mirror of
https://github.com/microsoft/autogen.git
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763 lines
24 KiB
Plaintext
763 lines
24 KiB
Plaintext
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"# Anthropic Claude\n",
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"\n",
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"In this notebook, we demonstrate how a to use Anthropic Claude model for AgentChat.\n",
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"\n",
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"## Requirements\n",
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"To use Anthropic Claude with AutoGen, first you need to install the `pyautogen` and `anthropic` package.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install pyautogen anthropic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import inspect\n",
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"from typing import Any, Dict, List, Union\n",
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"\n",
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"from anthropic import Anthropic\n",
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"from anthropic.types import Completion, Message\n",
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"\n",
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"import autogen\n",
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"from autogen import AssistantAgent, UserProxyAgent\n",
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"from autogen.oai.openai_utils import OAI_PRICE1K"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create Anthropic Model Client following ModelClient Protocol\n",
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"\n",
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"We will implement our Anthropic client adhere to the `ModelClient` protocol and response structure which is defined in client.py and shown below.\n",
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"\n",
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"\n",
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"```python\n",
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"class ModelClient(Protocol):\n",
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" \"\"\"\n",
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" A client class must implement the following methods:\n",
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" - create must return a response object that implements the ModelClientResponseProtocol\n",
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" - cost must return the cost of the response\n",
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" - get_usage must return a dict with the following keys:\n",
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" - prompt_tokens\n",
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" - completion_tokens\n",
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" - total_tokens\n",
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" - cost\n",
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" - model\n",
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"\n",
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" This class is used to create a client that can be used by OpenAIWrapper.\n",
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" The response returned from create must adhere to the ModelClientResponseProtocol but can be extended however needed.\n",
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" The message_retrieval method must be implemented to return a list of str or a list of messages from the response.\n",
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" \"\"\"\n",
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"\n",
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" RESPONSE_USAGE_KEYS = [\"prompt_tokens\", \"completion_tokens\", \"total_tokens\", \"cost\", \"model\"]\n",
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"\n",
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" class ModelClientResponseProtocol(Protocol):\n",
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" class Choice(Protocol):\n",
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" class Message(Protocol):\n",
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" content: Optional[str]\n",
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"\n",
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" message: Message\n",
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"\n",
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" choices: List[Choice]\n",
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" model: str\n",
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"\n",
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" def create(self, params) -> ModelClientResponseProtocol:\n",
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" ...\n",
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"\n",
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" def message_retrieval(\n",
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" self, response: ModelClientResponseProtocol\n",
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" ) -> Union[List[str], List[ModelClient.ModelClientResponseProtocol.Choice.Message]]:\n",
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" \"\"\"\n",
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" Retrieve and return a list of strings or a list of Choice.Message from the response.\n",
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"\n",
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" NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object,\n",
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" since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used.\n",
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" \"\"\"\n",
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" ...\n",
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"\n",
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" def cost(self, response: ModelClientResponseProtocol) -> float:\n",
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" ...\n",
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"\n",
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" @staticmethod\n",
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" def get_usage(response: ModelClientResponseProtocol) -> Dict:\n",
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" \"\"\"Return usage summary of the response using RESPONSE_USAGE_KEYS.\"\"\"\n",
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" ...\n",
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"```\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Implementation of AnthropicClient\n",
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"\n",
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"You can find the introduction to Claude-3-Opus model [here](https://docs.anthropic.com/claude/docs/intro-to-claude). \n",
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"\n",
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"Since anthropic provides their Python SDK with similar structure as OpenAI's, we will following the implementation from `autogen.oai.client.OpenAIClient`.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"TOOL_ENABLED = False\n",
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"\n",
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"\n",
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"class AnthropicClient:\n",
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" def __init__(self, config: Dict[str, Any]):\n",
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" self._config = config\n",
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" self.model = config[\"model\"]\n",
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" anthropic_kwargs = set(inspect.getfullargspec(Anthropic.__init__).kwonlyargs)\n",
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" filter_dict = {k: v for k, v in config.items() if k in anthropic_kwargs}\n",
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" self._client = Anthropic(**filter_dict)\n",
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"\n",
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" def message_retrieval(self, response: Message) -> Union[List[str], List]:\n",
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" \"\"\"Retrieve the messages from the response.\"\"\"\n",
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" choices = response.content\n",
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" if isinstance(response, Message):\n",
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" return [choice.text for choice in choices] # type: ignore [union-attr]\n",
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"\n",
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" # claude python SDK and API not yet support function calls\n",
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"\n",
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" def create(self, params: Dict[str, Any]) -> Completion:\n",
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" \"\"\"Create a completion for a given config using openai's client.\n",
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"\n",
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" Args:\n",
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" client: The openai client.\n",
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" params: The params for the completion.\n",
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"\n",
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" Returns:\n",
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" The completion.\n",
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" \"\"\"\n",
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" if \"messages\" in params:\n",
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" raw_contents = params[\"messages\"]\n",
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" if raw_contents[0][\"role\"] == \"system\":\n",
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" raw_contents = raw_contents[1:]\n",
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" params[\"messages\"] = raw_contents\n",
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" completions: Completion = self._client.messages # type: ignore [attr-defined]\n",
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" else:\n",
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" completions: Completion = self._client.completions\n",
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"\n",
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" # Not yet support stream\n",
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" params = params.copy()\n",
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" params[\"stream\"] = False\n",
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" params.pop(\"model_client_cls\")\n",
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" response = completions.create(**params)\n",
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"\n",
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" return response\n",
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"\n",
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" def cost(self, response: Completion) -> float:\n",
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" \"\"\"Calculate the cost of the response.\"\"\"\n",
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" total = 0.0\n",
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" tokens = {\n",
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" \"input\": response.usage.input_tokens if response.usage is not None else 0,\n",
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" \"output\": response.usage.output_tokens if response.usage is not None else 0,\n",
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" }\n",
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" price_per_million = {\n",
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" \"input\": 15,\n",
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" \"output\": 75,\n",
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" }\n",
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" for key, value in tokens.items():\n",
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" total += value * price_per_million[key] / 1_000_000\n",
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"\n",
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" return total\n",
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"\n",
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" @staticmethod\n",
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" def get_usage(response: Completion) -> Dict:\n",
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"\n",
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" return {\n",
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" \"prompt_tokens\": response.usage.input_tokens if response.usage is not None else 0,\n",
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" \"completion_tokens\": response.usage.output_tokens if response.usage is not None else 0,\n",
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" \"total_tokens\": (\n",
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" response.usage.input_tokens + response.usage.output_tokens if response.usage is not None else 0\n",
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" ),\n",
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" \"cost\": response.cost if hasattr(response, \"cost\") else 0,\n",
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" \"model\": response.model,\n",
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" }"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set the config for the Anthropic API\n",
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"\n",
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"You can add any parameters that are needed for the custom model loading in the same configuration list.\n",
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"\n",
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"It is important to add the `model_client_cls` field and set it to a string that corresponds to the class name: `\"CustomModelClient\"`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"config_list_claude = [\n",
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" {\n",
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" # Choose your model name.\n",
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" \"model\": \"claude-3-opus-20240229\",\n",
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" # You need to provide your API key here.\n",
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" \"api_key\": os.getenv(\"ANTHROPIC_API_KEY\"),\n",
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" \"base_url\": \"https://api.anthropic.com\",\n",
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" \"api_type\": \"anthropic\",\n",
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" \"model_client_cls\": \"AnthropicClient\",\n",
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" }\n",
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"]"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Construct Agents\n",
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"\n",
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"Construct a simple conversation between a User proxy and an ConversableAgent based on Claude-3 model.\n",
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"\n",
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"\n",
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"`max_tokens` argument is mandatory in the `llm_config`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[autogen.oai.client: 04-02 22:48:52] {418} INFO - Detected custom model client in config: AnthropicClient, model client can not be used until register_model_client is called.\n"
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]
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}
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],
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"source": [
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"assistant = AssistantAgent(\n",
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" \"assistant\",\n",
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" llm_config={\n",
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" \"config_list\": config_list_claude,\n",
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" \"max_tokens\": 100,\n",
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" },\n",
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")\n",
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"user_proxy = UserProxyAgent(\n",
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" \"user_proxy\",\n",
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" code_execution_config=False,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Register the custom client class to the assistant agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"assistant.register_model_client(model_client_cls=AnthropicClient)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No default IOStream has been set, defaulting to IOConsole.\n",
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"No default IOStream has been set, defaulting to IOConsole.\n",
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"No default IOStream has been set, defaulting to IOConsole.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"user_proxy (to assistant):\n",
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"\n",
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"Who are you?\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No default IOStream has been set, defaulting to IOConsole.\n",
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"No default IOStream has been set, defaulting to IOConsole.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"assistant (to user_proxy):\n",
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"\n",
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"I am an artificial intelligence called Claude. I was created by Anthropic to be an intelligent conversational assistant, but I'm not a real person.\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No default IOStream has been set, defaulting to IOConsole.\n",
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"No default IOStream has been set, defaulting to IOConsole.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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|
|
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]
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},
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|||
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|||
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|
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|||
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|
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|
|||
|
|
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