How does this skill handle categorical data like sentiment or status?

It utilizes JSON Enums to constrain the model's output to a specific list of valid categories, removing the need for post-processing validation.

What is the main advantage of using JSON Schema mode with Cohere?

JSON Schema mode guarantees that the LLM's response strictly follows your defined structure, preventing parsing errors and ensuring data integrity for downstream applications.

Can I extract multiple different entities in a single request?

Yes, the skill provides patterns for extracting arrays of objects, allowing you to capture lists of people, organizations, or line items from a single block of text.

Is this suitable for production-grade data pipelines?

Absolutely. By enforcing schemas and handling validation at the API level, it creates a reliable bridge between unstructured text and structured databases.

Which Cohere models are supported by this skill?

This skill is designed for the latest generation models, such as command-a-03-2025, which are optimized for the Cohere v2 Chat API.

Cohere v2 Entity Extraction

Name: Cohere v2 Entity Extraction
Author: ActiveInferenceInstitute

byActiveInferenceInstitute

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数据科学与机器学习

Automates structured entity extraction and data validation using the Cohere v2 Python Chat API and strict JSON Schema enforcement.

关于

This skill empowers developers to leverage the full power of the Cohere v2 Chat API, focusing specifically on JSON Schema mode to guarantee structured, validated outputs. It provides sophisticated implementation patterns for Named Entity Recognition (NER), multi-entity extraction, and complex document parsing (such as resumes, invoices, and medical reports). By using strict schema enforcement, this skill eliminates the need for brittle post-processing logic, making it an essential tool for building robust data pipelines, classification systems, and production-ready LLM integrations that require consistent JSON responses.

主要功能

Classification capabilities using JSON Enums to constrain categorical data
Integration patterns for the latest command-a-03-2025 model
Advanced Named Entity Recognition (NER) patterns for unstructured text
Pre-configured templates for complex parsing (Invoices, Resumes, Medical Reports)
Strict JSON Schema enforcement for guaranteed structured LLM outputs
9 GitHub stars

使用场景

Creating structured classification systems for sentiment analysis and aspect-based feedback categorization
Automating data extraction from business documents like invoices, receipts, and contracts
Building high-accuracy NER systems for processing and indexing large-scale text datasets

关于

主要功能

Classification capabilities using JSON Enums to constrain categorical data
Integration patterns for the latest command-a-03-2025 model
Advanced Named Entity Recognition (NER) patterns for unstructured text
Pre-configured templates for complex parsing (Invoices, Resumes, Medical Reports)
Strict JSON Schema enforcement for guaranteed structured LLM outputs
9 GitHub stars

使用场景

Creating structured classification systems for sentiment analysis and aspect-based feedback categorization
Automating data extraction from business documents like invoices, receipts, and contracts
Building high-accuracy NER systems for processing and indexing large-scale text datasets