Installing Cody in VS Code

Learn how to use Cody and its features with the VS Code editor.

The Cody extension by Sourcegraph enhances your coding experience in VS Code by providing intelligent code suggestions, context-aware autocomplete, and advanced code analysis. This guide will walk you through the steps to install and set up Cody within your VS Code environment.

Cody for VS Code

Install Cody's free extension for VS Code.

Prerequisites

• You have the latest version of VS Code installed
• You have a Free or Pro account via Sourcegraph.com or a Sourcegraph Enterprise account

Install the VS Code extension

Follow these steps to install the Cody AI extension for VS Code:

• Open VS Code editor on your local machine
• Click the Extensions icon in the Activity Bar on the side of VS Code, or use the keyboard shortcut Cmd+Shift+X (macOS) or Ctrl+Shift+X (Windows/Linux)
• Type Cody AI in the search bar and click the Install button
• After installing, you may be prompted to reload VS Code to activate the extension

Alternatively, you can also download and install the extension from the VS Code Marketplace directly.

Connect the extension to Sourcegraph

After a successful installation, the Cody icon appears in the Activity sidebar.

Cody Free or Cody Pro Users

Cody Free and Cody Pro users can sign in to their Sourcegraph.com accounts through GitHub, GitLab, or Google.

Sourcegraph Enterprise Cody Users

Sourcegraph Enterprise users should connect Cody to their Enterprise instance by clicking Sign In to Your Enterprise Instance.

You'll be prompted to choose how to sign-in, select Sign In to Sourcegraph Instances v5.1 and above.

Enter the URL of your Enterprise instance. If you are unsure, please contact your administrator.

A pop-up will ask if you want to Open the URL in a new window. Click Open to open the URL in a new window.

Sign in to your instance. If you do not yet have a login, please contact your administrator.

Create an access token from Account Settings - Access Tokens. Click + Generate new token

Name the token and click + Generate token.

Copy the token and return to VSCode.

Again, click Sign In to Your Enterprise Instance and choose Sign In to Sourcegraph Instances v5.1 and above. Enter the URL of your instance.

You should now be prompted to authorize Sourcegraph to connect to your VSCode extension using the token you created. Click Authorize.

Finally, you will be asked to allow the extension access. CLick Open.

VSCode should now display the Cody panel and you're ready to go.

Verifying the installation

Once connected, click the Cody icon from the sidebar again. The Cody extension will open in a configurable side panel.

Let's create an autocomplete suggestion to verify that the Cody extension has been successfully installed and is working as expected.

Cody provides intelligent code suggestions and context-aware autocompletions for numerous programming languages like JavaScript, Python, TypeScript, Go, etc.

• Create a new file in VS Code, for example, code.js
• Next, type the following algorithm function to sort an array of numbers

function bubbleSort(array){
 
}

• As you start typing, Cody will automatically provide suggestions and context-aware completions based on your coding patterns and the code context

• These autocomplete suggestions appear as grayed text. To accept the suggestion, press the Tab key

Chat

Cody chat in VS Code is available in a tab next to your code. Once connected to Sourcegraph, a New Chat button opens the chat window on the right. You can have multiple Cody Chats going simultaneously in separate tabs.

All previous and existing chats are stored under the chats panel on the left. You can download these to share or use later in a .json file, or delete them altogether.

Chat history

There is a Chat History button at the top of your chat tabs, so you can navigate between chats (and search chats) without opening the Cody sidebar.

Chat interface

The chat interface is designed intuitively, which makes it easier to edit chat messages or start a new chat quickly. You can hit Enter to edit your message immediately without waiting for the stream to finish. This allows for quicker experimentation and iteration and less dependence on sending follow-up messages to clarify your question (which can also fill up your chat's context window).

Selecting Context with @-mentions

Cody's chat allows you to add files and symbols as context in your messages.

• Type @-file and then a filename to include a file as context
• Type @# and then a symbol name to include the symbol's definition as context. Functions, methods, classes, types, etc., are all symbols

The @-file also supports line numbers to query the context of large files. You can add ranges of large files to your context by @-mentioning a large file and appending a number range to the filename, for example, @filepath/filename:1-10.

When you @-mention files to add to Cody’s context window, the file lookup takes files.exclude, search.exclude, and .gitgnore files into account. This makes the file search faster as a result up to 100ms.

Moreover, when you @-mention files, Cody will track the number of characters in those files against the context window limit of the selected chat model. As you @-mention multiple files, Cody will calculate how many tokens of the context window remain. When the remaining context window size becomes too small, you get File too large errors for further more @-mention files.

Context fetching mechanism

VS Code users on the Free or Pro plan can leverage local context (local keyword search) as the primary context source for Cody chat with access to local embeddings. Remote embeddings will no longer be produced or used as a context source.

Enterprise users can leverage the full power of the Sourcegraph search engine as the primary context provider to Cody.

Context scope

VS Code users on the Free or Pro plan get single-repo support and can use one repo for context fetching. Enterprise users get multi-repo support and can explicitly specify up to 10 additional repos they would like Cody to use for context.

Enhanced Context Selector

Cody's Enhanced Context enables Cody to leverage search and embeddings-based context. Community users can generate local embeddings for their projects by clicking the icon next to the chat input. Users can also disable Enhanced Context or configure more granular control of Cody’s context by including @-files or @#-symbols in the chat input. This feature only supports local files and paths relative to your workspace. Start typing @, and Cody will suggest files for you to include.

The following tables shows what happens when Enhanced Context Selection is enabled or disabled.

Commands

Cody offers quick, ready-to-use Commands for common actions to write, describe, fix, and smell code. These allow you to run predefined actions with smart context-fetching anywhere in the editor, like:

• Ask Cody a question
• Add code documentation
• Edit code with instructions
• Explain code
• Identify code smells
• Generate unit tests
• Custom commands

Let's understand how the /doc command generates code documentation for a function.

Custom Commands

For customization and advanced use cases, you can create Custom Commands tailored to your requirements. You can also bind keyboard shortcuts to run your custom commands quickly. To bind a keyboard shortcut, open the Keyboard Shortcuts editor and search for cody.command.custom. to see the list of your custom commands.

Cody Natural Language Search

Cody's Natural Language Search is an AI-powered code search that allows users to input a natural language search query and look for it within their project. For example, "password hashing" or "connection retries".

In the left-hand panel, type your queries in the Search field, and the search results are displayed. You can select one of the search results and verify that the correct file opens in a new tab. Natural Language Search works for all Cody users with the ability to search across your entire local codebase from within the IDE.

Updating the extension

VS Code will typically notify you when updates are available for installed extensions. Follow the prompts to update the Cody AI extension to the latest version.

Authenticating Cody with VS Code forks

Cody also works with Cursor, Gitpod, IDX, and other similar VS Code forks. To access VS Code forks like Cursor, select Sign in with URL and access token and generate an access token. Next, copy and paste into the allocated field, using https://sourcegraph.com as the URL.

Supported LLM models

Claude 3 Sonnet is the default LLM model for Cody Free users for Chat and Commands. Users on Cody Pro can choose from a list of supported LLM models for Chat, Commands, and Autocomplete. These LLMs are Claude Instant, Claude 2.0, Claude 2.1, Claude 3 (Haiku, Opus and Sonnet), ChatGPT 3.5 Turbo, ChatGPT 4 Turbo Preview, and Mixtral.

Cody Enterprise users get Claude 3 (Opus and Sonnet) as the default LLM models without extra cost. In addition, Enterprise users also get capabilities like BYOLLM (Bring Your Own LLM) key. Your site administrator determines the LLM; you cannot change it from the editor. However, when using Cody Gateway, you can configure custom models Anthropic (like Claude 2.0 and Claude Instant) and OpenAI (GPT 3.5 and GPT 4).

Supported local Ollama models with Cody

Cody Autocomplete with Ollama

To generate autocomplete suggestions with Ollama locally, follow these steps:

• Install and run Ollama
• Download one of the support local models:
  • ollama pull deepseek-coder:6.7b-base-q4_K_M for deepseek-coder
  • ollama pull codellama:7b-code for codellama
• Update Cody's VS Code settings to use the experimental-ollama autocomplete provider and configure the right model:

 
   {
     "cody.autocomplete.advanced.provider": "experimental-ollama",
     "cody.autocomplete.experimental.ollamaOptions": {
       "url": "http://localhost:11434",
       "model": "deepseek-coder:6.7b-base-q4_K_M"
     }
   }

• Confirm Cody uses Ollama by looking at the Cody output channel or the autocomplete trace view (in the command palette)

Cody Chat and Commands with Ollama

To generate chat and commands with Ollama locally, follow these steps:

• Download Ollama
• Start Ollama (make sure the Ollama logo is showing up in your menu bar)
• Select a chat model (model that includes instruct or chat, for example, gemma:7b-instruct-q4_K_M) from the Ollama Library
• Pull the chat model locally (for example, ollama pull gemma:7b-instruct-q4_K_M)
• Once the chat model is downloaded successfully, open Cody in VS Code
• Enable the cody.experimental.ollamaChat configuration
• Open a new Cody chat
• In the new chat panel, you should see the chat model you've pulled in the dropdown list
• Currently, you will need to restart VS Code to see the new models