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update colab links
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mrmer1 committed Jan 2, 2025
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keywords: "Cohere, chatbot"
---

<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt3.ipynb">Open in Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb">Open in Colab</a>

As its name implies, the Chat endpoint enables developers to build chatbots that can handle conversations. At the core of a conversation is a multi-turn dialog between the user and the chatbot. This requires the chatbot to have the state (or “memory”) of all the previous turns to maintain the state of the conversation.

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keywords: "Cohere, agents"
---

<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt7.ipynb">Open in Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb">Open in Colab</a>

Tool use extends the ideas from [RAG](/v2/docs/rag-with-cohere), where external systems are used to guide the response of an LLM, but by leveraging a much bigger set of tools than what’s possible with RAG. The concept of tool use leverages LLMs' useful feature of being able to act as a reasoning and decision-making engine.

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keywords: "Cohere, retrieval-augmented generation, RAG"
---

<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt6.ipynb">Open in Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb">Open in Colab</a>

The Chat endpoint provides comprehensive support for various text generation use cases, including retrieval-augmented generation (RAG).

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keywords: "Cohere, language models, ReRank models"
---

<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt5.ipynb">Open in Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb">Open in Colab</a>

Reranking is a technique that leverages [embeddings](/v2/docs/embeddings) as the last stage of a retrieval process, and is especially useful in [RAG systems](/v2/docs/retrieval-augmented-generation-rag).

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keywords: "Cohere, language models, "
---

<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt4.ipynb">Open in Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb">Open in Colab</a>

[Text embeddings](/v2/docs/embeddings) are lists of numbers that represent the context or meaning inside a piece of text. This is particularly useful in search or information retrieval applications. With text embeddings, this is called semantic search.

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keywords: "Cohere, how do LLMs generate text"
---

<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt2.ipynb">Open in Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb">Open in Colab</a>

Command is Cohere’s flagship LLM. It generates a response based on a user message or prompt. It is trained to follow user commands and to be instantly useful in practical business applications, like summarization, copywriting, extraction, and question-answering.

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