\u968f\u7740\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u7684\u5feb\u901f\u53d1\u5c55，\u6211\u4eec\u6b63\u7ad9\u5728AI\u5e94\u7528\u5f00\u53d1\u7684\u65b0\u62d0\u70b9\u3002\u8fc7\u53bb，\u6784\u5efaAI\u9a71\u52a8\u7684\u5e94\u7528\u5f80\u5f80\u9700\u8981\u590d\u6742\u7684\u5de5\u7a0b\u67b6\u6784\u548c\u5927\u91cf\u7684\u5b9a\u5236\u5316\u5f00\u53d1\u3002\u800c\u4eca，LangChain\u6846\u67b6\u6b63\u5728\u6539\u53d8\u8fd9\u4e00\u5c40\u9762\u2014\u2014\u5b83\u662f\u4e00\u4e2a\u4e13\u95e8\u4e3a\u6784\u5efa\u57fa\u4e8e\u5927\u8bed\u8a00\u6a21\u578b\u7684\u5e94\u7528\u7a0b\u5e8f\u800c\u8bbe\u8ba1\u7684\u5f00\u6e90\u6846\u67b6，\u8ba9\u5f00\u53d1\u8005\u80fd\u591f\u66f4\u8f7b\u677e\u3001\u66f4\u9ad8\u6548\u5730\u521b\u5efa\u590d\u6742\u7684AI\u5e94\u7528\u3002<\/p>\n
LangChain\u662f\u4e00\u4e2a\u7528\u4e8e\u5f00\u53d1\u7531\u8bed\u8a00\u6a21\u578b\u9a71\u52a8\u7684\u5e94\u7528\u7a0b\u5e8f\u7684\u6846\u67b6，\u5b83\u63d0\u4f9b\u4e86\u4e00\u5957\u5b8c\u6574\u7684\u5de5\u5177\u94fe，\u5e2e\u52a9\u5f00\u53d1\u8005\u5c06LLM\u80fd\u529b\u96c6\u6210\u5230\u5b9e\u9645\u5e94\u7528\u4e2d\u3002\u5176\u6838\u5fc3\u8bbe\u8ba1\u7406\u5ff5\u662f\u6a21\u5757\u5316\u548c\u53ef\u7ec4\u5408\u6027\u2014\u2014\u4f60\u53ef\u4ee5\u50cf\u642d\u79ef\u6728\u4e00\u6837\u7ec4\u5408\u4e0d\u540c\u7684\u7ec4\u4ef6\u6765\u6784\u5efa\u590d\u6742\u7684\u5e94\u7528\u3002<\/p>\n
\u51cf\u5c11\u6837\u677f\u4ee3\u7801：\u6807\u51c6\u5316\u4e86\u4e0eLLM\u4ea4\u4e92\u7684\u6a21\u5f0f<\/p>\n<\/li>\n
\u63d0\u9ad8\u5f00\u53d1\u6548\u7387：\u63d0\u4f9b\u9884\u6784\u5efa\u7684\u7ec4\u4ef6\u548c\u94fe<\/p>\n<\/li>\n
\u589e\u5f3a\u5e94\u7528\u80fd\u529b：\u8f7b\u677e\u96c6\u6210\u8bb0\u5fc6\u3001\u5de5\u5177\u4f7f\u7528\u7b49\u9ad8\u7ea7\u529f\u80fd<\/p>\n<\/li>\n<\/ul>\n
LangChain\u652f\u6301\u591a\u79cd\u6a21\u578b\u63d0\u4f9b\u5546，\u7edf\u4e00\u4e86\u4e0d\u540c\u6a21\u578b\u7684\u8c03\u7528\u63a5\u53e3\u3002<\/p>\n
from langchain.llms import OpenAI
\nfrom langchain.chat_models import ChatOpenAI<\/p>\n
# \u4f7f\u7528OpenAI GPT-3
\nllm = OpenAI(model_name="text-davinci-003", temperature=0.7)<\/p>\n
# \u4f7f\u7528ChatGPT
\nchat_model = ChatOpenAI(temperature=0.7)<\/p>\n
\u7ba1\u7406\u7cfb\u7edf\u5316\u63d0\u793a\u6a21\u677f，\u5b9e\u73b0\u63d0\u793a\u5de5\u7a0b\u7684\u6700\u4f73\u5b9e\u8df5\u3002<\/p>\n
from langchain.prompts import PromptTemplate<\/p>\n
# \u521b\u5efa\u63d0\u793a\u6a21\u677f
\nprompt = PromptTemplate(
\n input_variables=["product"],
\n template="\u4e3a{product}\u5199\u4e00\u4e2a\u5438\u5f15\u4eba\u7684\u5e7f\u544a\u6807\u8bed："
\n)<\/p>\n
# \u4f7f\u7528\u6a21\u677f
\nformatted_prompt = prompt.format(product="\u667a\u80fd\u624b\u8868")<\/p>\n
\u5c06\u591a\u4e2aLLM\u8c03\u7528\u6216\u64cd\u4f5c\u7ec4\u5408\u6210\u6709\u5e8f\u7684\u5de5\u4f5c\u6d41\u3002<\/p>\n
from langchain.chains import LLMChain
\nfrom langchain.chains import SimpleSequentialChain<\/p>\n
# \u521b\u5efa\u5355\u4e2a\u94fe
\nllm_chain = LLMChain(llm=llm, prompt=prompt)<\/p>\n
# \u521b\u5efa\u987a\u5e8f\u94fe
\nsummary_chain = SimpleSequentialChain(
\n chains=[llm_chain, translate_chain],
\n verbose=True
\n)<\/p>\n
\u4f7f\u94fe\u80fd\u591f\u5728\u591a\u6b21\u4ea4\u4e92\u4e2d\u8bb0\u4f4f\u4e0a\u4e0b\u6587\u4fe1\u606f\u3002<\/p>\n
from langchain.memory import ConversationBufferMemory<\/p>\n
memory = ConversationBufferMemory()
\nconversation = ConversationChain(
\n llm=llm,
\n memory=memory,
\n verbose=True
\n)<\/p>\n
\u8ba9LLM\u80fd\u591f\u4f7f\u7528\u5916\u90e8\u5de5\u5177\u548cAPI，\u5927\u5927\u6269\u5c55\u4e86\u5e94\u7528\u80fd\u529b\u3002<\/p>\n
from langchain.agents import initialize_agent, Tool
\nfrom langchain.agents import AgentType<\/p>\n
tools = [
\n Tool(
\n name="\u641c\u7d22",
\n func=search_tool,
\n description="\u7528\u4e8e\u641c\u7d22\u6700\u65b0\u4fe1\u606f"
\n )
\n]<\/p>\n
agent = initialize_agent(
\n tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
\n)<\/p>\n
\u4f18\u5316\u6587\u6863\u7684\u52a0\u8f7d\u3001\u7d22\u5f15\u548c\u68c0\u7d22，\u6784\u5efa\u9ad8\u6548\u7684RAG（\u68c0\u7d22\u589e\u5f3a\u751f\u6210）\u5e94\u7528\u3002<\/p>\n
from langchain.document_loaders import TextLoader
\nfrom langchain.text_splitter import CharacterTextSplitter
\nfrom langchain.embeddings import OpenAIEmbeddings
\nfrom langchain.vectorstores import Chroma<\/p>\n
# \u52a0\u8f7d\u548c\u5206\u5272\u6587\u6863
\nloader = TextLoader("document.txt")
\ndocuments = loader.load()
\ntext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
\ndocs = text_splitter.split_documents(documents)<\/p>\n
# \u521b\u5efa\u5411\u91cf\u5b58\u50a8
\nembeddings = OpenAIEmbeddings()
\nvectorstore = Chroma.from_documents(docs, embeddings)<\/p>\n
\u4e0b\u9762\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b，\u5c55\u793a\u5982\u4f55\u4f7f\u7528LangChain\u6784\u5efa\u4e00\u4e2a\u667a\u80fd\u6587\u6863\u95ee\u7b54\u7cfb\u7edf\u3002<\/p>\n
from langchain.chains import RetrievalQA
\nfrom langchain.llms import OpenAI<\/p>\n
# 1. \u51c6\u5907\u6587\u6863\u548c\u5411\u91cf\u6570\u636e\u5e93
\nloader = DirectoryLoader('.\/documents\/', glob="**\/*.txt")
\ndocuments = loader.load()<\/p>\n
# \u5206\u5272\u6587\u6863
\ntext_splitter = RecursiveCharacterTextSplitter(
\n chunk_size=1000,
\n chunk_overlap=200
\n)
\ntexts = text_splitter.split_documents(documents)<\/p>\n
# \u521b\u5efa\u5411\u91cf\u5b58\u50a8
\nembeddings = OpenAIEmbeddings()
\nvectorstore = Chroma.from_documents(texts, embeddings)<\/p>\n
# 2. \u521b\u5efa\u68c0\u7d22QA\u94fe
\nqa_chain = RetrievalQA.from_chain_type(
\n llm=OpenAI(temperature=0),
\n chain_type="stuff",
\n retriever=vectorstore.as_retriever(),
\n return_source_documents=True
\n)<\/p>\n
# 3. \u4f7f\u7528\u7cfb\u7edf
\nquery = "\u4ec0\u4e48\u662f\u673a\u5668\u5b66\u4e60？"
\nresult = qa_chain({"query": query})
\nprint(f"\u7b54\u6848：{result['result']}")<\/p>\n
LangChain\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u56de\u8c03\u7cfb\u7edf，\u65b9\u4fbf\u76d1\u63a7\u548c\u8c03\u8bd5\u5e94\u7528\u3002<\/p>\n
from langchain.callbacks import StdOutCallbackHandler<\/p>\n
handler = StdOutCallbackHandler()
\nchain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])<\/p>\n
\u5b9e\u73b0\u5b9e\u65f6\u54cd\u5e94\u7684\u804a\u5929\u4f53\u9a8c\u3002<\/p>\n
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler<\/p>\n
streaming_llm = OpenAI(
\n streaming=True,
\n callbacks=[StreamingStdOutCallbackHandler()],
\n temperature=0
\n)<\/p>\n
LangChain\u652f\u6301\u9ad8\u5ea6\u5b9a\u5236\u5316，\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u521b\u5efa\u81ea\u5df1\u7684\u94fe\u548c\u5de5\u5177\u3002<\/p>\n
from langchain.tools import BaseTool<\/p>\n
class CustomCalculatorTool(BaseTool):
\n name = "\u8ba1\u7b97\u5668"
\n description = "\u7528\u4e8e\u8fdb\u884c\u6570\u5b66\u8ba1\u7b97"<\/p>\n
def _run(self, expression: str) -> str:
\n return str(eval(expression))<\/p>\n
\u4f7f\u7528\u5c11\u6837\u672c\u5b66\u4e60（Few-shot learning）\u63d0\u9ad8\u51c6\u786e\u6027<\/p>\n<\/li>\n
\u5b9e\u73b0\u601d\u7ef4\u94fe（Chain-of-Thought）\u63a8\u7406<\/p>\n<\/li>\n
\u8bbe\u8ba1\u6e05\u6670\u7684\u6307\u4ee4\u548c\u7ea6\u675f<\/p>\n<\/li>\n<\/ul>\n
from langchain.cache import InMemoryCache
\nimport langchain
\nlangchain.llm_cache = InMemoryCache()<\/p>\n
from langchain.llms import OpenAI
\nfrom tenacity import retry, stop_after_attempt, wait_exponential<\/p>\n
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
\ndef robust_llm_call(prompt):
\n llm = OpenAI()
\n return llm(prompt)<\/p>\n
\u667a\u80fd\u5ba2\u670d\u548c\u95ee\u7b54\u7cfb\u7edf<\/p>\n<\/li>\n
\u6587\u6863\u5206\u6790\u548c\u6458\u8981\u751f\u6210<\/p>\n<\/li>\n
\u6570\u636e\u67e5\u8be2\u548c\u5206\u6790\u52a9\u624b<\/p>\n<\/li>\n<\/ul>\n
\u4e2a\u6027\u5316\u5b66\u4e60\u52a9\u624b<\/p>\n<\/li>\n
\u667a\u80fd\u9898\u76ee\u751f\u6210\u548c\u89e3\u7b54<\/p>\n<\/li>\n
\u5b66\u4e60\u5185\u5bb9\u63a8\u8350\u7cfb\u7edf<\/p>\n<\/li>\n<\/ul>\n
\u8425\u9500\u6587\u6848\u81ea\u52a8\u751f\u6210<\/p>\n<\/li>\n
\u6280\u672f\u6587\u6863\u64b0\u5199\u52a9\u624b<\/p>\n<\/li>\n
\u521b\u610f\u5199\u4f5c\u652f\u6301\u5de5\u5177<\/p>\n<\/li>\n<\/ul>\n
\u591a\u6a21\u6001\u6269\u5c55：\u652f\u6301\u56fe\u50cf\u3001\u97f3\u9891\u7b49\u591a\u7c7b\u578b\u6570\u636e<\/p>\n<\/li>\n
\u66f4\u597d\u7684\u672c\u5730\u5316\u90e8\u7f72：\u4f18\u5316\u79bb\u7ebf\u8fd0\u884c\u80fd\u529b<\/p>\n<\/li>\n
\u4f01\u4e1a\u7ea7\u529f\u80fd\u589e\u5f3a：\u5b89\u5168\u3001\u76d1\u63a7\u3001\u6743\u9650\u7ba1\u7406\u7b49<\/p>\n<\/li>\n
\u5904\u7406\u590d\u6742\u903b\u8f91\u7684\u80fd\u529b\u6709\u9650<\/p>\n<\/li>\n
\u6210\u672c\u63a7\u5236\u548c\u4f18\u5316<\/p>\n<\/li>\n
\u9690\u79c1\u548c\u6570\u636e\u5b89\u5168\u95ee\u9898<\/p>\n<\/li>\n<\/ul>\n
LangChain\u6846\u67b6\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u4e86\u6784\u5efa\u4e0b\u4e00\u4ee3AI\u5e94\u7528\u7684\u5f3a\u5927\u5de5\u5177\u96c6\u3002\u901a\u8fc7\u5176\u6a21\u5757\u5316\u8bbe\u8ba1\u548c\u4e30\u5bcc\u7684\u7ec4\u4ef6，\u5f00\u53d1\u8005\u53ef\u4ee5\u4e13\u6ce8\u4e8e\u5e94\u7528\u903b\u8f91\u800c\u4e0d\u662f\u5e95\u5c42\u5b9e\u73b0\u7ec6\u8282\u3002\u968f\u7740\u6846\u67b6\u7684\u4e0d\u65ad\u6210\u719f\u548c\u751f\u6001\u7cfb\u7edf\u7684\u5b8c\u5584，\u6211\u4eec\u6709\u7406\u7531\u76f8\u4fe1，LangChain\u5c06\u6210\u4e3aAI\u5e94\u7528\u5f00\u53d1\u7684\u6807\u51c6\u5de5\u5177\u4e4b\u4e00\u3002<\/p>\n
\u5bf9\u4e8e\u60f3\u8981\u8fdb\u5165AI\u5e94\u7528\u5f00\u53d1\u9886\u57df\u7684\u5f00\u53d1\u8005\u6765\u8bf4，\u73b0\u5728\u6b63\u662f\u5b66\u4e60LangChain\u7684\u6700\u4f73\u65f6\u673a\u3002\u65e0\u8bba\u662f\u6784\u5efa\u4f01\u4e1a\u5185\u90e8\u5de5\u5177，\u8fd8\u662f\u5f00\u53d1\u9762\u5411\u7528\u6237\u7684AI\u4ea7\u54c1，LangChain\u90fd\u80fd\u63d0\u4f9b\u5f3a\u6709\u529b\u7684\u652f\u6301\u3002<\/p>\n
\u6280\u672f\u7684\u4ef7\u503c\u5728\u4e8e\u5e94\u7528，\u800cLangChain\u6b63\u5728\u8ba9AI\u5e94\u7528\u5f00\u53d1\u53d8\u5f97\u524d\u6240\u672a\u6709\u7684\u7b80\u5355\u3002\u00a0\u5f00\u59cb\u4f60\u7684LangChain\u4e4b\u65c5，\u6784\u5efa\u5c5e\u4e8e\u81ea\u5df1\u7684\u667a\u80fd\u5e94\u7528\u5427！<\/p>\n
\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u5206\u4eab\u4f60\u7684LangChain\u4f7f\u7528\u7ecf\u9a8c！\u5982\u679c\u4f60\u5728\u5b9e\u8df5\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u95ee\u9898，\u6216\u8005\u6709\u72ec\u7279\u7684\u5e94\u7528\u6848\u4f8b，\u6b22\u8fce\u4ea4\u6d41\u8ba8\u8bba\u3002<\/p>\n
\u5173\u6ce8\u6211，\u83b7\u53d6\u66f4\u591aAI\u5f00\u53d1\u6280\u672f\u5e72\u8d27！<\/p>\n","protected":false},"excerpt":{"rendered":"
\u5f15\u8a00：AI\u5e94\u7528\u5f00\u53d1\u7684\u8303\u5f0f\u8f6c\u53d8\u968f\u7740\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u7684\u5feb\u901f\u53d1\u5c55，\u6211\u4eec\u6b63\u7ad9\u5728AI\u5e94\u7528\u5f00\u53d1\u7684\u65b0\u62d0\u70b9\u3002\u8fc7\u53bb，\u6784\u5efaAI\u9a71\u52a8\u7684\u5e94\u7528\u5f80\u5f80\u9700\u8981\u590d\u6742\u7684\u5de5\u7a0b\u67b6\u6784\u548c\u5927\u91cf\u7684\u5b9a\u5236\u5316\u5f00\u53d1\u3002\u800c\u4eca，LangChain\u6846\u67b6\u6b63\u5728\u6539\u53d8\u8fd9\u4e00\u5c40\u9762\u2014\u2014\u5b83\u662f\u4e00\u4e2a\u4e13\u95e8\u4e3a\u6784\u5efa\u57fa\u4e8e\u5927\u8bed\u8a00\u6a21\u578b\u7684\u5e94\u7528\u7a0b\u5e8f\u800c\u8bbe\u8ba1\u7684\u5f00\u6e90\u6846\u67b6，\u8ba9\u5f00\u53d1\u8005\u80fd\u591f\u66f4\u8f7b\u677e\u3001\u66f4\u9ad8\u6548\u5730\u521b\u5efa\u590d\u6742\u7684AI\u5e94\u7528\u3002\u4e00\u3001LangChain\u662f\u4ec0\u4e48？LangChain\u662f<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[997,50],"topic":[],"class_list":["post-60933","post","type-post","status-publish","format-standard","hentry","category-server","tag-langchain","tag-50"],"yoast_head":"\n