{"id":53811,"date":"2025-02-16T09:55:47","date_gmt":"2025-02-16T01:55:47","guid":{"rendered":"https:\/\/fwq.ai\/blog\/53811\/"},"modified":"2025-02-16T09:55:47","modified_gmt":"2025-02-16T01:55:47","slug":"langsmith%e7%ae%80%e6%98%8e%e6%95%99%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/fwq.ai\/blog\/53811\/","title":{"rendered":"LangSmith\u7b80\u660e\u6559\u7a0b"},"content":{"rendered":"<p>\u672c\u6587\u57fa\u4e8e\u6211\u7684\u4e66\u548c\u5173\u4e8e LLM \u7684\u7684\u5185\u5bb9\u3002<\/p>\n<p>\u4e3a\u4e86\u521b\u5efa\u8fd9\u7bc7\u6587\u7ae0\uff0c\u6211\u5c06\u4f7f\u7528\u4e0a\u4e00\u7bc7\u6587\u7ae0\u4e2d\u6784\u5efa\u7684\u4ee3\u7406\uff1a\u5982\u4f55\u521b\u5efa\u533b\u7597\u4ee3\u7406\/RAG \u7cfb\u7edf\u3002<\/p>\n<p>\u4f46\u522b\u62c5\u5fc3\uff1b\u6587\u7ae0\u548c\u968f\u9644\u7684\u7b14\u8bb0\u672c\u90fd\u5305\u542b\u6240\u6709\u5fc5\u8981\u7684\u4ee3\u7801\u3002\u5173\u4e8e\u5982\u4f55\u521b\u5efa\u4ee3\u7406\u7684\u89e3\u91ca\u4e0d\u4f1a\u50cf\u63d0\u5230\u7684\u6587\u7ae0\u4e2d\u90a3\u6837\u8be6\u7ec6\uff0c\u56e0\u4e3a\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u5c06\u66f4\u591a\u5730\u5173\u6ce8 LangSmith \u4ee5\u53ca\u5982\u4f55\u8ddf\u8e2a\u4ee3\u7406\u7684\u5185\u90e8\u6d41\u91cf\u3002<\/p>\n<p>\u4e0e\u5f80\u5e38\u4e00\u6837\uff0c\u672c\u6587\u5c06\u57fa\u4e8e\u7b14\u8bb0\u672c\u4e2d\u7684\u4ee3\u7801\uff0c\u53ef\u4ee5\u5728\u627e\u5230\u3002<\/p>\n<p>\u6211\u7684\u5efa\u8bae\u662f\u5728\u9605\u8bfb\u6587\u7ae0\u65f6\u6253\u5f00\u7b14\u8bb0\u672c\uff0c\u5e76\u5728\u5b8c\u6210\u540e\u521b\u5efa\u81ea\u5df1\u7684\u7248\u672c\u3002<\/p>\n<h2>1\u3001\u52a0\u8f7d\u6570\u636e\u96c6<\/h2>\n<p>\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u52a0\u8f7d\u6570\u636e\u96c6\u3002\u6b63\u5982\u6211\u4e4b\u524d\u63d0\u5230\u7684\uff0c\u6211\u5c06\u5305\u542b\u4ee3\u7801\uff0c\u4f46\u4e0d\u63d0\u4f9b\u8be6\u7ec6\u89e3\u91ca\uff1a<\/p>\n<pre><code>!pip install -q langchain==0.3.0\n!pip install -q langchain-openai==0.2.0\n!pip install -q langchainhub==0.1.21\n!pip install -q datasets==3.0.0\n!pip install -q chromadb==0.5.5\n!pip install -q langchain-community==0.3.0\n\nfrom datasets import load_dataset\ndata = load_dataset(\"keivalya\/MedQuad-MedicalQnADataset\", split='train')\ndata = data.to_pandas()\n# Uncoment this line if you want to limit the size of the data.\n# data = data[0:100]<\/code><\/pre>\n<p>\u73b0\u5728\u4f60\u5df2\u5c06\u6570\u636e\u96c6\u52a0\u8f7d\u5230\u53d8\u91cf Data \u4e2d\u3002\u8bf7\u8bb0\u4f4f\uff0c\u5982\u679c\u4f60\u6b63\u5728\u8fd0\u884c\u6d4b\u8bd5\uff0c\u6700\u597d\u53ea\u4f7f\u7528\u90e8\u5206\u6570\u636e\u4ee5\u8282\u7701\u6267\u884c\u65f6\u95f4\u3002<\/p>\n<p>\u6211\u4eec\u7684\u4ee3\u7406\u4f7f\u7528\u7684\u5de5\u5177\u662f\u4e00\u4e2a\u68c0\u7d22\u5668\uff0c\u5b83\u5c06\u641c\u7d22\u5b58\u50a8\u5728\u77e2\u91cf\u6570\u636e\u5e93\u4e2d\u7684\u4fe1\u606f\uff0c\u672c\u8d28\u4e0a\u5145\u5f53\u4e00\u4e2a\u7b80\u5355\u7684 RAG \u7cfb\u7edf\u3002\u6211\u5c06\u4f7f\u7528\u7684\u77e2\u91cf\u6570\u636e\u5e93\u662f ChromaDB\uff0c\u7531\u4e8e\u4ee3\u7406\u5c06\u4f7f\u7528 LangChain \u6784\u5efa\uff0c\u56e0\u6b64\u9700\u8981\u52a0\u8f7d\u4e00\u4e9b\u5e93\u5e76\u52a0\u8f7d\u6570\u636e\u3002<\/p>\n<p>\u5728\u7ee7\u7eed\u4e4b\u524d\uff0c\u6211\u60f3\u5f3a\u8c03\u4e00\u4e0b\uff0c\u521b\u5efa\u4ee3\u7406\u4e0d\u662f\u5fc5\u987b\u4f7f\u7528 LangChain\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u4efb\u4f55\u5176\u4ed6\u6846\u67b6\uff0c\u4f8b\u5982 LlamaIndex\uff0c\u5b83\u4e0e LangSmith \u914d\u5408\u4f7f\u7528\u6548\u679c\u4e5f\u4e00\u6837\u597d\u3002<\/p>\n<h2>2\u3001\u5b58\u50a8\u5728\u77e2\u91cf\u6570\u636e\u5e93\u4e2d<\/h2>\n<pre><code>from langchain.document_loaders import DataFrameLoader\nfrom langchain.vectorstores import Chroma\n\ndf_loader = DataFrameLoader(data, page_content_column=\"Answer\")<\/code><\/pre>\n<p>\u6211\u4eec\u7684\u4ee3\u7406\u7684\u76f8\u5173\u6570\u636e\u5b58\u50a8\u5728 <code>df_loader<\/code> \u53d8\u91cf\u4e2d\uff0c\u9700\u8981\u5c06\u5176\u4f20\u8f93\u5230\u77e2\u91cf\u6570\u636e\u5e93\u3002\u4f46\u662f\uff0c\u7531\u4e8e\u8fd9\u4e9b\u662f\u76f8\u5f53\u957f\u7684\u6587\u672c\uff0c\u56e0\u6b64\u9700\u8981\u5148\u5c06\u5b83\u4eec\u5206\u6210\u5757\u3002<\/p>\n<p>\u8981\u5c06\u6587\u6863\u62c6\u5206\u6210\u4e0d\u540c\u7684\u5757\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 Langchain \u4e2d\u7684 <code>CharacterTextSplitter<\/code> \u7c7b\u3002<\/p>\n<pre><code>from langchain.text_splitter import CharacterTextSplitter\n\ntext_splitter = CharacterTextSplitter(chunk_size=1250,\n                                      separator=\"\\n\",\n                                      chunk_overlap=100)\ntexts = text_splitter.split_documents(df_document)<\/code><\/pre>\n<p><code>chunk_overlap<\/code> (\u91cd\u53e0) \u662f\u5757\u4e4b\u95f4\u91cd\u590d\u7684\u6587\u672c\u91cf\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u6307\u5b9a\u91cd\u53e0\u4e3a 100\uff0c\u5219\u5757\u7684\u524d 100 \u4e2a\u5b57\u7b26\u5c06\u4e0e\u524d\u4e00\u4e2a\u5757\u7684\u540e 100 \u4e2a\u5b57\u7b26\u5339\u914d\u3002<\/p>\n<p>\u5757\u4e0d\u4f1a\u603b\u662f\u5728\u5b57\u7b26 1250 \u5904\u62c6\u5206\uff1b\u4e8b\u5b9e\u4e0a\uff0c\u8fd9\u79cd\u60c5\u51b5\u5f88\u5c11\u53d1\u751f\u3002\u8981\u62c6\u5206\u6587\u672c\uff0c\u8be5\u51fd\u6570\u4f1a\u7b49\u5f85\uff0c\u76f4\u5230\u627e\u5230\u6307\u5b9a\u7684\u5206\u9694\u7b26\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u662f <code>\\n\\n<\/code>\uff0c\u4f46\u6211\u5c06\u5176\u66f4\u6539\u4e3a <code>\\n<\/code>\uff0c\u56e0\u6b64\u6709\u65f6\u4f1a\u5728\u4e4b\u524d\u62c6\u5206\uff0c\u6709\u65f6\u4f1a\u5728\u4e4b\u540e\u62c6\u5206\u3002<\/p>\n<p>\u662f\u65f6\u5019\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u5d4c\u5165\u5e76\u5c06\u5176\u5b58\u50a8\u5230 ChromaDB \u4e2d\u4e86\u3002<\/p>\n<p>\u4f46\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u4f7f\u7528\u5fc5\u8981\u7684\u5bc6\u94a5\u914d\u7f6e\u73af\u5883\u4ee5\u7ee7\u7eed\u5f00\u53d1\uff1a<\/p>\n<pre><code>os.environ[\"LANGCHAIN_API_KEY\"] = getpass(\"LangChain API Key: \")\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"]=\"https:\/\/api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_PROJECT\"]=\"langsmith_test_agent\"<\/code><\/pre>\n<p>\u4f60\u53ef\u4ee5\u4ece LangSmith \u9762\u677f\u4e2d\u7684\u4e2a\u4eba-&gt;\u8bbe\u7f6e\u533a\u57df\u83b7\u53d6\u4f60\u7684 LangChain API \u5bc6\u94a5\u3002<\/p>\n<p>\u73b0\u5728\u4e00\u5207\u90fd\u5df2\u51c6\u5907\u5c31\u7eea\uff0c\u53ef\u4ee5\u521b\u5efa\u6570\u636e\u5e93\u5e76\u5c06\u6b63\u786e\u683c\u5f0f\u7684\u4fe1\u606f\u52a0\u8f7d\u5230\u5176\u4e2d\uff1a<\/p>\n<pre><code># We load the text-embedding-ada-002 model from OpenAI.\nfrom langchain_openai import OpenAIEmbeddings\n\nmodel_name = 'text-embedding-ada-002'\n\nembed = OpenAIEmbeddings(\n    model=model_name\n)<\/code><\/pre>\n<p>\u8981\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u5d4c\u5165\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 OpenAI \u7684\u6a21\u578b\u201ctext-embedding-ada-002\u201d\u3002<\/p>\n<p>\u4f60\u53ef\u4ee5\u901a\u8fc7\u5c06\u6587\u672c\u548c\u5d4c\u5165\u6a21\u578b\u4f20\u9012\u7ed9\u5b83\u6765\u521b\u5efa\u77e2\u91cf\u6570\u636e\u5e93\uff1a<\/p>\n<pre><code>directory_cdb = '\/content\/drive\/MyDrive\/chromadb'\nchroma_db = Chroma.from_documents(\n    df_document, embed, persist_directory=directory_cdb\n)\n\nmodel=\"gpt-4o\"\n#model=\"gpt-3.5-turbo\"\n\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain_openai import OpenAI\nfrom langchain.chains.conversation.memory import ConversationBufferWindowMemory\nfrom langchain.chains import RetrievalQA\n\nllm=OpenAI(temperature=0.0)\n\nconversational_memory = ConversationBufferWindowMemory(\n    memory_key='chat_history',\n    k=4, #Number of messages stored in memory\n    return_messages=True #Must return the messages in the response.\n)\n\nqa = RetrievalQA.from_chain_type(\n    llm=llm,\n    #chain_type=\"stuff\",\n    retriever=chroma_db.as_retriever()\n)<\/code><\/pre>\n<p>\u770b\u8d77\u6765\u6211\u4eec\u53ea\u521b\u5efa\u4e86\u4ee3\u7406\u5c06\u7528\u6765\u8bbf\u95ee\u77e2\u91cf\u6570\u636e\u5e93\u4e2d\u4fe1\u606f\u7684\u68c0\u7d22\u5668\u3002\u8fd9\u662f\u771f\u7684\uff0c\u4f46\u662f\u7531\u4e8e\u4f60\u5df2\u7ecf\u914d\u7f6e\u4e86 LangSmith \u8fd0\u884c\u6240\u9700\u7684\u73af\u5883\u53d8\u91cf\uff0c\u56e0\u6b64\u4e00\u65e6\u4f7f\u7528\u68c0\u7d22\u5668\uff0c\u4fe1\u606f\u5c31\u4f1a\u5f00\u59cb\u8bb0\u5f55\u5728 LangSmith \u4e2d\u3002<\/p>\n<h2>3\u3001\u5728 LangSmith \u4e2d\u8bb0\u5f55<\/h2>\n<p>\u4f8b\u5982\uff0c\u50cf\u8fd9\u6837\u8c03\u7528\u68c0\u7d22\u5668\uff1a<\/p>\n<pre><code>qa.run(\"What is the main symptom of LCM?\")<\/code><\/pre>\n<p>\u5728 LangSmith \u4e2d\u751f\u6210\u4e00\u4e2a\u6761\u76ee\uff0c\u5176\u4e2d\u5305\u542b\uff1a<\/p>\n<ul>\n<li>\u67e5\u8be2\u3002<\/li>\n<li>\u627e\u5230\u7684\u6587\u6863\u3002<\/li>\n<li>\u8fd4\u56de\u7684\u6587\u672c\u3002<\/li>\n<li>\u4ee5\u53ca\u6bcf\u4e2a\u64cd\u4f5c\u6240\u82b1\u8d39\u7684\u65f6\u95f4\u3002<\/li>\n<\/ul>\n<p>  \u68c0\u7d22\u5668\u5728 langSmith \u4e2d\u8bb0\u5f55 <\/p>\n<p>\u4f46\u8fd8\u6709\u66f4\u591a\uff01\u4f60\u8fd8\u4f1a\u53d1\u73b0\u4e0e\u6a21\u578b\u54cd\u5e94\u4e00\u8d77\u521b\u5efa\u7684\u63d0\u793a\u3002<\/p>\n<p>  LLM \u5728 langSmith \u4e2d\u8bb0\u5f55 <\/p>\n<h2>4\u3001\u521b\u5efa\u5e76\u8ddf\u8e2a\u4ee3\u7406<\/h2>\n<p>\u6211\u60f3\u4f60\u548c\u6211\u7b2c\u4e00\u6b21\u4f7f\u7528 LangSmith \u65f6\u4e00\u6837\u60ca\u8bb6\u3002\u201c\u4f46\u6211\u4ec0\u4e48\u90fd\u6ca1\u505a\uff01\u6211\u53ea\u9700\u8981\u8bbe\u7f6e\u51e0\u4e2a\u73af\u5883\u53d8\u91cf\uff0c\u5c31\u8fd9\u6837\uff01\u201d\u55ef\uff0c\u662f\u7684\uff0c\u5b83\u771f\u7684\u5c31\u8fd9\u4e48\u7b80\u5355\u2014\u2014\u800c\u4e14\u8fd8\u6709\u66f4\u591a\u3002<\/p>\n<p>\u8981\u7ee7\u7eed\uff0c\u7b2c\u4e00\u6b65\u662f\u521b\u5efa\u4ee3\u7406\uff0c\u4ece\u5b83\u5c06\u7528\u4e8e\u8bbf\u95ee\u4fe1\u606f\u7684\u5de5\u5177\u5f00\u59cb\uff1a<\/p>\n<pre><code>from langchain.agents import Tool\n\n#Defining the list of tool objects to be used by LangChain.\ntools = [\n    Tool(\n        name='Medical KB',\n        func=qa.run,\n        description=(\n            \"\"\"use this tool when answering medical knowledge queries to get\n            more information about the topic\"\"\"\n        )\n    )\n]<\/code><\/pre>\n<p>\u521b\u5efa\u4ee3\u7406\u548c\u4ee3\u7406\u6267\u884c\u5668\uff1a<\/p>\n<pre><code>from langchain.agents import create_react_agent\nfrom langchain import hub\n\nprompt = hub.pull(\"hwchase17\/react-chat\")\nagent = create_react_agent(\n    tools=tools,\n    llm=llm,\n    prompt=prompt,\n)\n\n# Create an agent executor by passing in the agent and tools\nfrom langchain.agents import AgentExecutor\nagent_executor2 = AgentExecutor(agent=agent,\n                               tools=tools,\n                               verbose=True,\n                               memory=conversational_memory,\n                               max_iterations=30,\n                               max_execution_time=600,\n                               handle_parsing_errors=True\n                               )<\/code><\/pre>\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u53ef\u4ee5\u8c03\u7528\u4ee3\u7406\u5e76\u67e5\u770b\u4fe1\u606f\u5982\u4f55\u5b58\u50a8\u5728 LangChain \u4e2d\uff1a<\/p>\n<pre><code>agent_executor2.invoke({\"input\": \"\"\"I have a patient that can have Botulism,\nhow can I confirm the diagnosis?\"\"\"})<\/code><\/pre>\n<p>\u53ef\u4ee5\u770b\u5230\u8ddf\u8e2a\u7684\u4fe1\u606f\u91cf\u76f8\u5f53\u9ad8\u3002\u5bf9 Retriever \u7684\u8c03\u7528\u5206\u7ec4\u5728 RetrievalQA \u4e0b\uff0c\u6700\u7ec8\u54cd\u5e94\u5728 OpenAI \u4e0b\u3002<\/p>\n<p>\u6b63\u5982\u4f60\u6240\u7406\u89e3\u7684\uff0c\u6211\u65e0\u6cd5\u5c06\u6240\u6709 LangSmith \u5c4f\u5e55\u4e0e\u6240\u6709\u751f\u6210\u7684\u4fe1\u606f\u4e00\u8d77\u5305\u542b\u5728\u5185\u3002\u6211\u8ba4\u4e3a\u8fd9\u4e0d\u4f1a\u589e\u52a0\u592a\u591a\u4ef7\u503c\uff0c\u800c\u4e14\u4f1a\u6d6a\u8d39\u65f6\u95f4\u548c\u7a7a\u95f4\u3002\u5982\u679c\u4f60\u6709\u5174\u8da3\u63a2\u7d22\u5b58\u50a8\u7684\u4fe1\u606f\uff0c\u6700\u597d\u7684\u529e\u6cd5\u662f\u8fd0\u884c\u7b14\u8bb0\u672c\u5e76\u4eb2\u81ea\u67e5\u770b\uff01<\/p>\n<h2>5\u3001\u7ed3\u675f\u8bed<\/h2>\n<p>\u5982\u679c\u4f60\u8fd8\u4e0d\u719f\u6089 LangSmith\uff0c\u6211\u60f3\u4f60\u73b0\u5728\u4f1a\u5bf9\u5b83\u7684\u5f3a\u5927\u529f\u80fd\u548c\u6613\u7528\u6027\u611f\u5230\u60ca\u8bb6\u3002<\/p>\n<p>\u6211\u5efa\u8bae\u4f60\u4e5f\u9605\u8bfb\u8fd9\u7bc7\u6587\u7ae0\uff1a\u3002\u5b83\u4f7f\u7528 LangSmith \u7684\u8bc4\u4f30\u5668\u6765\u6536\u96c6\u6709\u5173\u5927\u578b\u8bed\u8a00\u6a21\u578b\u6027\u80fd\u7684\u6307\u6807\u3002<\/p>\n<p>LangSmith \u662f\u4e0e\u5927\u578b\u8bed\u8a00\u6a21\u578b\u4e16\u754c\u76f8\u5173\u7684\u65b0\u4e00\u6ce2\u5de5\u5177\u7684\u4e00\u90e8\u5206\uff0c\u65e8\u5728\u4f7f\u6784\u5efa\u5e94\u7528\u7a0b\u5e8f\u53d8\u5f97\u66f4\u5bb9\u6613\u3002<\/p>\n<hr>\n","protected":false},"excerpt":{"rendered":"<p>\u672c\u6587\u57fa\u4e8e\u6211\u7684\u4e66\u548c\u5173\u4e8e LLM \u7684\u7684\u5185\u5bb9\u3002 \u4e3a\u4e86\u521b\u5efa\u8fd9\u7bc7\u6587\u7ae0\uff0c\u6211\u5c06\u4f7f\u7528\u4e0a\u4e00\u7bc7\u6587\u7ae0\u4e2d\u6784\u5efa\u7684\u4ee3\u7406\uff1a\u5982\u4f55\u521b\u5efa\u533b\u7597\u4ee3\u7406\/RAG \u7cfb\u7edf\u3002 \u4f46\u522b\u62c5\u5fc3\uff1b\u6587\u7ae0\u548c\u968f\u9644\u7684\u7b14\u8bb0\u672c\u90fd\u5305\u542b\u6240\u6709\u5fc5\u8981\u7684\u4ee3\u7801\u3002\u5173\u4e8e\u5982\u4f55\u521b\u5efa\u4ee3\u7406\u7684\u89e3\u91ca\u4e0d\u4f1a\u50cf\u63d0\u5230\u7684\u6587\u7ae0\u4e2d\u90a3\u6837\u8be6\u7ec6\uff0c\u56e0\u4e3a\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u5c06\u66f4\u591a\u5730\u5173\u6ce8 LangSmith \u4ee5\u53ca\u5982\u4f55\u8ddf\u8e2a\u4ee3\u7406\u7684\u5185\u90e8\u6d41\u91cf\u3002 \u4e0e\u5f80\u5e38\u4e00\u6837\uff0c\u672c\u6587\u5c06\u57fa\u4e8e\u7b14\u8bb0\u672c\u4e2d\u7684\u4ee3\u7801\uff0c\u53ef\u4ee5\u5728\u627e\u5230\u3002 \u6211\u7684\u5efa\u8bae\u662f\u5728\u9605\u8bfb\u6587\u7ae0\u65f6\u6253\u5f00\u7b14\u8bb0\u672c\uff0c\u5e76\u5728\u5b8c\u6210\u540e\u521b\u5efa\u81ea\u5df1\u7684\u7248\u672c\u3002 1\u3001\u52a0\u8f7d\u6570\u636e\u96c6 \u9996\u5148\uff0c\u8ba9\u6211\u4eec\u52a0\u8f7d\u6570\u636e\u96c6\u3002\u6b63\u5982\u6211\u4e4b\u524d\u63d0\u5230\u7684\uff0c\u6211\u5c06\u5305\u542b\u4ee3\u7801\uff0c\u4f46\u4e0d\u63d0\u4f9b\u8be6\u7ec6\u89e3\u91ca\uff1a !pip install -q langchain==0.3.0 !pip install -q langchain-openai==0.2.0 !pip install -q langchainhub==0.1.21 !pip install -q datasets==3.0.0 !pip install -q chromadb==0.5.5 !pip install -q langchain-community==0.3.0 from datasets import load_dataset data = load_dataset(&#8220;keivalya\/MedQuad-MedicalQnADataset&#8221;, split=&#8217;train&#8217;) data = data.to_pandas() # Uncoment this line if you want to 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