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## talk-codebase: Tool for chatting with your codebase and docs using OpenAI, LlamaCpp, and GPT-4-All
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[](https://github.com/rsaryev/talk-codebase/actions/workflows/python-publish.yml)
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<p align="center">
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<img src="https://github.com/rsaryev/talk-codebase/assets/70219513/b5d338f9-14a5-417b-9690-83f5cd66facf" width="800" alt="chat">
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</p>
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## Description
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Talk-codebase is a tool that allows you to converse with your codebase using LLMs to answer your queries. It supports
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offline code processing using [GPT4All](https://github.com/nomic-ai/gpt4all) without sharing your code with third
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parties, or you can use OpenAI if privacy is not a concern for you. It is only recommended for educational purposes and
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not for production use.
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## Installation
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To install `talk-codebase`, you need to have Python 3.9 and an OpenAI API
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key [api-keys](https://platform.openai.com/account/api-keys).
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Additionally, if you want to use the GPT4All model, you need to download
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the [ggml-gpt4all-j-v1.3-groovy.bin](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin) model. If you prefer a
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different model, you can download it from [GPT4All](https://gpt4all.io) and configure path to it in the configuration
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and specify its
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path in the configuration. If you want some files to be ignored, add them to .gitignore.
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To install `talk-codebase`, run the following command in your terminal:
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```bash
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pip install talk-codebase
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```
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Once `talk-codebase` is installed, you can use it to chat with your codebase by running the following command:
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```bash
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talk-codebase chat <path-to-your-codebase>
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```
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If you need to configure or edit the configuration, you can run:
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```bash
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talk-codebase configure
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```
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You can also edit the configuration manually by editing the `~/.config.yaml` file.
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If for some reason you cannot find the configuration file, just run the tool and at the very beginning it will output
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the path to the configuration file.
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```yaml
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# The OpenAI API key. You can get it from https://beta.openai.com/account/api-keys
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api_key: sk-xxx
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# maximum overlap between chunks. It can be nice to have some overlap to maintain some continuity between chunks
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chunk_overlap: '50'
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# maximum size of a chunk
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chunk_size: '500'
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# number of samples to generate for each prompt.
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k: '4'
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# maximum tokens for the LLMs
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max_tokens: '1048'
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# token limit for the LLM model only OpenAI
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model_name: gpt-3.5-turbo
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# path to the llm file on disk.
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model_path: models/ggml-gpt4all-j-v1.3-groovy.bin
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# type of the LLM model. It can be either local or openai
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model_type: openai
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```
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## The supported extensions:
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- [x] `.csv`
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- [x] `.doc`
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- [x] `.docx`
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- [x] `.epub`
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- [x] `.md`
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- [x] `.pdf`
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- [x] `.txt`
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- [x] `popular programming languages`
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## Contributing
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Contributions are always welcome!
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