Enabling Cody on Sourcegraph Enterprise

This guide will walk you through the steps to install and set up Cody with your Sourcegraph Enterprise instance.

Cody enhances your coding experience by providing intelligent code suggestions, context-aware completions, and advanced code analysis.

Cody Enterprise

Get in touch with our team to try Cody for Sourcegraph Enterprise.

Setting up Cody Enterprise

You can set up Cody for your Enterprise instance in one of the following ways:

• Self-hosted Sourcegraph
• Sourcegraph Cloud

Cody on self-hosted Sourcegraph Enterprise

Prerequisites

• You have Sourcegraph version 5.1.0 or above
• A Sourcegraph enterprise subscription with Cody Gateway access or an account with a third-party LLM provider

Enable Cody on your Sourcegraph instance

Cody uses one or more third-party LLM (Large Language Model) providers. Make sure you review Cody's usage and privacy notice. Code snippets are sent to a third-party language model provider when you use the Cody extension or enable embeddings.

This requires site-admin privileges. To do so,

1. First, configure your desired LLM provider either by Using Sourcegraph Cody Gateway (recommended) or Using a third-party LLM provider directly

2. Next, go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

    {
      // [...]
      "cody.enabled": true
    }

1. Finally, set up a policy to automatically create and configure embeddings for your repositories.

Cody is now fully enabled on your self-hosted Sourcegraph enterprise instance!

Cody on Sourcegraph Cloud

• With Sourcegraph Cloud, you get Cody as a managed service, and you do not need to enable Cody as is required for self-hosted setup
• However, by contacting your account manager, Cody can still be enabled on-demand on your Sourcegraph instance. The Sourcegraph team will refer to the handbook
• Next, you can configure the VS Code extension by following the same steps as mentioned for the self-hosted environment
• After which, you are all set to use Cody with Sourcegraph Cloud

Learn more about running Cody on Sourcegraph Cloud.

Enabling codebase-aware answers

The Cody: Codebase setting in VS Code enables codebase-aware answers for the Cody extension.

• Open your VS Code workspace settings via Cmd/Ctrl+,, (or File > Preferences (Settings) on Windows & Linux)
• Search for the Cody: Codebase setting
• Enter the repository name as listed in your Sourcegraph instance, for example, github.com/sourcegraph/sourcegraph without the https protocol

By setting this configuration to the repository name, Cody can provide more accurate and relevant answers to your coding questions based on the context of your current codebase.

Disable Cody

To turn Cody off:

• Go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

    {
      // [...]
      "cody.enabled": false
    }

• Next, remove completions and embeddings configuration if they exist

Enable Cody only for some users

To enable Cody only for some users, for example, when rolling out a Cody POC, follow all the steps mentioned in Enabling Cody on your Sourcegraph instance. Then, do the following:

Sourcegraph 5.3+

In Sourcegraph 5.3+, access to Cody is managed via user roles. By default, all users have access.

First, ensure Cody is enabled in your site configuration. Go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

    {
      // [...]
      "cody.enabled": true,
      // Make sure cody.restrictUsersFeatureFlag is not in your configuration! If it is, remove it.
    }

Next, go to Site admin > Users & Auth > Roles (/site-admin/roles) on your instance. On that page, you can:

• Control whether users by default have access to Cody (expand User [System] and toggle Cody > Access as desired)
• Control whether groups of users have access to Cody (+Create role and enable the Cody > Access toggle as desired)

Sourcegraph 5.2 and earlier

In Sourcegraph 5.2 and earlier, you should use the feature flag cody to turn Cody on selectively for some users. To do so:

• Go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

    {
      // [...]
      "cody.enabled": true,
      "cody.restrictUsersFeatureFlag": true
    }

• Next, go to Site admin > Feature flags (/site-admin/feature-flags)
• Add a feature flag called cody
• Select the boolean type and set it to false
• Once added, click on the feature flag and use add overrides to pick users that will have access to Cody

Using a third-party LLM provider

Instead of Sourcegraph Cody Gateway, you can also configure Sourcegraph to use a third-party provider directly, like:

• Anthropic
• OpenAI
• Azure OpenAI
• AWS Bedrock

Anthropic

Create your own key with Anthropic here. Once you have the key, go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

{
  // [...]
  "cody.enabled": true,
  "completions": {
    "provider": "anthropic",
    "chatModel": "claude-2", // Or any other model you would like to use
    "fastChatModel": "claude-instant-1", // Or any other model you would like to use
    "completionModel": "claude-instant-1", // Or any other model you would like to use
    "accessToken": "<key>"
  }
}

OpenAI

Create your own key with OpenAI here. Once you have the key, go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

{
  // [...]
  "cody.enabled": true,
  "completions": {
    "provider": "openai",
    "chatModel": "gpt-4", // Or any other model you would like to use
    "fastChatModel": "gpt-3.5-turbo", // Or any other model you would like to use
    "completionModel": "gpt-3.5-turbo-instruct", // Or any other model that supports the legacy completions endpoint
    "accessToken": "<key>"
  }
}

Azure OpenAI

Create a project in the Azure OpenAI portal. Go to Keys and Endpoint from the project overview and get one of the keys on that page and the endpoint.

Next, under Model deployments, click "manage deployments" and ensure you deploy the models you want, for example, gpt-35-turbo. Take note of the deployment name.

Once done, go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

{
  // [...]
  "cody.enabled": true,
  "completions": {
    "provider": "azure-openai",
    "chatModel": "<deployment name of the model>",
    "fastChatModel": "<deployment name of the model>",
    "completionModel": "<deployment name of the model>",
    "endpoint": "<endpoint>",
    "accessToken": "<See below>"
  }
}

For the access token, you can either:

• As of 5.2.4 the access token can be left empty and it will rely on Environmental, Workload Identity or Managed Identity credentials configured for the frontend and worker services
• Set it to <API_KEY> if directly configuring the credentials using the API key specified in the Azure portal

Anthropic Claude through AWS Bedrock

First, make sure you can access AWS Bedrock. Then, request access to the Anthropic Claude models in Bedrock. This may take some time to provision.

Next, create an IAM user with programmatic access in your AWS account. Depending on your AWS setup, different ways may be required to provide access. All completion requests are made from the frontend service, so this service needs to be able to access AWS. You can use instance role bindings or directly configure the IAM user credentials in the configuration.

Once ready, go to Site admin > Site configuration (/site-admin/configuration) on your instance and set:

{
  // [...]
  "cody.enabled": true,
  "completions": {
    "provider": "aws-bedrock",
    "chatModel": "anthropic.claude-v2",
    "fastChatModel": "anthropic.claude-instant-v1",
    "endpoint": "<AWS-Region>", // For example: us-west-2.
    "accessToken": "<See below>"
  }
}

For the access token, you can either:

• Leave it empty and rely on instance role bindings or other AWS configurations in the frontend service
• Set it to <ACCESS_KEY_ID>:<SECRET_ACCESS_KEY> if directly configuring the credentials
• Set it to <ACCESS_KEY_ID>:<SECRET_ACCESS_KEY>:<SESSION_TOKEN> if a session token is also required

Similarly, you can also use a third-party LLM provider directly for embeddings.