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// The `chat` endpoint allows users to have conversations with a Large Language Model (LLM) from Cohere. Users can send messages as part of a persisted conversation using the `conversation_id` parameter, or they can pass in their own conversation history using the `chat_history` parameter.
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// The endpoint features additional parameters such as [connectors](https://docs.cohere.com/docs/connectors) and `documents` that enable conversations enriched by external knowledge. We call this "Retrieval Augmented Generation", or "RAG".
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//
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// The endpoint features additional parameters such as [connectors](https://docs.cohere.com/docs/connectors) and `documents` that enable conversations enriched by external knowledge. We call this ["Retrieval Augmented Generation"](https://docs.cohere.com/docs/retrieval-augmented-generation-rag), or "RAG". For a full breakdown of the Chat API endpoint, document and connector modes, and streaming (with code samples), see [this guide](https://docs.cohere.com/docs/cochat-beta).
// The `chat` endpoint allows users to have conversations with a Large Language Model (LLM) from Cohere. Users can send messages as part of a persisted conversation using the `conversation_id` parameter, or they can pass in their own conversation history using the `chat_history` parameter.
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// The endpoint features additional parameters such as [connectors](https://docs.cohere.com/docs/connectors) and `documents` that enable conversations enriched by external knowledge. We call this "Retrieval Augmented Generation", or "RAG".
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//
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// The endpoint features additional parameters such as [connectors](https://docs.cohere.com/docs/connectors) and `documents` that enable conversations enriched by external knowledge. We call this ["Retrieval Augmented Generation"](https://docs.cohere.com/docs/retrieval-augmented-generation-rag), or "RAG". For a full breakdown of the Chat API endpoint, document and connector modes, and streaming (with code samples), see [this guide](https://docs.cohere.com/docs/cochat-beta).
// This endpoint makes a prediction about which label fits the specified text inputs best. To make a prediction, Classify uses the provided `examples` of text + label pairs as a reference.
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// Note: [Custom Models](/training-representation-models) trained on classification examples don't require the `examples` parameter to be passed in explicitly.
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// Note: [Fine-tuned models](https://docs.cohere.com/docs/classify-fine-tuning) trained on classification examples don't require the `examples` parameter to be passed in explicitly.
// This endpoint splits input text into smaller units called tokens using byte-pair encoding (BPE). To learn more about tokenization and byte pair encoding, see the tokens page.
// This endpoint takes tokens using byte-pair encoding and returns their text representation. To learn more about tokenization and byte pair encoding, see the tokens page.
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