📄 中文摘要
大型语言模型（LLMs）如 LangChain 和 AutoGPT 存在“幻觉”问题，容易生成与输入数据不符的信息。这是因为它们基于学习到的模式生成文本，而无法实时访问外部可靠来源。为了解决这一问题，整合特定处理服务至关重要，例如 ETL-D 服务的 '/v1/documents/invoice' 端点。该端点能够从发票等来源中以确定性方式提取结构化数据，确保 LLM 不会猜测，而是接收来自真实文档的准确信息。通过这种方式，可以提高生成内容的准确性和可靠性。
为人工智能赋能：V1 文档发票 (ETL-D API)
出处: Dotando a IAs con: V1 Documents Invoice (ETL-D API)
发布: 2026年3月24日
📄 English Summary
Dotando a IAs con: V1 Documents Invoice (ETL-D API)
Large language models (LLMs) like LangChain and AutoGPT face the 'hallucination' problem, where they tend to generate information that is not present in the input data. This occurs because they generate text based on learned patterns without real-time access to external reliable sources. To address this issue, it is crucial to integrate specific processing services, such as the '/v1/documents/invoice' endpoint from the ETL-D service. This endpoint allows for deterministic extraction of structured data from sources like invoices, ensuring that the LLM does not guess but receives accurate information extracted from real documents. This approach enhances the accuracy and reliability of the generated content.