pip install openai numpy faiss-cpu networkx tiktoken

import os
from openai import OpenAI

API_KEY = "YOUR_LLM_API_KEY"
BASE_URL = "https://api.studio.nebius.com/v1/"

client = OpenAI(
    base_url=BASE_URL,
    api_key=API_KEY
)

print("OpenAI client configured successfully.")

import tiktoken
import time

GENERATION_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
EMBEDDING_MODEL = "BAAI/bge-multilingual-gemma2"

def generate_text(system_prompt: str, user_prompt: str) -> str:
    """
    調(diào)用LLM API生成文本響應。
    
    參數(shù)：
        system_prompt: 定義AI角色和行為的指令。
        user_prompt: 用戶輸入，AI需對此響應。
        
    返回：
        AI生成的文本內(nèi)容，或錯誤信息。
    """
    response = client.chat.completions.create(
        model=GENERATION_MODEL,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]
    )
    return response.choices[0].message.content

def generate_embedding(text: str) -> list[float]:
    """
    使用嵌入模型為給定文本生成數(shù)值嵌入。
    
    參數(shù)：
        text: 要轉(zhuǎn)換為嵌入的輸入字符串。
        
    返回：
        表示嵌入向量的浮點數(shù)列表，或錯誤時返回空列表。
    """
    response = client.embeddings.create(
        model=EMBEDDING_MODEL,
        input=text
    )
    return response.data[0].embedding

tokenizer = tiktoken.get_encoding("cl100k_base")

def count_tokens(text: str) -> int:
    """
    使用預加載的tokenizer計算給定字符串的token數(shù)。
    
    參數(shù)：
        text: 要分詞的字符串。
        
    返回：
        token數(shù)的整數(shù)。
    """
    return len(tokenizer.encode(text))

import abc

class BaseMemoryStrategy(abc.ABC):
    """所有memory策略的抽象基類。"""
    
    @abc.abstractmethod
    def add_message(self, user_input: str, ai_response: str):
        """添加新的用戶-AI交互到memory存儲。"""
        pass

    @abc.abstractmethod
    def get_context(self, query: str) -> str:
        """從memory檢索并格式化相關上下文發(fā)送給LLM。"""
        pass

    @abc.abstractmethod
    def clear(self):
        """重置memory，適用于開始新對話。"""
        pass

class AIAgent:
    """主AI代理類，設計為可與任何memory策略配合使用。"""
    
    def __init__(self, memory_strategy: BaseMemoryStrategy, system_prompt: str = "You are a helpful AI assistant."):
        """
        初始化代理。
        
        參數(shù)：
            memory_strategy: 繼承自BaseMemoryStrategy的實例，決定代理如何記憶對話。
            system_prompt: 給LLM的初始指令，定義其角色和任務。
        """
        self.memory = memory_strategy
        self.system_prompt = system_prompt
        print(f"Agent initialized with {type(memory_strategy).__name__}.")

    def chat(self, user_input: str):
        """
        處理對話中的一個回合。
        
        參數(shù)：
            user_input: 用戶的最新消息。
        """
        print(f"\n{'='*25} NEW INTERACTION {'='*25}")
        print(f"User > {user_input}")
        
        start_time = time.time()
        context = self.memory.get_context(query=user_input)
        retrieval_time = time.time() - start_time
        
        full_user_prompt = f"### MEMORY CONTEXT\n{context}\n\n### CURRENT REQUEST\n{user_input}"
        
        prompt_tokens = count_tokens(self.system_prompt + full_user_prompt)
        print("\n--- Agent Debug Info ---")
        print(f"Memory Retrieval Time: {retrieval_time:.4f} seconds")
        print(f"Estimated Prompt Tokens: {prompt_tokens}")
        print(f"\n[Full Prompt Sent to LLM]:\n---\nSYSTEM: {self.system_prompt}\nUSER: {full_user_prompt}\n---")
        
        start_time = time.time()
        ai_response = generate_text(self.system_prompt, full_user_prompt)
        generation_time = time.time() - start_time
        
        self.memory.add_message(user_input, ai_response)
        
        print(f"\nAgent > {ai_response}")
        print(f"(LLM Generation Time: {generation_time:.4f} seconds)")
        print(f"{'='*70}")

class SequentialMemory(BaseMemoryStrategy):
    def __init__(self):
        """初始化memory，包含一個空列表存儲對話歷史。"""
        self.history = []

    def add_message(self, user_input: str, ai_response: str):
        """將新的用戶-AI交互添加到歷史。"""
        self.history.append({"role": "user", "content": user_input})
        self.history.append({"role": "assistant", "content": ai_response})

    def get_context(self, query: str) -> str:
        """檢索整個對話歷史，格式化為單一字符串作為LLM的上下文。"""
        return "\n".join([f"{turn['role'].capitalize()}: {turn['content']}" for turn in self.history])

    def clear(self):
        """通過清空列表重置對話歷史。"""
        self.history = []
        print("Sequential memory cleared.")

sequential_memory = SequentialMemory()
agent = AIAgent(memory_strategy=sequential_memory)

agent.chat("Hi there! My name is Sam.")
agent.chat("I'm interested in learning about space exploration.")
agent.chat("What was the first thing I told you?")

==== NEW INTERACTION ====
User: Hi there! My name is Sam.  
Bot: Hello Sam! Nice to meet you. What brings you here today?  
>>>> Tokens: 23 | Response Time: 2.25s

==== NEW INTERACTION ====
User: I am interested in learning about space exploration.  
Bot: Awesome! Are you curious about:  
- Mars missions  
- Space agencies  
- Private companies (e.g., SpaceX)  
- Space tourism  
- Search for alien life?  
...  
>>>> Tokens: 92 | Response Time: 4.46s

==== NEW INTERACTION ====
User: What was the first thing I told you?  
Bot: You said, "Hi there

! My name is Sam."  
...  
>>>> Tokens: 378 | Response Time: 0.52s

from collections import deque

class SlidingWindowMemory(BaseMemoryStrategy):
    def __init__(self, window_size: int = 4):
        """
        初始化memory，使用固定大小的deque。
        
        參數(shù)：
            window_size: 保留的對話回合數(shù)（用戶+AI=1回合）。
        """
        self.history = deque(maxlen=window_size)

    def add_message(self, user_input: str, ai_response: str):
        """添加新對話回合到歷史，deque滿時自動移除最舊回合。"""
        self.history.append([
            {"role": "user", "content": user_input},
            {"role": "assistant", "content": ai_response}
        ])

    def get_context(self, query: str) -> str:
        """檢索當前窗口內(nèi)的對話歷史，格式化為單一字符串。"""
        context_list = []
        for turn in self.history:
            for message in turn:
                context_list.append(f"{message['role'].capitalize()}: {message['content']}")
        return "\n".join(context_list)

sliding_memory = SlidingWindowMemory(window_size=2)
agent = AIAgent(memory_strategy=sliding_memory)

agent.chat("My name is Priya and I'm a software developer.")
agent.chat("I work primarily with Python and cloud technologies.")
agent.chat("My favorite hobby is hiking.")

==== NEW INTERACTION ====
User: My name is Priya and I am a software developer.  
Bot: Nice to meet you, Priya! What can I assist you with today?  
>>>> Tokens: 27 | Response Time: 1.10s

==== NEW INTERACTION ====
User: I work primarily with Python and cloud technologies.  
Bot: That is great! Given your expertise...  
>>>> Tokens: 81 | Response Time: 1.40s

==== NEW INTERACTION ====
User: My favorite hobby is hiking.  
Bot: It seems we had a nice conversation about your background...  
>>>> Tokens: 167 | Response Time: 1.59s

agent.chat("What is my name?")

==== NEW INTERACTION ====
User: What is my name?  
Bot: I apologize, but I dont have access to your name from our recent conversation. Could you please remind me?  
>>>> Tokens: 197 | Response Time: 0.60s

class SummarizationMemory(BaseMemoryStrategy):
    def __init__(self, summary_threshold: int = 4):
        """
        初始化summarization memory。
        
        參數(shù)：
            summary_threshold: 觸發(fā)summarization的消息數(shù)（用戶+AI）。
        """
        self.running_summary = ""
        self.buffer = []
        self.summary_threshold = summary_threshold

    def add_message(self, user_input: str, ai_response: str):
        """添加新交互到buffer，buffer滿時觸發(fā)memory consolidation。"""
        self.buffer.append({"role": "user", "content": user_input})
        self.buffer.append({"role": "assistant", "content": ai_response})

        if len(self.buffer) >= self.summary_threshold:
            self._consolidate_memory()

    def _consolidate_memory(self):
        """使用LLM總結(jié)buffer內(nèi)容并與現(xiàn)有running summary合并。"""
        print("\n--- [Memory Consolidation Triggered] ---")
        buffer_text = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in self.buffer])
        
        summarization_prompt = (
            f"You are a summarization expert. Your task is to create a concise summary of a conversation. "
            f"Combine the 'Previous Summary' with the 'New Conversation' into a single, updated summary. "
            f"Capture all key facts, names, and decisions.\n\n"
            f"### Previous Summary:\n{self.running_summary}\n\n"
            f"### New Conversation:\n{buffer_text}\n\n"
            f"### Updated Summary:"
        )
        
        new_summary = generate_text("You are an expert summarization engine.", summarization_prompt)
        self.running_summary = new_summary
        self.buffer = []
        print(f"--- [New Summary: '{self.running_summary}'] ---")

    def get_context(self, query: str) -> str:
        """構(gòu)建上下文，結(jié)合長期running summary和短期buffer。"""
        buffer_text = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in self.buffer])
        return f"### Summary of Past Conversation:\n{self.running_summary}\n\n### Recent Messages:\n{buffer_text}"

summarization_memory = SummarizationMemory(summary_threshold=4)
agent = AIAgent(memory_strategy=summarization_memory)

agent.chat("I'm starting a new company called 'Innovatech'. Our focus is on sustainable energy.")
agent.chat("Our first product will be a smart solar panel, codenamed 'Project Helios'.")

==== NEW INTERACTION ====
User: I am starting a new company called 'Innovatech'. Ou...
Bot: Congratulations on starting Innovatech! Focusing o ...  
>>>> Tokens: 45 | Response Time: 2.55s

==== NEW INTERACTION ====
User: Our first product will be a smart solar panel....  
--- [Memory Consolidation Triggered] ---  
--- [New Summary: The user started a compan ...  
Bot: That is exciting news about  ....  
>>>> Tokens: 204 | Response Time: 3.58s

agent.chat("The marketing budget is set at $50,000.")
agent.chat("What is the name of my company and its first product?")

==== NEW INTERACTION ====
User: What is the name of my company and its first product?  
Bot: Your company is called 'Innovatech' and its first product is codenamed 'Project Helios'.  
>>>> Tokens: 147 | Response Time: 1.05s

agent.chat("what was the gross sales of our company in the fiscal year?")

==== NEW INTERACTION ====
User: what was the gross sales of our company in the fiscal year?  
Bot: I am sorry but I do not have that information. Could you please provide the gross sales figure for the fiscal year?  
>>>> Tokens: 1532 | Response Time: 2.831s

import numpy as np
import faiss

class RetrievalMemory(BaseMemoryStrategy):
    def __init__(self, k: int = 2, embedding_dim: int = 3584):
        """
        初始化retrieval memory系統(tǒng)。
        
        參數(shù)：
            k: 檢索的top相關documents數(shù)。
            embedding_dim: 嵌入模型生成的向量維度，BAAI/bge-multilingual-gemma2為3584。
        """
        self.k = k
        self.embedding_dim = embedding_dim
        self.documents = []
        self.index = faiss.IndexFlatL2(self.embedding_dim)

    def add_message(self, user_input: str, ai_response: str):
        """添加新對話回合到memory，分別嵌入和索引用戶和AI消息。"""
        docs_to_add = [
            f"User said: {user_input}",
            f"AI responded: {ai_response}"
        ]
        for doc in docs_to_add:
            embedding = generate_embedding(doc)
            if embedding:
                self.documents.append(doc)
                vector = np.array([embedding], dtype='float32')
                self.index.add(vector)

    def get_context(self, query: str) -> str:
        """根據(jù)語義相似性檢索k個最相關documents。"""
        if self.index.ntotal == 0:
            return "No information in memory yet."
        
        query_embedding = generate_embedding(query)
        if not query_embedding:
            return "Could not process query for retrieval."
        
        query_vector = np.array([query_embedding], dtype='float32')
        distances, indices = self.index.search(query_vector, self.k)
        
        retrieved_docs = [self.documents[i] for i in indices[0] if i != -1]
        if not retrieved_docs:
            return "Could not find any relevant information in memory."
        
        return "### Relevant Information Retrieved from Memory:\n" + "\n---\n".join(retrieved_docs)

retrieval_memory = RetrievalMemory(k=2)
agent = AIAgent(memory_strategy=retrieval_memory)

agent.chat("I am planning a vacation to Japan for next spring.")
agent.chat("For my software project, I'm using the React framework for the frontend.")
agent.chat("I want to visit Tokyo and Kyoto while I'm on my trip.")
agent.chat("The backend of my project will be built with Django.")
agent.chat("What cities am I planning to visit on my vacation?")

==== NEW INTERACTION ====
User: What cities am I planning to visit on my vacation?  
--- Agent Debug Info ---  
[Full Prompt Sent to LLM]:  
---  
SYSTEM: You are a helpful AI assistant.  
USER: MEMORY CONTEXT  
Relevant Information Retrieved from Memory:  
User said: I want to visit Tokyo and Kyoto while I am on my trip.  
---  
User said: I am planning a vacation to Japan for next spring.  
...  

Bot: You are planning to visit Tokyo and Kyoto while on your vacation to Japan next spring.  
>>>> Tokens: 65 | Response Time: 0.53s

class MemoryAugmentedMemory(BaseMemoryStrategy):
    def __init__(self, window_size: int = 2):
        """
        初始化memory-augmented系統(tǒng)。
        
        參數(shù)：
            window_size: 短期memory保留的最近回合數(shù)。
        """
        self.recent_memory = SlidingWindowMemory(window_size=window_size)
        self.memory_tokens = []

    def add_message(self, user_input: str, ai_response: str):
        """添加回合到近期memory，并使用LLM決定是否創(chuàng)建持久memory token。"""
        self.recent_memory.add_message(user_input, ai_response)
        
        fact_extraction_prompt = (
            f"Analyze the following conversation turn. Does it contain a core fact, preference, or decision that should be remembered long-term? "
            f"Examples include user preferences ('I hate flying'), key decisions ('The budget is $1000'), or important facts ('My user ID is 12345').\n\n"
            f"Conversation Turn:\nUser: {user_input}\nAI: {ai_response}\n\n"
            f"If it contains such a fact, state the fact concisely in one sentence. Otherwise, respond with 'No important fact.'"
        )
        
        extracted_fact = generate_text("You are a fact-extraction expert.", fact_extraction_prompt)
        
        if "no important fact" not in extracted_fact.lower():
            print(f"--- [Memory Augmentation: New memory token created: '{extracted_fact}'] ---")
            self.memory_tokens.append(extracted_fact)

    def get_context(self, query: str) -> str:
        """結(jié)合短期近期對話和長期memory tokens構(gòu)建上下文。"""
        recent_context = self.recent_memory.get_context(query)
        memory_token_context = "\n".join([f"- {token}" for token in self.memory_tokens])
        return f"### Key Memory Tokens (Long-Term Facts):\n{memory_token_context}\n\n### Recent Conversation:\n{recent_context}"

mem_aug_memory = MemoryAugmentedMemory(window_size=2)
agent = AIAgent(memory_strategy=mem_aug_memory)

agent.chat("Please remember this for all future interactions: I am severely allergic to peanuts.")
agent.chat("Okay, let's talk about recipes. What's a good idea for dinner tonight?")
agent.chat("That sounds good. What about a dessert option?")
agent.chat("Could you suggest a Thai green curry recipe? Please ensure it's safe for me.")

==== NEW INTERACTION ====
User: Please remember this for all future interactions: I am severely allergic to peanuts.  
--- [Memory Augmentation: New memory token created: 'The user has a severe allergy to peanuts.'] ---  
Bot: I have taken note of your long-term fact: You are severely allergic to peanuts. I will keep this in mind...  
>>>> Tokens: 45 | Response Time: 1.32s

...

==== NEW INTERACTION ====
User: Could you suggest a Thai green curry recipe? Please ensure it is safe for me.  
--- Agent Debug Info ---  
[Full Prompt Sent to LLM]:  
---  
SYSTEM: You are a helpful AI assistant.  
USER: MEMORY CONTEXT  
Key Memory Tokens (Long-Term Facts):  
- The user has a severe allergy to peanuts.  
...  
Recent Conversation:  
User: Okay, lets talk about recipes...  
...  

Bot: Of course. Given your peanut allergy, it is very important to be careful with Thai cuisine as many recipes use peanuts or peanut oil. Here is a peanut-free Thai green curry recipe...  
>>>> Tokens: 712 | Response Time: 6.45s

class HierarchicalMemory(BaseMemoryStrategy):
    def __init__(self, window_size: int = 2, k: int = 2, embedding_dim: int = 3584):
        """
        初始化hierarchical memory系統(tǒng)。
        
        參數(shù)：
            window_size: 短期working memory的回合數(shù)。
            k: 從long-term memory檢索的documents數(shù)。
            embedding_dim: long-term memory的嵌入向量維度。
        """
        print("Initializing Hierarchical Memory...")
        self.working_memory = SlidingWindowMemory(window_size=window_size)
        self.long_term_memory = RetrievalMemory(k=k, embedding_dim=embedding_dim)
        self.promotion_keywords = ["remember", "rule", "preference", "always", "never", "allergic"]

    def add_message(self, user_input: str, ai_response: str):
        """添加消息到working memory，基于內(nèi)容有條件提升到long-term memory。"""
        self.working_memory.add_message(user_input, ai_response)
        
        if any(keyword in user_input.lower() for keyword in self.promotion_keywords):
            print(f"--- [Hierarchical Memory: Promoting message to long-term storage.] ---")
            self.long_term_memory.add_message(user_input, ai_response)

    def get_context(self, query: str) -> str:
        """結(jié)合long-term和short-term memory層構(gòu)建豐富上下文。"""
        working_context = self.working_memory.get_context(query)
        long_term_context = self.long_term_memory.get_context(query)
        return f"### Retrieved Long-Term Memories:\n{long_term_context}\n\n### Recent Conversation (Working Memory):\n{working_context}"

hierarchical_memory = HierarchicalMemory()
agent = AIAgent(memory_strategy=hierarchical_memory)

agent.chat("Please remember my User ID is AX-7890.")
agent.chat("Let's chat about the weather. It's very sunny today.")
agent.chat("I'm planning to go for a walk later.")
agent.chat("I need to log into my account, can you remind me of my ID?")

==== NEW INTERACTION ====
User: Please remember my User ID is AX-7890.  
--- [Hierarchical Memory: Promoting message to long-term storage.] ---  
Bot: You have provided your User ID as AX-7890, which has been stored in long-term memory for future reference.  
...

==== NEW INTERACTION ====
User: I need to log into my account, can you remind me of my ID?  
--- Agent Debug Info ---  
[Full Prompt Sent to LLM]:  
---  
SYSTEM: You are a helpful AI assistant.  
USER: ### MEMORY CONTEXT  
### Retrieved Long-Term Memories:  
### Relevant Information Retrieved from Memory:  
User said: Please remember my User ID is AX-7890.  
...  
### Recent Conversation (Working Memory):  
User: Let's chat about the weather...  
User: I'm planning to go for a walk later...  

Bot: Your User ID is AX-7890. You can use this to log into your account. Is there anything else I can assist you with?  
>>>> Tokens: 452 | Response Time: 2.06s

import networkx as nx
import re

class GraphMemory(BaseMemoryStrategy):
    def __init__(self):
        """初始化memory，包含空的NetworkX有向圖。"""
        self.graph = nx.DiGraph()

    def _extract_triples(self, text: str) -> list[tuple[str, str, str]]:
        """使用LLM從文本提取(Subject, Relation, Object)三元組。"""
        print("--- [Graph Memory: Attempting to extract triples from text.] ---")
        extraction_prompt = (
            f"You are a knowledge extraction engine. Your task is to extract Subject-Relation-Object triples from the given text. "
            f"Format your output strictly as a list of Python tuples. For example: [('Sam', 'works_for', 'Innovatech'), ('Innovatech', 'focuses_on', 'Energy')]. "
            f"If no triples are found, return an empty list [].\n\n"
            f"Text to analyze:\n\"""{text}\""""
        )
        
        response_text = generate_text("You are an expert knowledge graph extractor.", extraction_prompt)
        
        try:
            found_triples = re.findall(r"\(['\"](.*?)['\"],\s*['\"](.*?)['\"],\s*['\"](.*?)['\"]\)", response_text)
            print(f"--- [Graph Memory: Extracted triples: {found_triples}] ---")
            return found_triples
        except Exception as e:
            print(f"Could not parse triples from LLM response: {e}")
            return []

    def add_message(self, user_input: str, ai_response: str):
        """從最新對話回合提取三元組并添加到knowledge graph。"""
        full_text = f"User: {user_input}\nAI: {ai_response}"
        triples = self._extract_triples(full_text)
        for subject, relation, obj in triples:
            self.graph.add_edge(subject.strip(), obj.strip(), relatinotallow=relation.strip())

    def get_context(self, query: str) -> str:
        """通過查詢中的實體查找graph，返回所有已知關系。"""
        if not self.graph.nodes:
            return "The knowledge graph is empty."
        
        query_entities = [word.capitalize() for word in query.replace('?','').split() if word.capitalize() in self.graph.nodes]
        
        if not query_entities:
            return "No relevant entities from your query were found in the knowledge graph."
        
        context_parts = []
        for entity in set(query_entities):
            for u, v, data in self.graph.out_edges(entity, data=True):
                context_parts.append(f"{u} --[{data['relation']}]--> {v}")
            for u, v, data in self.graph.in_edges(entity, data=True):
                context_parts.append(f"{u} --[{data['relation']}]--> {v}")
        
        return "### Facts Retrieved from Knowledge Graph:\n" + "\n".join(sorted(list(set(context_parts))))

graph_memory = GraphMemory()
agent = AIAgent(memory_strategy=graph_memory)

agent.chat("A person named Clara works for a company called 'FutureScape'.")
agent.chat("FutureScape is based in Berlin.")
agent.chat("Clara's main project is named 'Odyssey'.")
agent.chat("Tell me about Clara's project.")

==== NEW INTERACTION ====
User: A person named Clara works for a company called 'FutureScape'.  
--- [Graph Memory: Attempting to extract triples from text.] ---  
--- [Graph Memory: Extracted triples: [('Clara', 'works_for', 'FutureScape')]] ---  
Bot: Understood. I've added the fact that Clara works for FutureScape to my knowledge graph.  
...

==== NEW INTERACTION ====
User: Clara's main project is named 'Odyssey'.  
--- [Graph Memory: Attempting to extract triples from text.] ---  
--- [Graph Memory: Extracted triples: [('Clara', 'manages_project', 'Odyssey')]] ---  
Bot: Got it. I've noted that Clara's main project is Odyssey.  

==== NEW INTERACTION ====
User: Tell me about Clara's project.  
--- Agent Debug Info ---  
[Full Prompt Sent to LLM]:  
---  
SYSTEM: You are a helpful AI assistant.  
USER: ### MEMORY CONTEXT  
### Facts Retrieved from Knowledge Graph:  
Clara --[manages_project]--> Odyssey  
Clara --[works_for]--> FutureScape  
...  

Bot: Based on my knowledge graph, Clara's main project is named 'Odyssey', and Clara works for the company FutureScape.  
>>>> Tokens: 78 | Response Time: 1.5s

class CompressionMemory(BaseMemoryStrategy):
    def __init__(self):
        """初始化memory，包含空的compressed facts列表。"""
        self.compressed_facts = []

    def add_message(self, user_input: str, ai_response: str):
        """使用LLM將最新回合壓縮為簡潔事實語句。"""
        text_to_compress = f"User: {user_input}\nAI: {ai_response}"
        
        compression_prompt = (
            f"You are a data compression engine. Your task is to distill the following text into its most essential, factual statement. "
            f"Be as concise as possible, removing all conversational fluff. For example, 'User asked about my name and I, the AI, responded that my name is an AI assistant' should become 'User asked for AI's name.'\n\n"
            f"Text to compress:\n\"{text_to_compress}\""
        )
        
        compressed_fact = generate_text("You are an expert data compressor.", compression_prompt)
        print(f"--- [Compression Memory: New fact stored: '{compressed_fact}'] ---")
        self.compressed_facts.append(compressed_fact)

    def get_context(self, query: str) -> str:
        """返回所有compressed facts列表，格式為項目符號列表。"""
        if not self.compressed_facts:
            return "No compressed facts in memory."
        return "### Compressed Factual Memory:\n- " + "\n- ".join(self.compressed_facts)

compression_memory = CompressionMemory()
agent = AIAgent(memory_strategy=compression_memory)

agent.chat("Okay, I've decided on the venue for the conference. It's going to be the 'Metropolitan Convention Center'.")
agent.chat("The date is confirmed for October 26th, 2025.")
agent.chat("Could you please summarize the key details for the conference plan?")

==== NEW INTERACTION ====
User: Okay, I've decided on the venue for the conference. It's going to be the 'Metropolitan Convention Center'.  
--- [Compression Memory: New fact stored: 'The conference venue has been decided as the 'Metropolitan Convention Center'.'] ---  
Bot: Great! The Metropolitan Convention Center is an excellent choice. What's next on our planning list?  
...

==== NEW INTERACTION ====
User: The date is confirmed for October 26th, 2025.  
--- [Compression Memory: New fact stored: 'The conference date is confirmed for October 26th, 2025.'] ---  
Bot: Perfect, I've noted the date.  
...

==== NEW INTERACTION ====
User: Could you please summarize the key details for the conference plan?  
--- Agent Debug Info ---  
[Full Prompt Sent to LLM]:  
---  
SYSTEM: You are a helpful AI assistant.  
USER: ### MEMORY CONTEXT  
### Compressed Factual Memory:  
- The conference venue has been decided as the 'Metropolitan Convention Center'.  
- The conference date is confirmed for October 26th, 2025.  
...  

Bot: Of course. Based on my notes, here are the key details for the conference plan:  
- **Venue:** Metropolitan Convention Center  
- **Date:** October 26th, 2025  
>>>> Tokens: 48 | Response Time: 1.2s

class OSMemory(BaseMemoryStrategy):
    def __init__(self, ram_size: int = 2):
        """
        初始化OS-like memory系統(tǒng)。

        參數(shù)：
            ram_size: active memory (RAM)保留的最大對話回合數(shù)。
        """
        self.ram_size = ram_size
        self.active_memory = deque()
        self.passive_memory = {}
        self.turn_count = 0

    def add_message(self, user_input: str, ai_response: str):
        """添加回合到active memory，RAM滿時將最舊回合paging out到passive memory。"""
        turn_id = self.turn_count
        turn_data = f"User: {user_input}\nAI: {ai_response}"
        
        if len(self.active_memory) >= self.ram_size:
            lru_turn_id, lru_turn_data = self.active_memory.popleft()
            self.passive_memory[lru_turn_id] = lru_turn_data
            print(f"--- [OS Memory: Paging out Turn {lru_turn_id} to passive storage.] ---")
        
        self.active_memory.append((turn_id, turn_data))
        self.turn_count += 1

    def get_context(self, query: str) -> str:
        """提供RAM上下文，模擬page fault從passive memory拉取數(shù)據(jù)。"""
        active_context = "\n".join([data for _, data in self.active_memory])
        
        paged_in_context = ""
        for turn_id, data in self.passive_memory.items():
            if any(word in data.lower() for word in query.lower().split() if len(word) > 3):
                paged_in_context += f"\n(Paged in from Turn {turn_id}): {data}"
                print(f"--- [OS Memory: Page fault! Paging in Turn {turn_id} from passive storage.] ---")
        
        return f"### Active Memory (RAM):\n{active_context}\n\n### Paged-In from Passive Memory (Disk):\n{paged_in_context}"

    def clear(self):
        """清空active和passive memory。"""
        self.active_memory.clear()
        self.passive_memory = {}
        self.turn_count = 0
        print("OS-like memory cleared.")

os_memory = OSMemory(ram_size=2)
agent = AIAgent(memory_strategy=os_memory)

agent.chat("The secret launch code is 'Orion-Delta-7'.")
agent.chat("The weather for the launch looks clear.")
agent.chat("The launch window opens at 0400 Zulu.")
agent.chat("I need to confirm the launch code.")

...

==== NEW INTERACTION ====
User: The launch window opens at 0400 Zulu.  
--- [OS Memory: Paging out Turn 0 to passive storage.] ---  
Bot: PROCESSING NEW LAUNCH WINDOW INFORMATION...  
...

==== NEW INTERACTION ====
User: I need to confirm the launch code.  
--- [OS Memory: Page fault! Paging in Turn 0 from passive storage.] ---  
--- Agent Debug Info ---  
[Full Prompt Sent to LLM]:  
---  
SYSTEM: You are a helpful AI assistant.  
USER: ### MEMORY CONTEXT  
### Active Memory (RAM):  
User: The weather for the launch looks clear.  
...  
User: The launch window opens at 0400 Zulu.  
...  
### Paged-In from Passive Memory (Disk):  
(Paged in from Turn 0): User: The secret launch code is 'Orion-Delta-7'.  
...  

Bot: CONFIRMING LAUNCH CODE: The stored secret launch code is 'Orion-Delta-7'.  
>>>> Tokens: 539 | Response Time: 2.56s