363 lines
17 KiB
Python
363 lines
17 KiB
Python
from __future__ import annotations
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import asyncio
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import base64
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import json
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import secrets
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from collections.abc import AsyncIterator
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from typing import Any
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import httpx
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from app.models.contracts import ChatMessage
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from app.services.engine_service import build_revision, load_engine
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from app.services.library import CdslLibrary
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from app.services.sse import event
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from app.services.storage import WorkspaceStore
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from app.settings import ProviderConfig, ProviderModel, Settings
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TOOL_SCHEMAS: list[dict[str, Any]] = [
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{
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"type": "function",
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"function": {
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"name": "search_cdsl_library",
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"description": "Search the official local CDSL library for similar geometry and feature sequences.",
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"parameters": {
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"type": "object",
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"properties": {"query": {"type": "string"}, "limit": {"type": "integer", "minimum": 1, "maximum": 8}},
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"required": ["query"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "read_cdsl_reference",
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"description": "Read one official CDSL sample by part_id. Use this before creating geometry based on a reference.",
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"parameters": {"type": "object", "properties": {"part_id": {"type": "string"}}, "required": ["part_id"]},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "read_current_cdsl",
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"description": "Read the current task's latest CDSL before making a natural-language revision.",
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"parameters": {"type": "object", "properties": {}, "additionalProperties": False},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "generate_cdsl_model",
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"description": "Validate and execute a complete parameterized CDSL model. Use only for explicit CAD generation or revision.",
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"parameters": {
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"type": "object",
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"properties": {
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"cdsl": {"type": "object", "description": "Complete cad.cdsl.llm.v1 JSON object."},
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"summary": {"type": "string"},
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"assumptions": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["cdsl", "summary"],
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},
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},
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},
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]
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def text_from_message(message: ChatMessage) -> str:
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return "\n".join(part.text or "" for part in message.parts if part.type == "text").strip()
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def messages_for_model(messages: list[ChatMessage]) -> list[dict[str, Any]]:
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result: list[dict[str, Any]] = []
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for message in messages[-20:]:
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text = text_from_message(message)
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if text:
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result.append({"role": message.role, "content": text})
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return result
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def system_prompt(settings: Settings) -> str:
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skill_path = settings.engine_root.parent.parent / "agent" / "skills" / "cad-engine" / "SKILL.md"
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skill = skill_path.read_text(encoding="utf-8") if skill_path.is_file() else ""
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readme_path = settings.engine_root / "README.md"
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engine_readme = readme_path.read_text(encoding="utf-8") if readme_path.is_file() else ""
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supported_profiles = ", ".join(sorted(load_engine(settings).SHAPE_GENERATORS))
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return f"""You are the CDSL CAD Agent for CDSL CAD Studio.
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You generate parameterized CDSL, never raw CAD source code. For new CAD requests:
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1. Search the local official CDSL library.
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2. Read at least one relevant reference when a match exists.
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3. Call generate_cdsl_model only when the request is sufficiently specified.
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4. Never claim success unless the tool returns a successful CDSL-only STEP and GLB artifact.
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For a revision, call read_current_cdsl first and preserve unrelated features.
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Do not output compiler_context, unknown_shape, complex_arc_shape, entities,
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contour_edges_mm, or contour_regions_mm. Use only self-contained named profiles
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supported by the engine. Ask a concise clarification question when essential
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dimensions or intent are missing. Ordinary explanations must not create CAD.
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Local skill:
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{skill}
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Local engine guide:
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{engine_readme}
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Supported named profile types:
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{supported_profiles}
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"""
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class AgentService:
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def __init__(self, settings: Settings, store: WorkspaceStore, library: CdslLibrary) -> None:
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self.settings = settings
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self.store = store
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self.library = library
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async def stream(
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self,
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messages: list[ChatMessage],
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conversation_id: str | None,
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selected_task_id: str | None,
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provider_id: str | None = None,
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model_id: str | None = None,
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) -> AsyncIterator[bytes]:
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latest_user = next((message for message in reversed(messages) if message.role == "user"), None)
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if latest_user is None:
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yield event("cad_error", {"stage": "request", "message": "A user message is required."})
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yield event("done", {})
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return
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user_text = text_from_message(latest_user)
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conversation = self.store.ensure_conversation(conversation_id, selected_task_id)
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self.store.append_conversation_message(conversation["conversation_id"], latest_user.model_dump(), selected_task_id)
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task_id = selected_task_id or conversation.get("current_task_id") or ""
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assistant_parts: list[dict[str, Any]] = []
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assistant_id = f"assistant_{secrets.token_hex(8)}"
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successful_result: dict[str, Any] | None = None
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error_payload: dict[str, Any] | None = None
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try:
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provider, model = self.settings.resolve_model(provider_id, model_id)
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except ValueError as error:
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provider = None
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model = None
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configuration_error = str(error)
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else:
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configuration_error = ""
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if not self.settings.llm_configured or provider is None or model is None:
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message = "Agent 尚未配置模型。请设置 CDSL_LLM_BASE_URL、CDSL_LLM_API_KEY 和 CDSL_LLM_MODEL。"
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if configuration_error:
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message = configuration_error
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error_payload = {"stage": "configuration", "message": message}
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assistant_parts.append({"type": "data-cad-error", "data": error_payload})
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yield event("cad_error", error_payload)
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self._persist_assistant(conversation["conversation_id"], assistant_id, assistant_parts, task_id)
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yield event("done", {})
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return
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try:
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attachment_message = self._attachment_message(conversation, model)
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except ValueError as error:
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error_payload = {"stage": "attachment", "message": str(error)}
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assistant_parts.append({"type": "data-cad-error", "data": error_payload})
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yield event("cad_error", error_payload)
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self._persist_assistant(conversation["conversation_id"], assistant_id, assistant_parts, task_id)
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yield event("done", {})
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return
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yield event("progress", {"step": "analyze_request", "label": "分析需求", "status": "running", "message": "正在整理当前会话和 CAD 需求。"})
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references: list[str] = []
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model_messages: list[dict[str, Any]] = [{"role": "system", "content": system_prompt(self.settings)}]
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model_messages.extend(messages_for_model(messages))
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if attachment_message:
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model_messages.append({"role": "user", "content": attachment_message})
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tools = TOOL_SCHEMAS
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try:
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for iteration in range(8):
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response = await self._complete(model_messages, tools, provider, model)
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choice = response["choices"][0]["message"]
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tool_calls = choice.get("tool_calls") or []
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content = str(choice.get("content") or "")
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if content:
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assistant_parts.append({"type": "text", "text": content})
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for chunk in self._chunks(content):
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yield event("text_delta", {"text": chunk})
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if not tool_calls:
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break
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model_messages.append(choice)
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for call in tool_calls:
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name = str(call.get("function", {}).get("name") or "")
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arguments = json.loads(call.get("function", {}).get("arguments") or "{}")
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yield event("progress", {
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"step": name,
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"label": self._tool_label(name),
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"status": "running",
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"message": "Agent 正在调用本地 CAD 工具。",
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})
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result, generated = await self._run_tool(name, arguments, task_id, user_text, references)
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if generated:
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task_id = generated["task_id"]
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model_messages.append({
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"role": "tool",
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"tool_call_id": call.get("id", ""),
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"content": json.dumps(result, ensure_ascii=False),
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})
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yield event("progress", {
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"step": name,
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"label": self._tool_label(name),
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"status": "success" if result.get("ok", True) else "error",
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"message": result.get("message") or result.get("summary") or "",
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})
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if generated:
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result_payload = {
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"taskId": generated["task_id"],
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"revisionId": generated["revision_id"],
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"cdslPath": generated["cdsl_path"],
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"stepPath": generated["step_path"],
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"glbPath": generated["glb_path"],
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"reportPath": generated["report_path"],
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"parametersPath": generated.get("parameters_path"),
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"selectorPath": generated.get("selector_path"),
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"edgesPath": generated.get("edges_path"),
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"summary": generated["summary"],
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"referenceIds": generated["reference_ids"],
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"engine": generated["engine"],
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}
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successful_result = result_payload
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assistant_parts.append({"type": "data-cad-result", "data": result_payload})
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yield event("cad_result", result_payload)
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if iteration == 7:
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error_payload = {"stage": "agent", "message": "Agent tool loop reached its safety limit."}
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assistant_parts.append({"type": "data-cad-error", "data": error_payload})
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yield event("cad_error", error_payload)
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except Exception as error:
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error_payload = {"stage": "agent", "message": str(error)}
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assistant_parts.append({"type": "data-cad-error", "data": error_payload})
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yield event("cad_error", error_payload)
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if task_id:
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self.store.ensure_conversation(conversation["conversation_id"], task_id)
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if not assistant_parts:
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assistant_parts.append({
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"type": "text",
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"text": "我暂时没有生成可执行的 CAD 结果。请补充尺寸、形状或修改目标。",
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})
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if successful_result and not any(part.get("type") == "text" for part in assistant_parts):
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assistant_parts.insert(0, {"type": "text", "text": f"已生成：{successful_result['summary']}。"})
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self._persist_assistant(conversation["conversation_id"], assistant_id, assistant_parts, task_id)
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yield event("progress", {"step": "agent_stream", "label": "调用模型和工具", "status": "success", "message": "Agent 请求已完成。"})
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yield event("done", {})
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def _persist_assistant(
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self,
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conversation_id: str,
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assistant_id: str,
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parts: list[dict[str, Any]],
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task_id: str,
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) -> None:
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self.store.append_conversation_message(
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conversation_id,
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{
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"id": assistant_id or f"assistant_{conversation_id}_{len(parts)}",
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"role": "assistant",
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"parts": parts,
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},
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task_id or None,
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)
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async def _complete(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]],
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provider: ProviderConfig,
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model: ProviderModel,
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) -> dict[str, Any]:
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url = f"{provider.base_url}/chat/completions"
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headers = {"Authorization": f"Bearer {provider.api_key}", "Content-Type": "application/json"}
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payload = {"model": model.id, "messages": messages, "tools": tools, "tool_choice": "auto", "temperature": 0.1}
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async with httpx.AsyncClient(timeout=self.settings.llm_timeout_s) as client:
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response = await client.post(url, headers=headers, json=payload)
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if response.status_code >= 400:
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raise RuntimeError(f"LLM request failed ({response.status_code}): {response.text[:800]}")
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return response.json()
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async def _run_tool(self, name: str, arguments: dict[str, Any], task_id: str, request: str, references: list[str]) -> tuple[dict[str, Any], dict[str, Any] | None]:
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if name == "search_cdsl_library":
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results = self.library.search(str(arguments.get("query") or request), int(arguments.get("limit") or 5))
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return {"ok": True, "results": results}, None
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if name == "read_cdsl_reference":
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part_id = str(arguments.get("part_id") or "")
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sample = self.library.read_sample(part_id)
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if part_id not in references:
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references.append(part_id)
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return {"ok": True, "part_id": part_id, "cdsl": sample}, None
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if name == "read_current_cdsl":
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if not task_id:
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return {"ok": False, "message": "No current task exists. This is a new model request."}, None
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path = self.store.current_cdsl_path(task_id)
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if path is None:
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return {"ok": False, "message": "The current task has no successful CDSL revision."}, None
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return {"ok": True, "task_id": task_id, "cdsl": json.loads(path.read_text(encoding="utf-8"))}, None
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if name == "generate_cdsl_model":
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cdsl = arguments.get("cdsl")
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if isinstance(cdsl, str):
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cdsl = json.loads(cdsl)
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if not isinstance(cdsl, dict):
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raise ValueError("generate_cdsl_model requires a CDSL JSON object")
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summary = str(arguments.get("summary") or "CDSL CAD model")
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yieldable = await asyncio.to_thread(
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build_revision,
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settings=self.settings,
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store=self.store,
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task_id=task_id or None,
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request=request,
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cdsl=cdsl,
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reference_ids=list(references),
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summary=summary,
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)
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return {"ok": True, "summary": summary, "task_id": yieldable["task_id"], "revision_id": yieldable["revision_id"]}, yieldable
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raise ValueError(f"Unknown agent tool: {name}")
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@staticmethod
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def _chunks(text: str) -> list[str]:
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return [text[index:index + 96] for index in range(0, len(text), 96)]
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@staticmethod
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def _tool_label(name: str) -> str:
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return {
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"search_cdsl_library": "检索 CDSL 模型库",
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"read_cdsl_reference": "读取 CDSL 参考模型",
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"read_current_cdsl": "读取当前 CDSL",
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"generate_cdsl_model": "生成 CDSL CAD 模型",
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}.get(name, "调用 CAD 工具")
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def _attachment_message(self, conversation: dict[str, Any], model: ProviderModel) -> list[dict[str, Any]] | str:
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attachments = conversation.get("attachments") or []
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if not attachments:
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return ""
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content: list[dict[str, Any]] = [{"type": "text", "text": "The following local attachments are part of the CAD request."}]
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for attachment in attachments:
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if not isinstance(attachment, dict):
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continue
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kind = str(attachment.get("kind") or "")
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task_id = str(attachment.get("task_id") or "")
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relative = str(attachment.get("path") or "")
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if not task_id or not relative:
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continue
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path = self.store.artifact_path(task_id, relative)
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if kind == "image":
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if not model.vision:
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raise ValueError("The selected model does not support images. Choose a vision-capable OpenAI or Kimi model.")
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mime = str(attachment.get("mime") or "image/png")
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encoded = base64.b64encode(path.read_bytes()).decode("ascii")
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content.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{encoded}"}})
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elif kind == "document":
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extracted = str(attachment.get("extracted_path") or "")
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if extracted:
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text_path = self.store.artifact_path(task_id, extracted)
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text = text_path.read_text(encoding="utf-8")[:30_000]
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content.append({"type": "text", "text": f"Document {attachment.get('name')}:\n{text}"})
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return content
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