Entity Recognition with LLMs: A Step‑by‑Step Guide

说明与用途

使用 Oxlo.ai 的 LLM 进行命名实体识别的逐步自动化流程，包括客户端配置、系统提示编写、提取函数、测试和批量处理。

问题

Extract named entities (people, organizations, locations, dates) from unstructured text without maintaining separate NLP models, while controlling token costs.

结果

The pipeline produces a JSON object listing all named entities per document, with support for batching to reduce per‑request costs.

工作流

• Configure OpenAI client to point at Oxlo.ai endpoint
• Write a system prompt specifying entity types and JSON schema
• Create an extraction function that calls the LLM with JSON mode and parses the result
• Test the function on a realistic document and inspect output
• Extend the prompt for batch processing of multiple documents
• Wrap the batch function to handle lists of texts
• Run the script locally, setting the API key and executing the pipeline
• Optional: expose the extraction function via a FastAPI endpoint for external use

证据

We are going to build a named entity recognition pipeline that turns unstructured text into structured JSON using an LLM.

来源

Because Oxlo.ai charges a flat rate per request, you can feed it long documents or batch multiple inputs without watching token costs climb.

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The system prompt tells the model exactly which entities to extract and what schema to return.

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