--- layout: single title: "Tabby - GitHub Copilot 的开源替代解决方案" date: 2024-01-05 10:00:00 +0800 categories: [编程开发, 操作系统] tags: [Tabby, CodeLLM, GitHubCopilot, Tabnine, Leaderboard, DeepSeek-Coder, IDE, IntelliJIDEA] --- ## [Tabby](https://tabby.tabbyml.com/) - [Tabby Playground](https://tabby.tabbyml.com/playground/) - [Tabby Docs](https://tabby.tabbyml.com/docs/getting-started/) - [Tabby Blog](https://tabby.tabbyml.com/blog/) - [GitHub: Tabby](https://github.com/TabbyML/tabby) - [There's An AI For That](https://theresanaiforthat.com/coding/) - [Run Curl Commands Online](https://reqbin.com/curl) ## [Coding LLMs Leaderboard](https://leaderboard.tabbyml.com/) (TabbyML Team) [Introducing the Coding LLM Leaderboard](https://tabby.tabbyml.com/blog/2023/11/23/coding-llm-leaderboard/) ![](/images/2024/Tabby/Coding-LLMs-Leaderboard.png) > 更新日期：2023-11-13 ### Next Line Accuracy 什么是 Next Line Accuracy ？ 在代码补全中，模型预测的是跨越多行的代码块。一种朴素的方法是直接将预测的代码块与实际提交的代码进行比较。虽然这种方法看起来理想，但它通常被认为是一个“过于稀疏”的度量标准。另一方面，下一行准确度可以作为整体代码块匹配准确度的可靠代理。 ![](/images/2024/Tabby/next-line-accuracy.png) > 只有红色框内的内容被用于与真实值进行比较，以计算准确度指标。 ## 安装 Tabby ### [Homebrew (Apple M1/M2)](https://tabby.tabbyml.com/docs/installation/apple/) #### 安装 tabby ```shell brew install tabbyml/tabby/tabby ``` ``` ==> Fetching tabbyml/tabby/tabby ==> Downloading https://github.com/TabbyML/tabby/releases/download/v0.7.0/tabby_aarch64-apple-darwin ==> Running `brew cleanup tabby`... Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP. Hide these hints with HOMEBREW_NO_ENV_HINTS (see `man brew`). ``` - `brew cleanup tabby` 命令的意思是清理所有旧版本的 tabby 包和缓存的 tabby 下载。 #### 运行 Tabby 服务（模型：DeepseekCoder-1.3B） ```shell tabby serve --device metal --model TabbyML/DeepseekCoder-1.3B ``` ``` 00:01:21 ▕████████████████████▏ 1.33 GiB/1.33 GiB 16.75 MiB/s ETA 0s. 2024-01-05T11:38:38.816761Z INFO tabby::serve: crates/tabby/src/serve.rs:111: Starting server, this might takes a few minutes... 2024-01-05T11:38:40.713532Z INFO tabby::routes: crates/tabby/src/routes/mod.rs:35: Listening at 0.0.0.0:8080 TELEMETRY As an open source project, we collect usage statistics to inform development priorities. For more information, read https://tabby.tabbyml.com/docs/configuration#usage-collection We will not see or any code in your development process. To opt-out, add the TABBY_DISABLE_USAGE_COLLECTION=1 to your tabby server's environment variables. Welcome to Tabby! If you have any questions or would like to engage with the Tabby team, please join us on Slack (https://tinyurl.com/35sv9kz2). ``` 上面是从 [HuggingFace TabbyML](https://huggingface.co/TabbyML) 上下载的模型，中国的用户不能访问，可以使用 [ModelScope TabbyML](https://modelscope.cn/organization/TabbyML) 下载模型。 ```shell TABBY_DOWNLOAD_HOST=modelscope.cn tabby serve --model TabbyML/StarCoder-1B ``` ``` Writing to new file. 🎯 Downloaded https://modelscope.cn/api/v1/models/TabbyML/StarCoder-1B/repo?FilePath=ggml/q8_0.v2.gguf to /Users/junjian/.tabby/models/TabbyML/StarCoder-1B/ggml/q8_0.v2.gguf.tmp 00:00:26 ▕████████████████████▏ 1.23 GiB/1.23 GiB 46.58 MiB/s ETA 0s. 2024-01-06T03:08:13.393167Z INFO tabby::serve: crates/tabby/src/serve.rs:111: Starting server, this might takes a few minutes... 2024-01-06T03:08:14.976912Z INFO tabby::routes: crates/tabby/src/routes/mod.rs:35: Listening at 0.0.0.0:8080 ``` #### 模型缓存目录 ```shell tree ~/.tabby ``` ``` /Users/junjian/.tabby ├── events │   ├── 2024-01-05.json │   └── 2024-01-06.json ├── models │   └── TabbyML │   ├── DeepseekCoder-1.3B │   │   ├── ggml │   │   │   └── q8_0.v2.gguf │   │   └── tabby.json │   ├── DeepseekCoder-6.7B │   │   ├── ggml │   │   │   └── q8_0.v2.gguf │   │   └── tabby.json │   ├── StarCoder-1B │   │   ├── ggml │   │   │   └── q8_0.v2.gguf │   │   └── tabby.json │   └── models.json └── usage_anonymous_id ``` ### [Docker (CUDA)](https://tabby.tabbyml.com/docs/installation/docker) > cuda>=11.7 #### [安装 NVIDIA Driver](https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html) 1. [NVIDIA Driver Downloads](https://www.nvidia.com/download/index.aspx) 2. NVIDIA Driver Installation ```shell sudo sh NVIDIA-Linux-x86_64-535.129.03.run ``` #### 模型：DeepSeek Coder - deepseek-coder-1.3b-base ```shell huggingface-cli download deepseek-ai/deepseek-coder-1.3b-base huggingface-cli download deepseek-ai/deepseek-coder-1.3b-base --local-dir deepseek-ai/deepseek-coder-1.3b-base --local-dir-use-symlinks False ``` - deepseek-coder-6.7b-base ```shell huggingface-cli download deepseek-ai/deepseek-coder-6.7b-base huggingface-cli download deepseek-ai/deepseek-coder-6.7b-base --local-dir deepseek-ai/deepseek-coder-6.7b-base --local-dir-use-symlinks False ``` #### 运行 Tabby 服务（模型：DeepseekCoder-1.3B / DeepseekCoder-6.7B） * --runtime nvidia ```shell docker run -d --runtime nvidia --name tabby \ -e NVIDIA_VISIBLE_DEVICES=3 \ -e RUST_BACKTRACE=full \ -p 8080:8080 -v `pwd`/.tabby:/data tabbyml/tabby \ serve --model TabbyML/DeepseekCoder-6.7B \ --device cuda ``` 在 `Rust` 中，设置环境变量 `RUST_BACKTRACE` 来显式调用栈的信息： - RUST_BACKTRACE=1: 打印简单信息 - RUST_BACKTRACE=full：打印全部信息 * --gpus '"device=3"' ```shell docker run -d --gpus '"device=3"' --name tabby -p 8080:8080 \ -e RUST_BACKTRACE=full \ -v `pwd`/.tabby:/data tabbyml/tabby \ serve --model TabbyML/DeepseekCoder-6.7B \ --device cuda ``` 在一张卡上部署模型 `DeepseekCoder-1.3B`，并设置并行度为 `3`： ```shell docker run -d --gpus '"device=3"' --name tabby -p 8080:8080 \ -e RUST_BACKTRACE=full \ -v `pwd`/.tabby:/data tabbyml/tabby \ serve --model TabbyML/DeepseekCoder-1.3B \ --device cuda --parallelism 3 ``` * --gpus all ```shell docker run -d --gpus all --name tabby -p 8080:8080 \ -v `pwd`/.tabby:/data tabbyml/tabby \ serve --model TabbyML/DeepseekCoder-6.7B \ --device cuda --parallelism 4 ``` - --parallelism 模型服务的并行度（`4` 张卡的 `NVIDIA T4 16GB` 的服务器） - `TabbyML/DeepseekCoder-1.3B` 模型最多设置 `12` 个并行度 - `TabbyML/DeepseekCoder-6.7B` 模型最多设置 `4` 个并行度 ### [SkyPilot Serving](https://tabby.tabbyml.com/docs/installation/skypilot/) ## 测试服务 ```shell curl -X 'POST' \ 'http://localhost:8080/v1/completions' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "language": "python", "segments": { "prefix": "def swap(x, y):\n ", "suffix": "\n return x, y" } }' ``` ```py { "id": "cmpl-4f3f4974-7c19-4a17-86ad-d6cb92515181", "choices": [ { "index": 0, "text": "x, y = y, x" } ] } ``` ## 模型 - [Models Registry](https://tabby.tabbyml.com/docs/models/) - [Tabby Registry](https://github.com/TabbyML/registry-tabby) - [registry-tabby/models.json](https://github.com/TabbyML/registry-tabby/blob/main/models.json) ### Completion models (`--model`) 官方推荐 - For **1B to 3B models**, it's advisable to have at least **NVIDIA T4, 10 Series, or 20 Series GPUs**. - For **7B to 13B models**, we recommend using **NVIDIA V100, A100, 30 Series, or 40 Series GPUs**. | Model ID | License | | -------- | ------- | | [TabbyML/StarCoder-1B](https://huggingface.co/bigcode/starcoderbase-1b) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | | [TabbyML/StarCoder-3B](https://huggingface.co/bigcode/starcoderbase-3b) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | | [TabbyML/StarCoder-7B](https://huggingface.co/bigcode/starcoderbase-7b) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | | [TabbyML/CodeLlama-7B](https://huggingface.co/codellama/CodeLlama-7b-hf) | [Llama 2](https://github.com/facebookresearch/llama/blob/main/LICENSE) | | [TabbyML/CodeLlama-13B](https://huggingface.co/codellama/CodeLlama-13b-hf) | [Llama 2](https://github.com/facebookresearch/llama/blob/main/LICENSE) | | [TabbyML/DeepseekCoder-1.3B](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) | [Deepseek License](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) | | [TabbyML/DeepseekCoder-6.7B](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) | [Deepseek License](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) | ### Chat models (`--chat-model`) 官方推荐至少使用 3B 参数的模型. | Model ID | License | | -------- | ------- | | [TabbyML/WizardCoder-3B](https://huggingface.co/WizardLM/WizardCoder-3B-V1.0) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | | [TabbyML/Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) | [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) | ### Benchmark | GPU | Model | Parallelism | QPS | Latency (Avg) | Latency (Med) | Latency (p90) | Latency (p95) | | --- | ----- | ----------- | --- | ------------- | ------------- | ------------- | ------------- | | T4 | TabbyML/StarCoder-1B | 7 | 4.14 | 1.69 | 1.58 | 2 | 2.05 | | T4 | TabbyML/StarCoder-1B | 10 | 4.85 | 2.06 | 1.98 | 2.49 | 2.63 | | T4 | TabbyML/StarCoder-1B | 8 | 4.22 | 1.9 | 1.85 | 2.29 | 2.38 | | A10G | TabbyML/StarCoder-1B | 17 | 12.01 | 1.42 | 1.35 | 1.66 | 1.9 | | A10G | TabbyML/StarCoder-1B | 25 | 14.61 | 1.71 | 1.68 | 1.97 | 2.07 | | A10G | TabbyML/StarCoder-1B | 29 | 15.94 | 1.82 | 1.79 | 2.11 | 2.17 | | A10G | TabbyML/StarCoder-1B | 27 | 14.89 | 1.81 | 1.79 | 2.03 | 2.37 | | A10G | TabbyML/StarCoder-1B | 26 | 14.79 | 1.76 | 1.72 | 2.08 | 2.22 | | A100 | TabbyML/StarCoder-1B | 33 | 13.16 | 2.51 | 1.57 | 2.58 | 11.52 | | A100 | TabbyML/StarCoder-1B | 17 | 12.94 | 1.31 | 1.29 | 1.46 | 1.53 | | A100 | TabbyML/StarCoder-1B | 25 | 16.98 | 1.47 | 1.43 | 1.69 | 1.86 | | A100 | TabbyML/StarCoder-1B | 29 | 10.46 | 2.77 | 2.8 | 3.11 | 3.18 | | A100 | TabbyML/StarCoder-1B | 27 | 13.58 | 1.99 | 1.96 | 2.37 | 2.42 | | A100 | TabbyML/StarCoder-1B | 26 | 16.52 | 1.57 | 1.47 | 1.96 | 2.19 | | T4 | TabbyML/DeepseekCoder-1.3B | 7 | 3.82 | 1.83 | 1.86 | 1.94 | 1.96 | | T4 | TabbyML/DeepseekCoder-1.3B | 4 | 3.05 | 1.31 | 1.32 | 1.45 | 1.49 | | T4 | TabbyML/DeepseekCoder-1.3B | 5 | 3.18 | 1.57 | 1.54 | 1.92 | 1.97 | | T4 | TabbyML/DeepseekCoder-1.3B | 6 | 3.33 | 1.8 | 1.84 | 1.92 | 1.95 | | A10G | TabbyML/DeepseekCoder-1.3B | 17 | 11.66 | 1.46 | 1.46 | 1.65 | 1.77 | | A10G | TabbyML/DeepseekCoder-1.3B | 21 | 6.83 | 3.07 | 1.63 | 14.32 | 14.56 | | A10G | TabbyML/DeepseekCoder-1.3B | 19 | 12.63 | 1.5 | 1.5 | 1.69 | 1.78 | | A10G | TabbyML/DeepseekCoder-1.3B | 20 | 12.88 | 1.55 | 1.53 | 1.79 | 1.92 | | A100 | TabbyML/DeepseekCoder-1.3B | 33 | 20.78 | 1.59 | 1.55 | 1.84 | 1.9 | | A100 | TabbyML/DeepseekCoder-1.3B | 49 | 18.2 | 2.69 | 2.62 | 3.15 | 3.38 | | A100 | TabbyML/DeepseekCoder-1.3B | 41 | 16.44 | 2.49 | 2.36 | 2.9 | 3.49 | | A100 | TabbyML/DeepseekCoder-1.3B | 37 | 21.15 | 1.75 | 1.67 | 2.07 | 2.18 | | A100 | TabbyML/DeepseekCoder-1.3B | 39 | 14.78 | 2.64 | 2.6 | 3.02 | 3.13 | | A100 | TabbyML/DeepseekCoder-1.3B | 38 | 20.88 | 1.82 | 1.76 | 2.08 | 2.19 | | T4 | TabbyML/StarCoder-3B | 7 | 1.89 | 3.7 | 3.68 | 3.89 | 3.96 | | T4 | TabbyML/StarCoder-3B | 4 | 1.53 | 2.62 | 2.62 | 2.75 | 2.78 | | T4 | TabbyML/StarCoder-3B | 2 | 0.86 | 2.32 | 2.34 | 2.43 | 2.47 | | A10G | TabbyML/StarCoder-3B | 17 | 5.42 | 3.14 | 3.16 | 3.47 | 3.84 | | A10G | TabbyML/StarCoder-3B | 9 | 4.31 | 2.09 | 2.05 | 2.39 | 2.69 | | A10G | TabbyML/StarCoder-3B | 5 | 2.85 | 1.75 | 1.73 | 1.95 | 2.28 | | A10G | TabbyML/StarCoder-3B | 7 | 3.61 | 1.94 | 1.94 | 2.18 | 2.29 | | A10G | TabbyML/StarCoder-3B | 6 | 3.17 | 1.89 | 1.91 | 2.04 | 2.06 | | A100 | TabbyML/StarCoder-3B | 33 | 8.12 | 4.07 | 4.12 | 4.53 | 4.61 | | A100 | TabbyML/StarCoder-3B | 17 | 7.9 | 2.15 | 2.1 | 2.34 | 2.81 | | A100 | TabbyML/StarCoder-3B | 9 | 4.47 | 2.01 | 1.99 | 2.19 | 2.26 | | A100 | TabbyML/StarCoder-3B | 5 | 3.21 | 1.56 | 1.56 | 1.68 | 1.72 | | A100 | TabbyML/StarCoder-3B | 7 | 4.29 | 1.63 | 1.61 | 1.78 | 1.8 | | A100 | TabbyML/StarCoder-3B | 8 | 4.63 | 1.73 | 1.73 | 1.92 | 2.03 | | A10G | TabbyML/DeepseekCoder-6.7B | 5 | 1.3 | 3.85 | 3.83 | 4.25 | 4.31 | | A10G | TabbyML/DeepseekCoder-6.7B | 3 | 1.14 | 2.63 | 2.6 | 2.81 | 2.86 | | A10G | TabbyML/DeepseekCoder-6.7B | 2 | 0.83 | 2.4 | 2.4 | 2.48 | 2.5 | | A100 | TabbyML/DeepseekCoder-6.7B | 9 | 3.14 | 2.87 | 2.85 | 3.08 | 3.13 | | A100 | TabbyML/DeepseekCoder-6.7B | 5 | 2.08 | 2.4 | 2.46 | 2.58 | 2.63 | | A100 | TabbyML/DeepseekCoder-6.7B | 3 | 1.32 | 2.27 | 2.3 | 2.54 | 2.69 | | A100 | TabbyML/DeepseekCoder-6.7B | 2 | 1.2 | 1.67 | 1.66 | 1.84 | 1.93 | | A100 | TabbyML/CodeLlama-7B | 9 | 3.69 | 2.44 | 2.45 | 2.59 | 2.63 | | A100 | TabbyML/CodeLlama-7B | 5 | 2.14 | 2.34 | 2.31 | 2.61 | 3.26 | | A100 | TabbyML/CodeLlama-7B | 3 | 1.52 | 1.97 | 2.02 | 2.3 | 2.37 | | A100 | TabbyML/CodeLlama-7B | 5 | 2.37 | 2.11 | 2.13 | 2.24 | 2.26 | | A100 | TabbyML/CodeLlama-7B | 3 | 1.59 | 1.89 | 1.95 | 2.04 | 2.07 | | A100 | TabbyML/CodeLlama-7B | 2 | 1.45 | 1.38 | 1.39 | 1.54 | 1.56 | | A100 | TabbyML/CodeLlama-13B | 5 | 1.21 | 4.14 | 4.15 | 4.38 | 4.5 | | A100 | TabbyML/CodeLlama-13B | 3 | 0.89 | 3.36 | 3.4 | 3.71 | 3.73 | - **Parallelism**：并行性，指的是系统同时处理多个任务的能力。在多核处理器或多处理器的环境中，多个任务可以同时执行。 - **QPS**：每秒查询率，是一个衡量服务器负载能力的指标，表示服务器每秒能处理的查询请求的数量。 - **Latency (Avg)**：平均延迟，表示系统处理请求的平均时间。 - **Latency (Med)**：中位数延迟，表示所有请求处理时间的中位数。与平均延迟相比，中位数延迟对极端值不敏感，更能反映系统的典型性能。 - **Latency (p90)**：90th 百分位延迟，表示 90% 的请求处理时间都在这个值以下。这是一个衡量系统在大多数情况下性能的指标。 - **Latency (p95)**：95th 百分位延迟，表示 95% 的请求处理时间都在这个值以下。这个指标可以帮助我们理解系统在几乎所有情况下的性能。 ## [IDE/Editor 插件](https://tabby.tabbyml.com/docs/extensions/) ### [IntelliJ Platform](https://tabby.tabbyml.com/docs/extensions/installation/intellij) Tabby IntelliJ 平台插件适用于所有构建版本为 2022.2.5 或更高版本的 IntelliJ 平台 IDE，如 IDEA、PyCharm、GoLand、Android Studio 等。 - [Tabby Plugin for IntelliJ Platform](https://plugins.jetbrains.com/plugin/22379-tabby) #### Tabby Plugin 安装 ![](/images/2024/Tabby/IDEA-Tabby-Install.png) #### Tabby 配置 ![](/images/2024/Tabby/IDEA-Tabby-Settings.png) 安装完插件最好重启一下 `IntelliJ IDEA`，否则可能会出现不能触发`自动补全`的情况。 #### 配置文件路径 ```shell tree ~/.tabby-client ``` ``` /Users/junjian/.tabby-client └── agent ├── config.toml └── data.json ``` #### 代码自动补全 自动补全默认 `4秒` 触发一次，也可以通过快捷键触发 `Option + \`。 下面是自动补全的例子。 ![](/images/2024/Tabby/Write-Code.png) #### IDEA 日志 通过日志文件 `~/Library/Logs/JetBrains/IdeaIC2023.3/idea.log` 可以看到 Tabby 的日志信息 `com.tabbyml.intellijtabby.agent.Agent`。 在工作期间，IDE 将其内部监控数据导出到 open-telemetry-metrics.*.csv 文件中。该页面 `~/Library/Logs/JetBrains/IdeaIC2023.3/open-telemetry-metrics-plotter.html` 可以解析这些文件并绘制漂亮的时间序列图表。这些数据主要供 JetBrains 支持和开发工程师使用。 ![](/images/2024/Tabby/open-telemetry-metrics-plotter-html.png) ### [Visual Studio Code](https://tabby.tabbyml.com/docs/extensions/installation/vscode) - [Tabby VSCode Extension](https://marketplace.visualstudio.com/items?itemName=TabbyML.vscode-tabby) #### Tabby Extension 安装 ![](/images/2024/Tabby/VSCode-Tabby-Install.png) #### Tabby 配置 单击状态栏中的 `Tabby` 图标，打开 `Tabby` 配置页面。 ![](/images/2024/Tabby/VSCode-Tabby-Settings.png) 我在使用的过程中发现，我把 `Tabby` Disable 之后，之前生成的代码还可以继续使用，只是不能再生成新的代码了，好像在本地增加了 `RAG` 的功能。 ## 配置 ### [Tabby Agent 配置 - IDE 插件](https://tabby.tabbyml.com/docs/extensions/configurations) Tabby Agent 作为 Tabby IDE 扩展的核心组件，从 `~/.tabby-client/agent/config.toml` 文件中读取配置。当你首次运行 Tabby IDE 扩展时，这个文件会自动创建。你可以编辑这个文件来修改配置。当检测到变化时，Tabby IDE 扩展会自动重新加载配置文件。 ~/.tabby-client/agent/config.toml ```toml ## Tabby agent configuration file ## Online documentation: https://tabby.tabbyml.com/docs/extensions/configurations ## You can uncomment and edit the values below to change the default settings. ## Configurations in this file have lower priority than the IDE settings. ## Server ## You can set the server endpoint here and an optional authentication token if required. # [server] # endpoint = "http://localhost:8080" # http or https URL # token = "your-token-here" # if token is set, request header Authorization = "Bearer $token" will be added automatically ## You can add custom request headers. # [server.requestHeaders] # Header1 = "Value1" # list your custom headers here # Header2 = "Value2" # values can be strings, numbers or booleans ## Completion ## (Since 1.1.0) You can set the completion request timeout here. ## Note that there is also a timeout config at the server side. # [completion] # timeout = 4000 # 4s ## Logs ## You can set the log level here. The log file is located at ~/.tabby-client/agent/logs/. # [logs] # level = "silent" # "silent" or "error" or "debug" ## Anonymous usage tracking ## Tabby collects anonymous usage data and sends it to the Tabby team to help improve our products. ## Your code, generated completions, or any sensitive information is never tracked or sent. ## For more details on data collection, see https://tabby.tabbyml.com/docs/extensions/configurations#usage-collection ## Your contribution is greatly appreciated. However, if you prefer not to participate, you can disable anonymous usage tracking here. # [anonymousUsageTracking] # disable = false # set to true to disable ``` 默认这个文件的配置都是注释的，发现通过 UI 配置并不会更改这个文件。 - [Tabby Agent Debug Logs](https://tabby.tabbyml.com/docs/extensions/troubleshooting/#tabby-agent-debug-logs) ### Tabby Server 配置 Tabby Server 将在 `~/.tabby/config.toml` 中查找配置文件以获取高级功能。 #### [Repository context for code completion](https://tabby.tabbyml.com/docs/configuration) - 编辑配置文件 `~/.tabby/config.toml` 设置源代码存储库 ```toml # Index three repositories' source code as additional context for code completion. [[repositories]] name = "tabby" git_url = "https://github.com/TabbyML/tabby.git" # git through ssh protocol. [[repositories]] name = "CTranslate2" git_url = "git@github.com:OpenNMT/CTranslate2.git" # local directory is also supported! [[repositories]] name = "repository_a" git_url = "file:///home/users/repository_a" ``` - 索引源代码存储库 默认情况下，`tabby scheduler` 程序在守护进程中运行，并每 5 小时处理一次其管道。要立即运行管道，请使用 `tabby Scheduler --now`。 ```shell tabby scheduler --now ``` ## [编程语言](https://tabby.tabbyml.com/docs/programming-languages) 在 Tabby 中，我们需要为每种语言添加配置，以最大限度地提高性能和完成质量。 目前，每种语言需要添加两个方面的支持。 - 停用词（Stop Words） 停止词决定了语言模型何时可以提前停止其解码步骤，从而导致更好的延迟并影响完成的质量。我们建议将所有顶级关键字添加为停用词的一部分。 - 存储库上下文（Repository Context） 我们将语言解析为块（chunks）并计算基于 Token 的索引以用于服务时间检索增强代码完成（Retrieval Augmented Code Completion）。在 Tabby 中，我们将这些存储库上下文定义为 [treesitter 查询](https://tree-sitter.github.io/tree-sitter/using-parsers#query-syntax)，查询结果将被索引。 有关添加上述支持的问题或拉取请求的实际示例，请查看 https://github.com/TabbyML/tabby/issues/553 作为参考。 [Tree-sitter](https://tree-sitter.github.io/tree-sitter/) 是一个解析器生成工具和增量解析库。它可以为源文件构建一个具体的语法树，并在源文件被编辑时有效地更新语法树。 ### 支持的编程语言 - Rust - Python - JavaScript - TypeScript - Golang - Ruby - Java - Kotlin - C/C++ ## 代码助手 - [GitHub Copilot](https://github.com/features/copilot) - [Tabnine](https://www.tabnine.com/) - [Tabby](https://github.com/TabbyML/tabby) - [10 Free GitHub Copilot Alternatives for VS Code 2023](https://bito.ai/blog/free-github-copilot-alternatives-for-vs-code/) - [Sourcegraph](https://github.com/sourcegraph/sourcegraph) - [Cody](https://github.com/sourcegraph/cody) - [FauxPilot](https://github.com/fauxpilot/fauxpilot) ## Tabnine 代码助手功能 ### 生成（Completion） - 自动完成代码行 - 建议基于函数声明的完整函数完成 - 根据自然语言注释生成代码块 ### 聊天（Chat） - 生成代码 - 发现并学习如何使用新的 API - 生成测试 - 从一种编程语言翻译为另一种编程语言 - 了解当前代码库支持的内容 - 使用自然语言搜索您的组织代码库 - 重构和改进现有代码或添加功能 - 编写文档字符串 ## 参考资料 - [Troubleshooting](https://tabby.tabbyml.com/docs/extensions/troubleshooting/) - [Awesome Mac 软件](https://github.com/jaywcjlove/awesome-mac/blob/master/README-zh.md)