Principle:Cohere ai Cohere python Chat Completion Request
| Metadata | |
|---|---|
| Source Repo | Cohere Python SDK |
| Source Doc | Cohere Chat API |
| Domains | NLP, Text_Generation, Chat_API |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
A synchronous request pattern for generating text responses from large language models given a conversation context.
Description
Chat Completion is the process of sending a structured conversation (messages) and model parameters to an LLM endpoint and receiving a complete text response. The request includes model selection, message history, generation parameters (temperature, max_tokens, top-k, top-p), and optional features (tools, documents, citations, response format). The model processes the full conversation context and returns a single complete response with finish reason and usage statistics. This is the non-streaming variant where the entire response is returned at once.
Usage
Use this principle for standard chat interactions where you need the complete response before processing. Suitable for automated pipelines, batch processing, or when response latency is acceptable. For real-time interactive UIs, prefer the streaming variant.
Theoretical Basis
Chat completion implements the autoregressive text generation paradigm where a language model predicts the next token given all previous tokens (the conversation context). Sampling parameters (temperature, top-k, top-p) control the probability distribution over the vocabulary at each generation step. Temperature scales logits before softmax, top-k truncates to the k most likely tokens, and top-p (nucleus sampling) truncates to the smallest set of tokens whose cumulative probability exceeds p.