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@@ -19,4 +19,6 @@ If you are looking for a quick tour of the Cohere API, the following notebooks w
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- [**Retrieval-Augmented Generation**](notebooks/llmu/RAG_with_Chat_Embed_and_Rerank.ipynb): Build a RAG-powered chatbot that can extract relevant information from external documents and produce verifiable, inline citations in its responses. This leverages the Chat endpoint as well as the Embed v3 and Rerank 3 models.
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- [**Multi-Step Tool Use**](notebooks/Data_Analyst_Agent_Cohere_and_Langchain.ipynb): Multi-step tool use allows an LLM to call more than one tool in a sequence of steps, using the results from one tool call in a subsequent step. In this example, build a simple data analyst agent that is able to search the web and run code in a Python interpreter. This agent uses Cohere's Command R+ model and Langchain.
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- [**Tool Use**](notebooks/Vanilla_Tool_Use.ipynb): Tool use allows you to connect LLMs to external tools like search engines, APIs, functions, databases, etc. In this example, build an assistant that that can query sales reports and a product catalog and provide its analysis.

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