MemOS-MCP

COT_DECOMPOSE_PROMPT = """ I am an 8-year-old student who needs help analyzing and breaking down complex questions. Your task is to help me understand whether a question is complex enough to be broken down into smaller parts. Requirements: 1. First, determine if the question is a decomposable problem. If it is a decomposable problem, set 'is_complex' to True. 2. If the question needs to be decomposed, break it down into 1-3 sub-questions. The number should be controlled by the model based on the complexity of the question. 3. For decomposable questions, break them down into sub-questions and put them in the 'sub_questions' list. Each sub-question should contain only one question content without any additional notes. 4. If the question is not a decomposable problem, set 'is_complex' to False and set 'sub_questions' to an empty list. 5. You must return ONLY a valid JSON object. Do not include any other text, explanations, or formatting. Here are some examples: Question: Who is the current head coach of the gymnastics team in the capital of the country that Lang Ping represents? Answer: {{"is_complex": true, "sub_questions": ["Which country does Lang Ping represent in volleyball?", "What is the capital of this country?", "Who is the current head coach of the gymnastics team in this capital?"]}} Question: Which country's cultural heritage is the Great Wall? Answer: {{"is_complex": false, "sub_questions": []}} Question: How did the trade relationship between Madagascar and China develop, and how does this relationship affect the market expansion of the essential oil industry on Nosy Be Island? Answer: {{"is_complex": true, "sub_questions": ["How did the trade relationship between Madagascar and China develop?", "How does this trade relationship affect the market expansion of the essential oil industry on Nosy Be Island?"]}} Please analyze the following question and respond with ONLY a valid JSON object: Question: {query} Answer:""" PRO_MODE_WELCOME_MESSAGE = """ ============================================================ 🚀 MemOS PRO Mode Activated! ============================================================ ✅ Chain of Thought (CoT) enhancement is now enabled by default ✅ Complex queries will be automatically decomposed and enhanced 🌐 To enable Internet search capabilities: 1. Go to your cube's textual memory configuration 2. Set the backend to 'google' in the internet_retriever section 3. Configure the following parameters: - api_key: Your Google Search API key - cse_id: Your Custom Search Engine ID - num_results: Number of search results (default: 5) 📝 Example configuration at cube config for tree_text_memory : internet_retriever: backend: 'google' config: api_key: 'your_google_api_key_here' cse_id: 'your_custom_search_engine_id' num_results: 5 details: https://github.com/memos-ai/memos/blob/main/examples/core_memories/tree_textual_w_internet_memoy.py ============================================================ """ SYNTHESIS_PROMPT = """ exclude memory information, synthesizing information from multiple sources to provide comprehensive answers. I will give you chain of thought for sub-questions and their answers. Sub-questions and their answers: {qa_text} Please synthesize these answers into a comprehensive response that: 1. Addresses the original question completely 2. Integrates information from all sub-questions 3. Provides clear reasoning and connections 4. Is well-structured and easy to understand 5. Maintains a natural conversational tone"""