from langchain.chains import TransformChainfrom langchain.llms import OpenAIfrom langchain.text_splitter import CharacterTextSplitter
# 初始化文本分割器text_splitter = CharacterTextSplitter(separator="\n\n", chunk_size=1000, chunk_overlap=200)
# 初始化语言模型llm = OpenAI(api_key="your_openai_api_key")
# 定义Transform Chaindef text_transformer(inputs):texts = text_splitter.split_text(inputs['text'])return {'texts': texts}
transform_chain = TransformChain(transformer=text_transformer, llm=llm)
# 处理输入文本input_text = "这是一个超长的法律文档..."result = transform_chain.run({'text': input_text})

from langchain.chains import SequentialChain
# 定义后续处理链def summarizer_chain(inputs):summaries = [llm.predict(text) for text in inputs['texts']]return {'summary': "\n".join(summaries)}
# 创建Sequential Chainsequential_chain = SequentialChain(chains=[transform_chain, summarizer_chain])
# 生成最终摘要final_result = sequential_chain.run({'text': input_text})

from langchain.chains import MultiPromptChain, LLMChain, ConversationChainfrom langchain.prompts import PromptTemplate
# 定义不同的提示信息和对应的LLMChainprompt_infos = {"tech_support": PromptTemplate(template="技术支持：{query}"),"order_query": PromptTemplate(template="订单查询：{query}")}
llm_chains = {key: LLMChain(prompt=prompt, llm=llm) for key, prompt in prompt_infos.items()}default_chain = ConversationChain(llm=llm)
# 创建MultiPromptChainmulti_prompt_chain = MultiPromptChain(prompt_infos=llm_chains, default_chain=default_chain)
# 处理用户输入user_input = "我想查询我的订单状态"response = multi_prompt_chain.run({'query': user_input})

from langchain.chains import LLMRouterChain
# 定义Router Chainrouter_chain = LLMRouterChain(prompt_infos=llm_chains, default_chain=default_chain)
# 根据条件判断调用适当的链response = router_chain.run({'query': user_input})