v0.0.9

RAG

README.md

@@ -1,14 +1,15 @@
 # LangChain Learning
 
-[![](https://img.shields.io/badge/version-0.0.6-blue.svg)](https://github.com/your-repo/langchain-learning)
+[![](https://img.shields.io/badge/version-0.0.7-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/)
 
-> LangChain 框架学习项目，集成 SiliconFlow & Ollama API
+> LangChain 框架学习项目，集成 SiliconFlow & Ollama API
 
 ## 功能特性
 
 - **多 LLM 集成**：支持 OpenAI API、SiliconFlow、Ollama 及 LangChain 抽象层
+- **RAG 检索增强生成**：基于向量库（FAISS）的文档检索与问答
 - **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
@@ -65,6 +66,12 @@ OLLAMA_API_KEY=ollama
 |------|------|------|
 | JSON 解析器 | `python parser/json_parser_demo.py` | 使用 JsonOutputParser 解析 LLM 输出 |
 
+**RAG 示例**
+
+| 示例 | 命令 | 说明 |
+|------|------|------|
+| 基础 RAG | `python rag/rag_demo.py` | 基于 FAISS 向量库的检索问答系统 |
+
 **Token 用量示例**
 
 | 示例 | 命令 | 说明 |
@@ -104,6 +111,8 @@ langchain-learning/
 │   └── prompt_from_file.json          # Prompt JSON 模板文件
 ├── parser/
 │   └── json_parser_demo.py            # JSON 输出解析示例
+├── rag/
+│   └── rag_demo.py                    # RAG 检索增强生成示例
 ├── token/
 │   └── token_demo.py                  # Token 用量追踪示例
 ├── memory/
@@ -137,6 +146,7 @@ langchain-learning/
 |------|------|
 | 框架 | LangChain |
 | LLM 提供商 | SiliconFlow, Ollama |
+| 向量库 | FAISS |
 | 终端美化 | Rich |
 | 语言 | Python 3.11+ |
 

pyproject.toml

@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
+    "faiss-cpu>=1.13.2",
     "langchain==0.3.27",
     "langchain-community==0.3.31",
     "langchain-siliconflow==0.1.3",

rag/rag_demo.py

@@ -0,0 +1,68 @@
+import logging
+
+from langchain.chains.retrieval_qa.base import RetrievalQA
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+import os
+import dotenv
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+
+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="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
+
+## 1. 准备某个领域的文档：测试相关的知识
+docs = [
+    "等价类划分是一种黑盒测试方法，将输入数据划分为有效等价类和无效等价类。",
+    "边界值分析通常作为等价类划分的补充，重点测试输入输出的边界条件。",
+    "集成测试用于验证模块间接口的正确性，常见策略包括自顶向下和自底向上。",
+    "回归测试是在软件变更后执行的测试，确保原有功能不受新修改影响。",
+    "性能测试包括负载测试、压力测试和耐久性测试，用于评估系统的响应能力。",
+    "测试用例应包含测试ID、模块、前置条件、步骤、预期结果和优先级信息。"
+]
+
+splitter = CharacterTextSplitter()
+
+## 2. 切割文档（可选）
+texts = []
+for doc in docs:
+    chunks = splitter.split_text(doc)
+    texts.extend(chunks)
+    logging.info("文档切分：原文=%s -> %d 个分片",doc,len(chunks))
+logging.info(texts)
+
+## 3. embedding（向量化） 以及 建立向量库
+embeddings = OpenAIEmbeddings(model="netease-youdao/bce-embedding-base_v1")
+## 第一次调用 embedding模型：HTTP Request: POST https://api.siliconflow.cn/v1/embeddings "HTTP/1.1 200 OK"
+vectorstore = FAISS.from_texts(texts,embeddings)
+logging.info("构建向量数据库完成")
+logging.info(vectorstore)
+
+## 4. 构建 RAG的调用链    k参数： topK
+retriever = vectorstore.as_retriever(search_type='similarity',search_kwargs={"k":2})
+## HTTP Request: POST https://api.siliconflow.cn/v1/embeddings "HTTP/1.1 200 OK"
+chain = RetrievalQA.from_chain_type(llm=llm,retriever=retriever)
+
+query = "什么是等价类划分？"
+
+## 检索过程探索
+retrieved_docs = retriever.get_relevant_documents(query)
+logging.info("---------")
+for retrieved_doc in retrieved_docs:
+    logging.info(retrieved_doc)
+logging.info("---------")
+
+## 5. 查询数据（通过模型自己去查数据库）
+## HTTP Request: POST https://api.siliconflow.cn/v1/chat/completions "HTTP/1.1 200 OK"
+response = chain.invoke(query)
+logging.info(response)
+

uv.lock

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 ]
 
+[[package]]
+name = "faiss-cpu"
+version = "1.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+]
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+]
+
 [[package]]
 name = "frozenlist"
 version = "1.8.0"
@@ -673,6 +698,7 @@ name = "langchain-learning"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "faiss-cpu" },
     { name = "langchain" },
     { name = "langchain-community" },
     { name = "langchain-siliconflow" },
@@ -682,6 +708,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "faiss-cpu", specifier = ">=1.13.2" },
     { name = "langchain", specifier = "==0.3.27" },
     { name = "langchain-community", specifier = "==0.3.31" },
     { name = "langchain-siliconflow", specifier = "==0.1.3" },