v0.0.2

- prompt

README.md

@@ -1,6 +1,6 @@
 # LangChain Learning
 
-[![](https://img.shields.io/badge/version-0.0.1-blue.svg)](https://github.com/your-repo/langchain-learning)
+[![](https://img.shields.io/badge/version-0.0.2-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/)
 
@@ -10,6 +10,7 @@
 
 - **多 LLM 集成**：支持 OpenAI API、SiliconFlow 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
 - **实战示例**：从基础到进阶的使用模式
 
 ## 快速开始
@@ -31,25 +32,22 @@ SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1
 
 ### 3. 运行示例
 
-**直接调用 API（requests）**
+**Hello World 示例**
-```bash
-python helloworld/helloworld.py
-```
 
-**LangChain + ChatOpenAI 接口**
+| 示例 | 命令 | 说明 |
-```bash
+|------|------|------|
-python helloworld/helloworld_langchain_openai.py
+| 直接调用 API | `python helloworld/helloworld.py` | 使用 requests 直接调用 SiliconFlow API |
-```
+| LangChain + ChatOpenAI | `python helloworld/helloworld_langchain_openai.py` | 通过 OpenAI 接口调用 LLM |
+| LangChain + ChatSiliconFlow | `python helloworld/helloworld_siliconflow.py` | 使用 LangChain SiliconFlow 集成 |
+| OpenAI 客户端 + SiliconFlow | `python helloworld/openai_siliconflow.py` | OpenAI 客户端兼容 SiliconFlow |
 
-**LangChain + ChatSiliconFlow**
+**Prompt 示例**
-```bash
-python helloworld/helloworld_siliconflow.py
-```
 
-**OpenAI 客户端 + SiliconFlow**
+| 示例 | 命令 | 说明 |
-```bash
+|------|------|------|
-python helloworld/openai_siliconflow.py
+| PromptTemplate | `python prompt/prompt_demo.py` | 演示 PromptTemplate 模板构建 |
-```
+| Few-shot Learning | `python prompt/fewshot_demo.py` | 带示例的少样本提示学习 |
+| 从文件加载 Prompt | `python prompt/promt_from_file.py` | 从 YAML 文件加载提示词模板 |
 
 ## 项目结构
 
@@ -60,6 +58,11 @@ langchain-learning/
 │   ├── helloworld_langchain_openai.py # LangChain + ChatOpenAI
 │   ├── helloworld_siliconflow.py      # LangChain + ChatSiliconFlow
 │   └── openai_siliconflow.py          # OpenAI 客户端 + SiliconFlow
+├── prompt/
+│   ├── prompt_demo.py                 # PromptTemplate 模板示例
+│   ├── fewshot_demo.py                # Few-shot Learning 示例
+│   ├── promt_from_file.py             # 从文件加载 Prompt
+│   └── prompt_from_file.yaml          # Prompt 模板文件
 ├── main.py                            # 入口文件
 ├── pyproject.toml                     # 项目配置
 └── README.md

prompt/fewshot_demo.py

@@ -0,0 +1,58 @@
+import logging
+
+from langchain_core.prompts import FewShotChatMessagePromptTemplate, ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+import os
+import dotenv
+
+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")
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+
+examples = [
+    {"input": "1 || 1", "output": "2"},
+    {"input": "1 || 2", "output": "3"},
+    {"input": "1 || 3", "output": "4"}
+]
+
+example_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("human", "{input}"),
+        ("ai", "{output}"),
+    ]
+)
+
+few_show_prompt = FewShotChatMessagePromptTemplate(
+    examples=examples,
+    example_prompt=example_prompt
+)
+
+
+final_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system","你是一个数学天才"),
+        few_show_prompt,
+        ("human", "{input}")
+    ]
+)
+# question = final_prompt.invoke(input = {"input":"1 || 10"})
+# # llm  : 1||10  ?
+# response = llm.invoke(question)
+# print(response)
+# 链式调用
+llm.invoke(final_prompt.invoke(input = {"input":"1 || 10"}))
+
+
+# 提示词的invoke输出给到了llm作为输入，和管道的概念一模一样
+chain = final_prompt | llm
+chain.invoke(input = {"input":"1 || 10"})

prompt/prompt_demo.py

@@ -0,0 +1,42 @@
+import logging
+
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.prompts import PromptTemplate
+import langchain
+
+from langchain_openai import ChatOpenAI
+import os
+import dotenv
+
+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")
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+print(langchain.__version__)
+
+## prompt
+system_message = SystemMessage(
+    content="你是一个大数据方向的专家，用户提问时，你只需要精简的回答问题，回答内容不超过100个token")
+human_message = HumanMessage(content="我现在想要学习hive，你帮我指定一个学习计划把")
+message = [system_message, human_message]
+
+print(human_message)
+
+## 1. 创建PromptTemplate
+template = PromptTemplate.from_template(template="我现在想要学习{topic}和{topic2}，你帮我指定一个学习计划把")
+## 2. 构建完整的提示词
+hadoop_prompt = template.format(topic="hadoop",topic2="spark")
+hadoop_prompt2 = template.invoke(input={"topic":"hadoop","topic2":"spark"})
+print(hadoop_prompt)
+print(hadoop_prompt2)
+# response = llm.invoke(hadoop_prompt)
+# print(response)

prompt/prompt_from_file.json

@@ -0,0 +1,5 @@
+{
+  "_type": "prompt",
+  "input_variables": ["topic","role"],
+  "template": "请以{role}的身份介绍一下{topic}"
+}

prompt/prompt_from_file.yaml

@@ -0,0 +1,5 @@
+_type: "prompt"
+input_variables:
+  - topic
+  - role
+template: "请以{role}的身份介绍一下{topic}"

prompt/promt_from_file.py

@@ -0,0 +1,5 @@
+from langchain_core.prompts import load_prompt
+
+prompt = load_prompt("prompt_from_file.yaml",encoding="utf-8")
+print(prompt.format(topic = "spark",role = "老师"))
+print(prompt.invoke(input = {"topic":"hive","role":"大数据专家"}))