473 lines
16 KiB
Python
473 lines
16 KiB
Python
"""Persistence and context reconstruction for stateful Responses calls."""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from datetime import UTC, datetime
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from typing import Any
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from db.models import ConversationMessage, ConversationSession, OpenAIResponse
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from db.session import SessionLocal
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from models import AssistantConfig
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from services.openai_realtime.events import assistant_id_from_model
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from services.openai_responses.protocol import (
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ResponseRequest,
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ResponsesAPIError,
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new_id,
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)
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from services.realtime.launcher import resolve_assistant_config
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from sqlalchemy import func, select, update
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CONTINUABLE_STATUSES = {"completed"}
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@dataclass(frozen=True)
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class PreparedResponse:
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row: OpenAIResponse
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config: AssistantConfig
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history_messages: list[dict[str, Any]]
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current_messages: list[dict[str, Any]]
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runtime_state: dict[str, Any]
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client_tool_results: list[dict[str, Any]]
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def _content_text(item: dict[str, Any]) -> str:
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return "\n".join(
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str(part.get("text") or "")
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for part in item.get("content") or []
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if isinstance(part, dict)
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).strip()
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def items_to_messages(items: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert the supported Responses item subset to OpenAI LLM messages."""
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messages: list[dict[str, Any]] = []
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pending_calls: list[dict[str, Any]] = []
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def flush_calls() -> None:
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if not pending_calls:
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return
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messages.append(
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{"role": "assistant", "content": "", "tool_calls": list(pending_calls)}
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)
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pending_calls.clear()
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for item in items:
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item_type = item.get("type")
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if item_type == "function_call":
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pending_calls.append(
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{
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"id": str(item.get("call_id") or ""),
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"type": "function",
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"function": {
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"name": str(item.get("name") or ""),
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"arguments": str(item.get("arguments") or "{}"),
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},
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}
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)
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continue
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flush_calls()
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if item_type == "message":
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messages.append(
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{
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"role": str(item.get("role") or "user"),
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"content": _content_text(item),
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}
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)
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elif item_type == "function_call_output":
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messages.append(
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{
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"role": "tool",
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"tool_call_id": str(item.get("call_id") or ""),
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"content": str(item.get("output") or ""),
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}
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)
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flush_calls()
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return messages
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async def _load_chain(
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response_id: str,
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*,
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api_key_id: str,
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) -> list[OpenAIResponse]:
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chain: list[OpenAIResponse] = []
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seen: set[str] = set()
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async with SessionLocal() as db:
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current_id: str | None = response_id
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while current_id:
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if current_id in seen or len(chain) >= 1000:
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raise ResponsesAPIError(
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"Response chain is invalid", code="invalid_response_chain"
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)
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seen.add(current_id)
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row = await db.get(OpenAIResponse, current_id)
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if row is None or row.api_key_id != api_key_id:
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raise ResponsesAPIError(
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"Response not found", code="response_not_found", status_code=404
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)
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chain.append(row)
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current_id = row.previous_response_id
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chain.reverse()
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return chain
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def _validate_tool_outputs(
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chain: list[OpenAIResponse],
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current_items: list[dict[str, Any]],
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) -> None:
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unresolved: dict[str, dict[str, Any]] = {}
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for row in chain:
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for item in row.output_items or []:
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if item.get("type") == "function_call":
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unresolved[str(item.get("call_id") or "")] = item
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for item in row.input_items or []:
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if item.get("type") == "function_call_output":
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unresolved.pop(str(item.get("call_id") or ""), None)
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supplied = [
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str(item.get("call_id") or "")
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for item in current_items
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if item.get("type") == "function_call_output"
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]
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if len(set(supplied)) != len(supplied):
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raise ResponsesAPIError(
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"A function call output can only be supplied once",
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code="duplicate_tool_output",
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param="input",
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)
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unknown = sorted(set(supplied) - set(unresolved))
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if unknown:
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raise ResponsesAPIError(
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f"Unknown or already resolved call_id: {unknown[0]}",
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code="invalid_tool_output",
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param="input",
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)
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if unresolved and set(supplied) != set(unresolved):
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missing = sorted(set(unresolved) - set(supplied))
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raise ResponsesAPIError(
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f"Missing function_call_output for call_id: {missing[0]}",
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code="missing_tool_output",
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param="input",
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)
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def _client_tool_results(
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chain: list[OpenAIResponse],
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current_items: list[dict[str, Any]],
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) -> list[dict[str, Any]]:
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calls = {
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str(item.get("call_id") or ""): item
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for row in chain
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for item in row.output_items or []
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if item.get("type") == "function_call"
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}
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results: list[dict[str, Any]] = []
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for item in current_items:
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if item.get("type") != "function_call_output":
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continue
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call = calls.get(str(item.get("call_id") or "")) or {}
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output = str(item.get("output") or "")
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try:
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value = json.loads(output)
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except json.JSONDecodeError:
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value = output
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if not isinstance(value, dict) or "status" not in value:
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value = {"status": "ok", "data": value}
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results.append(
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{
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"call_id": item.get("call_id"),
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"name": call.get("name"),
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"result": value,
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}
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)
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return results
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async def prepare_response(
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request: ResponseRequest,
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*,
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api_key_id: str,
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) -> PreparedResponse:
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chain: list[OpenAIResponse] = []
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parent: OpenAIResponse | None = None
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if request.previous_response_id:
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chain = await _load_chain(
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request.previous_response_id,
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api_key_id=api_key_id,
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)
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parent = chain[-1]
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if parent.status not in CONTINUABLE_STATUSES:
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raise ResponsesAPIError(
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f"Previous response is {parent.status}",
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code="response_not_ready",
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param="previous_response_id",
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status_code=409,
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)
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if (parent.runtime_state or {}).get("status") == "ended":
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raise ResponsesAPIError(
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"The workflow conversation has ended",
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code="conversation_ended",
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param="previous_response_id",
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status_code=409,
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)
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if not parent.assistant_id:
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raise ResponsesAPIError(
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"The assistant used by the previous response no longer exists",
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code="invalid_model",
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param="previous_response_id",
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)
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assistant_id = parent.assistant_id
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if request.model:
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requested_id = assistant_id_from_model(request.model)
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if requested_id != assistant_id:
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raise ResponsesAPIError(
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"model must match the previous response",
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code="invalid_model",
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param="model",
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)
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conversation_id = parent.conversation_id
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variables = dict(parent.dynamic_variables or {})
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variables.update(request.dynamic_variables)
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_validate_tool_outputs(chain, request.input_items)
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client_tool_results = _client_tool_results(chain, request.input_items)
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runtime_state = dict(parent.runtime_state or {})
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else:
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try:
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assistant_id = assistant_id_from_model(request.model)
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except ValueError as exc:
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raise ResponsesAPIError(
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str(exc), code="invalid_model", param="model"
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) from exc
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conversation_id = new_id("conv")[:40]
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variables = dict(request.dynamic_variables)
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client_tool_results = []
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runtime_state = {}
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if any(
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item.get("type") == "function_call_output"
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for item in request.input_items
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):
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raise ResponsesAPIError(
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"function_call_output requires previous_response_id",
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code="invalid_tool_output",
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param="input",
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)
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try:
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config = await resolve_assistant_config(
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assistant_id,
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dynamic_variables=variables,
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conversation_id=conversation_id,
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)
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except ValueError as exc:
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raise ResponsesAPIError(
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str(exc), code="invalid_model", param="model"
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) from exc
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if config.runtimeMode != "pipeline":
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raise ResponsesAPIError(
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"Responses API only supports pipeline assistants",
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code="unsupported_runtime_mode",
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param="model",
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)
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if config.type not in {"prompt", "workflow", "dify", "fastgpt"}:
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raise ResponsesAPIError(
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f"Assistant type {config.type} is not supported by Responses API",
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code="unsupported_assistant_type",
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param="model",
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)
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if config.conversation_id != conversation_id:
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config = config.model_copy(update={"conversation_id": conversation_id})
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effective_variables = {
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name: value
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for name, value in (config.dynamic_variables or {}).items()
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if not name.startswith(("system__", "secret__"))
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}
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history_items: list[dict[str, Any]] = []
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for item in chain:
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history_items.extend(item.input_items or [])
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history_items.extend(item.output_items or [])
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history_messages = (
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list(parent.context_messages or [])
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if parent is not None and parent.context_messages
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else items_to_messages(history_items)
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)
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row = OpenAIResponse(
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id=new_id("resp")[:40],
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api_key_id=api_key_id,
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assistant_id=assistant_id,
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conversation_id=conversation_id,
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previous_response_id=request.previous_response_id,
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status="in_progress",
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input_items=request.input_items,
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output_items=[],
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context_messages=[],
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runtime_state={},
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dynamic_variables=effective_variables,
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)
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async with SessionLocal() as db:
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if parent is None:
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db.add(
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ConversationSession(
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id=conversation_id,
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assistant_id=assistant_id,
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assistant_name=config.name,
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channel="responses",
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runtime_mode="pipeline",
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status="active",
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message_count=0,
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analysis_status="none",
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analysis_data=(
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{"plan": config.analysis_config}
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if config.analysis_config.get("enabled")
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else {}
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),
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extra={},
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)
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)
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else:
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conversation = await db.get(ConversationSession, conversation_id)
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if conversation:
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conversation.status = "active"
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conversation.ended_at = None
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db.add(row)
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await db.commit()
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await db.refresh(row)
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return PreparedResponse(
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row=row,
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config=config,
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history_messages=history_messages,
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current_messages=items_to_messages(request.input_items),
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runtime_state=runtime_state,
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client_tool_results=client_tool_results,
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)
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async def get_response(
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response_id: str,
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*,
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api_key_id: str,
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) -> OpenAIResponse:
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async with SessionLocal() as db:
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row = await db.get(OpenAIResponse, response_id)
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if row is None or row.api_key_id != api_key_id:
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raise ResponsesAPIError(
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"Response not found", code="response_not_found", status_code=404
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)
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return row
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async def finish_response(
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response_id: str,
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*,
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status: str,
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output_items: list[dict[str, Any]],
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context_messages: list[dict[str, Any]] | None = None,
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dynamic_variables: dict[str, Any] | None = None,
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runtime_state: dict[str, Any] | None = None,
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error: dict[str, Any] | None = None,
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) -> OpenAIResponse:
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now = datetime.now(UTC)
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async with SessionLocal() as db:
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row = await db.get(OpenAIResponse, response_id)
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if row is None:
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raise RuntimeError(f"Response disappeared while running: {response_id}")
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row.status = status
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row.output_items = output_items
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row.context_messages = context_messages or []
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if dynamic_variables is not None:
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row.dynamic_variables = dynamic_variables
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if runtime_state is not None:
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row.runtime_state = runtime_state
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row.error = error
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row.completed_at = now
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conversation = (
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await db.execute(
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select(ConversationSession)
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.where(ConversationSession.id == row.conversation_id)
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.with_for_update()
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)
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).scalar_one_or_none()
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if conversation:
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max_sequence = await db.scalar(
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select(func.max(ConversationMessage.sequence)).where(
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ConversationMessage.session_id == row.conversation_id
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)
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)
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sequence = int(max_sequence or 0)
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for item in row.input_items or []:
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if item.get("type") != "message" or item.get("role") != "user":
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continue
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text = _content_text(item)
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if not text:
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continue
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sequence += 1
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db.add(
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ConversationMessage(
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id=new_id("msg")[:40],
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session_id=row.conversation_id,
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sequence=sequence,
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role="user",
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content_type="text",
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content=text,
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extra={"response_id": row.id},
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)
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)
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for item in output_items:
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if item.get("type") == "message":
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text = _content_text(item)
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elif item.get("type") == "function_call":
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text = f"[function_call] {item.get('name') or ''}"
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else:
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continue
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if not text:
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continue
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sequence += 1
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db.add(
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ConversationMessage(
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id=new_id("msg")[:40],
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session_id=row.conversation_id,
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sequence=sequence,
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role="assistant",
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content_type="text",
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content=text,
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extra={"response_id": row.id, "item": item},
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)
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)
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conversation.message_count = sequence
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conversation.status = "completed" if status == "completed" else status
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conversation.ended_at = now
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await db.commit()
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await db.refresh(row)
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return row
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async def recover_interrupted_responses() -> None:
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"""Mark generations interrupted by a process restart as cancelled."""
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now = datetime.now(UTC)
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async with SessionLocal() as db:
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conversation_ids = select(OpenAIResponse.conversation_id).where(
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OpenAIResponse.status == "in_progress"
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)
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await db.execute(
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update(ConversationSession)
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.where(ConversationSession.id.in_(conversation_ids))
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.values(status="cancelled", ended_at=now)
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)
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await db.execute(
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update(OpenAIResponse)
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.where(OpenAIResponse.status == "in_progress")
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.values(
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status="cancelled",
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error={
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"code": "server_restarted",
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"message": "Response was interrupted by a server restart",
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},
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completed_at=now,
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)
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)
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await db.commit()
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