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88 lines
2.6 KiB
Markdown
88 lines
2.6 KiB
Markdown
---
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toAstro: true
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astroType: null
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published: true
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title: ChainForge简单介绍
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date: 2024-05-22T00:00:00.000Z
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author: KazooTTT
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tags:
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- 工具
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- chainforge
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- llm
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finished: true
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category: AI
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slug: chainforge-intro
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description: >-
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ChainForge is a visual programming environment designed for prompt
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engineering, allowing users to evaluate and test prompts for large language
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models (LLMs). It supports various use cases such as assessing prompt
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effectiveness, conducting adversarial testing, and evaluating model
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performance. The platform also facilitates the customization of LLMs through
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its documentation on adding custom providers. Users can find practical
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examples and tutorials on the ChainForge website and GitHub repository,
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including a case study on Twitter and a video demonstrating LLM prompt
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injection attacks and vulnerability testing. Additionally, ChainForge is
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working on enhancing its functionality by enabling internal and proxy network
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environments.
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NotionID-notionnext: 40ec4f8d-2030-4ce1-b8c7-c1c9f56ef55b
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link-notionnext: 'https://kazoottt.notion.site/ChainForge-40ec4f8d20304ce1b8c7c1c9f56ef55b'
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date_created: 20250104
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date_modified: 20250304
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---
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# ChainForge 简单介绍
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官网：
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[ChainForge: A visual programming environment for prompt engineering](<https://chainforge.ai/>)
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github 地址：
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[GitHub - ianarawjo/ChainForge: An open-source visual programming environment for battle-testing prompts to LLMs.](<https://github.com/ianarawjo/ChainForge>)
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## 简介
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## 使用场景
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评估提示词
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攻击性测试
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评估模型
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评估某项指标
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## 实践
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这两个例子单独拿出来讲
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## TODO
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1. 内部环境运行
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2. 内网环境 proxy [Add Custom Providers - ChainForge Documentation](<https://chainforge.ai/docs/custom_providers/> 提供了自定义 llm
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## 参考
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使用案例：
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[X](<https://twitter.com/9hills/status/1772467763165958602>)
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自动化 Prompts
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[\[2402.10949\] The Unreasonable Effectiveness of Eccentric Automatic Prompts](<https://arxiv.org/abs/2402.10949>)
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相关的视频
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[LLM Prompt Injection Attacks & Testing Vulnerabilities With ChainForge - YouTube](<https://www.youtube.com/watch?v=x1_uEnoxvNs>)
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使用 ChainForge 进行 LLM 提示注入攻击和漏洞测试
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