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## What is this?
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Autodoc is a **experimental** toolkit for auto-generating codebase documention for git repositories using Large Language Models, like [GPT-4](https://openai.com/research/gpt-4) or [Alpaca](https://github.com/ggerganov/llama.cpp). Autodoc can be [installed](#get-started) in your repo in about 5 minutes. It indexes your codebase through a depth-first traversal of all repository contents and calls an LLM to write documentation for each file and folder. These documents can be combined to describe the different components of your system and how they work together.
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Autodoc is a **experimental** toolkit for auto-generating codebase documentation for git repositories using Large Language Models, like [GPT-4](https://openai.com/research/gpt-4) or [Alpaca](https://github.com/ggerganov/llama.cpp). Autodoc can be [installed](#get-started) in your repo in about 5 minutes. It indexes your codebase through a depth-first traversal of all repository contents and calls an LLM to write documentation for each file and folder. These documents can be combined to describe the different components of your system and how they work together.
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The generated documentation lives in your codebase, and travels where your code travels. Developers who download your code can use the `doc` command to ask questions about your codebase and get highly specific answers with reference links back to code files.
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In the near future, documentation will be re-indexed as part your CI pipeline, so it is always up-to-date. If your interested in working contributing to this work, see [this issue](https://github.com/context-labs/autodoc/issues/7).
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### Status
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Autodoc is in the early stages of development. It is functional, but not ready for production use. Things may break, or not work as expected. If you're interested in working on the core Autodoc framwork, please see [contributing](#contributing). We would love to have your help!
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Autodoc is in the early stages of development. It is functional, but not ready for production use. Things may break, or not work as expected. If you're interested in working on the core Autodoc framework, please see [contributing](#contributing). We would love to have your help!
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### FAQs
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**Question:** I'm not getting good responses. How can I improve response quality?
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### Examples
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Below are a few examples of how Autodoc can be used.
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1.[Autodoc](https://github.com/context-labs/autodoc) - This repository contains documentation for itself, generated by Autodoc. It lives in the `.autodoc` folder. Follow the instructions [here](#querying) to learn how to query it.
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2.[TolyGPT.com](https://tolygpt.com) - TolyGPT is an Autodoc chatbot trained on the [Solana validator](https://github.com/solana-labs/solana) codebase and deployed to the web for easy access. In the near future, Autodoc will support a web version in additon to the existing CLI tool.
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2.[TolyGPT.com](https://tolygpt.com) - TolyGPT is an Autodoc chatbot trained on the [Solana validator](https://github.com/solana-labs/solana) codebase and deployed to the web for easy access. In the near future, Autodoc will support a web version in addition to the existing CLI tool.
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## Get Started
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For every file in your project, Autodoc calculates the number of tokens in the file based on the file content. The more lines of code, the larger the number of tokens. Using this number, it determine which model it will use on per file basis, always choosing the cheapest model whose context length supports the number of tokens in the file. If you're interested in helping make model selection configurable in Autodoc, check out [this issue](https://github.com/context-labs/autodoc/issues/9).
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**Note:** This naive model selection strategy means that files under ~4000 tokens will be documented using GPT-3.5, which will result in less accurate documenation. **We recommend using GPT-4 8K at a minimum.** Indexing with GPT-4 results in signficantly better output. You can apply for access [here](https://openai.com/waitlist/gpt-4-api).
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**Note:** This naive model selection strategy means that files under ~4000 tokens will be documented using GPT-3.5, which will result in less accurate documentation. **We recommend using GPT-4 8K at a minimum.** Indexing with GPT-4 results in significantly better output. You can apply for access [here](https://openai.com/waitlist/gpt-4-api).
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For large projects, the cost can be several hundred dollars. View OpenAI pricing [here](https://openai.com/pricing).
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In the near future, we will support self-hosted models, such as [Llama](https://github.com/facebookresearch/llama) and [Alpaca](https://github.com/tatsu-lab/stanford_alpaca). Read [this issue](https://github.com/context-labs/autodoc/issues/8) if you're interesting in contributing to this work.
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When you're done repository is done being indexed, you should see a screen like this:
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When your repository is done being indexed, you should see a screen like this:
You can now query your application using the steps outlined in [querying](#querying).
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## Community
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There is a small group of us that are working full time on Autodoc. Join us on [Discord](https://discord.gg/zpFEXXWSNg), or follow us on [Twitter](https://twitter.com/autodoc_) for updates. We'll be posting reguarly and continuing to improve the Autodoc applicatioin. What to contribute? Read below.
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There is a small group of us that are working full time on Autodoc. Join us on [Discord](https://discord.gg/zpFEXXWSNg), or follow us on [Twitter](https://twitter.com/autodoc_) for updates. We'll be posting regularly and continuing to improve the Autodoc application. Want to contribute? Read below.
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