simonw--llm
Chris Mungall
50 行
1.8 KiB
Markdown
50 行
1.8 KiB
Markdown
(related-tools)=
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# Related tools
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The following tools are designed to be used with LLM:
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## strip-tags
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[strip-tags](https://github.com/simonw/strip-tags) is a command for stripping tags from HTML. This is useful when working with LLMs because HTML tags can use up a lot of your token budget.
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Here's how to summarize the front page of the New York Times, by both stripping tags and filtering to just the elements with `class="story-wrapper"`:
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```bash
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curl -s https://www.nytimes.com/ \
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| strip-tags .story-wrapper \
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| llm -s 'summarize the news'
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```
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[llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs](https://simonwillison.net/2023/May/18/cli-tools-for-llms/) describes ways to use `strip-tags` in more detail.
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## ttok
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[ttok](https://github.com/simonw/ttok) is a command-line tool for counting OpenAI tokens. You can use it to check if input is likely to fit in the token limit for GPT 3.5 or GPT4:
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```bash
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cat my-file.txt | ttok
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```
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```
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125
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```
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It can also truncate input down to a desired number of tokens:
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```bash
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ttok This is too many tokens -t 3
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```
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```
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This is too
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```
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This is useful for truncating a large document down to a size where it can be processed by an LLM.
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## symbex
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[symbex](https://github.com/simonw/symbex) is a tool for searching for symbols in Python codebases. It's useful for extracting just the code for a specific problem and then piping that into LLM for explanation, refactoring or other tasks.
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Here's how to use it to find all functions that match `test*csv*` and use those to guess what the software under test does:
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```bash
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symbex 'test*csv*' | \
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llm --system 'based on these tests guess what this tool does'
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```
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For more examples see [symbex: search Python code for functions and classes, then pipe them into a LLM](https://simonwillison.net/2023/Jun/18/symbex/).
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