gabrii
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261 lines
9.8 KiB
Python
261 lines
9.8 KiB
Python
"""Request adaptation helpers for Azure Responses API.
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This module defines RequestAdapter, which transforms incoming OpenAI-style
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requests into Azure Responses API request parameters.
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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 Any, Dict, List, Optional
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from flask import Request, Response, current_app
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class RequestAdapter:
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"""Handle pre-request adaptation for the Azure Responses API.
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Transforms OpenAI Completions/Chat-style inputs into Azure Responses API
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request parameters suitable for streaming completions in this codebase.
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Returns request_kwargs for requests.request(**kwargs). If an early
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short-circuit is needed (for example, missing config), sets
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self.adapter.early_response and returns an empty dict. Also sets
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per-request state on the adapter (model).
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"""
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def __init__(self, adapter: Any) -> None:
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"""Initialize the adapter with a reference to the AzureAdapter."""
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self.adapter = adapter # AzureAdapter instance for shared config/env
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# ---- Helpers (kept local to minimize cross-module coupling) ----
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def _parse_json_body(self, req: Request, body: bytes) -> Optional[Any]:
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if not body:
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return None
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data = req.get_json(silent=True, force=False)
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if data is not None:
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return data
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try:
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return json.loads(body.decode(req.charset or "utf-8", errors="replace"))
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except json.JSONDecodeError:
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return None
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def _copy_request_headers_for_azure(
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self, src: Request, *, api_key: str
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) -> Dict[str, str]:
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headers: Dict[str, str] = {k: v for k, v in src.headers.items()}
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headers.pop("Host", None)
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# Azure prefers api-key header
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headers.pop("Authorization", None)
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headers["api-key"] = api_key
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return headers
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def _messages_to_responses_input_and_instructions(
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self, messages: List[Dict[str, Any]]
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) -> Dict[str, Any]:
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instructions_parts: List[str] = []
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input_items: List[Dict[str, Any]] = []
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def content_to_text(c: Any) -> str:
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if c is None:
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return ""
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if isinstance(c, str):
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return c
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if isinstance(c, list):
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parts: List[str] = []
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for it in c:
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if isinstance(it, dict):
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if it.get("type") in {"text", "input_text"} and "text" in it:
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parts.append(str(it.get("text", "")))
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elif "content" in it and isinstance(it["content"], str):
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parts.append(it["content"])
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else:
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parts.append(str(it))
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return "\n".join([p for p in parts if p])
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return json.dumps(c, ensure_ascii=False)
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for m in messages:
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role = m.get("role")
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c = m.get("content")
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if role in {"system", "developer"}:
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text = content_to_text(c)
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if text:
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instructions_parts.append(text)
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continue
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# For user/assistant/tools as inputs
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if role == "tool":
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item = {
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"type": "function_call_output",
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"output": content_to_text(c),
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"status": "completed",
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"call_id": m.get("tool_call_id"),
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}
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input_items.append(item)
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else:
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text = content_to_text(c)
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item = {
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"role": role or "user",
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"content": [
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{
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"type": "input_text" if role == "user" else "output_text",
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"text": text,
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},
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],
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}
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input_items.append(item)
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if tool_calls := m.get("tool_calls"):
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for tool_call in tool_calls:
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function = tool_call.get("function", {})
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item = {
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"type": "function_call",
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"name": function.get("name"),
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"arguments": function.get("arguments"),
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"call_id": tool_call.get("id"),
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}
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input_items.append(item)
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instructions = "\n\n".join(instructions_parts) if instructions_parts else None
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return {
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"input": input_items if input_items else None,
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"instructions": instructions,
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}
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def _transform_tools_for_responses(self, tools: Any) -> Any:
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if not isinstance(tools, list):
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return tools
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out: List[Dict[str, Any]] = []
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for t in tools:
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if not isinstance(t, dict):
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out.append(t)
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continue
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ttype = t.get("type")
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if ttype == "function" and isinstance(t.get("function"), dict):
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f = t["function"]
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transformed: Dict[str, Any] = {
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"type": "function",
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"name": f.get("name"),
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}
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if "description" in f:
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transformed["description"] = f["description"]
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if "parameters" in f:
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transformed["parameters"] = f["parameters"]
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transformed["strict"] = False
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out.append(transformed)
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else:
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out.append(t)
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return out
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def _transform_tool_choice(self, tool_choice: Any) -> Any:
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if tool_choice in (None, "auto", "none"):
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return tool_choice
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if isinstance(tool_choice, dict):
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t = tool_choice.get("type")
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if t == "function":
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fn = tool_choice.get("function") or {}
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name = fn.get("name")
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if name:
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return {"type": "function", "name": name}
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return tool_choice
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# ---- Main adaptation (always streaming completions-like) ----
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def adapt(self, req: Request) -> Dict[str, Any]:
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"""Build requests.request kwargs for the Azure Responses API call.
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Validates the inbound request, sets early_response on error, maps inputs
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to the Responses schema, and returns a dict suitable for
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requests.request(**kwargs).
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"""
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# Reset per-request state
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self.adapter.inbound_model = None
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self.adapter.early_response = None
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# Validate method
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if (req.method or "").upper() != "POST":
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self.adapter.early_response = Response(
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"Only POST supported for Azure backend",
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status=405,
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mimetype="text/plain",
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)
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return {}
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# Parse request body
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raw_body = req.get_data(cache=True)
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payload = self._parse_json_body(req, raw_body)
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if not isinstance(payload, dict):
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payload = {}
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# Determine target model: prefer env AZURE_MODEL/AZURE_DEPLOYMENT
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inbound_model = payload.get("model") if isinstance(payload, dict) else None
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self.adapter.inbound_model = inbound_model
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settings = current_app.config
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upstream_headers = self._copy_request_headers_for_azure(
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req, api_key=settings["AZURE_API_KEY"]
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)
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# Map Chat/Completions to Responses (always streaming)
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messages = payload.get("messages") or []
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tools_in = payload.get("tools") or []
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tool_choice_in = payload.get("tool_choice")
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top_p = payload.get("top_p")
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max_tokens = payload.get("max_tokens") or payload.get("max_output_tokens")
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prompt_cache_key = payload.get("user") or payload.get("prompt_cache_key")
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mapped = (
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self._messages_to_responses_input_and_instructions(messages)
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if isinstance(messages, list)
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else {"input": None, "instructions": None}
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)
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responses_body: Dict[str, Any] = {}
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if mapped.get("instructions"):
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responses_body["instructions"] = mapped["instructions"]
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if mapped.get("input") is not None:
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responses_body["input"] = mapped["input"]
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responses_body["model"] = settings["AZURE_DEPLOYMENT"]
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# Transform tools and tool choice
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if tools_in:
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responses_body["tools"] = self._transform_tools_for_responses(tools_in)
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mapped_tool_choice = self._transform_tool_choice(tool_choice_in)
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if mapped_tool_choice is not None:
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responses_body["tool_choice"] = mapped_tool_choice
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# Optional sampling/limits
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if top_p is not None:
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responses_body["top_p"] = top_p
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if max_tokens is not None:
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responses_body["max_output_tokens"] = max_tokens
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if prompt_cache_key is not None:
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responses_body["prompt_cache_key"] = prompt_cache_key
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# Always streaming
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responses_body["stream"] = True
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reasoning_effort = inbound_model.replace("gpt-", "").lower()
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if reasoning_effort not in {"high", "medium", "low"}:
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raise ValueError(
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"Model name must be either gpt-high, gpt-medium, or gpt-low"
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)
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responses_body["reasoning"] = {
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"effort": reasoning_effort,
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"summary": settings["AZURE_SUMMARY_LEVEL"],
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}
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responses_body["store"] = False
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responses_body["stream_options"] = {"include_obfuscation": False}
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responses_body["truncation"] = settings["AZURE_TRUNCATION"]
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request_kwargs: Dict[str, Any] = {
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"method": "POST",
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"url": settings["AZURE_RESPONSES_API_URL"],
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"headers": upstream_headers,
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"json": responses_body,
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"data": None,
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"stream": True,
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"timeout": (60, None),
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}
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return request_kwargs
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