datasette-llm 0.1a11 发布：支持 OpenAI 兼容端点接入本地模型
datasette-llm 0.1a11
Simon Willison 的 Datasette 现在能直接接 LM Studio、Ollama 这些本地模型了，跟着文中几行 uvx 命令就能跑起一个本地模型驱动的数据 Agent，数据不用上传云端。经常用 Datasette 做数据分析、又在意数据隐私的开发者可以试试。
Simon Willison 发布 datasette-llm 0.1a11，这是 Datasette 的中间层插件，负责为其他 Datasette 插件配置 LLM。新版本增加了对 OpenAI 兼容端点的支持，任何遵循 OpenAI 伪标准 Web 协议的模型服务都能接入 Datasette。
技术上，用户可以通过配置 default_model 参数指定模型名和 api_base，将本地运行的 LM Studio、Ollama 或 llama.cpp（刚发布 v1.0.0）作为后端。文中给出了完整命令示例：用 uvx 启动带 datasette-agent 的 Datasette 实例，指向 localhost:1234/v1 的 qwen3.8-27b 本地模型，即可获得一个由本地模型驱动的 Datasette Agent 服务。
对开发者而言，这意味着数据分析 Agent 可以完全脱离云端 API 运行，数据不出本机。相比绑定 OpenAI 官方 API 的方案，这种方式兼容本地推理工具链，适合处理敏感数据的团队。
datasette-llm 0.1a11
Release: datasette-llm 0.1a11 datasette-llm is the intermediate plugin for Datasette that supports configuring LLMs for use with other Datasette plugins. This release adds support for OpenAI-compatible endpoints, so you can configure Datasette to use an LLM hosted anywhere that talks the OpenAI pseudo-standard web protocol. This means that if you're running local models with LM Studio or Ollama or llama.cpp (which just hit v1.0.0 ) you can run Datasette via uvx like this: wget https://datasette.io/content.db uvx --with datasette-agent datasette \ --root --secret 1 \ -s plugins.datasette-llm.default_model ' { "model": "lmstudio-qwen", "model_name": "qwen/qwen3.8-27b", "api_base": "http://localhost:1234/v1" } ' content.db And you'll get a local server running a Datasette Agent instance powered by your local model! Tags: uv , datasette-agent , llm , datasette , llms , ai , generative-ai
This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Datasette, Ollama, LM Studio, llama.cpp.
Source attribution: curated from Simon Willison’s Weblog.