1 files changed, 74 insertions, 0 deletions
diff --git a/server/continuedev/libs/llm/anthropic.py b/server/continuedev/libs/llm/anthropic.py
new file mode 100644
index 00000000..7d0708f1
--- /dev/null
+++ b/server/continuedev/libs/llm/anthropic.py
@@ -0,0 +1,74 @@
+from typing import Any, Callable, Coroutine
+
+from anthropic import AI_PROMPT, HUMAN_PROMPT, AsyncAnthropic
+
+from .base import LLM, CompletionOptions
+from .prompts.chat import anthropic_template_messages
+
+
+class AnthropicLLM(LLM):
+    """
+    Import the `AnthropicLLM` class and set it as the default model:
+
+    ```python title="~/.continue/config.py"
+    from continuedev.libs.llm.anthropic import AnthropicLLM
+
+    config = ContinueConfig(
+        ...
+        models=Models(
+            default=AnthropicLLM(api_key="<API_KEY>", model="claude-2")
+        )
+    )
+    ```
+
+    Claude 2 is not yet publicly released. You can request early access [here](https://www.anthropic.com/earlyaccess).
+
+    """
+
+    api_key: str
+    "Anthropic API key"
+
+    model: str = "claude-2"
+
+    _async_client: AsyncAnthropic = None
+
+    template_messages: Callable = anthropic_template_messages
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    async def start(self, **kwargs):
+        await super().start(**kwargs)
+        self._async_client = AsyncAnthropic(api_key=self.api_key)
+
+        if self.model == "claude-2":
+            self.context_length = 100_000
+
+    def collect_args(self, options: CompletionOptions):
+        options.stop = None
+        args = super().collect_args(options)
+
+        if "max_tokens" in args:
+            args["max_tokens_to_sample"] = args["max_tokens"]
+            del args["max_tokens"]
+        if "frequency_penalty" in args:
+            del args["frequency_penalty"]
+        if "presence_penalty" in args:
+            del args["presence_penalty"]
+        return args
+
+    async def _stream_complete(self, prompt: str, options):
+        args = self.collect_args(options)
+        prompt = f"{HUMAN_PROMPT} {prompt} {AI_PROMPT}"
+
+        async for chunk in await self._async_client.completions.create(
+            prompt=prompt, stream=True, **args
+        ):
+            yield chunk.completion
+
+    async def _complete(self, prompt: str, options) -> Coroutine[Any, Any, str]:
+        args = self.collect_args(options)
+        prompt = f"{HUMAN_PROMPT} {prompt} {AI_PROMPT}"
+        return (
+            await self._async_client.completions.create(prompt=prompt, **args)
+        ).completion