cohere-v2-pythonlisted

# Cohere v2 Python ## Overview Cohere's v2 Chat API provides powerful conversational AI capabilities with a specialized focus on structured outputs through JSON Schema mode. This skill covers entity extraction, data validation, and integration patterns for building production-ready systems that require consistent, validated responses from LLMs. ## When to Use This Skill Apply this skill when: - Extracting structured entities from unstructured text (names, dates, locations, organizations) - Building Named Entity Recognition (NER) systems - Implementing data extraction pipelines with validated outputs - Requiring JSON responses that conform to specific schemas - Processing documents for information extraction - Building classification systems with constrained outputs - Integrating LLM responses with downstream databases or APIs ## Core Capabilities ### 1. Basic Chat API Initialize and use the Cohere Client for conversational tasks: ```python import cohere co = cohere.ClientV2(api_key="<YOUR API KEY>") response = co.chat( model="command-a-03-2025", messages=[ {"role": "user", "content": "Summarize the key features of quantum computing."} ], ) print(response.message.content[0].text) ``` Available models: - `command-a-03-2025` - Latest generation model For comprehensive API parameters, streaming, RAG, and tool use, refer to `references/chat_api.md`. ### 2. Entity Extraction with JSON Schema Mode The primary strength of Cohere v2 is structured out