Search Results for "json tool"

json_repair is an open-source Python library designed to automatically fix malformed JSON data and convert it into valid, parseable structures. The tool is particularly useful in scenarios where JSON output is generated by large language models or external services that may produce syntactically invalid responses. Instead of failing when encountering errors such as missing quotes, trailing commas, or incomplete objects, the library analyzes the malformed data and reconstructs it into valid JSON.

Easy DataSet is a comprehensive open-source tool designed to make creating high-quality datasets for large language model fine-tuning, retrieval-augmented generation (RAG), and evaluation as easy and automated as possible by providing intuitive interfaces and powerful parsing, segmentation, and labeling tools. It supports ingesting domain-specific documents in a wide range of formats — including PDF, Markdown, DOCX, EPUB, and plain text — and can intelligently segment, clean, and structure...

webclaw is a high-performance web content extraction tool designed specifically for AI agents and large language models, focusing on delivering clean, structured data instead of raw HTML. It is built in Rust and operates without a headless browser, using advanced techniques such as TLS fingerprinting to bypass common scraping barriers and mimic real browser behavior. The tool addresses a major inefficiency in AI workflows by removing irrelevant elements like navigation menus, ads, and scripts, significantly reducing token usage when feeding data into language models. ...

...The model extends traditional chat-based language models by enabling them to determine when external functions should be called and how to extract the necessary parameters from natural language input. Function definitions are typically provided in JSON schema format, allowing the model to generate structured function calls compatible with modern tool-calling interfaces used in AI applications. Functionary can decide whether to execute tools sequentially or in parallel and can analyze the outputs of those tools to produce context-aware responses. This capability allows AI systems to interact with external services, APIs, or computation engines rather than relying solely on knowledge embedded in the model.

...Cohere recommends W4A4 for most users because it offers a smaller hardware footprint with negligible benchmark differences compared to BF16 and FP8 versions. The model supports a 128K input context and 64K output length, covers 48 languages, and includes conversational tool-use capabilities with JSON-schema tools and optional citation grounding.