mirror of
https://github.com/NCBM/plyngent.git
synced 2026-11-07 23:47:54 +08:00
core/agent: wire anthropic messages bridge and dispatch
This commit is contained in:
@@ -159,6 +159,13 @@ async def summarize_messages(
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stream=False,
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),
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)
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elif kind == "messages":
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from .messages_dispatch import dispatch_messages
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response = cast(
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"ChatCompletionResponse",
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await dispatch_messages(cast("Any", client), param, stream=False),
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)
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else:
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msg = f"summarization for client.kind={kind!r} is not implemented"
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raise NotImplementedError(msg)
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@@ -54,6 +54,7 @@ from .usage import resolve_round_usage, token_usage_from_api
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if TYPE_CHECKING:
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from collections.abc import AsyncIterator, Awaitable, Callable, Sequence
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from plyngent.lmproto.anthropic.client import AnthropicClient
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from plyngent.lmproto.openai.client import OpenAIClient
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from plyngent.lmproto.openai_compatible.model import AnyChatMessage, AnyToolItem
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@@ -210,8 +211,13 @@ async def _dispatch_chat_completions(
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stream=stream,
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)
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if kind == "messages":
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msg = "Anthropic /messages dispatch is not wired in the agent loop yet"
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raise NotImplementedError(msg)
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from .messages_dispatch import dispatch_messages
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return await dispatch_messages(
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cast("AnthropicClient", client),
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param,
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stream=stream,
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)
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msg = f"client kind {kind!r} is not supported by the agent loop"
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raise NotImplementedError(msg)
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@@ -0,0 +1,418 @@
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"""Convert agent chat history/tools to Anthropic Messages API shapes and back.
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Agent memory and events stay chat-completions-shaped; only the transport uses
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``POST /messages``. OpenAI / DeepSeek paths never enter this module.
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"""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING, Any, cast
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from msgspec import UNSET
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from plyngent.lmproto.anthropic.model import (
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AnthropicAssistantMessage,
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AnthropicMessageResponse,
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AnthropicMessagesParam,
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AnthropicResponseText,
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AnthropicTextContent,
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AnthropicToolChoice,
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AnthropicToolDefinition,
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AnthropicToolResultContent,
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AnthropicToolUseContent,
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AnthropicUsage,
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AnthropicUserMessage,
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)
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from plyngent.lmproto.openai_compatible.model import (
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AnyAssistantToolCall,
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AssistantChatMessage,
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AssistantFunctionTool,
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AssistantFunctionToolCall,
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ChatCompletionChoice,
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ChatCompletionChunk,
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ChatCompletionResponse,
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ChatCompletionsParam,
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ChunkChoice,
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DeltaMessage,
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DeveloperChatMessage,
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StreamFunctionDelta,
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StreamToolCallDelta,
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SystemChatMessage,
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ToolFunctionItem,
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UserChatMessage,
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)
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from plyngent.lmproto.anthropic.model import AnthropicMessage
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from plyngent.lmproto.openai_compatible.model import AnyChatMessage, AnyToolItem
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from plyngent.typedef import Unset
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def _parse_tool_arguments(arguments: str) -> dict[str, Any]:
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"""Decode tool-call argument JSON into an object; empty/invalid → ``{}``."""
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text = arguments.strip() if arguments else ""
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if not text:
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return {}
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try:
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parsed = json.loads(text)
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except json.JSONDecodeError:
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return {}
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if isinstance(parsed, dict):
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return cast("dict[str, Any]", parsed)
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return {}
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def _encode_tool_arguments(obj: dict[str, Any]) -> str:
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return json.dumps(obj, ensure_ascii=False, separators=(",", ":"))
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def _as_nonneg_int(value: object) -> int:
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if isinstance(value, bool):
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return 0
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if isinstance(value, int):
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return max(0, value)
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if isinstance(value, float):
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return max(0, int(value))
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return 0
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def tool_items_to_anthropic_tools(
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tools: Sequence[AnyToolItem] | None,
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) -> list[AnthropicToolDefinition]:
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"""Map chat ``ToolFunctionItem`` list to Anthropic tool definitions."""
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if not tools:
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return []
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result: list[AnthropicToolDefinition] = []
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for item in tools:
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if not isinstance(item, ToolFunctionItem):
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continue
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fn = item.function
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result.append(
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AnthropicToolDefinition(
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name=fn.name,
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description=fn.description if fn.description is not UNSET else UNSET,
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input_schema=fn.parameters if fn.parameters is not UNSET else UNSET,
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)
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)
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return result
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def _tool_choice_from_chat(param: ChatCompletionsParam) -> AnthropicToolChoice | Unset:
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if param.tool_choice is UNSET:
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return UNSET
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choice = param.tool_choice
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if not isinstance(choice, str):
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return UNSET
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if choice in {"none", "auto"}:
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# Anthropic has no "none"; keep auto (callers can omit tools for hard none).
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return AnthropicToolChoice(type="auto")
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if choice == "required":
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return AnthropicToolChoice(type="any")
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return UNSET
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def _assistant_to_anthropic(message: AssistantChatMessage) -> AnthropicAssistantMessage | None:
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blocks: list[AnthropicTextContent | AnthropicToolUseContent] = []
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if isinstance(message.content, str) and message.content:
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blocks.append(AnthropicTextContent(text=message.content))
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if message.tool_calls is not UNSET and message.tool_calls:
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for call in message.tool_calls:
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if not isinstance(call, AssistantFunctionToolCall):
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continue
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blocks.append(
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AnthropicToolUseContent(
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id=call.id,
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name=call.function.name,
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input=_parse_tool_arguments(call.function.arguments),
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)
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)
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if not blocks:
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return None
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if len(blocks) == 1 and isinstance(blocks[0], AnthropicTextContent):
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return AnthropicAssistantMessage(content=blocks[0].text)
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return AnthropicAssistantMessage(content=blocks)
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def chat_messages_to_anthropic( # noqa: C901 — multi-role conversion
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messages: Sequence[AnyChatMessage],
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) -> tuple[str | None, list[AnthropicMessage]]:
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"""Split leading system prompts; convert the rest to Anthropic messages.
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Mid-turn developer messages become user text with a ``[developer]`` prefix
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(Anthropic has no developer role). Consecutive tool results are merged into
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a single user message with multiple ``tool_result`` blocks, which is the
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shape Anthropic expects after an assistant ``tool_use`` turn.
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"""
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system_parts: list[str] = []
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out: list[AnthropicMessage] = []
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pending_tool_results: list[AnthropicToolResultContent] = []
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def flush_tool_results() -> None:
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nonlocal pending_tool_results
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if not pending_tool_results:
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return
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out.append(AnthropicUserMessage(content=list(pending_tool_results)))
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pending_tool_results = []
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for message in messages:
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if isinstance(message, SystemChatMessage):
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# Leading system only becomes top-level ``system``; mid-history
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# system (unusual) is treated as developer-style control text.
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if not out and not pending_tool_results:
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if message.content.strip():
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system_parts.append(message.content)
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else:
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flush_tool_results()
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if message.content.strip():
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out.append(AnthropicUserMessage(content=f"[system] {message.content}"))
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continue
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if isinstance(message, DeveloperChatMessage):
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flush_tool_results()
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if message.content.strip():
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out.append(AnthropicUserMessage(content=f"[developer] {message.content}"))
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continue
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if isinstance(message, UserChatMessage):
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flush_tool_results()
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out.append(AnthropicUserMessage(content=message.content))
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continue
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if isinstance(message, AssistantChatMessage):
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flush_tool_results()
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converted = _assistant_to_anthropic(message)
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if converted is not None:
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out.append(converted)
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continue
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# ToolChatMessage
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pending_tool_results.append(
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AnthropicToolResultContent(
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tool_use_id=message.tool_call_id,
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content=message.content,
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)
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)
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flush_tool_results()
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system = "\n\n".join(system_parts) if system_parts else None
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return system, out
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def anthropic_response_to_assistant(response: AnthropicMessageResponse) -> AssistantChatMessage:
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"""Map a completed Anthropic message to agent ``AssistantChatMessage``."""
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text_parts: list[str] = []
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tool_calls: list[AnyAssistantToolCall] = []
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for block in response.content:
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if isinstance(block, AnthropicResponseText):
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if block.text:
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text_parts.append(block.text)
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continue
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# AnthropicResponseToolUse (remaining content arm)
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tool_calls.append(
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AssistantFunctionToolCall(
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id=block.id,
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function=AssistantFunctionTool(
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name=block.name,
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arguments=_encode_tool_arguments(block.input),
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),
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)
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)
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text = "".join(text_parts)
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return AssistantChatMessage(
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content=text or None,
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tool_calls=tool_calls or UNSET,
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)
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def anthropic_stop_to_finish_reason(
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stop_reason: str | None,
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*,
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has_tool_calls: bool,
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) -> str:
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"""Map Anthropic ``stop_reason`` to a chat-style finish_reason."""
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reason = (stop_reason or "").lower() or None
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if reason == "max_tokens":
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return "length"
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if reason in {"refusal", "content_filter"}:
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return "content_filter"
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if reason == "tool_use" or has_tool_calls:
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return "tool_calls"
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# end_turn, stop_sequence, None, …
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return "stop"
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def anthropic_usage_to_dict(usage: AnthropicUsage | dict[str, Any] | None) -> dict[str, Any] | None:
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"""Normalize Anthropic usage Struct/dict to chat-compatible usage fields."""
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if usage is None:
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return None
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if isinstance(usage, AnthropicUsage):
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prompt = _as_nonneg_int(usage.input_tokens)
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completion = _as_nonneg_int(usage.output_tokens)
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else:
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raw = cast("dict[str, object]", usage)
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prompt = _as_nonneg_int(raw.get("input_tokens") or raw.get("prompt_tokens") or 0)
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completion = _as_nonneg_int(raw.get("output_tokens") or raw.get("completion_tokens") or 0)
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if prompt == 0 and completion == 0:
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return None
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return {
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"prompt_tokens": prompt,
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"completion_tokens": completion,
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"total_tokens": prompt + completion,
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"input_tokens": prompt,
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"output_tokens": completion,
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}
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def anthropic_response_to_chat_completion(
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response: AnthropicMessageResponse,
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) -> ChatCompletionResponse:
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"""Wrap an Anthropic message as a synthetic chat completion for the agent loop."""
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assistant = anthropic_response_to_assistant(response)
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has_tools = assistant.tool_calls is not UNSET and bool(assistant.tool_calls)
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finish = anthropic_stop_to_finish_reason(response.stop_reason, has_tool_calls=has_tools)
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usage = anthropic_usage_to_dict(response.usage)
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return ChatCompletionResponse(
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id=response.id,
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object="chat.completion",
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created=0,
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model=response.model or "",
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choices=[
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ChatCompletionChoice(
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index=0,
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message=assistant,
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finish_reason=cast("Any", finish),
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)
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],
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usage=cast("Any", usage) if usage is not None else UNSET,
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)
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def finish_reason_chunk(
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*,
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model: str,
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finish_reason: str,
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created: int = 0,
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) -> ChatCompletionChunk:
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return ChatCompletionChunk(
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id="anthropic-stream",
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object="chat.completion.chunk",
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created=created,
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model=model,
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choices=[
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ChunkChoice(
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index=0,
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delta=DeltaMessage(),
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finish_reason=cast("Any", finish_reason),
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)
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],
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)
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def text_delta_chunk(*, model: str, content: str, created: int = 0) -> ChatCompletionChunk:
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return ChatCompletionChunk(
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id="anthropic-stream",
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object="chat.completion.chunk",
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created=created,
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model=model,
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choices=[
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ChunkChoice(
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index=0,
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delta=DeltaMessage(content=content),
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)
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],
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)
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def tool_call_delta_chunk(
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*,
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model: str,
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index: int,
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call_id: str | None = None,
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name: str | None = None,
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arguments: str | None = None,
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created: int = 0,
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) -> ChatCompletionChunk:
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return ChatCompletionChunk(
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id="anthropic-stream",
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object="chat.completion.chunk",
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created=created,
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model=model,
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choices=[
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ChunkChoice(
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index=0,
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delta=DeltaMessage(
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tool_calls=[
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StreamToolCallDelta(
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index=index,
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id=call_id if call_id is not None else UNSET,
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type="function",
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function=StreamFunctionDelta(
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name=name if name is not None else UNSET,
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arguments=arguments if arguments is not None else UNSET,
|
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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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def usage_chunk(*, model: str, usage: dict[str, Any], created: int = 0) -> ChatCompletionChunk:
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return ChatCompletionChunk(
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id="anthropic-stream",
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object="chat.completion.chunk",
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created=created,
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model=model,
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choices=[],
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usage=usage,
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)
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def _max_tokens_from_param(param: ChatCompletionsParam) -> int:
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if param.max_completion_tokens is not UNSET:
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return int(param.max_completion_tokens)
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if param.max_tokens is not UNSET:
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return int(param.max_tokens)
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return 8192
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def chat_param_to_anthropic_param(param: ChatCompletionsParam) -> AnthropicMessagesParam:
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"""Build :class:`AnthropicMessagesParam` from a chat completions param."""
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system, messages = chat_messages_to_anthropic(param.messages)
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tools = tool_items_to_anthropic_tools(param.tools if param.tools is not UNSET else None)
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|
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kwargs: dict[str, Any] = {
|
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"model": param.model,
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"max_tokens": _max_tokens_from_param(param),
|
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"messages": messages,
|
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}
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if system:
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kwargs["system"] = system
|
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if tools:
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kwargs["tools"] = tools
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tool_choice = _tool_choice_from_chat(param)
|
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if tool_choice is not UNSET:
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kwargs["tool_choice"] = tool_choice
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if param.parallel_tool_calls is not UNSET and param.parallel_tool_calls is False:
|
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existing = kwargs.get("tool_choice")
|
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if isinstance(existing, AnthropicToolChoice):
|
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kwargs["tool_choice"] = AnthropicToolChoice(
|
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type=existing.type,
|
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name=existing.name,
|
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disable_parallel_tool_use=True,
|
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)
|
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else:
|
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kwargs["tool_choice"] = AnthropicToolChoice(
|
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type="auto",
|
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disable_parallel_tool_use=True,
|
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)
|
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if param.temperature is not UNSET:
|
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kwargs["temperature"] = param.temperature
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if param.top_p is not UNSET:
|
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kwargs["top_p"] = param.top_p
|
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|
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return AnthropicMessagesParam(**kwargs)
|
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@@ -0,0 +1,253 @@
|
||||
"""Anthropic Messages dispatch: present ``/messages`` as chat completions to the loop.
|
||||
|
||||
Agent memory and events stay chat-completions-shaped; only the transport uses
|
||||
``POST /messages``. OpenAI / DeepSeek paths never enter this module.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Any, Final
|
||||
|
||||
from plyngent.agent.messages_bridge import (
|
||||
anthropic_response_to_chat_completion,
|
||||
anthropic_stop_to_finish_reason,
|
||||
anthropic_usage_to_dict,
|
||||
chat_param_to_anthropic_param,
|
||||
finish_reason_chunk,
|
||||
text_delta_chunk,
|
||||
tool_call_delta_chunk,
|
||||
usage_chunk,
|
||||
)
|
||||
from plyngent.lmproto.anthropic.model import (
|
||||
AnthropicContentBlockDelta,
|
||||
AnthropicContentBlockStart,
|
||||
AnthropicErrorEvent,
|
||||
AnthropicMessageDelta,
|
||||
AnthropicMessageStart,
|
||||
AnthropicMessageStop,
|
||||
AnthropicRawContentBlock,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import AsyncIterator
|
||||
|
||||
from plyngent.lmproto.anthropic.client import AnthropicClient
|
||||
from plyngent.lmproto.anthropic.model import AnthropicStreamEvent
|
||||
from plyngent.lmproto.openai_compatible.model import (
|
||||
ChatCompletionChunk,
|
||||
ChatCompletionResponse,
|
||||
ChatCompletionsParam,
|
||||
)
|
||||
|
||||
|
||||
class _ToolBlockState:
|
||||
"""Tracks an in-flight tool_use content block during SSE streaming."""
|
||||
|
||||
__slots__: Final = ("call_id", "index", "name", "tool_index")
|
||||
index: int
|
||||
call_id: str
|
||||
name: str
|
||||
tool_index: int
|
||||
|
||||
def __init__(self, *, index: int, call_id: str, name: str, tool_index: int) -> None:
|
||||
self.index = index
|
||||
self.call_id = call_id
|
||||
self.name = name
|
||||
self.tool_index = tool_index
|
||||
|
||||
|
||||
def _merge_usage(previous: dict[str, Any] | None, new: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Merge message_start input tokens with message_delta output tokens."""
|
||||
if previous is None:
|
||||
return new
|
||||
prompt = int(previous.get("prompt_tokens", 0) or new.get("prompt_tokens", 0) or 0)
|
||||
completion = int(new.get("completion_tokens", 0) or previous.get("completion_tokens", 0) or 0)
|
||||
return {
|
||||
"prompt_tokens": prompt,
|
||||
"completion_tokens": completion,
|
||||
"total_tokens": prompt + completion,
|
||||
"input_tokens": prompt,
|
||||
"output_tokens": completion,
|
||||
}
|
||||
|
||||
|
||||
def _error_detail(error: dict[str, Any]) -> str:
|
||||
msg = error.get("message")
|
||||
if isinstance(msg, str) and msg.strip():
|
||||
return msg
|
||||
return str(error)
|
||||
|
||||
|
||||
class _StreamState:
|
||||
"""Mutable accumulator while converting Anthropic SSE to chat chunks."""
|
||||
|
||||
model: str
|
||||
tool_blocks: dict[int, _ToolBlockState]
|
||||
next_tool_index: int
|
||||
stop_reason: str | None
|
||||
last_usage: dict[str, Any] | None
|
||||
saw_message_stop: bool
|
||||
has_tool_calls: bool
|
||||
|
||||
def __init__(self, model: str) -> None:
|
||||
self.model = model
|
||||
self.tool_blocks = {}
|
||||
self.next_tool_index = 0
|
||||
self.stop_reason = None
|
||||
self.last_usage = None
|
||||
self.saw_message_stop = False
|
||||
self.has_tool_calls = False
|
||||
|
||||
def start_tool(self, *, block_index: int, call_id: str, name: str) -> _ToolBlockState:
|
||||
state = _ToolBlockState(
|
||||
index=block_index,
|
||||
call_id=call_id,
|
||||
name=name,
|
||||
tool_index=self.next_tool_index,
|
||||
)
|
||||
self.tool_blocks[block_index] = state
|
||||
self.next_tool_index += 1
|
||||
self.has_tool_calls = True
|
||||
return state
|
||||
|
||||
def ensure_tool(self, block_index: int) -> _ToolBlockState:
|
||||
state = self.tool_blocks.get(block_index)
|
||||
if state is not None:
|
||||
return state
|
||||
return self.start_tool(block_index=block_index, call_id=f"toolu_{block_index}", name="")
|
||||
|
||||
|
||||
def _handle_tool_use_start(
|
||||
event: AnthropicContentBlockStart,
|
||||
state: _StreamState,
|
||||
) -> list[ChatCompletionChunk]:
|
||||
block = event.content_block
|
||||
if block.type != "tool_use":
|
||||
return []
|
||||
call_id = block.id or f"toolu_{event.index}"
|
||||
name = block.name or ""
|
||||
tool_state = state.start_tool(block_index=event.index, call_id=call_id, name=name)
|
||||
return [
|
||||
tool_call_delta_chunk(
|
||||
model=state.model,
|
||||
index=tool_state.tool_index,
|
||||
call_id=call_id,
|
||||
name=name,
|
||||
arguments="",
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
def _handle_message_delta(event: AnthropicMessageDelta, state: _StreamState) -> None:
|
||||
raw_stop = event.delta.get("stop_reason")
|
||||
if isinstance(raw_stop, str) and raw_stop:
|
||||
state.stop_reason = raw_stop
|
||||
usage = anthropic_usage_to_dict(event.usage)
|
||||
if usage is not None:
|
||||
state.last_usage = _merge_usage(state.last_usage, usage)
|
||||
|
||||
|
||||
def _chunks_for_event(event: AnthropicStreamEvent, state: _StreamState) -> list[ChatCompletionChunk]:
|
||||
"""Convert one Anthropic stream event into zero or more chat chunks."""
|
||||
if isinstance(event, AnthropicErrorEvent):
|
||||
detail = _error_detail(event.error)
|
||||
msg = f"anthropic stream error: {detail}"
|
||||
raise RuntimeError(msg) # noqa: TRY004 — stream transport failure
|
||||
|
||||
if isinstance(event, AnthropicMessageStart):
|
||||
usage = anthropic_usage_to_dict(event.message.usage)
|
||||
if usage is not None:
|
||||
state.last_usage = usage
|
||||
return []
|
||||
|
||||
if isinstance(event, AnthropicContentBlockStart):
|
||||
return _handle_tool_use_start(event, state)
|
||||
|
||||
if isinstance(event, AnthropicContentBlockDelta):
|
||||
return _chunks_for_content_delta(event, state)
|
||||
|
||||
if isinstance(event, AnthropicMessageDelta):
|
||||
_handle_message_delta(event, state)
|
||||
return []
|
||||
|
||||
if isinstance(event, AnthropicMessageStop):
|
||||
state.saw_message_stop = True
|
||||
|
||||
return []
|
||||
|
||||
|
||||
def _chunks_for_content_delta(
|
||||
event: AnthropicContentBlockDelta,
|
||||
state: _StreamState,
|
||||
) -> list[ChatCompletionChunk]:
|
||||
delta: AnthropicRawContentBlock = event.delta
|
||||
if delta.type in {"text_delta", "text"} and isinstance(delta.text, str) and delta.text:
|
||||
return [text_delta_chunk(model=state.model, content=delta.text)]
|
||||
|
||||
is_json_delta = delta.type in {"input_json_delta", "input_json"} or (
|
||||
isinstance(delta.partial_json, str) and bool(delta.partial_json)
|
||||
)
|
||||
if not is_json_delta:
|
||||
return []
|
||||
|
||||
tool_state = state.ensure_tool(event.index)
|
||||
fragment = delta.partial_json if isinstance(delta.partial_json, str) else ""
|
||||
if not fragment:
|
||||
return []
|
||||
return [
|
||||
tool_call_delta_chunk(
|
||||
model=state.model,
|
||||
index=tool_state.tool_index,
|
||||
arguments=fragment,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
async def _stream_as_chat_chunks(
|
||||
client: AnthropicClient,
|
||||
param: ChatCompletionsParam,
|
||||
) -> AsyncIterator[ChatCompletionChunk]:
|
||||
"""Yield chat-completions chunks from an Anthropic Messages SSE stream.
|
||||
|
||||
Text deltas stream as they arrive. Tool calls stream as OpenAI-style
|
||||
tool-call deltas (id/name on start, argument JSON fragments on delta).
|
||||
A finish_reason (and optional usage) is emitted on ``message_delta`` /
|
||||
``message_stop``. Errors raise ``RuntimeError`` so the agent loop can
|
||||
surface them as retryable failures.
|
||||
"""
|
||||
create = chat_param_to_anthropic_param(param)
|
||||
stream = await client.messages(create, stream=True)
|
||||
state = _StreamState(param.model)
|
||||
|
||||
async for event in stream:
|
||||
for chunk in _chunks_for_event(event, state):
|
||||
yield chunk
|
||||
if state.saw_message_stop:
|
||||
break
|
||||
|
||||
if not state.saw_message_stop and state.stop_reason is None and not state.has_tool_calls:
|
||||
# Stream ended without a terminal — yield nothing more so the loop can
|
||||
# treat empty + no finish_reason as a missing terminal glitch.
|
||||
return
|
||||
|
||||
finish = anthropic_stop_to_finish_reason(state.stop_reason, has_tool_calls=state.has_tool_calls)
|
||||
yield finish_reason_chunk(model=state.model, finish_reason=finish)
|
||||
if state.last_usage is not None:
|
||||
yield usage_chunk(model=state.model, usage=state.last_usage)
|
||||
|
||||
|
||||
async def dispatch_messages(
|
||||
client: AnthropicClient,
|
||||
param: ChatCompletionsParam,
|
||||
*,
|
||||
stream: bool = False,
|
||||
) -> ChatCompletionResponse | AsyncIterator[ChatCompletionChunk]:
|
||||
"""Run one Anthropic Messages turn and return a chat-completions-shaped result."""
|
||||
create = chat_param_to_anthropic_param(param)
|
||||
if stream:
|
||||
return _stream_as_chat_chunks(client, param)
|
||||
response = await client.messages(create, stream=False)
|
||||
return anthropic_response_to_chat_completion(response)
|
||||
|
||||
|
||||
__all__ = ["dispatch_messages"]
|
||||
@@ -147,7 +147,7 @@ class ModelConfig(Struct, omit_defaults=True):
|
||||
``preset`` / ``url`` override the parent provider for this model only.
|
||||
``preset`` keeps the same meanings as provider presets:
|
||||
``openai`` → /responses, ``openai-compatible`` → /chat/completions,
|
||||
``anthropic`` → /messages (native support planned), ``deepseek`` → DeepSeek.
|
||||
``anthropic`` → /messages, ``deepseek`` → DeepSeek.
|
||||
"""
|
||||
|
||||
text: bool = True
|
||||
|
||||
@@ -0,0 +1,200 @@
|
||||
"""Tests for Anthropic Messages chat ↔ native conversion."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from msgspec import UNSET
|
||||
|
||||
from plyngent.agent.messages_bridge import (
|
||||
anthropic_response_to_assistant,
|
||||
anthropic_response_to_chat_completion,
|
||||
anthropic_stop_to_finish_reason,
|
||||
anthropic_usage_to_dict,
|
||||
chat_messages_to_anthropic,
|
||||
chat_param_to_anthropic_param,
|
||||
tool_items_to_anthropic_tools,
|
||||
)
|
||||
from plyngent.lmproto.anthropic.model import (
|
||||
AnthropicAssistantMessage,
|
||||
AnthropicMessageResponse,
|
||||
AnthropicResponseText,
|
||||
AnthropicResponseToolUse,
|
||||
AnthropicToolResultContent,
|
||||
AnthropicToolUseContent,
|
||||
AnthropicUsage,
|
||||
AnthropicUserMessage,
|
||||
)
|
||||
from plyngent.lmproto.openai_compatible.model import (
|
||||
AssistantChatMessage,
|
||||
AssistantFunctionTool,
|
||||
AssistantFunctionToolCall,
|
||||
ChatCompletionsParam,
|
||||
DeveloperChatMessage,
|
||||
SystemChatMessage,
|
||||
ToolChatMessage,
|
||||
ToolFunction,
|
||||
ToolFunctionItem,
|
||||
UserChatMessage,
|
||||
)
|
||||
|
||||
|
||||
def test_tool_items_to_anthropic_tools() -> None:
|
||||
items = [
|
||||
ToolFunctionItem(
|
||||
function=ToolFunction(
|
||||
name="read_file",
|
||||
description="Read a file",
|
||||
parameters={"type": "object", "properties": {"path": {"type": "string"}}},
|
||||
)
|
||||
)
|
||||
]
|
||||
tools = tool_items_to_anthropic_tools(items)
|
||||
assert len(tools) == 1
|
||||
assert tools[0].name == "read_file"
|
||||
assert tools[0].input_schema is not UNSET
|
||||
|
||||
|
||||
def test_chat_messages_system_and_tool_round() -> None:
|
||||
messages = [
|
||||
SystemChatMessage(content="You are helpful."),
|
||||
UserChatMessage(content="hi"),
|
||||
AssistantChatMessage(
|
||||
content=None,
|
||||
tool_calls=[
|
||||
AssistantFunctionToolCall(
|
||||
id="call_1",
|
||||
function=AssistantFunctionTool(name="read_file", arguments='{"path":"a"}'),
|
||||
)
|
||||
],
|
||||
),
|
||||
ToolChatMessage(content="file body", tool_call_id="call_1"),
|
||||
]
|
||||
system, items = chat_messages_to_anthropic(messages)
|
||||
assert system == "You are helpful."
|
||||
assert len(items) == 3
|
||||
assert isinstance(items[0], AnthropicUserMessage)
|
||||
assert items[0].content == "hi"
|
||||
assert isinstance(items[1], AnthropicAssistantMessage)
|
||||
assert isinstance(items[1].content, list)
|
||||
assert any(isinstance(b, AnthropicToolUseContent) and b.id == "call_1" for b in items[1].content)
|
||||
assert isinstance(items[2], AnthropicUserMessage)
|
||||
assert isinstance(items[2].content, list)
|
||||
assert isinstance(items[2].content[0], AnthropicToolResultContent)
|
||||
assert items[2].content[0].tool_use_id == "call_1"
|
||||
assert items[2].content[0].content == "file body"
|
||||
|
||||
|
||||
def test_developer_becomes_user_prefix() -> None:
|
||||
system, items = chat_messages_to_anthropic(
|
||||
[
|
||||
SystemChatMessage(content="persona"),
|
||||
UserChatMessage(content="go"),
|
||||
DeveloperChatMessage(content="checkpoint"),
|
||||
]
|
||||
)
|
||||
assert system == "persona"
|
||||
assert len(items) == 2
|
||||
assert isinstance(items[1], AnthropicUserMessage)
|
||||
assert items[1].content == "[developer] checkpoint"
|
||||
|
||||
|
||||
def test_merge_consecutive_tool_results() -> None:
|
||||
messages = [
|
||||
UserChatMessage(content="go"),
|
||||
AssistantChatMessage(
|
||||
content=None,
|
||||
tool_calls=[
|
||||
AssistantFunctionToolCall(
|
||||
id="c1",
|
||||
function=AssistantFunctionTool(name="a", arguments="{}"),
|
||||
),
|
||||
AssistantFunctionToolCall(
|
||||
id="c2",
|
||||
function=AssistantFunctionTool(name="b", arguments="{}"),
|
||||
),
|
||||
],
|
||||
),
|
||||
ToolChatMessage(content="r1", tool_call_id="c1"),
|
||||
ToolChatMessage(content="r2", tool_call_id="c2"),
|
||||
]
|
||||
_, items = chat_messages_to_anthropic(messages)
|
||||
assert len(items) == 3 # user, assistant, one merged tool-result user
|
||||
assert isinstance(items[2], AnthropicUserMessage)
|
||||
assert isinstance(items[2].content, list)
|
||||
assert len(items[2].content) == 2
|
||||
|
||||
|
||||
def test_anthropic_response_to_assistant_with_tools() -> None:
|
||||
response = AnthropicMessageResponse(
|
||||
id="msg_1",
|
||||
model="claude-test",
|
||||
stop_reason="tool_use",
|
||||
content=[
|
||||
AnthropicResponseText(text="calling"),
|
||||
AnthropicResponseToolUse(id="tu_1", name="add", input={"a": 1, "b": 2}),
|
||||
],
|
||||
usage=AnthropicUsage(input_tokens=12, output_tokens=4),
|
||||
)
|
||||
assistant = anthropic_response_to_assistant(response)
|
||||
assert assistant.content == "calling"
|
||||
assert assistant.tool_calls is not UNSET
|
||||
assert len(assistant.tool_calls) == 1
|
||||
call = assistant.tool_calls[0]
|
||||
assert isinstance(call, AssistantFunctionToolCall)
|
||||
assert call.id == "tu_1"
|
||||
assert call.function.name == "add"
|
||||
assert '"a":1' in call.function.arguments or '"a": 1' in call.function.arguments
|
||||
|
||||
|
||||
def test_anthropic_response_to_chat_completion_usage() -> None:
|
||||
response = AnthropicMessageResponse(
|
||||
id="msg_1",
|
||||
model="claude-test",
|
||||
stop_reason="end_turn",
|
||||
content=[AnthropicResponseText(text="done")],
|
||||
usage=AnthropicUsage(input_tokens=9, output_tokens=3),
|
||||
)
|
||||
chat = anthropic_response_to_chat_completion(response)
|
||||
assert chat.choices[0].message.content == "done"
|
||||
assert chat.choices[0].finish_reason == "stop"
|
||||
assert chat.usage is not UNSET
|
||||
assert chat.usage["prompt_tokens"] == 9
|
||||
assert chat.usage["completion_tokens"] == 3
|
||||
|
||||
|
||||
def test_stop_reason_mapping() -> None:
|
||||
assert anthropic_stop_to_finish_reason("end_turn", has_tool_calls=False) == "stop"
|
||||
assert anthropic_stop_to_finish_reason("tool_use", has_tool_calls=True) == "tool_calls"
|
||||
assert anthropic_stop_to_finish_reason("max_tokens", has_tool_calls=False) == "length"
|
||||
assert anthropic_stop_to_finish_reason("refusal", has_tool_calls=False) == "content_filter"
|
||||
assert anthropic_stop_to_finish_reason(None, has_tool_calls=True) == "tool_calls"
|
||||
|
||||
|
||||
def test_usage_to_dict() -> None:
|
||||
assert anthropic_usage_to_dict(AnthropicUsage(input_tokens=1, output_tokens=2)) == {
|
||||
"prompt_tokens": 1,
|
||||
"completion_tokens": 2,
|
||||
"total_tokens": 3,
|
||||
"input_tokens": 1,
|
||||
"output_tokens": 2,
|
||||
}
|
||||
assert anthropic_usage_to_dict(AnthropicUsage()) is None
|
||||
|
||||
|
||||
def test_chat_param_to_anthropic_param() -> None:
|
||||
param = ChatCompletionsParam(
|
||||
model="claude-test",
|
||||
messages=[
|
||||
SystemChatMessage(content="Be brief."),
|
||||
UserChatMessage(content="hi"),
|
||||
],
|
||||
tools=[ToolFunctionItem(function=ToolFunction(name="f", description="d", parameters={"type": "object"}))],
|
||||
temperature=0.2,
|
||||
max_completion_tokens=1024,
|
||||
)
|
||||
body = chat_param_to_anthropic_param(param)
|
||||
assert body.model == "claude-test"
|
||||
assert body.max_tokens == 1024
|
||||
assert body.system == "Be brief."
|
||||
assert body.tools is not UNSET
|
||||
assert len(body.tools) == 1
|
||||
assert body.temperature == 0.2
|
||||
@@ -0,0 +1,300 @@
|
||||
"""Tests for kind-based Anthropic Messages dispatch in the agent loop."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Any, Literal, cast, overload
|
||||
|
||||
from msgspec import UNSET
|
||||
|
||||
from plyngent.agent import (
|
||||
AssistantMessageEvent,
|
||||
TextDeltaEvent,
|
||||
ToolCallEvent,
|
||||
ToolRegistry,
|
||||
ToolResultEvent,
|
||||
UsageEvent,
|
||||
run_chat_loop,
|
||||
tool,
|
||||
)
|
||||
from plyngent.agent.messages_dispatch import dispatch_messages
|
||||
from plyngent.lmproto.anthropic.model import (
|
||||
AnthropicContentBlockDelta,
|
||||
AnthropicContentBlockStart,
|
||||
AnthropicMessageDelta,
|
||||
AnthropicMessageResponse,
|
||||
AnthropicMessagesParam,
|
||||
AnthropicMessageStart,
|
||||
AnthropicMessageStop,
|
||||
AnthropicRawContentBlock,
|
||||
AnthropicResponseText,
|
||||
AnthropicResponseToolUse,
|
||||
AnthropicStreamEvent,
|
||||
AnthropicToolResultContent,
|
||||
AnthropicUsage,
|
||||
AnthropicUserMessage,
|
||||
)
|
||||
from plyngent.lmproto.openai_compatible.model import (
|
||||
AssistantChatMessage,
|
||||
ChatCompletionsParam,
|
||||
UserChatMessage,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import AsyncIterator, Sequence
|
||||
|
||||
|
||||
def _message_response(
|
||||
*,
|
||||
text: str = "hello",
|
||||
tool_uses: list[tuple[str, str, dict[str, Any]]] | None = None,
|
||||
stop_reason: str = "end_turn",
|
||||
usage: AnthropicUsage | None = None,
|
||||
) -> AnthropicMessageResponse:
|
||||
content: list[Any] = []
|
||||
if text:
|
||||
content.append(AnthropicResponseText(text=text))
|
||||
if tool_uses:
|
||||
for call_id, name, inp in tool_uses:
|
||||
content.append(AnthropicResponseToolUse(id=call_id, name=name, input=inp))
|
||||
stop_reason = "tool_use"
|
||||
return AnthropicMessageResponse(
|
||||
id="msg_test",
|
||||
model="claude-test",
|
||||
content=content,
|
||||
stop_reason=stop_reason,
|
||||
usage=usage or AnthropicUsage(input_tokens=10, output_tokens=5),
|
||||
)
|
||||
|
||||
|
||||
class ScriptedMessagesClient:
|
||||
"""Fake AnthropicClient that scripts ``messages()`` results."""
|
||||
|
||||
kind: str = "messages"
|
||||
calls: list[AnthropicMessagesParam]
|
||||
_non_stream: list[AnthropicMessageResponse]
|
||||
_stream_events: list[list[AnthropicStreamEvent]]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
non_stream: Sequence[AnthropicMessageResponse] | None = None,
|
||||
stream_events: Sequence[Sequence[AnthropicStreamEvent]] | None = None,
|
||||
) -> None:
|
||||
self.calls = []
|
||||
self._non_stream = list(non_stream or ())
|
||||
self._stream_events = [list(events) for events in (stream_events or ())]
|
||||
|
||||
@overload
|
||||
async def messages(
|
||||
self, param: AnthropicMessagesParam, *, stream: Literal[False] = False
|
||||
) -> AnthropicMessageResponse: ...
|
||||
|
||||
@overload
|
||||
async def messages(
|
||||
self, param: AnthropicMessagesParam, *, stream: Literal[True]
|
||||
) -> AsyncIterator[AnthropicStreamEvent]: ...
|
||||
|
||||
async def messages(
|
||||
self, param: AnthropicMessagesParam, *, stream: bool = False
|
||||
) -> AnthropicMessageResponse | AsyncIterator[AnthropicStreamEvent]:
|
||||
self.calls.append(param)
|
||||
if stream:
|
||||
if not self._stream_events:
|
||||
msg = "no more scripted stream events"
|
||||
raise RuntimeError(msg)
|
||||
events = self._stream_events.pop(0)
|
||||
|
||||
async def _gen() -> AsyncIterator[AnthropicStreamEvent]:
|
||||
for event in events:
|
||||
yield event
|
||||
|
||||
return _gen()
|
||||
if not self._non_stream:
|
||||
msg = "no more scripted messages"
|
||||
raise RuntimeError(msg)
|
||||
return self._non_stream.pop(0)
|
||||
|
||||
|
||||
async def test_dispatch_messages_non_stream_text() -> None:
|
||||
client = ScriptedMessagesClient(non_stream=[_message_response(text="hi there")])
|
||||
param = ChatCompletionsParam(
|
||||
model="claude-test",
|
||||
messages=[UserChatMessage(content="hello")],
|
||||
)
|
||||
result = await dispatch_messages(cast("Any", client), param, stream=False)
|
||||
assert result.choices[0].message.content == "hi there"
|
||||
assert result.choices[0].finish_reason == "stop"
|
||||
assert len(client.calls) == 1
|
||||
assert client.calls[0].max_tokens == 8192
|
||||
|
||||
|
||||
async def test_dispatch_messages_stream_text_and_usage() -> None:
|
||||
events: list[AnthropicStreamEvent] = [
|
||||
AnthropicMessageStart(
|
||||
message=AnthropicMessageResponse(
|
||||
id="msg_s",
|
||||
model="claude-test",
|
||||
content=[],
|
||||
usage=AnthropicUsage(input_tokens=8, output_tokens=0),
|
||||
)
|
||||
),
|
||||
AnthropicContentBlockStart(
|
||||
index=0,
|
||||
content_block=AnthropicRawContentBlock(type="text", text=""),
|
||||
),
|
||||
AnthropicContentBlockDelta(
|
||||
index=0,
|
||||
delta=AnthropicRawContentBlock(type="text_delta", text="stream"),
|
||||
),
|
||||
AnthropicContentBlockDelta(
|
||||
index=0,
|
||||
delta=AnthropicRawContentBlock(type="text_delta", text="ed"),
|
||||
),
|
||||
AnthropicMessageDelta(
|
||||
delta={"stop_reason": "end_turn"},
|
||||
usage=AnthropicUsage(input_tokens=0, output_tokens=4),
|
||||
),
|
||||
AnthropicMessageStop(),
|
||||
]
|
||||
client = ScriptedMessagesClient(stream_events=[events])
|
||||
param = ChatCompletionsParam(
|
||||
model="claude-test",
|
||||
messages=[UserChatMessage(content="hi")],
|
||||
)
|
||||
stream = await dispatch_messages(cast("Any", client), param, stream=True)
|
||||
chunks = [chunk async for chunk in cast("Any", stream)]
|
||||
texts = [c.choices[0].delta.content for c in chunks if c.choices and isinstance(c.choices[0].delta.content, str)]
|
||||
assert texts == ["stream", "ed"]
|
||||
assert any(c.choices and c.choices[0].finish_reason == "stop" for c in chunks)
|
||||
assert any(getattr(c, "usage", UNSET) not in (UNSET, None) for c in chunks)
|
||||
|
||||
|
||||
async def test_run_chat_loop_messages_kind_text() -> None:
|
||||
client = ScriptedMessagesClient(non_stream=[_message_response(text="done")])
|
||||
messages: list[Any] = [UserChatMessage(content="go")]
|
||||
events = [
|
||||
event
|
||||
async for event in run_chat_loop(
|
||||
cast("Any", client),
|
||||
messages,
|
||||
model="claude-test",
|
||||
stream=False,
|
||||
)
|
||||
]
|
||||
assert any(isinstance(e, TextDeltaEvent) and e.content == "done" for e in events)
|
||||
assert any(isinstance(e, AssistantMessageEvent) for e in events)
|
||||
assert any(isinstance(e, UsageEvent) and e.usage.prompt_tokens == 10 for e in events)
|
||||
assert isinstance(messages[-1], AssistantChatMessage)
|
||||
assert messages[-1].content == "done"
|
||||
|
||||
|
||||
async def test_run_chat_loop_messages_kind_tool_round() -> None:
|
||||
first = _message_response(
|
||||
text="",
|
||||
tool_uses=[("call_1", "add", {"a": 1, "b": 2})],
|
||||
)
|
||||
second = _message_response(text="sum is 3")
|
||||
client = ScriptedMessagesClient(non_stream=[first, second])
|
||||
|
||||
@tool(register=False)
|
||||
def add(a: int, b: int) -> str:
|
||||
return str(a + b)
|
||||
|
||||
registry = ToolRegistry([add])
|
||||
messages: list[Any] = [UserChatMessage(content="add 1 and 2")]
|
||||
events = [
|
||||
event
|
||||
async for event in run_chat_loop(
|
||||
cast("Any", client),
|
||||
messages,
|
||||
model="claude-test",
|
||||
tools=registry,
|
||||
stream=False,
|
||||
)
|
||||
]
|
||||
assert any(isinstance(e, ToolCallEvent) for e in events)
|
||||
assert any(isinstance(e, ToolResultEvent) and "3" in e.message.content for e in events)
|
||||
assert any(isinstance(e, AssistantMessageEvent) and e.message.content == "sum is 3" for e in events)
|
||||
assert len(client.calls) == 2
|
||||
# Second turn must include tool_result in a user message.
|
||||
second_msgs = client.calls[1].messages
|
||||
assert any(
|
||||
isinstance(m, AnthropicUserMessage)
|
||||
and isinstance(m.content, list)
|
||||
and any(isinstance(b, AnthropicToolResultContent) and b.tool_use_id == "call_1" for b in m.content)
|
||||
for m in second_msgs
|
||||
)
|
||||
|
||||
|
||||
async def test_stream_tool_call_deltas() -> None:
|
||||
events: list[AnthropicStreamEvent] = [
|
||||
AnthropicMessageStart(
|
||||
message=AnthropicMessageResponse(
|
||||
id="msg_s",
|
||||
model="claude-test",
|
||||
content=[],
|
||||
usage=AnthropicUsage(input_tokens=5, output_tokens=0),
|
||||
)
|
||||
),
|
||||
AnthropicContentBlockStart(
|
||||
index=0,
|
||||
content_block=AnthropicRawContentBlock(type="tool_use", id="tu_9", name="add"),
|
||||
),
|
||||
AnthropicContentBlockDelta(
|
||||
index=0,
|
||||
delta=AnthropicRawContentBlock(type="input_json_delta", partial_json='{"a":'),
|
||||
),
|
||||
AnthropicContentBlockDelta(
|
||||
index=0,
|
||||
delta=AnthropicRawContentBlock(type="input_json_delta", partial_json="1}"),
|
||||
),
|
||||
AnthropicMessageDelta(
|
||||
delta={"stop_reason": "tool_use"},
|
||||
usage=AnthropicUsage(input_tokens=0, output_tokens=6),
|
||||
),
|
||||
AnthropicMessageStop(),
|
||||
]
|
||||
client = ScriptedMessagesClient(stream_events=[events])
|
||||
|
||||
@tool(register=False)
|
||||
def add(a: int) -> str:
|
||||
return str(a)
|
||||
|
||||
registry = ToolRegistry([add])
|
||||
# Second streamed round after tool execution.
|
||||
client._stream_events.append(
|
||||
[
|
||||
AnthropicMessageStart(
|
||||
message=AnthropicMessageResponse(
|
||||
id="msg_2",
|
||||
model="claude-test",
|
||||
content=[],
|
||||
usage=AnthropicUsage(input_tokens=12, output_tokens=0),
|
||||
)
|
||||
),
|
||||
AnthropicContentBlockDelta(
|
||||
index=0,
|
||||
delta=AnthropicRawContentBlock(type="text_delta", text="ok"),
|
||||
),
|
||||
AnthropicMessageDelta(
|
||||
delta={"stop_reason": "end_turn"},
|
||||
usage=AnthropicUsage(input_tokens=0, output_tokens=1),
|
||||
),
|
||||
AnthropicMessageStop(),
|
||||
]
|
||||
)
|
||||
|
||||
messages: list[Any] = [UserChatMessage(content="go")]
|
||||
events_out = [
|
||||
event
|
||||
async for event in run_chat_loop(
|
||||
cast("Any", client),
|
||||
messages,
|
||||
model="claude-test",
|
||||
tools=registry,
|
||||
stream=True,
|
||||
)
|
||||
]
|
||||
assert any(isinstance(e, ToolCallEvent) for e in events_out)
|
||||
assert any(isinstance(e, ToolResultEvent) for e in events_out)
|
||||
assert any(isinstance(e, AssistantMessageEvent) and e.message.content == "ok" for e in events_out)
|
||||
Reference in New Issue
Block a user