如文件体积过大,还可分批处理缩小文件体积 通过分布式“映射-规约”模式,系统高效 parallel 地从海量文件中找到答案: 映射工作使每个文件独立处理,提高效率; 规约环节再整合答案,精度不恨损失。 这样既利用了分布技术,又保证了答案质量,帮助用户快速解决问题。 MapRerankDocumentsChain 每个文档上运行一个初始提示,再给...
map_as_doc_chain = ( RunnableParallel({"doc": RunnablePassthrough(), "content": map_chain}) | (lambda x: Document(page_content=x["content"], metadata=x["doc"].metadata)) ).with_config(run_name="Summarize (return doc)")# The chain we'll repeatedly apply to collapse subsets of the...
MapReduceDocumentsChain 将LLM链应用于每个单独的文档(Map步骤),将链的输出视为新文档。然后,将所有新文档传递给单独的合并文档链以获得单一输出(Reduce步骤)。在执行Map步骤前也可以对每个单独文档进行压缩或合并映射,以确保它们适合合并文档链;可以将这个步骤递归执行直到满足要求。(适合大规模文档的情况) 为提升问答...
Q1: 解释 LangChain 中的 LCEL (LangChain Expression Language) 的工作原理,并给出一个复杂的 LCEL 表达式示例。 Q2: 描述 LangChain 中的 Pydantic 集成,并解释如何创建一个自定义的 Pydantic 输出解析器来处理复杂的嵌套结构。 Q3: 解释 LangChain 中的 Self-querying Retriever 的工作原理,并提供一个实...
Hi guys, I'm trying build a map_reduce chain to handle the long document summarization. Per my understanding, a long document will be cut into several parts firstly and then query the summary in map_reduce mode, that really make sense. H...
Faster processing time as chunks are processed in parallel. Cons: Loss of information may be possible as the chunks are processed in parallel without any connection with previous chunks. Output: Pattern 3 - Map Reduce chain In this approach first each document is mapped to an individual sum...
1. Map-Reduce Branches: LangGraph's implementation of map-reduce allows for the creation of flexible branches that can handle parallel execution. This is particularly useful when dealing with tasks where the number of subtasks may be unknown during the initial graph design, or when each subtask...
//使用reduce求所有作者年龄的最大值 Integer maxAge = getAuthors().stream() .distinct() .map(author -> author.getAge()) .reduce(Integer.MIN_VALUE, (result, age) -> age > result ? age : result); System.out.println(maxAge); 1. 2. 3. 4. 5. 6. 两个参数的重载形式的reduce源码计...
"""prompt=PromptTemplate.from_template(template,partial_variables={"format_instructions":output_parser.get_format_instructions()},)# Create a chain with the prompt and language modelchain=LLMChain(prompt=prompt,llm=OpenAI())# Define a query to prompt the modelquery="when did Neil Armstrong land...
Deprecated since version 0.2.13: Refer here for a recommended map-reduce implementation using langgraph: https://langchain-ai.github.io/langgraph/how-tos/map-reduce/. See also migration guide: https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_reduce_chain/ chains.natbot.bas...