📄 中文摘要
大规模语言模型（LLM）在生成流畅文本方面表现出色，但常常会出现“幻觉”现象，即生成看似合理但实际上不真实的信息。为了解决这一问题，整合'/v1/documents/custom'端点可以提供一个确定性的框架，从预定义的PDF文档中提取和处理准确的数据，而不是完全依赖模型的推断。这种方法有助于提高信息的准确性和可靠性，减少错误信息的传播。
为人工智能赋能：V1 文档自定义 (ETL-D API)
出处: Dotando a IAs con: V1 Documents Custom (ETL-D API)
发布: 2026年3月19日
📄 English Summary
Dotando a IAs con: V1 Documents Custom (ETL-D API)
Large language models (LLMs) excel at generating fluent text but often exhibit a phenomenon known as 'hallucination,' where they produce information that sounds plausible but is not true. To address this issue, integrating the '/v1/documents/custom' endpoint offers a deterministic framework for extracting and processing accurate, predefined data directly from PDF documents, rather than relying solely on the model's inferences. This approach enhances the accuracy and reliability of information, reducing the spread of misinformation.