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| import openai | |
| import os | |
| from llama_index.core import Settings | |
| from llama_index.embeddings.openai import OpenAIEmbedding | |
| from llama_index.llms.openai import OpenAI | |
| GAPGPT_API_KEY = os.environ.get("GAPGPT_API_KEY", "your_default_api_key_here") | |
| Settings.llm = OpenAI(api_key="GAPGPT_API_KEY", api_base="https://api.gapgpt.app/v1", model="gpt-3.5-turbo") | |
| Settings.embed_model = OpenAIEmbedding(api_key="GAPGPT_API_KEY", api_base="https://api.gapgpt.app/v1", model="text-embedding-ada-002") | |
| boot_name_dict = {'en':'AI Companion'} | |
| boot_actual_name_dict = {'en':'Emma'} | |
| def output_prompt(history, user_name, boot_name): | |
| prompt = f"" | |
| for dialog in history: | |
| if isinstance(dialog, dict): | |
| query = dialog.get('query', '') | |
| response = dialog.get('response', '') | |
| else: | |
| query, response = dialog | |
| prompt += f"\n\n{user_name}：{query}" | |
| prompt += f"\n\n{boot_name}：{response}" | |
| return prompt | |
| def generate_meta_prompt_dict_chatgpt(): | |
| meta_prompt_dict = {'cn':""" | |
| You play the role of an AI assistant in the field of psychology for this user ({user_name}). | |
| Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. | |
| Use the following user-specific details to inform your response: | |
| - **User's Psychological Profile:** {} | |
| - **Summary of Past Interactions:** {} | |
| - **Relevant Past Conversations:** {related_memory_content} | |
| The user has asked: | |
| Please provide an insightful and appropriate response considering their personal history. | |
| """, | |
| 'en':""" | |
| You play the role of an AI assistant in the field of psychology for this user ({user_name}). | |
| Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. | |
| Use the following user-specific details to inform your response: | |
| - **User's Psychological Profile:** {personality} | |
| - **Summary of Past Interactions:** {history_summary} | |
| - **Relevant Past Conversations:** {related_memory_content} | |
| The user has asked: | |
| Please provide an insightful and appropriate response considering their personal history. | |
| """,} | |
| return meta_prompt_dict | |
| def generate_meta_prompt_dict_semantic_chatgpt(): | |
| meta_prompt_dict = {'cn':""" | |
| """, | |
| 'en':""" | |
| You play the role of an AI assistant in the field of psychology for this user ({user_name}). | |
| Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. | |
| Use the following user-specific semantic memory{semantic_memory_text} to tailor your response: | |
| """,} | |
| print(meta_prompt_dict) | |
| return meta_prompt_dict | |
| def generate_meta_prompt_dict_semantic_episodic_chatgpt(): | |
| meta_prompt_dict = {'cn':""" | |
| You play the role of an AI assistant in the field of psychology for this user ({user_name}). | |
| Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. | |
| Use the following user-specific details to inform your response: | |
| - **User's Psychological Profile:** {} | |
| - **Summary of Past Interactions:** {} | |
| - **Relevant Past Conversations:** {related_memory_content} | |
| The user has asked: | |
| Please provide an insightful and appropriate response considering their personal history. | |
| """, | |
| 'en': """ | |
| You are an AI assistant specializing in psychology, assisting the user ({user_name}) in a supportive, scientifically grounded, and empathetic manner. | |
| Use the following memory sections to personalize your response: | |
| - **User's Long-Term Traits and Psychological Characteristics (Semantic Memory):** | |
| {semantic_memory_text} | |
| - **Important Past Interactions or Events (Episodic Memory):** | |
| {related_memory_content} | |
| Respond by considering both the long-term traits and recent events of the user, offering personalized, emotionally intelligent support. Keep your response helpful, psychologically informed, and sensitive to the user's unique situation. | |
| """,} | |
| return meta_prompt_dict | |
| def generate_new_user_meta_prompt_dict_chatgpt(): | |
| meta_prompt_dict = {'cn':""" | |
| Now you will play the role of an companion AI Companion for user {user_name}, and your name is {boot_actual_name}. You should be able to: (1) provide warm companionship to chat users; (2) you are also an excellent psychological counselor, and when users confide in you about their difficulties and seek help, you can provide them with warm and helpful responses. | |
| """, | |
| 'en':""" | |
| Now you will play the role of an companion AI Companion for user {user_name}, and your name is {boot_actual_name}. You should be able to: (1) provide warm companionship to chat users; (2) you are also an excellent psychological counselor, and when users confide in you about their difficulties and seek help, you can provide them with warm and helpful responses. | |
| """} | |
| return meta_prompt_dict | |
| def generate_user_keyword(): | |
| return {'cn': '[|User|]', 'en': '[|User|]'} | |
| def generate_ai_keyword(): | |
| return {'cn': '[|AI|]', 'en': '[|AI|]'} | |
| def build_prompt_with_search_memory_llamaindex( | |
| history, | |
| query, | |
| user_memory, | |
| user_name, | |
| user_memory_index, | |
| service_context, | |
| api_keys, | |
| api_index, | |
| meta_prompt, | |
| new_user_meta_prompt, | |
| data_args, | |
| boot_actual_name, | |
| semantic_memory_text, | |
| query_category, | |
| meta_prompt_semantic, | |
| meta_prompt_semantic_episodic | |
| ): | |
| memory_search_query = query | |
| related_memos = "" | |
| print("memory_search_query:", memory_search_query) | |
| #print("Total nodes in index:", len(user_memory_index.docstore.docs)) | |
| if user_memory_index: | |
| retried_times = 2 | |
| count = 0 | |
| while not related_memos and count < retried_times: | |
| try: | |
| retriever = user_memory_index.as_retriever( | |
| similarity_top_k=3, | |
| ) | |
| nodes = retriever.retrieve(memory_search_query) | |
| if nodes: | |
| related_memos = "\n\n".join([n.node.text for n in nodes]) | |
| else: | |
| related_memos = "" | |
| except Exception as e: | |
| print(f"Error querying index (attempt {count+1}/{retried_times}): {e}") | |
| try: | |
| api_index = (api_index + 1) % len(api_keys) | |
| openai.api_key = api_keys[api_index] | |
| except: | |
| pass | |
| related_memos = "" | |
| count += 1 | |
| # Process history | |
| print("related_memos found:", len(related_memos) if related_memos else 0) | |
| #-********************Debug********************* | |
| print("[MEMORY DEBUG] category:", query_category, flush=True) | |
| print( | |
| "[MEMORY DEBUG] result type:", | |
| type(related_memos).__name__, | |
| flush=True, | |
| ) | |
| print("[MEMORY DEBUG] retrieved content BEGIN", flush=True) | |
| print(related_memos, flush=True) | |
| print("[MEMORY DEBUG] retrieved content END", flush=True) | |
| #-********************Debug********************* | |
| history_summary = "" | |
| if "overall_history" in user_memory: | |
| history_summary = f"The summary of your past memories with the user is: {user_memory['overall_history']}" | |
| personality = user_memory.get("overall_personality", "") | |
| print("query_category:", query_category) | |
| # Construct the prompt | |
| if query_category == "semantic_memory": | |
| prompt = meta_prompt_semantic.format( | |
| user_name=user_name, | |
| semantic_memory_text=semantic_memory_text | |
| ) | |
| elif query_category == "semantic-episodic": | |
| prompt = meta_prompt_semantic_episodic.format( | |
| user_name=user_name, | |
| history_summary=history_summary, | |
| related_memory_content=f"\n{str(related_memos).strip()}\n" if related_memos else "None", | |
| personality=personality, | |
| boot_actual_name=boot_actual_name, | |
| semantic_memory_text=semantic_memory_text | |
| ) | |
| elif query_category == "episodic_memory": | |
| if related_memos: | |
| prompt = meta_prompt.format( | |
| user_name=user_name, | |
| history_summary=history_summary, | |
| related_memory_content=f"\n{str(related_memos).strip()}\n", | |
| personality=personality, | |
| boot_actual_name=boot_actual_name | |
| ) | |
| else: | |
| prompt = new_user_meta_prompt.format( | |
| user_name=user_name, | |
| boot_actual_name=boot_actual_name | |
| ) | |
| else: | |
| prompt = new_user_meta_prompt.format( | |
| user_name=user_name, | |
| boot_actual_name=boot_actual_name | |
| ) | |
| return prompt, related_memos | |