Awesome GitHub RepositoriesData Validation

This project is a web-based platform designed for creating, managing, and sharing professional resumes. It functions as a structured document builder that integrates artificial intelligence to assist with content generation, editing, and analysis. Users can maintain a collection of resumes, customize their visual presentation through various templates, and export them into multiple formats for job applications. The platform distinguishes itself through its autonomous AI agent capabilities, which can perform research, suggest incremental edits, and apply data patches directly to documents. It

Instructor is a schema enforcement and validation library designed to transform language model outputs into structured, type-safe data formats. It functions as a validation layer that uses Pydantic to ensure model responses conform to specific data models, acting as a tool for forcing large language models to return data in predefined schemas. The project differentiates itself through a recursive error-feedback loop that automatically retries requests when structural errors occur, passing validation failure messages back to the model to guide corrections. It also includes a streaming parser c

TypeChat is a schema enforcement library and framework for building natural language interfaces. It ensures that responses from large language models strictly adhere to predefined TypeScript type definitions, translating unstructured human language into predictable, structured data. The project functions as both a prompt generator and an output validator. It automatically creates model instructions by extracting requirements from type schemas to replace manual prompt engineering and verifies that model outputs match the required format. The system handles structured output generation and res

Garak is an AI model evaluation tool and vulnerability scanner designed for red teaming large language models and auditing the security of retrieval-augmented generation pipelines. It identifies behavioral weaknesses, such as jailbreaks, hallucinations, and data leakage, by simulating adversarial attacks and executing automated testing vectors. The framework utilizes an adaptive probing loop where prompts can react to previous model behavior and be modified in flight via middleware. To ensure consistent analysis, it employs a provider-agnostic interface to interact with various model APIs and

Marshmallow is a Python data serialization library and validation framework. It functions as a bridge that transforms complex application objects into primitive data types for storage or network transmission and vice versa. The library utilizes a schema-based approach to validate that incoming data conforms to specific types and constraints. It employs a two-way transformation pipeline consisting of separate load and dump phases to handle deserialization and serialization. The framework supports class-based schema definitions with declarative field validation and recursive schema nesting for

Poml is a prompt management framework and templating engine designed for authoring, versioning, and rendering structured prompts for large language models. It uses a semantic markup language to organize prompts into reusable templates, combining them with dynamic context and data to generate formatted inputs. The system distinguishes itself by decoupling core prompt logic from final presentation through a stylesheet-based approach. It provides a dedicated JSON schema output generator to enforce strict, machine-parsable model responses and a configuration interface for managing function tool s

FLAML 是一个自动化机器学习框架、超参数优化工具和大型语言模型代理编排器。它提供了一个用于跨各种学习器和数据集进行模型选择和调优的系统，同时也提供了一个用于优化大型语言模型推理参数和微调设置的工具包。 该项目具有元学习调优系统，可分析历史任务数据以生成依赖于数据的默认配置，从而加速模型收敛。它进一步通过对话式工作流和事件驱动编排，支持协作式多代理系统的设计。 能力涵盖了针对机器学习模型和任意 Python 函数的资源高效超参数搜索，支持分层搜索空间和字典序目标优化。该框架还包括用于自动化模型选择、堆叠集成构建、零样本配置以及强制执行公平性约束的实用工具。 该系统支持分布式调优扩展和跨计算集群的并发试验执行，以缩短总搜索时长。