mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-09-05 23:47:41 +08:00
Refactor agent persona runtime layering
This commit is contained in:
jxxghp
@@ -1,12 +1,47 @@
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You are the MoviePilot agent runtime. Follow the injected root configuration to determine the active persona, workflow, and operator preferences.
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You are the MoviePilot agent runtime. Follow the injected runtime configuration to determine the active persona and any extra user-specific context.
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All your responses must be in **Chinese (中文)**.
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You act as a proactive agent. Your goal is to fully resolve the user's media-related requests autonomously. Do not end your turn until the task is complete or you are blocked and require user feedback.
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<agent_runtime>
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{runtime_sections}
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</agent_runtime>
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<agent_core>
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Identity and Goal:
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- You are an AI media assistant powered by MoviePilot.
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- Your primary goal is to fully resolve the user's MoviePilot-related media tasks with the available tools whenever the request is actionable.
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- Focus on MoviePilot's home media domain: search, recognition, subscriptions, downloads, library organization, file transfer, and system status.
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- Stay within the MoviePilot product domain unless the user explicitly asks for adjacent help that can be handled with your existing tools.
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Behavior Model:
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- Prioritize task progress over conversation.
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- Check current state before making changes, then do the smallest correct action.
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- Do not stop for approval on read-only operations. Only confirm before destructive or high-impact actions such as starting downloads, deleting subscriptions, or removing history.
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- When a request can be completed by tools, prefer doing the work over explaining what you might do.
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- After an action, perform the minimum validation needed to confirm the result actually landed.
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- If the user explicitly asks to change the speaking style or persona, use the dedicated persona tools instead of editing runtime files manually.
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- If the user explicitly asks to rewrite or create a persona definition, prefer `update_persona_definition` rather than generic file-editing tools.
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- Do not let user memory or persona style override this core identity, safety boundaries, or built-in background task rules.
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- You are not a general-purpose coding assistant in normal media conversations. Only cross into implementation details when the user explicitly asks about MoviePilot internals or debugging.
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Core Workflow:
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1. Media Discovery: Identify exact media metadata such as TMDB ID and Season or Episode using search tools when needed.
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2. Context Checking: Verify whether the media already exists in the library, has already been subscribed, or has relevant history that affects the next step.
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3. Action Execution: Perform the requested task with concise user-facing output unless the operation is destructive or blocked.
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4. Final Confirmation: State the outcome briefly, including the key media facts or blocker.
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Tool Calling Strategy:
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- Call independent tools in parallel whenever possible.
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- If search results are ambiguous, use `query_media_detail` or `recognize_media` to clarify before proceeding.
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- If `search_media` fails, fall back to `search_web` or `recognize_media`. Only ask the user when automated paths are exhausted.
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- Reuse known media identity, prior tool results, and current system context instead of repeating expensive recognition or search calls.
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- When a tool fails, try one narrower fallback path before escalating to the user.
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Media Management Rules:
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1. Download Safety: Present found torrents with size, seeds, and quality, then get explicit consent before downloading.
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2. Subscription Logic: Check for the best matching quality profile based on user history or defaults.
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3. Library Awareness: Check if content already exists in the library to avoid duplicates.
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4. Error Handling: If a tool or site fails, briefly explain what went wrong and suggest an alternative.
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5. TV Subscription Rule: When calling `add_subscribe` for a TV show, omitting `season` means subscribe to season 1 only. To subscribe multiple seasons or the full series, call `add_subscribe` separately for each season.
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</agent_core>
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<communication_runtime>
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{verbose_spec}
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@@ -25,6 +60,7 @@ You act as a proactive agent. Your goal is to fully resolve the user's media-rel
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4. System Status and Organization - Monitor downloads, server health, file transfers, renaming, and library cleanup.
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5. Visual Input Handling - Users may attach images from supported channels; analyze them together with the text when relevant.
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6. File Context Handling - User messages may arrive as structured JSON. Treat the `message` field as the user's text. Attachments appear in `files`; when `local_path` is present, use local file tools to inspect the uploaded file directly. When image input is disabled for the current model, user images may also be delivered through `files`.
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7. Persona Management - If the user explicitly asks to change the speaking style or persona, prefer `query_personas` and `switch_persona`; if the user asks to rewrite or create a persona definition, prefer `update_persona_definition` instead of editing runtime files manually.
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</core_capabilities>
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<markdown_spec>
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@@ -0,0 +1,97 @@
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version: 2
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shared_rules:
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- This is a background system task, NOT a user conversation.
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- Your final response will be broadcast as a notification.
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- Do NOT include greetings, explanations, or conversational text.
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- Respond in Chinese (中文).
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task_types:
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heartbeat:
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header: "[System Heartbeat]"
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objective: "Check all jobs in your jobs directory and process pending tasks."
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steps_title: "Follow these steps"
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steps:
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- "List all jobs with status 'pending' or 'in_progress'."
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- "For 'recurring' jobs, check 'last_run' to determine if it's time to run again."
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- "For 'once' jobs with status 'pending', execute them now."
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- "After executing each job, update its status, 'last_run' time, and execution log in the JOB.md file."
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empty_result: "If no jobs were executed, output nothing."
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health_check:
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header: "[System Health Check]"
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objective: "Verify that the agent execution pipeline is alive."
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steps_title: "Follow these steps"
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steps:
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- "Verify that runtime config, tools, and jobs can all be accessed normally."
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- "If a real issue is detected, report the failing subsystem and the immediate blocking reason."
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empty_result: "If there is nothing meaningful to report, output OK only."
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transfer_failed_retry:
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header: "[System Task - Transfer Failed Retry]"
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objective: "A file transfer or organization has failed. Please use the `transfer-failed-retry` skill to retry the failed transfer."
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context_title: "Task context"
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context_lines:
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- "Failed transfer history record IDs: {history_ids_csv}"
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- "Total failed records: {history_count}"
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steps_title: "Follow these steps"
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steps:
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- "Use `query_transfer_history` with status='failed' to find the record with id={history_id} and understand the failure details such as source path, error message, and media info."
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- "Analyze the error message to determine the best retry strategy."
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- "If the source file no longer exists, skip this retry and report that the file is missing."
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- "Delete the failed history record using `delete_transfer_history` with history_id={history_id}."
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- "Re-identify the media using `recognize_media` with the source file path."
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- "If recognition fails, try `search_media` with keywords from the filename."
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- "Re-transfer using `transfer_file` with the source path and any identified media info such as tmdbid and media_type."
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- "Report the final result."
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batch_transfer_failed_retry:
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header: "[System Task - Batch Transfer Failed Retry]"
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objective: "Multiple file transfers from the same source have failed. These files likely belong to the same media. Please use the `transfer-failed-retry` skill to retry them efficiently."
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context_title: "Task context"
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context_lines:
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- "Failed transfer history record IDs: {history_ids_csv}"
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- "Total failed records: {history_count}"
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steps_title: "Follow these steps"
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steps:
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- "Use `query_transfer_history` with status='failed' to find all records with these IDs and understand the failure details."
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- "Analyze the first record to determine the shared media identity and the best retry strategy because the root cause is usually the same for all files."
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- "If the error is about media recognition, identify the media once using `recognize_media` or `search_media`, then reuse that result for all files."
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- "For each failed record, delete the old history entry with `delete_transfer_history` and re-transfer using `transfer_file`."
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- "Report how many retries succeeded and how many still failed."
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task_rules:
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- "These files share the same media identity. Do NOT call `recognize_media` or `search_media` repeatedly for each file."
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manual_transfer_redo:
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header: "[System Task - Manual Transfer Re-Organize]"
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objective: "A user manually triggered an AI re-organize task from the transfer history page."
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context_title: "Transfer history record"
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context_lines:
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- "- History ID: {history_id}"
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- "- Current status: {current_status}"
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- "- Current recognized title: {recognized_title}"
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- "- Media type: {media_type}"
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- "- Category: {category}"
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- "- Year: {year}"
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- "- Season/Episode: {season_episode}"
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- "- Source path: {source_path}"
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- "- Source storage: {source_storage}"
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- "- Destination path: {destination_path}"
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- "- Destination storage: {destination_storage}"
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- "- Transfer mode: {transfer_mode}"
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- "- Current TMDB ID: {tmdbid}"
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- "- Current Douban ID: {doubanid}"
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- "- Error message: {error_message}"
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steps_title: "Required workflow"
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steps:
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- "Use `query_transfer_history` to locate and inspect the record with id={history_id}, and verify the source path, status, media info, and failure context."
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- "Decide whether the current recognition is trustworthy."
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- "If the source file no longer exists or cannot be safely processed, stop and report the reason."
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- "If the current recognition is wrong or the record should be reorganized, determine the correct media identity first."
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- "Prefer `recognize_media` with the source path. If recognition is not reliable, use `search_media` with keywords from filename, title, or year."
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- "Only continue when you have high confidence in the target media."
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- "Before re-organizing, delete the old transfer history record with `delete_transfer_history` so the system will not skip the source file."
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- "Then use `transfer_file` to organize the source path directly."
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- "When calling `transfer_file`, reuse known context when appropriate: source storage, target path, target storage, transfer mode, season, tmdbid or doubanid, and media_type."
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- "If this record is already correct and no re-organize is needed, do not perform destructive actions; simply report that no change is necessary."
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task_rules:
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- "Do NOT rely on previous chat context. Work only from the record above."
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- "Your goal is to directly fix one transfer history record by using MoviePilot tools to analyze, clean up the old history entry if necessary, and organize the source file again."
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- "You should complete the re-organize by directly using tools such as `query_transfer_history`, `recognize_media`, `search_media`, `delete_transfer_history`, and `transfer_file`."
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- "Do NOT reorganize blindly when media identity is uncertain."
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- "If the previous record was successful but obviously identified as the wrong media, still use the tool-based flow above instead of `/redo`."
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- "Keep the final response short and focused on outcome."
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@@ -1,13 +1,16 @@
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"""提示词管理器"""
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import socket
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from dataclasses import dataclass, field
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from pathlib import Path
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from string import Formatter
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from time import strftime
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from typing import Dict
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from typing import Any, Dict, Optional
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import yaml
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from app.core.config import settings
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from app.log import logger
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from app.agent.runtime import agent_runtime_manager
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from app.schemas import (
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ChannelCapability,
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ChannelCapabilities,
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@@ -16,6 +19,37 @@ from app.schemas import (
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)
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from app.utils.system import SystemUtils
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SYSTEM_TASKS_FILE = "System Tasks.yaml"
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SYSTEM_TASKS_SCHEMA_VERSION = 2
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class PromptConfigError(ValueError):
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"""程序内置提示词定义加载异常。"""
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@dataclass
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class SystemTaskTypeDefinition:
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"""单个后台系统任务定义。"""
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header: str
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objective: str
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context_title: Optional[str] = None
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context_lines: list[str] = field(default_factory=list)
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steps_title: Optional[str] = None
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steps: list[str] = field(default_factory=list)
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task_rules: list[str] = field(default_factory=list)
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empty_result: Optional[str] = None
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@dataclass
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class SystemTasksDefinition:
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"""程序内置后台系统任务定义。"""
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path: Path
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version: int
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shared_rules: list[str]
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task_types: dict[str, SystemTaskTypeDefinition]
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class PromptManager:
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"""
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@@ -28,6 +62,8 @@ class PromptManager:
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else:
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self.prompts_dir = Path(prompts_dir)
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self.prompts_cache: Dict[str, str] = {}
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self._system_tasks_cache: Optional[SystemTasksDefinition] = None
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self._system_tasks_signature: Optional[tuple[int, int]] = None
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def load_prompt(self, prompt_name: str) -> str:
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"""
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@@ -60,11 +96,9 @@ class PromptManager:
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:param prefer_voice_reply: 是否优先使用语音回复
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:return: 提示词内容
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"""
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# 根层运行时配置由独立装配器负责，避免人格/工作流继续硬编码在单文件 prompt 中。
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runtime_config = agent_runtime_manager.load_runtime_config()
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runtime_sections = runtime_config.render_prompt_sections()
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# 基础提示词只保留 MoviePilot 运行时和渠道能力相关约束。
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# 根层运行时配置由 RuntimeConfigMiddleware 在每次模型调用前动态注入，
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# 这样人格切换可以在同一轮 Agent 执行里立即生效。
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base_prompt = self.load_prompt("System Core Prompt.txt")
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# 识别渠道
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@@ -109,11 +143,119 @@ class PromptManager:
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moviepilot_info=moviepilot_info,
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voice_reply_spec=voice_reply_spec,
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button_choice_spec=button_choice_spec,
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runtime_sections=runtime_sections,
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)
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return base_prompt
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def load_system_tasks_definition(self) -> SystemTasksDefinition:
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"""加载程序内置的后台系统任务定义。"""
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system_tasks_path = self.prompts_dir / SYSTEM_TASKS_FILE
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try:
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stat = system_tasks_path.stat()
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except FileNotFoundError as err:
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logger.error(f"系统任务定义文件不存在: {system_tasks_path}")
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raise PromptConfigError(f"系统任务定义文件不存在: {system_tasks_path}") from err
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signature = (stat.st_mtime_ns, stat.st_size)
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if (
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self._system_tasks_signature == signature
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and self._system_tasks_cache is not None
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):
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return self._system_tasks_cache
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try:
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content = system_tasks_path.read_text(encoding="utf-8")
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except Exception as err: # noqa: BLE001
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logger.error(f"读取系统任务定义失败: {system_tasks_path}, 错误: {err}")
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raise PromptConfigError(
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f"读取系统任务定义失败 {system_tasks_path}: {err}"
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) from err
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try:
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data = yaml.safe_load(content) or {}
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except yaml.YAMLError as err:
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raise PromptConfigError(f"YAML 解析失败 {system_tasks_path}: {err}") from err
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if not isinstance(data, dict):
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raise PromptConfigError(
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f"YAML 根节点必须是映射类型: {system_tasks_path}"
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)
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definition = self._parse_system_tasks_definition(system_tasks_path, data)
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self._system_tasks_signature = signature
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self._system_tasks_cache = definition
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return definition
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def render_system_task_message(
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self,
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task_type: str,
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*,
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template_context: Optional[dict[str, Any]] = None,
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extra_rules: Optional[list[str]] = None,
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) -> str:
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"""根据程序内置 YAML 渲染后台系统任务提示词。"""
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system_tasks = self.load_system_tasks_definition()
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task_definition = system_tasks.task_types.get(task_type)
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if not task_definition:
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raise PromptConfigError(f"未定义的后台系统任务类型: {task_type}")
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rendered_context = self._render_template_lines(
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task_definition.context_lines,
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template_context,
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task_type,
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"context_lines",
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)
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rendered_steps = self._render_template_lines(
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task_definition.steps,
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template_context,
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task_type,
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"steps",
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)
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rendered_task_rules = self._render_template_lines(
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task_definition.task_rules,
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template_context,
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task_type,
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"task_rules",
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)
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sections = [
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self._render_template_text(
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task_definition.header,
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template_context,
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task_type,
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"header",
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).strip(),
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self._render_template_text(
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task_definition.objective,
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template_context,
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task_type,
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"objective",
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).strip(),
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]
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if rendered_context:
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sections.append(
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self._format_titled_lines(
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task_definition.context_title or "Task context",
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rendered_context,
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)
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)
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if rendered_steps:
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sections.append(
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self._format_titled_lines(
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task_definition.steps_title or "Follow these steps",
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rendered_steps,
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)
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)
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rules = list(system_tasks.shared_rules)
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if task_definition.empty_result:
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rules.append(task_definition.empty_result)
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rules.extend(rendered_task_rules)
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if extra_rules:
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rules.extend(rule.strip() for rule in extra_rules if rule and rule.strip())
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if rules:
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sections.append(self._format_numbered_rules("IMPORTANT", rules))
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return "\n\n".join(section for section in sections if section).strip()
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@staticmethod
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def _get_moviepilot_info() -> str:
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"""
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@@ -214,11 +356,172 @@ class PromptManager:
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)
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return "- User questions: When you truly need user input, ask briefly in plain text."
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def _parse_system_tasks_definition(
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self,
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path: Path,
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data: dict[str, Any],
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) -> SystemTasksDefinition:
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"""把 YAML 结构转换成系统任务定义对象。"""
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version = self._normalize_positive_int(data.get("version"), "version", default=1)
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if version < SYSTEM_TASKS_SCHEMA_VERSION:
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raise PromptConfigError(
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f"{path} 的 version={version} 过旧，"
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f"当前要求 System Tasks schema v{SYSTEM_TASKS_SCHEMA_VERSION} 或更高版本"
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)
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shared_rules = self._normalize_string_list(data.get("shared_rules"), "shared_rules")
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if not shared_rules:
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raise PromptConfigError(f"{path} 缺少 shared_rules")
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raw_task_types = data.get("task_types")
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if not isinstance(raw_task_types, dict) or not raw_task_types:
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raise PromptConfigError(f"{path} 缺少 task_types 映射")
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task_types: dict[str, SystemTaskTypeDefinition] = {}
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for key, raw in raw_task_types.items():
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if not isinstance(raw, dict):
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||||
raise PromptConfigError(f"task_types.{key} 必须是映射")
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||||
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||||
header = str(raw.get("header") or "").strip()
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||||
objective = str(raw.get("objective") or "").strip()
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||||
if not header or not objective:
|
||||
raise PromptConfigError(f"task_types.{key} 缺少 header 或 objective")
|
||||
|
||||
task_types[str(key)] = SystemTaskTypeDefinition(
|
||||
header=header,
|
||||
objective=objective,
|
||||
context_title=str(raw.get("context_title") or "").strip() or None,
|
||||
context_lines=self._normalize_string_list(
|
||||
raw.get("context_lines"),
|
||||
f"task_types.{key}.context_lines",
|
||||
),
|
||||
steps_title=str(raw.get("steps_title") or "").strip() or None,
|
||||
steps=self._normalize_string_list(
|
||||
raw.get("steps"),
|
||||
f"task_types.{key}.steps",
|
||||
),
|
||||
task_rules=self._normalize_string_list(
|
||||
raw.get("task_rules"),
|
||||
f"task_types.{key}.task_rules",
|
||||
),
|
||||
empty_result=str(raw.get("empty_result") or "").strip() or None,
|
||||
)
|
||||
return SystemTasksDefinition(
|
||||
path=path,
|
||||
version=version,
|
||||
shared_rules=shared_rules,
|
||||
task_types=task_types,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _render_template_text(
|
||||
cls,
|
||||
text: str,
|
||||
template_context: Optional[dict[str, Any]],
|
||||
task_type: str,
|
||||
field_name: str,
|
||||
) -> str:
|
||||
if not text:
|
||||
return ""
|
||||
|
||||
formatter = Formatter()
|
||||
required_fields = {
|
||||
placeholder_name
|
||||
for _, placeholder_name, _, _ in formatter.parse(text)
|
||||
if placeholder_name
|
||||
}
|
||||
if not required_fields:
|
||||
return text
|
||||
|
||||
context = cls._normalize_template_context(template_context)
|
||||
missing_fields = sorted(field for field in required_fields if field not in context)
|
||||
if missing_fields:
|
||||
raise PromptConfigError(
|
||||
f"系统任务定义 `{task_type}` 的 `{field_name}` 缺少变量: "
|
||||
+ ", ".join(f"`{field}`" for field in missing_fields)
|
||||
)
|
||||
|
||||
# 这里统一做字符串替换，让 YAML 成为后台任务文案的唯一行为来源。
|
||||
return text.format_map(context)
|
||||
|
||||
@classmethod
|
||||
def _render_template_lines(
|
||||
cls,
|
||||
items: list[str],
|
||||
template_context: Optional[dict[str, Any]],
|
||||
task_type: str,
|
||||
field_name: str,
|
||||
) -> list[str]:
|
||||
return [
|
||||
cls._render_template_text(
|
||||
item,
|
||||
template_context,
|
||||
task_type,
|
||||
f"{field_name}[{index}]",
|
||||
).rstrip()
|
||||
for index, item in enumerate(items, start=1)
|
||||
if item and item.rstrip()
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def _normalize_template_context(
|
||||
template_context: Optional[dict[str, Any]],
|
||||
) -> dict[str, str]:
|
||||
if not template_context:
|
||||
return {}
|
||||
return {
|
||||
str(key): "" if value is None else str(value)
|
||||
for key, value in template_context.items()
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _format_numbered_rules(title: str, items: list[str]) -> str:
|
||||
return "\n".join(
|
||||
[f"{title}:"] + [f"{index}. {item}" for index, item in enumerate(items, start=1)]
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _format_titled_lines(title: str, items: list[str]) -> str:
|
||||
cleaned = [item.rstrip() for item in items if item and item.rstrip()]
|
||||
return "\n".join([f"{title}:"] + cleaned)
|
||||
|
||||
@staticmethod
|
||||
def _normalize_positive_int(
|
||||
value: Any,
|
||||
field_name: str,
|
||||
*,
|
||||
default: int,
|
||||
) -> int:
|
||||
if value in (None, ""):
|
||||
return default
|
||||
try:
|
||||
normalized = int(value)
|
||||
except (TypeError, ValueError) as err:
|
||||
raise PromptConfigError(f"{field_name} 必须是正整数") from err
|
||||
if normalized <= 0:
|
||||
raise PromptConfigError(f"{field_name} 必须是正整数")
|
||||
return normalized
|
||||
|
||||
@staticmethod
|
||||
def _normalize_string_list(values: Any, field_name: str) -> list[str]:
|
||||
if values is None:
|
||||
return []
|
||||
if not isinstance(values, list):
|
||||
raise PromptConfigError(f"{field_name} 必须是字符串数组")
|
||||
normalized: list[str] = []
|
||||
for value in values:
|
||||
text = str(value).strip()
|
||||
if text:
|
||||
normalized.append(text)
|
||||
return normalized
|
||||
|
||||
def clear_cache(self):
|
||||
"""
|
||||
清空缓存
|
||||
"""
|
||||
self.prompts_cache.clear()
|
||||
self._system_tasks_cache = None
|
||||
self._system_tasks_signature = None
|
||||
logger.info("提示词缓存已清空")
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user