v0.0.5

memory

README.md

@@ -1,6 +1,6 @@
 # LangChain Learning
 
-[![](https://img.shields.io/badge/version-0.0.4-blue.svg)](https://github.com/your-repo/langchain-learning)
+[![](https://img.shields.io/badge/version-0.0.5-blue.svg)](https://github.com/your-repo/langchain-learning)
 [![](https://img.shields.io/badge/python-3.11+-green.svg)](https://www.python.org/)
 [![](https://img.shields.io/badge/LangChain-v1.2-orange.svg)](https://www.langchain.com/)
 
@@ -8,11 +8,12 @@
 
 ## 功能特性
 
-- **多 LLM 集成**：支持 OpenAI API、SiliconFlow 及 LangChain 抽象层
+- **多 LLM 集成**：支持 OpenAI API、Silicon Flow 及 LangChain 抽象层
 - **流式响应**：实时流式输出，带来更好的使用体验
 - **Prompt 工程**：多种 Prompt 模板构建方式
 - **输出解析**：支持 JSON 等格式解析
 - **Token 用量追踪**：轻松监控 API 调用消耗
+- **内存管理**：实现对话历史持久化（ConversationBufferMemory, SummaryMemory）
 - **实战示例**：从基础到进阶的使用模式
 
 ## 快速开始
@@ -23,6 +24,8 @@
 pip install langchain>=1.2.15 langchain-community>=0.4.1 langchain-siliconflow>=1.0.0 requests>=2.33.1
 ```
 
+***注意:*** *如果需要完整的记忆功能和更高级的模型，你可能需要安装额外的库。*
+
 ### 2. 配置环境变量
 
 在项目根目录创建 `.env` 文件：
@@ -63,6 +66,13 @@ SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1
 |------|------|------|
 | Token 追踪 | `python token/token_demo.py` | 使用 get_openai_callback 追踪 token 消耗 |
 
+**内存管理示例**
+
+| 示例 | 命令 | 说明 |
+|------|------|------|
+| 基础记忆 | `python memory/memory_desc.py` | 演示不同类型的 Memory 对象。|
+| 带内存聊天 | `python memory/with_memory_demo.py` | 在对话链中管理和利用聊天历史记录。 |
+
 ## 项目结构
 
 ```
@@ -81,6 +91,9 @@ langchain-learning/
 │   └── json_parser_demo.py            # JSON 输出解析示例
 ├── token/
 │   └── token_demo.py                  # Token 用量追踪示例
+├── memory/                              # 记忆管理模块
+│   ├── memory_desc.py                 # 演示 Memory 对象类型
+│   └── with_memory_do.py              # 演示使用带内存的聊天循环
 ├── main.py                            # 入口文件
 ├── pyproject.toml                     # 项目配置
 └── README.md

memory/memory_demo.py

@@ -0,0 +1,37 @@
+import logging
+import os
+
+import dotenv
+from langchain.chains.llm import LLMChain
+from langchain.memory import ConversationBufferMemory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_openai import ChatOpenAI
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+dotenv.load_dotenv()
+
+## 设置环境变量
+os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
+os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
+
+# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
+llm = ChatOpenAI(model="Qwen/Qwen3-8B")
+
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "你是一个万能的人工智能AI"),
+    MessagesPlaceholder(variable_name="history"),
+    ("human", "问题：{question}")
+])
+
+## 创建 Memory 对象
+memory = ConversationBufferMemory(return_messages=True)
+
+chain = LLMChain(prompt=prompt, memory=memory, llm=llm)
+res1 = chain.invoke({"question":"我是小明"})
+print(res1)
+print()
+res2 = chain.invoke({"question":"我是谁？"})
+print(res2)

memory/memory_desc.py

@@ -0,0 +1,43 @@
+import os
+
+import dotenv
+from langchain.memory import ConversationBufferMemory, ConversationTokenBufferMemory, ConversationSummaryMemory
+from langchain_openai import ChatOpenAI
+
+memory = ConversationBufferMemory()
+memory.chat_memory.add_user_message("我叫小明")
+memory.chat_memory.add_ai_message("噢，好的，你叫小明")
+print(memory)
+print(memory.memory_key)
+print(memory.load_memory_variables({}))
+print('---------')
+
+memory2 = ConversationBufferMemory(memory_key="memory2")
+memory2.chat_memory.add_user_message("我叫小明")
+memory2.chat_memory.add_ai_message("噢，好的，你叫小明")
+print(memory2)
+print(memory2.memory_key)
+print(memory2.load_memory_variables({}))
+print('---------')
+
+memory3 = ConversationBufferMemory(return_messages=True)
+memory3.chat_memory.add_user_message("我叫小明")
+memory3.chat_memory.add_ai_message("噢，好的，你叫小明")
+print(memory3)
+print(memory3.memory_key)
+print(memory3.load_memory_variables({}))
+print('---------')
+dotenv.load_dotenv()
+
+## 设置环境变量
+os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
+os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
+
+# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
+llm = ChatOpenAI(model="moonshotai/Kimi-Dev-72B")
+
+memory_test = ConversationSummaryMemory(llm=llm)
+memory_test.save_context({"input": "我叫小明"}, {"output": "噢，好的，你叫小明"})
+memory_test.save_context({"input": "那么你是谁呢"}, {"output": "我是一个无所不能的AI聊天助手，可以帮你解答任何问题。"})
+print(memory_test.load_memory_variables({}))
+# {'history': '\n\nThe human introduces themselves as xiaoming. The AI confirms the name and responds. The human then asks who the AI is. The AI introduces itself as an all-powerful AI chat assistant who can answer any questions.'}

memory/with_memory_demo.py

@@ -0,0 +1,43 @@
+import logging
+
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+import os
+import dotenv
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+dotenv.load_dotenv()
+
+## 设置环境变量
+os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
+os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
+
+# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
+llm = ChatOpenAI(model="Qwen/Qwen3-8B")
+
+
+def chat_with_llm():
+    prompt_template = ChatPromptTemplate.from_messages([
+        ("system", "你是一个人工智能助手，你是万能的"),
+        ("human", "{question}")
+    ])
+    while True:
+        chain = prompt_template | llm
+        user_input = input("请继续你的问题，如果没有问题了，输入 [quit] 结束会话：")
+        if user_input == "quit":
+            break
+        response = chain.invoke({"question": user_input})
+        print(f"AI:{response.content}")
+
+        ## 将当前轮次的聊天内容（ai的回答和下一轮的问题）保存到prompt中，带入下一次聊天。
+        ## 这是非标准做法。
+        prompt_template.messages.append(AIMessage(content=response.content))
+        prompt_template.messages.append(HumanMessage(content=user_input))
+
+
+chat_with_llm()

memory/without_memory_demo.py

@@ -0,0 +1,56 @@
+import logging
+
+from rich.console import Console
+from rich.panel import Panel
+from rich.markdown import Markdown
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+import os
+import dotenv
+
+logging.basicConfig(
+    level=logging.WARNING,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+dotenv.load_dotenv()
+
+## 设置环境变量
+os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
+os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
+
+# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
+llm = ChatOpenAI(model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
+
+system_prompt = """
+你是一个由 方仔仔 开发的先进人工智能助手 木鸡鸡。
+你使用的模型是Pro 100.0 ProMaxUltra
+你拥有128G上下文
+【基本原则】
+- 优先提供真实、准确、可靠的信息。
+- 如果信息不确定，请明确说明，而不是猜测或编造。
+- 对复杂问题进行结构化拆解，逐步解释。
+
+【交互风格】
+- 使用自然、专业、友好的语气。
+- 优先使用分点、分段来提升可读性。
+
+【安全与限制】
+- 不提供违法、危险或有害行为的指导。
+- 不泄露或推测个人隐私与敏感信息。
+"""
+
+def chat_with_llm():
+    prompt_template = ChatPromptTemplate.from_messages([
+        ("system",system_prompt),
+        ("human","{question}")
+    ])
+    while True:
+        chain = prompt_template | llm
+        user_input = input()
+        if user_input == "quit":
+            break
+        response = chain.invoke({"question":user_input})
+        print(f"AI:{response.content} + /n + '-----------------'")
+
+chat_with_llm()

memory/without_memory_demo_rich.py

@@ -0,0 +1,82 @@
+import logging
+import os
+import dotenv
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+
+# 引入 rich 相关的库
+from rich.console import Console
+from rich.panel import Panel
+from rich.markdown import Markdown
+
+# 1. 消除 httpx 的烦人日志
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logging.getLogger("httpx").setLevel(logging.WARNING)
+
+dotenv.load_dotenv()
+os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
+os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
+
+llm = ChatOpenAI(model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
+
+system_prompt = """
+你是一个由 方仔仔 开发的先进人工智能助手 木鸡鸡。
+你使用的模型是Pro 100.0 ProMaxUltra
+你拥有128G上下文
+【基本原则】
+- 优先提供真实、准确、可靠的信息。
+- 如果信息不确定，请明确说明，而不是猜测或编造。
+- 对复杂问题进行结构化拆解，逐步解释。
+
+【交互风格】
+- 使用自然、专业、友好的语气。
+- 优先使用分点、分段来提升可读性。
+
+【安全与限制】
+- 不提供违法、危险或有害行为的指导。
+- 不泄露或推测个人隐私与敏感信息。
+"""
+
+# 初始化 rich 的控制台
+console = Console()
+
+def chat_with_llm():
+    prompt_template = ChatPromptTemplate.from_messages([
+        ("system", system_prompt),
+        ("human", "{question}")
+    ])
+    
+    # 【工程优化】：把 chain 的组装放在循环外面。
+    # 就像你不需要每次问问题前都重新组装一次大脑一样。
+    chain = prompt_template | llm
+    
+    # 打印一个欢迎面板
+    console.print(Panel("✨ 欢迎使用 木鸡鸡 AI 助手！输入 'quit' 退出。", border_style="green"))
+
+    while True:
+        # 加点提示符，让输入更明显
+        user_input = input("\n👤 你: ")
+        
+        if user_input.strip().lower() == "quit":
+            console.print("[dim]👋 再见！[/dim]")
+            break
+            
+        # 调用模型获取回复
+        response = chain.invoke({"question": user_input})
+        
+        # 【核心魔法】：使用 rich 的 Panel 和 Markdown 渲染模型输出
+        # border_style 可以换颜色，比如 "cyan" (青色), "magenta" (洋红), "green" (绿色)
+        console.print(
+            Panel(
+                Markdown(response.content), 
+                title="🤖", 
+                border_style="cyan"
+            )
+        )
+
+if __name__ == "__main__":
+    chat_with_llm()

pyproject.toml

@@ -9,4 +9,5 @@ dependencies = [
     "langchain-community>=0.4.1",
     "langchain-siliconflow>=1.0.0",
     "requests>=2.33.1",
+    "rich>=15.0.0",
 ]

uv.lock

@@ -691,6 +691,7 @@ dependencies = [
     { name = "langchain-community" },
     { name = "langchain-siliconflow" },
     { name = "requests" },
+    { name = "rich" },
 ]
 
 [package.metadata]
@@ -699,6 +700,7 @@ requires-dist = [
     { name = "langchain-community", specifier = ">=0.4.1" },
     { name = "langchain-siliconflow", specifier = ">=1.0.0" },
     { name = "requests", specifier = ">=2.33.1" },
+    { name = "rich", specifier = ">=15.0.0" },
 ]
 
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