8000 models - anthropic (#12) · hrudu-dev/sdk-python@b213e34 · GitHub
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yonib05pgrayyrlatntjr
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models - anthropic (strands-agents#12)
Co-authored-by: Patrick Gray <pgrayy@amazon.com> Co-authored-by: Jason Kim <jkim@anthropic.com>
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‎pyproject.toml

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@@ -47,6 +47,9 @@ Documentation = "https://strandsagents.com"
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packages = ["src/strands"]
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[project.optional-dependencies]
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anthropic = [
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"anthropic>=0.21.0,<1.0.0",
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]
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dev = [
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"commitizen>=4.4.0,<5.0.0",
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"hatch>=1.0.0,<2.0.0",
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]
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[tool.hatch.envs.hatch-static-analysis]
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features = ["litellm", "ollama"]
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features = ["anthropic", "litellm", "ollama"]
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dependencies = [
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"mypy>=1.15.0,<2.0.0",
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"ruff>=0.11.6,<0.12.0",
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]
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[tool.hatch.envs.hatch-test]
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features = ["litellm", "ollama"]
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features = ["anthropic", "litellm", "ollama"]
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extra-dependencies = [
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"moto>=5.1.0,<6.0.0",
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"pytest>=8.0.0,<9.0.0",

‎src/strands/models/anthropic.py

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@@ -0,0 +1,357 @@
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"""Anthropic Claude model provider.
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- Docs: https://docs.anthropic.com/claude/reference/getting-started-with-the-api
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"""
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import base64
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import json
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import logging
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import mimetypes
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from typing import Any, Iterable, Optional, TypedDict, cast
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import anthropic
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from typing_extensions import Required, Unpack, override
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from ..types.content import ContentBlock, Messages
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from ..types.exceptions import ContextWindowOverflowException, ModelThrottledException
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from ..types.models import Model
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from ..types.streaming import StreamEvent
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from ..types.tools import ToolSpec
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logger = logging.getLogger(__name__)
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class AnthropicModel(Model):
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"""Anthropic model provider implementation."""
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EVENT_TYPES = {
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"message_start",
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"content_block_start",
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"content_block_delta",
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"content_block_stop",
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"message_stop",
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}
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OVERFLOW_MESSAGES = {
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"input is too long",
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"input length exceeds context window",
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"input and output tokens exceed your context limit",
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}
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class AnthropicConfig(TypedDict, total=False):
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"""Configuration options for Anthropic models.
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Attributes:
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max_tokens: Maximum number of tokens to generate.
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model_id: Calude model ID (e.g., "claude-3-7-sonnet-latest").
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For a complete list of supported models, see
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https://docs.anthropic.com/en/docs/about-claude/models/all-models.
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params: Additional model parameters (e.g., temperature).
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For a complete list of supported parameters, see https://docs.anthropic.com/en/api/messages.
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"""
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max_tokens: Required[str]
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model_id: Required[str]
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params: Optional[dict[str, Any]]
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def __init__(self, *, client_args: Optional[dict[str, Any]] = None, **model_config: Unpack[AnthropicConfig]):
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"""Initialize provider instance.
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Args:
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client_args: Arguments for the underlying Anthropic client (e.g., api_key).
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For a complete list of supported arguments, see https://docs.anthropic.com/en/api/client-sdks.
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**model_config: Configuration options for the Anthropic model.
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"""
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self.config = AnthropicModel.AnthropicConfig(**model_config)
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logger.debug("config=<%s> | initializing", self.config)
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client_args = client_args or {}
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self.client = anthropic.Anthropic(**client_args)
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@override
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def update_config(self, **model_config: Unpack[AnthropicConfig]) -> None: # type: ignore[override]
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"""Update the Anthropic model configuration with the provided arguments.
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Args:
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**model_config: Configuration overrides.
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"""
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self.config.update(model_config)
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@override
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def get_config(self) -> AnthropicConfig:
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"""Get the Anthropic model configuration.
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Returns:
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The Anthropic model configuration.
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"""
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return self.config
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def _format_request_message_content(self, content: ContentBlock) -> dict[str, Any]:
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"""Format an Anthropic content block.
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Args:
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content: Message content.
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Returns:
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Anthropic formatted content block.
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"""
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if "document" in content:
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return {
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"source": {
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"data": base64.b64encode(content["document"]["source"]["bytes"]).decode("utf-8"),
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"media_type": mimetypes.types_map.get(
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f".{content['document']['format']}", "application/octet-stream"
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),
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"type": "base64",
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},
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"title": content["document"]["name"],
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"type": "document",
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}
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if "image" in content:
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return {
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"source": {
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"data": base64.b64encode(content["image"]["source"]["bytes"]).decode("utf-8"),
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"media_type": mimetypes.types_map.get(f".{content['image']['format']}", "application/octet-stream"),
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"type": "base64",
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},
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"type": "image",
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}
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if "reasoningContent" in content:
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return {
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"signature": content["reasoningContent"]["reasoningText"]["signature"],
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"thinking": content["reasoningContent"]["reasoningText"]["text"],
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"type": "thinking",
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}
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if "text" in content:
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return {"text": content["text"], "type": "text"}
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if "toolUse" in content:
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return {
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"id": content["toolUse"]["toolUseId"],
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"input": content["toolUse"]["input"],
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"name": content["toolUse"]["name"],
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"type": "tool_use",
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}
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if "toolResult" in content:
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return {
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"content": [
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self._format_request_message_content(cast(ContentBlock, tool_result_content))
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for tool_result_content in content["toolResult"]["content"]
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],
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"is_error": content["toolResult"]["status"] == "error",
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"tool_use_id": content["toolResult"]["toolUseId"],
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"type": "tool_result",
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}
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return {"text": json.dumps(content), "type": "text"}
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def _format_request_messages(self, messages: Messages) -> list[dict[str, Any]]:
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"""Format an Anthropic messages array.
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Args:
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messages: List of message objects to be processed by the model.
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Returns:
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An Anthropic messages array.
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"""
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formatted_messages = []
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for message in messages:
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formatted_contents: list[dict[str, Any]] = []
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for content in message["content"]:
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if "cachePoint" in content:
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formatted_contents[-1]["cache_control"] = {"type": "ephemeral"}
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continue
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formatted_contents.append(self._format_request_message_content(content))
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if formatted_contents:
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formatted_messages.append({"content": formatted_contents, "role": message["role"]})
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return formatted_messages
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@override
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def format_request(
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self, messages: Messages, tool_specs: Optional[list[ToolSpec]] = None, system_prompt: Optional[str] = None
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) -> dict[str, Any]:
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"""Format an Anthropic streaming request.
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Args:
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messages: List of message objects to be processed by the model.
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tool_specs: List of tool specifications to make available to the model.
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system_prompt: System prompt to provide context to the model.
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Returns:
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An Anthropic streaming request.
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"""
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return {
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"max_tokens": self.config["max_tokens"],
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"messages": self._format_request_messages(messages),
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"model": self.config["model_id"],
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"tools": [
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{
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"name": tool_spec["name"],
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"description": tool_spec["description"],
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"input_schema": tool_spec["inputSchema"]["json"],
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}
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for tool_spec in tool_specs or []
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],
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**({"system": system_prompt} if system_prompt else {}),
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**(self.config.get("params") or {}),
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}
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@override
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def format_chunk(self, event: dict[str, Any]) -> StreamEvent:
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"""Format the Anthropic response events into standardized message chunks.
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Args:
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event: A response event from the Anthropic model.
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F438 Returns:
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The formatted chunk.
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Raises:
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RuntimeError: If chunk_type is not recognized.
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This error should never be encountered as we control chunk_type in the stream method.
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"""
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match event["type"]:
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case "message_start":
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return {"messageStart": {"role": "assistant"}}
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case "content_block_start":
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content = event["content_block"]
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if content["type"] == "tool_use":
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return {
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"contentBlockStart": {
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"contentBlockIndex": event["index"],
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"start": {
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"toolUse": {
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"name": content["name"],
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"toolUseId": content["id"],
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}
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},
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}
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}
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return {"contentBlockStart": {"contentBlockIndex": event["index"], "start": {}}}
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case "content_block_delta":
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delta = event["delta"]
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match delta["type"]:
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case "signature_delta":
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return {
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"contentBlockDelta": {
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"contentBlockIndex": event["index"],
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"delta": {
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"reasoningContent": {
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"signature": delta["signature"],
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},
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},
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},
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}
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case "thinking_delta":
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return {
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"contentBlockDelta": {
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"contentBlockIndex": event["index"],
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"delta": {
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"reasoningContent": {
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"text": delta["thinking"],
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},
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},
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},
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}
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case "input_json_delta":
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return {
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"contentBlockDelta": {
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"contentBlockIndex": event["index"],
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"delta": {
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"toolUse": {
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"input": delta["partial_json"],
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},
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},
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},
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}
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case "text_delta":
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return {
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"contentBlockDelta": {
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"contentBlockIndex": event["index"],
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"delta": {
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"text": delta["text"],
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},
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},
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}
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case _:
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raise RuntimeError(
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f"event_type=<content_block_delta>, delta_type=<{delta['type']}> | unknown type"
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)
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case "content_block_stop":
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return {"contentBlockStop": {"contentBlockIndex": event["index"]}}
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case "message_stop":
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message = event["message"]
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return {"messageStop": {"stopReason": message["stop_reason"]}}
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case "metadata":
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usage = event["usage"]
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return {
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"metadata": {
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"usage": {
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"inputTokens": usage["input_tokens"],
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"outputTokens": usage["output_tokens"],
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"totalTokens": usage["input_tokens"] + usage["output_tokens"],
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},
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"metrics": {
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"latencyMs": 0, # TODO
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},
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}
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}
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case _:
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raise RuntimeError(f"event_type=<{event['type']} | unknown type")
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@override
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def stream(self, request: dict[str, Any]) -> Iterable[dict[str, Any]]:
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"""Send the request to the Anthropic model and get the streaming response.
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Args:
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request: The formatted request to send to the Anthropic model.
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Returns:
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An iterable of response events from the Anthropic model.
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Raises:
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ContextWindowOverflowException: If the input exceeds the model's context window.
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ModelThrottledException: If the request is throttled by Anthropic.
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"""
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try:
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with self.client.messages.stream(**request) as stream:
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for event in stream:
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if event.type in AnthropicModel.EVENT_TYPES:
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yield event.dict()
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usage = event.message.usage # type: ignore
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yield {"type": "metadata", "usage": usage.dict()}
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except anthropic.RateLimitError as error:
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raise ModelThrottledException(str(error)) from error
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except anthropic.BadRequestError as error:
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if any(overflow_message in str(error).lower() for overflow_message in AnthropicModel.OVERFLOW_MESSAGES):
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raise ContextWindowOverflowException(str(error)) from error
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raise error

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