohere's foundation model for Retrieval-Augmented Generation (RAG). RAG can be used to improve the accuracy of language models by combining them with a retrieval system. This allows the model to generate completions that are grounded in provided sources of truth.",
    cards: ragCards,
  },
  {
    id: "summarization",
    title: "Summarization",
    description:
      "Learn how to summarize long documents, meeting summaries, and technical reports. Summarization is a key feature of Cohere's Command model, which can be used to generate summaries of long documents with citations.",
    cards: summarizationCards,
  },
  {
    id: "finetuning",
    title: "Finetuning",
    description:
      "Learn how to finetune Cohere's models using custom data. Finetuning allows you to adapt Cohere's models to your specific use case, improving performance and accuracy.",
    cards: finetuningCards,
  },
  {
    id: "other",
    title: "Other",
    description:
      "Here are a variety of other fun and useful guides to help you get started with Cohere. From text classification to document parsing, there's something for everyone!",
    cards: otherCards,
  },
];

export const AllCardsContainer = () => (
  <div className="all-cards-container">
    {sections.map(({ id, title, description, cards }) => (
      <div key={id}>
        <SecondaryTitleContainer
          id={id}
          title={title}
          description={description}
        />
        <div className="divider" />
        <CardsContainer cards={cards} />
      </div>
    ))}
  </div>
);

export const UseCasesContainer = () => (
  <div className="all-cards-container !py-0">
    <div className="page-secondary-title-container">
      <div className="toc-title">
        <h3 className="h2-title mb-3">Use Cases</h3>
        <p className="p-base mb-6">
          Click on one of the section headers below to jump to guides for that
          use case category.
        </p>
        <div className="toc-list">
          {sections.map(({ id, title }) => (
            <div key={id} className="toc-item">
              <a
                href={`#${id}`}
                className="group inline-block cursor-pointer font-medium"
                target="_self"
              >
                {title}
              </a>
            </div>
          ))}
        </div>
      </div>
    </div>
  </div>
);

<div>
  <div>
    <h1>
      Cookbooks
    </h1>

    <p>
      Explore what you can build on Cohere's generative AI platform. These
      ready-made guides will get you started with best-practices that get the
      most out of Cohere's models. Everything is set up and ready for you to
      start testing!
    </p>
  </div>
</div>

<div>
  <UseCasesContainer />

  <AllCardsContainer />
</div>