Upload langchain_react.py

langchain_react.py

@@ -0,0 +1,55 @@
+# 完整部署示例代码
+from langchain_community.llms import Ollama
+from langchain_community.document_loaders import DirectoryLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_ollama import OllamaEmbeddings
+from langchain import hub
+from langchain.agents import create_react_agent, AgentExecutor
+from langchain.tools.retriever import create_retriever_tool
+import nltk
+nltk.download('punkt_tab')
+nltk.download('averaged_perceptron_tagger_eng')
+# 首先还得保证下面的库安装了
+# pip install unstructured
+# pip install chromadb
+# pip install typing_extensions (如果python版本是3.8及以下)
+
+# 1. 初始化LLM
+llm = Ollama(model="llama3.1:8b-instruct", temperature=0, num_ctx=8192)
+
+# 2. 加载和准备文档 TODO: 修改path/to/articles为每个TCE对应的实际目录
+loader = DirectoryLoader('/home/weishaohang/workspace/Omni-Temp/test_articles', glob="**/*.txt")
+# test_articles是一个目录，下面是该次被检索的文件
+# .txt文件是该次被检索的文件
+
+documents = loader.load()
+"""[Document(metadata={'source': '/home/weishaohang/workspace/Omni-Temp/test_articles/article2.txt'}, page_content='bbbbbb'), Document(metadata={'source': '/home/weishaohang/workspace/Omni-Temp/test_articles/article3.txt'}, page_content='cccccc'), Document(metadata={'source': '/home/weishaohang/workspace/Omni-Temp/test_articles/article1.txt'}, page_content='aaaaaa')]"""
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=128)   # 512是每个chunk的大小，128是chunk之间的重叠大小。通过重复内容桥接相邻文本块，保障关键信息不因分割而丢失。
+texts = text_splitter.split_documents(documents)
+"""[Document(metadata={'source': '/home/weishaohang/workspace/Omni-Temp/test_articles/article2.txt'}, page_content='bbbbbb'), Document(metadata={'source': '/home/weishaohang/workspace/Omni-Temp/test_articles/article3.txt'}, page_content='cccccc'), Document(metadata={'source': '/home/weishaohang/workspace/Omni-Temp/test_articles/article1.txt'}, page_content='aaaaaa')]"""
+
+# 3. 创建向量数据库
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
+vectorstore = Chroma.from_documents(documents=texts, embedding=embeddings, persist_directory="./chroma_db")
+print(vectorstore)
+exit()
+
+# 4. 创建检索器工具
+retriever = vectorstore.as_retriever(search_kwargs={"k": 3})    # search_kwargs是搜索参数，k是返回的文档数量
+retriever_tool = create_retriever_tool(
+    retriever,
+    name="local_knowledge_base",
+    description="Search for information in local articles collection."
+)
+
+# 5. 创建ReAct Agent
+prompt = hub.pull("hwchase17/react")
+print(prompt)
+# agent = create_react_agent(llm, [retriever_tool], prompt)
+# agent_executor = AgentExecutor(agent=agent, tools=[retriever_tool], verbose=True)
+
+# # 6. 运行Agent
+# response = agent_executor.invoke({"input": "Your benchmark question here?"})
+# print(response['output'])