57be5868f9* Studio: fix OpenAI- and Anthropic-compatible API spec compliance * Studio: fix API spec-compliance gaps on passthrough and streaming paths * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: carry context_length_exceeded through the OpenAI passthrough error path * Studio: count tool-schema tokens in the Anthropic server-tool stream, and small stream-handling guards * Studio: guard message_delta usage against None and normalize developer role before proxying * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: honor max_completion_tokens on the external-provider proxy path * Studio: forward llama-server cached_tokens into OpenAI prompt_tokens_details * Studio: sanitize messages in count_tokens to match the /v1/messages prompt * Studio: report max_tokens for truncated tool calls and guard null usage in metadata events * Studio: drop the request-id middleware (headers aren't declared in either spec) * Studio: include the required request_id field in Anthropic error bodies * Studio: honor max_completion_tokens on the audio (TTS / audio-input) paths * Studio: add the _effective_max_tokens helper and route all max-token sites through it * Studio: align API compatibility edge cases * Studio: clarify multi-choice chat support * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: clarify logprobs chat support * Studio: opt the local chat UI into the streaming usage chunk so the context bar and tok/s repopulate * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: forward seed to llama-server, and fix Anthropic server-tool stop_reason, tool_result id correlation, and parallel-tool execution cap * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: align OpenAI chat completion spec edge cases * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: align backend API compatibility tests * Studio: honor tool caps and internal stream usage * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: coerce nullable stream usage counts * Studio: preserve system prompts with developer messages --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: wasimysaid <wasimysdev@gmail.com>
667 lines
24 KiB
Python
667 lines
24 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
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"""
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Anthropic Messages API ↔ OpenAI format translation utilities.
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Pure functions plus stateful stream emitters; no FastAPI, no I/O.
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"""
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from __future__ import annotations
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import json
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import uuid
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from typing import Any, Optional, Union
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def openai_finish_to_anthropic_stop(finish_reason, had_tool_calls = False) -> str:
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"""Map an OpenAI finish_reason to an Anthropic stop_reason.
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'length' -> 'max_tokens' (truncation wins even mid tool call, so a cut-off
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tool call isn't mislabeled tool_use); tool_calls / had_tool_calls -> 'tool_use';
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'stop_sequence' -> 'stop_sequence'; 'stop'/None/unknown -> 'end_turn'."""
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# Truncation takes precedence: a tool call cut off at max_tokens has possibly
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# incomplete arguments, so report max_tokens rather than telling the client to
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# run the tool.
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if finish_reason == "length":
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return "max_tokens"
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if finish_reason == "tool_calls" or had_tool_calls:
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return "tool_use"
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if finish_reason == "stop_sequence":
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return "stop_sequence"
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# "stop", None, and any unknown value collapse to end_turn.
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return "end_turn"
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def anthropic_tool_use_id(upstream_id = None) -> str:
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"""Return an Anthropic-style tool_use id (prefix 'toolu_'). Reuses an
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upstream id only if it already starts with 'toolu_'; otherwise mints a fresh
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'toolu_<24 hex>'."""
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if upstream_id and isinstance(upstream_id, str) and upstream_id.startswith("toolu_"):
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return upstream_id
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return f"toolu_{uuid.uuid4().hex[:24]}"
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def _anthropic_image_block_to_openai_part(block: dict) -> Optional[dict]:
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"""Translate one Anthropic ``image`` block to an OpenAI ``image_url`` part.
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Accepts both source shapes:
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- ``{"type": "base64", "media_type": "image/jpeg", "data": "..."}``
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- ``{"type": "url", "url": "https://..."}``
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Returns ``None`` when the source is malformed so the caller can skip it.
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"""
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source = block.get("source") or {}
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stype = source.get("type")
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if stype == "base64":
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data = source.get("data")
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if not data:
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return None
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media_type = source.get("media_type") or "image/jpeg"
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return {
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"type": "image_url",
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"image_url": {"url": f"data:{media_type};base64,{data}"},
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}
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if stype == "url":
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url = source.get("url")
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if not url:
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return None
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return {"type": "image_url", "image_url": {"url": url}}
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return None
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def anthropic_messages_to_openai(
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messages: list[dict], system: Optional[Union[str, list]] = None
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) -> list[dict]:
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"""Convert Anthropic messages + system to OpenAI-format message dicts.
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User messages with ``image`` blocks are emitted as OpenAI multimodal
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content arrays (``[{type: "text", ...}, {type: "image_url", ...}]``) so
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they flow through llama-server's native vision pathway.
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"""
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result: list[dict] = []
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# System prompt
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if system:
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if isinstance(system, str):
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result.append({"role": "system", "content": system})
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elif isinstance(system, list):
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parts = []
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for block in system:
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if isinstance(block, dict) and block.get("type") == "text":
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parts.append(block["text"])
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elif isinstance(block, str):
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parts.append(block)
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if parts:
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result.append({"role": "system", "content": "\n".join(parts)})
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for msg in messages:
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role = msg["role"] if isinstance(msg, dict) else msg.role
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content = msg["content"] if isinstance(msg, dict) else msg.content
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if isinstance(content, str):
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result.append({"role": role, "content": content})
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continue
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if role == "assistant":
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# Assistant content: text + tool_use only (no images in Anthropic's model).
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text_parts: list[str] = []
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tool_calls: list[dict] = []
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for block in content:
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b = block if isinstance(block, dict) else block.model_dump()
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btype = b.get("type", "")
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if btype == "text":
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text_parts.append(b["text"])
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elif btype == "tool_use":
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tool_calls.append(
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{
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"id": b["id"],
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"type": "function",
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"function": {
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"name": b["name"],
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"arguments": json.dumps(b["input"]),
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},
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}
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)
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msg_dict: dict[str, Any] = {"role": "assistant"}
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if text_parts:
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msg_dict["content"] = "\n".join(text_parts)
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if tool_calls:
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msg_dict["tool_calls"] = tool_calls
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result.append(msg_dict)
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continue
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if role == "user":
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# Ordered parts preserve text/image interleaving; tool_result -> own "tool" messages.
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user_parts: list[dict] = []
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has_image = False
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tool_results: list[dict] = []
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for block in content:
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b = block if isinstance(block, dict) else block.model_dump()
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btype = b.get("type", "")
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if btype == "text":
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user_parts.append({"type": "text", "text": b["text"]})
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elif btype == "image":
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part = _anthropic_image_block_to_openai_part(b)
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if part is not None:
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user_parts.append(part)
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has_image = True
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elif btype == "tool_result":
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tc = b.get("content", "")
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if isinstance(tc, list):
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tc = " ".join(
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p["text"] for p in tc if isinstance(p, dict) and p.get("type") == "text"
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)
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tool_results.append(
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{
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"role": "tool",
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"tool_call_id": b["tool_use_id"],
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"content": str(tc),
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}
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)
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if has_image:
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result.append({"role": "user", "content": user_parts})
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else:
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# No images: collapse text parts to a plain string.
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text = "\n".join(p["text"] for p in user_parts)
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if text:
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result.append({"role": "user", "content": text})
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for tr in tool_results:
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result.append(tr)
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return result
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def anthropic_tools_to_openai(tools: list) -> list[dict]:
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"""Convert Anthropic client tools to OpenAI function-tool format."""
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result = []
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for t in tools:
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td = t if isinstance(t, dict) else t.model_dump()
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name = td.get("name")
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input_schema = td.get("input_schema")
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if not name or input_schema is None:
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continue
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result.append(
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{
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"type": "function",
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"function": {
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"name": name,
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"description": td.get("description", ""),
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"parameters": input_schema,
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},
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}
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)
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return result
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def anthropic_tool_choice_to_openai(tc: Any) -> Any:
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"""Translate Anthropic `tool_choice` into OpenAI `tool_choice`.
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Anthropic formats (all dict shapes with a ``type`` discriminator):
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- ``{"type": "auto"}`` → ``"auto"``
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- ``{"type": "any"}`` → ``"required"``
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- ``{"type": "none"}`` → ``"none"``
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- ``{"type": "tool", "name": "get_weather"}``
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→ ``{"type": "function", "function": {"name": "get_weather"}}``
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Returns ``None`` for ``None`` or any unrecognized shape (caller falls
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back to its own default, typically ``"auto"``).
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"""
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if tc is None:
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return None
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if not isinstance(tc, dict):
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return None
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t = tc.get("type")
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if t == "auto":
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return "auto"
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if t == "any":
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return "required"
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if t == "none":
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return "none"
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if t == "tool":
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name = tc.get("name")
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if not name:
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return None
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return {"type": "function", "function": {"name": name}}
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return None
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def build_anthropic_sse_event(event_type: str, data: dict) -> str:
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"""Format a single Anthropic SSE event."""
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return f"event: {event_type}\ndata: {json.dumps(data)}\n\n"
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def _message_delta_usage(usage: Optional[dict]) -> dict:
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"""Usage block for a message_delta event (cumulative token counts). Cache
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fields are always 0 — no prompt caching backend. ``usage`` may be None when a
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metadata event carried usage=None (e.g. only finish_reason set)."""
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usage = usage or {}
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return {
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"input_tokens": usage.get("prompt_tokens", 0),
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"cache_creation_input_tokens": 0,
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"cache_read_input_tokens": 0,
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"output_tokens": usage.get("completion_tokens", 0),
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}
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class AnthropicStreamEmitter:
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"""Converts generate_chat_completion_with_tools() events into Anthropic
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Messages SSE strings."""
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def __init__(self) -> None:
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self.block_index: int = 0
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self._text_block_open: bool = False
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self._open_tool_call_id: Optional[str] = None
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# The mapped Anthropic ``toolu_*`` id published in content_block_start,
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# reused for the paired tool_result so consumers can correlate them.
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self._open_tool_use_id: Optional[str] = None
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self._open_tool_args_sent: bool = False
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self._prev_text: str = ""
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self._usage: dict = {}
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def start(
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self,
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message_id: str,
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model: str,
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input_tokens: int = 0,
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) -> list[str]:
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"""Emit message_start and open the first text content block."""
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events = []
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events.append(
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build_anthropic_sse_event(
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"message_start",
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{
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"type": "message_start",
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"message": {
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"id": message_id,
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"type": "message",
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"role": "assistant",
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"content": [],
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"model": model,
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"stop_reason": None,
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"stop_sequence": None,
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"usage": {
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"input_tokens": input_tokens,
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"output_tokens": 0,
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"cache_creation_input_tokens": 0,
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"cache_read_input_tokens": 0,
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},
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},
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},
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)
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)
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events.extend(self._open_text_block())
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return events
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def feed(self, event: dict) -> list[str]:
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"""Process one generator event, return SSE strings."""
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etype = event.get("type", "")
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if etype == "content":
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return self._handle_content(event)
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elif etype == "tool_start":
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return self._handle_tool_start(event)
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elif etype == "tool_end":
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return self._handle_tool_end(event)
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elif etype == "metadata":
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self._usage = event.get("usage", {})
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return []
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# status events — no Anthropic equivalent
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return []
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def finish(
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self,
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stop_reason: str = "end_turn",
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stop_sequence = None,
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) -> list[str]:
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"""Close any open block and emit message_delta + message_stop."""
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events = []
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if self._text_block_open or self._open_tool_call_id is not None:
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events.append(self._close_block())
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self._open_tool_call_id = None
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self._open_tool_use_id = None
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self._open_tool_args_sent = False
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events.append(
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build_anthropic_sse_event(
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"message_delta",
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{
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"type": "message_delta",
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"delta": {
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"stop_reason": stop_reason,
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"stop_sequence": stop_sequence,
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},
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"usage": _message_delta_usage(self._usage),
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},
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)
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)
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events.append(
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build_anthropic_sse_event(
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"message_stop",
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{
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"type": "message_stop",
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},
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)
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)
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return events
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def _handle_content(self, event: dict) -> list[str]:
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cumulative = event.get("text", "")
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new_text = cumulative[len(self._prev_text) :]
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self._prev_text = cumulative
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if not new_text:
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return []
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if not self._text_block_open:
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events = self._open_text_block()
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else:
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events = []
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events.append(
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build_anthropic_sse_event(
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"content_block_delta",
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{
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"type": "content_block_delta",
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"index": self.block_index,
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"delta": {"type": "text_delta", "text": new_text},
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},
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)
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)
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return events
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def _handle_tool_start(self, event: dict) -> list[str]:
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tool_call_id = event.get("tool_call_id", "")
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args = event.get("arguments", {})
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if tool_call_id and self._open_tool_call_id == tool_call_id:
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return self._tool_arguments_delta(args)
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events = []
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if self._text_block_open:
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events.append(self._close_block())
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# Defensive: close a stale open tool_use block before starting another.
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elif self._open_tool_call_id is not None:
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events.append(self._close_block())
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self._open_tool_call_id = None
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self._open_tool_use_id = None
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self._open_tool_args_sent = False
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# Open a tool_use block.
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self.block_index += 1
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self._open_tool_call_id = tool_call_id
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self._open_tool_use_id = anthropic_tool_use_id(tool_call_id)
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self._open_tool_args_sent = False
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events.append(
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build_anthropic_sse_event(
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"content_block_start",
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{
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"type": "content_block_start",
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"index": self.block_index,
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"content_block": {
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"type": "tool_use",
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"id": self._open_tool_use_id,
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"name": event.get("tool_name", ""),
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"input": {},
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},
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},
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)
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)
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events.extend(self._tool_arguments_delta(args))
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return events
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def _tool_arguments_delta(self, args: dict) -> list[str]:
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if not args:
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return []
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if self._open_tool_args_sent:
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return []
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self._open_tool_args_sent = True
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return [
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build_anthropic_sse_event(
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"content_block_delta",
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{
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"type": "content_block_delta",
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"index": self.block_index,
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"delta": {
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"type": "input_json_delta",
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"partial_json": json.dumps(args),
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},
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},
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)
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]
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def _handle_tool_end(self, event: dict) -> list[str]:
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events = []
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# Close the tool_use block.
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if self._open_tool_call_id is not None or self._text_block_open:
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events.append(self._close_block())
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# Reuse the id published in content_block_start; fall back to mapping
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# the raw id only if no tool_start preceded this end.
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tool_use_id = self._open_tool_use_id or anthropic_tool_use_id(event.get("tool_call_id", ""))
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self._open_tool_call_id = None
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self._open_tool_use_id = None
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self._open_tool_args_sent = False
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# Emit custom tool_result event (non-standard, ignored by SDKs)
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events.append(
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build_anthropic_sse_event(
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"tool_result",
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{
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"type": "tool_result",
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"tool_use_id": tool_use_id,
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"content": event.get("result", ""),
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},
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)
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)
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# Open a new text block for the model's next response
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self.block_index += 1
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events.extend(self._open_text_block())
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# Reset text tracking for the next synthesis turn
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self._prev_text = ""
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return events
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def _open_text_block(self) -> list[str]:
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self._text_block_open = True
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return [
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build_anthropic_sse_event(
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"content_block_start",
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{
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"type": "content_block_start",
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"index": self.block_index,
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"content_block": {"type": "text", "text": ""},
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},
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)
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]
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def _close_block(self) -> str:
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self._text_block_open = False
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return build_anthropic_sse_event(
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"content_block_stop",
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{
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"type": "content_block_stop",
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"index": self.block_index,
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},
|
|
)
|
|
|
|
|
|
class AnthropicPassthroughEmitter:
|
|
"""Converts llama-server's OpenAI-format streaming chunks into Anthropic SSE.
|
|
|
|
Used for the client-side tool-use pass-through path: the client (e.g.
|
|
Claude Code) sends its own tool definitions in ``tools`` and executes
|
|
them itself. We forward them to llama-server and translate the streaming
|
|
response back to Anthropic format without executing anything.
|
|
"""
|
|
|
|
def __init__(self) -> None:
|
|
self.block_index: int = -1
|
|
self._current_block_type: Optional[str] = None # "text" | "tool_use" | None
|
|
self._tool_call_states: dict = {} # delta index -> {block_index, id, name}
|
|
self._usage: dict = {}
|
|
self._stop_reason: str = "end_turn"
|
|
self._stop_sequence: Optional[str] = None
|
|
|
|
def start(
|
|
self,
|
|
message_id: str,
|
|
model: str,
|
|
input_tokens: int = 0,
|
|
) -> list[str]:
|
|
return [
|
|
build_anthropic_sse_event(
|
|
"message_start",
|
|
{
|
|
"type": "message_start",
|
|
"message": {
|
|
"id": message_id,
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"content": [],
|
|
"model": model,
|
|
"stop_reason": None,
|
|
"stop_sequence": None,
|
|
"usage": {
|
|
"input_tokens": input_tokens,
|
|
"output_tokens": 0,
|
|
"cache_creation_input_tokens": 0,
|
|
"cache_read_input_tokens": 0,
|
|
},
|
|
},
|
|
},
|
|
)
|
|
]
|
|
|
|
def feed_chunk(self, chunk: dict) -> list[str]:
|
|
"""Process one OpenAI streaming chat.completion.chunk."""
|
|
events: list[str] = []
|
|
|
|
# usage-only chunks carry token totals
|
|
usage = chunk.get("usage")
|
|
if usage:
|
|
self._usage = usage
|
|
|
|
choices = chunk.get("choices") or []
|
|
if not choices:
|
|
return events
|
|
|
|
choice = choices[0]
|
|
delta = choice.get("delta") or {}
|
|
finish_reason = choice.get("finish_reason")
|
|
|
|
# ── Text content ──
|
|
content = delta.get("content")
|
|
if content:
|
|
if self._current_block_type != "text":
|
|
if self._current_block_type is not None:
|
|
events.append(self._close_current_block())
|
|
events.extend(self._open_text_block())
|
|
events.append(
|
|
build_anthropic_sse_event(
|
|
"content_block_delta",
|
|
{
|
|
"type": "content_block_delta",
|
|
"index": self.block_index,
|
|
"delta": {"type": "text_delta", "text": content},
|
|
},
|
|
)
|
|
)
|
|
|
|
# ── Tool calls (streaming deltas) ──
|
|
tool_calls = delta.get("tool_calls") or []
|
|
for tc in tool_calls:
|
|
tc_idx = tc.get("index", 0)
|
|
fn = tc.get("function") or {}
|
|
if tc_idx not in self._tool_call_states:
|
|
# New tool call — close prior block, open tool_use block
|
|
if self._current_block_type is not None:
|
|
events.append(self._close_current_block())
|
|
tc_id = anthropic_tool_use_id(tc.get("id", ""))
|
|
tc_name = fn.get("name", "")
|
|
self.block_index += 1
|
|
self._current_block_type = "tool_use"
|
|
self._tool_call_states[tc_idx] = {
|
|
"block_index": self.block_index,
|
|
"id": tc_id,
|
|
"name": tc_name,
|
|
}
|
|
events.append(
|
|
build_anthropic_sse_event(
|
|
"content_block_start",
|
|
{
|
|
"type": "content_block_start",
|
|
"index": self.block_index,
|
|
"content_block": {
|
|
"type": "tool_use",
|
|
"id": tc_id,
|
|
"name": tc_name,
|
|
"input": {},
|
|
},
|
|
},
|
|
)
|
|
)
|
|
|
|
args_delta = fn.get("arguments", "")
|
|
if args_delta:
|
|
events.append(
|
|
build_anthropic_sse_event(
|
|
"content_block_delta",
|
|
{
|
|
"type": "content_block_delta",
|
|
"index": self._tool_call_states[tc_idx]["block_index"],
|
|
"delta": {
|
|
"type": "input_json_delta",
|
|
"partial_json": args_delta,
|
|
},
|
|
},
|
|
)
|
|
)
|
|
|
|
# ── Finish reason ──
|
|
if finish_reason:
|
|
self._stop_reason = openai_finish_to_anthropic_stop(finish_reason)
|
|
|
|
return events
|
|
|
|
def finish(self) -> list[str]:
|
|
events: list[str] = []
|
|
if self._current_block_type is not None:
|
|
events.append(self._close_current_block())
|
|
events.append(
|
|
build_anthropic_sse_event(
|
|
"message_delta",
|
|
{
|
|
"type": "message_delta",
|
|
"delta": {
|
|
"stop_reason": self._stop_reason,
|
|
"stop_sequence": self._stop_sequence,
|
|
},
|
|
"usage": _message_delta_usage(self._usage),
|
|
},
|
|
)
|
|
)
|
|
events.append(
|
|
build_anthropic_sse_event(
|
|
"message_stop",
|
|
{"type": "message_stop"},
|
|
)
|
|
)
|
|
return events
|
|
|
|
def _open_text_block(self) -> list[str]:
|
|
self.block_index += 1
|
|
self._current_block_type = "text"
|
|
return [
|
|
build_anthropic_sse_event(
|
|
"content_block_start",
|
|
{
|
|
"type": "content_block_start",
|
|
"index": self.block_index,
|
|
"content_block": {"type": "text", "text": ""},
|
|
},
|
|
)
|
|
]
|
|
|
|
def _close_current_block(self) -> str:
|
|
idx = self.block_index
|
|
self._current_block_type = None
|
|
return build_anthropic_sse_event(
|
|
"content_block_stop",
|
|
{
|
|
"type": "content_block_stop",
|
|
"index": idx,
|
|
},
|
|
)
|