Configuring Adapters

An adapter is what connects Neovim to an LLM. It's the interface that allows data to be sent, received and processed and there are a multitude of ways to customize them.

Changing the Default Adapter

You can change the default adapter as follows:

require("codecompanion").setup({
  strategies = {
    chat = {
      adapter = "anthropic",
    },
    inline = {
      adapter = "copilot",
    },
  },
}),

Extending an Adapter

Extend a base adapter to set options like api_key or model:

require("codecompanion").setup({
  adapters = {
    anthropic = function()
      return require("codecompanion.adapters").extend("anthropic", {
        env = {
          api_key = "MY_OTHER_ANTHROPIC_KEY",
        },
      })
    end,
  },
}),

If you do not want to store secrets in plain text, prefix commands with cmd::

require("codecompanion").setup({
  adapters = {
    openai = function()
      return require("codecompanion.adapters").extend("openai", {
        env = {
          api_key = "cmd:op read op://personal/OpenAI/credential --no-newline",
        },
      })
    end,
  },
}),

Adding a Custom Adapter

NOTE

See the Creating Adapters section to learn how to create custom adapters

Custom adapters can be added to the plugin as follows:

require("codecompanion").setup({
  adapters = {
    my_custom_adapter = function()
      return {} -- My adapter logic
    end,
  },
}),

Setting a Proxy

A proxy can be configured by utilising the adapters.opts table in the config:

require("codecompanion").setup({
  adapters = {
    opts = {
      allow_insecure = true,
      proxy = "socks5://127.0.0.1:9999",
    },
  },
}),

Changing a Model

Many adapters allow model selection via the schema.model.default property:

require("codecompanion").setup({
  adapters = {
    openai = function()
      return require("codecompanion.adapters").extend("openai", {
        schema = {
          model = {
            default = "gpt-4",
          },
        },
      })
    end,
  },
}),

Example: Azure OpenAI

Below is an example of how you can leverage the azure_openai adapter within the plugin:

require("codecompanion").setup({
  adapters = {
    azure_openai = function()
      return require("codecompanion.adapters").extend("azure_openai", {
        env = {
          api_key = "YOUR_AZURE_OPENAI_API_KEY",
          endpoint = "YOUR_AZURE_OPENAI_ENDPOINT",
        },
        schema = {
          model = {
            default = "YOUR_DEPLOYMENT_NAME",
          },
        },
      })
    end,
  },
  strategies = {
    chat = {
      adapter = "azure_openai",
    },
    inline = {
      adapter = "azure_openai",
    },
  },
}),