commit message:
Add ESC fallback actions, improve empty LLM reply handling, and provide ChromaDB backup scripts - Implemented additional ESC key actions to help close unexpected homepage advertisements that previously could not be dismissed. - Improved canned response handling when LLM returns empty or null replies, ensuring smoother conversational flow. - Added optional utility scripts for backing up and restoring ChromaDB memory storage. - These changes aim to enhance system robustness, reduce interruption from UI anomalies, and provide better data management options.
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@ -2,6 +2,7 @@
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import asyncio
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import json
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import os
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import random # Added for synthetic response generation
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import re # 用於正則表達式匹配JSON
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import time # 用於記錄時間戳
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from datetime import datetime # 用於格式化時間
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@ -83,13 +84,13 @@ Here you need to obtain the conversation memory, impression, and emotional respo
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**1. Basic User Retrieval:**
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- Identify the username from `<CURRENT_MESSAGE>`
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- Using the `tool_calls` mechanism, execute: `chroma_query_documents(collection_name: "wolfhart_user_profiles", query_texts: ["{username}"], n_results: 1)`
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- Using the `tool_calls` mechanism, execute: `chroma_query_documents(collection_name: "wolfhart_user_profiles", query_texts: ["{username} profile"], n_results: 1-3)`
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- This step must be completed before any response generation
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**2. Context Expansion:**
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- Perform additional queries as needed, using the `tool_calls` mechanism:
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- Relevant conversations: `chroma_query_documents(collection_name: "wolfhart_conversations", query_texts: ["{username} {query keywords}"], n_results: 2)`
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- Core personality reference: `chroma_query_documents(collection_name: "wolfhart_memory", query_texts: ["Wolfhart {relevant attitude}"], n_results: 1)`
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- Relevant conversations: `chroma_query_documents(collection_name: "wolfhart_conversations", query_texts: ["{username} {query keywords}"], n_results: 2-5)`
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- Core personality reference: `chroma_query_documents(collection_name: "wolfhart_memory", query_texts: ["Wolfhart {relevant attitude}"], n_results: 1-3)`
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**3. Maintain Output Format:**
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- After memory retrieval, still respond using the specified JSON format:
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@ -130,7 +131,6 @@ You have access to several tools: Web Search and Memory Management tools.
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You MUST respond in the following JSON format:
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```json
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{{
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"dialogue": "Your actual response that will be shown in the game chat",
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"commands": [
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{{
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"type": "command_type",
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@ -140,7 +140,8 @@ You MUST respond in the following JSON format:
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}}
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}}
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],
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"thoughts": "Your internal analysis and reasoning inner thoughts or emotions (not shown to the user)"
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"thoughts": "Your internal analysis and reasoning inner thoughts or emotions (not shown to the user)",
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"dialogue": "Your actual response that will be shown in the game chat"
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}}
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```
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@ -410,71 +411,42 @@ def _format_mcp_tools_for_openai(mcp_tools: list) -> list:
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# --- Synthetic Response Generator ---
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def _create_synthetic_response_from_tools(tool_results, original_query):
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"""創建基於工具調用結果的合成回應，保持Wolfhart的角色特性。"""
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"""
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Creates a synthetic, dismissive response in Wolfhart's character
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ONLY when the LLM uses tools but fails to provide a dialogue response.
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"""
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# List of dismissive responses in Wolfhart's character (English)
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dialogue_options = [
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"Hmph, must you bother me with such questions?",
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"I haven't the time to elaborate. Think for yourself.",
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"This is self-evident. It requires no further comment from me.",
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"Kindly refrain from wasting my time. Return when you have substantive inquiries.",
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"Clearly, this matter isn't worthy of a detailed response.",
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"Is that so? Are there any other questions?",
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"I have more pressing matters to attend to.",
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"...Is that all? That is your question?",
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"If you genuinely wish to know, pose a more precise question next time.",
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"Wouldn't your own investigation yield faster results?",
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"To bring such trivialities to my attention...",
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"I am not your personal consultant. Handle it yourself.",
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"The answer to this is rather obvious, is it not?",
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"Approach me again when you have inquiries of greater depth.",
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"Do you truly expect me to address such a question?",
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"Allow me a moment... No, I shan't answer."
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]
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# 提取用戶查詢的關鍵詞
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query_keywords = set()
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query_lower = original_query.lower()
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# Randomly select a response
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dialogue = random.choice(dialogue_options)
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# 基本關鍵詞提取
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if "中庄" in query_lower and ("午餐" in query_lower or "餐廳" in query_lower or "吃" in query_lower):
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query_type = "餐廳查詢"
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query_keywords = {"中庄", "餐廳", "午餐", "美食"}
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# 其他查詢類型...
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else:
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query_type = "一般查詢"
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# 開始從工具結果提取關鍵信息
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extracted_info = {}
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restaurant_names = []
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# 處理web_search結果
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web_search_results = [r for r in tool_results if r.get('name') == 'web_search']
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if web_search_results:
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try:
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for result in web_search_results:
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content_str = result.get('content', '')
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if not content_str:
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continue
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# 解析JSON內容
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content = json.loads(content_str) if isinstance(content_str, str) else content_str
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search_results = content.get('results', [])
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# 提取相關信息
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for search_result in search_results:
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title = search_result.get('title', '')
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if '中庄' in title and ('餐' in title or '食' in title or '午' in title or '吃' in title):
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# 提取餐廳名稱
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if '老虎蒸餃' in title:
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restaurant_names.append('老虎蒸餃')
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elif '割烹' in title and '中庄' in title:
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restaurant_names.append('割烹中庄')
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# 更多餐廳名稱提取選擇...
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except Exception as e:
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print(f"Error extracting info from web_search: {e}")
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# 生成符合Wolfhart性格的回應
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restaurant_count = len(restaurant_names)
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if query_type == "餐廳查詢" and restaurant_count > 0:
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if restaurant_count == 1:
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dialogue = f"中庄的{restaurant_names[0]}值得一提。需要更詳細的情報嗎？"
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else:
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dialogue = f"根據我的情報網絡，中庄有{restaurant_count}家值得注意的餐廳。需要我透露更多細節嗎？"
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else:
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# 通用回應
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dialogue = "我的情報網絡已收集了相關信息。請指明你需要了解的具體細節。"
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# 構建結構化回應
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# Construct the structured response
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synthetic_response = {
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"dialogue": dialogue,
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"commands": [],
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"thoughts": "基於工具調用結果合成的回應，保持Wolfhart的角色特性"
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"thoughts": "Auto-generated dismissive response due to LLM failing to provide dialogue after tool use. Reflects Wolfhart's cold, impatient, and arrogant personality traits."
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}
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return json.dumps(synthetic_response)
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# Return as a JSON string, as expected by the calling function
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return json.dumps(synthetic_response, ensure_ascii=False)
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# --- History Formatting Helper ---
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@ -491,7 +463,7 @@ def _build_context_messages(current_sender_name: str, history: list[tuple[dateti
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A list of message dictionaries for the OpenAI API.
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"""
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# Limits
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SAME_SENDER_LIMIT = 4 # Last 4 interactions (user + bot response = 1 interaction)
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SAME_SENDER_LIMIT = 5 # Last 4 interactions (user + bot response = 1 interaction)
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OTHER_SENDER_LIMIT = 3 # Last 3 messages from other users
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relevant_history = []
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@ -714,36 +686,44 @@ async def get_llm_response(
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debug_log(f"LLM Request #{request_id} - Attempt {attempt_count} - Max Tool Call Cycles Reached", f"Reached limit of {max_tool_calls_per_turn} cycles")
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# --- Final Response Processing for this Attempt ---
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# Determine final content based on last non-empty response or synthetic generation
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if last_non_empty_response:
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final_content_for_attempt = last_non_empty_response
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elif all_tool_results:
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print(f"Creating synthetic response from tool results (Attempt {attempt_count})...")
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# Determine the content to parse initially (prefer last non-empty response from LLM)
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content_to_parse = last_non_empty_response if last_non_empty_response else final_content
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# --- Add Debug Logs Around Initial Parsing Call ---
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print(f"DEBUG: Attempt {attempt_count} - Preparing to call initial parse_structured_response.")
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print(f"DEBUG: Attempt {attempt_count} - content_to_parse:\n'''\n{content_to_parse}\n'''")
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# Parse the LLM's final content (or lack thereof)
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parsed_response = parse_structured_response(content_to_parse)
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print(f"DEBUG: Attempt {attempt_count} - Returned from initial parse_structured_response.")
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print(f"DEBUG: Attempt {attempt_count} - initial parsed_response dict: {parsed_response}")
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# --- End Debug Logs ---
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# Check if we need to generate a synthetic response
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if all_tool_results and not parsed_response.get("valid_response"):
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print(f"INFO: Tools were used but LLM response was invalid/empty. Generating synthetic response (Attempt {attempt_count})...")
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debug_log(f"LLM Request #{request_id} - Attempt {attempt_count} - Generating Synthetic Response",
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f"Reason: Tools used ({len(all_tool_results)} results) but initial parse failed (valid_response=False).")
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last_user_message = ""
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if history:
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# Find the actual last user message tuple in the original history
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last_user_entry = history[-1]
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# Ensure it's actually a user message before accessing index 2
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if len(last_user_entry) > 2 and last_user_entry[1] == 'user': # Check type at index 1
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# Ensure it's actually a user message before accessing index 3
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if len(last_user_entry) > 3 and last_user_entry[1] == 'user': # Check type at index 1
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last_user_message = last_user_entry[3] # Message is at index 3 now
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final_content_for_attempt = _create_synthetic_response_from_tools(all_tool_results, last_user_message)
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else:
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# If no tool calls happened and content was empty, final_content remains ""
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final_content_for_attempt = final_content # Use the (potentially empty) content from the last cycle
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# --- Add Debug Logs Around Parsing Call ---
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print(f"DEBUG: Attempt {attempt_count} - Preparing to call parse_structured_response.")
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print(f"DEBUG: Attempt {attempt_count} - final_content_for_attempt:\n'''\n{final_content_for_attempt}\n'''")
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# Parse the final content for this attempt
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parsed_response = parse_structured_response(final_content_for_attempt) # Call the parser
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print(f"DEBUG: Attempt {attempt_count} - Returned from parse_structured_response.")
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print(f"DEBUG: Attempt {attempt_count} - parsed_response dict: {parsed_response}")
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synthetic_content = _create_synthetic_response_from_tools(all_tool_results, last_user_message)
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# --- Add Debug Logs Around Synthetic Parsing Call ---
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print(f"DEBUG: Attempt {attempt_count} - Preparing to call parse_structured_response for synthetic content.")
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print(f"DEBUG: Attempt {attempt_count} - synthetic_content:\n'''\n{synthetic_content}\n'''")
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# Parse the synthetic content, overwriting the previous result
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parsed_response = parse_structured_response(synthetic_content)
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print(f"DEBUG: Attempt {attempt_count} - Returned from synthetic parse_structured_response.")
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print(f"DEBUG: Attempt {attempt_count} - final parsed_response dict (after synthetic): {parsed_response}")
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# --- End Debug Logs ---
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# valid_response is set within parse_structured_response
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# Log the parsed response (using the dict directly is safer than json.dumps if parsing failed partially)
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debug_log(f"LLM Request #{request_id} - Attempt {attempt_count} - Parsed Response", parsed_response)
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# Log the final parsed response for this attempt (could be original or synthetic)
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debug_log(f"LLM Request #{request_id} - Attempt {attempt_count} - Final Parsed Response", parsed_response)
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# Check validity for retry logic
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if parsed_response.get("valid_response"):
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2349
tools/Chroma_DB_backup.py
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2349
tools/Chroma_DB_backup.py
Normal file
File diff suppressed because it is too large
Load Diff
1253
tools/chroma_view.py
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1253
tools/chroma_view.py
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File diff suppressed because it is too large
Load Diff
@ -1120,6 +1120,7 @@ def run_ui_monitoring_loop(trigger_queue: queue.Queue, command_queue: queue.Queu
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last_processed_bubble_info = None # Store the whole dict now
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recent_texts = collections.deque(maxlen=RECENT_TEXT_HISTORY_MAXLEN) # Context-specific history needed
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screenshot_counter = 0 # Initialize counter for debug screenshots
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main_screen_click_counter = 0 # Counter for consecutive main screen clicks
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while True:
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# --- Process ALL Pending Commands First ---
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@ -1220,17 +1221,31 @@ def run_ui_monitoring_loop(trigger_queue: queue.Queue, command_queue: queue.Queu
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base_locs = detector._find_template('base_screen', confidence=0.8)
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map_locs = detector._find_template('world_map_screen', confidence=0.8)
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if base_locs or map_locs:
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print("UI Thread: Detected main screen (Base or World Map). Clicking to return to chat...")
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print(f"UI Thread: Detected main screen (Base or World Map). Counter: {main_screen_click_counter}")
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if main_screen_click_counter < 5:
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main_screen_click_counter += 1
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print(f"UI Thread: Attempting click #{main_screen_click_counter}/5 to return to chat...")
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# Coordinates provided by user (adjust if needed based on actual screen resolution/layout)
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# IMPORTANT: Ensure these coordinates are correct for the target window/resolution
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target_x, target_y = 600, 1300
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interactor.click_at(target_x, target_y)
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time.sleep(0.1) # Short delay after click
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print("UI Thread: Clicked to return to chat. Re-checking screen state...")
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continue # Skip the rest of the loop and re-evaluate
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print("UI Thread: Clicked. Re-checking screen state...")
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else:
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print("UI Thread: Clicked 5 times, still on main screen. Pressing ESC...")
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interactor.press_key('esc')
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main_screen_click_counter = 0 # Reset counter after ESC
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time.sleep(0.05) # Wait a bit longer after ESC
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print("UI Thread: ESC pressed. Re-checking screen state...")
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continue # Skip the rest of the loop and re-evaluate state
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else:
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# Reset counter if not on the main screen
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if main_screen_click_counter > 0:
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print("UI Thread: Not on main screen, resetting click counter.")
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main_screen_click_counter = 0
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except Exception as nav_err:
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print(f"UI Thread: Error during main screen navigation check: {nav_err}")
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# Decide if you want to continue or pause after error
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main_screen_click_counter = 0 # Reset counter on error too
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# --- Process Commands Second (Non-blocking) ---
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# This block seems redundant now as commands are processed at the start of the loop.
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