importosfrompathlibimportPathfromlangchain.document_loadersimportUnstructuredCSVLoaderfromlangchain.document_loaders.csv_loaderimportCSVLoaderEXAMPLE_DIRECTORY=file_path=Path(__file__).parent.parent/"examples"deftest_unstructured_csv_loader()->None:"""Test unstructured loader."""file_path=os.path.join(E...
prefix="Give the antonym of every input",suffix="Word: {input}\nAntonym:",input_variables=["input"],example_separator="\n\n",)longString="big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"print(dynamic_prompt.format...
该类的引用包为from langchain.docstore.document import Document。简单理解就是包括文本内容(page_content)、元数据(metadata)和类型(type)的类。源码如下所示: class Document(Serializable): """Class for storing a piece of text and associated metadata.""" page_content: str """String text.""" metadat...
); } embedQuery(document: string): Promise<number[]> { throw new Error("Method not implemented."); } } 深扒langchain库 下面是VoyageEmbedding的实现 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import { getEnvironmentVariable } from "@langchain/core/utils/env"; import { Embeddings, ...
该类的引用包为from langchain.docstore.document import Document。简单理解就是包括文本内容(page_content)、元数据(metadata)和类型(type)的类。源码如下所示: classDocument(Serializable):"""Class for storing a piece of text and associated metadata."""page_content:str"""String text."""metadata...
{query}\n",input_variables=["query"],partial_variables={"format_instructions": parser.get_format_instructions()})joke_query = "Tell me a joke."_input = prompt.format_prompt(query=joke_query)output = model(_input.to_string())print(parser.get_format_instructions())print(output)print(parser...
#Writefunctiontotakestringinputandreturnnumberoftokens defnum_tokens_from_string(string:str,encoding_name:str)->int: """Returnsthenumberoftokensinatextstring.""" encoding=tiktoken.encoding_for_model(encoding_name) num_tokens=len(encoding.encode(string)) ...
PageContent(rawText);List<Document> documents = embeddings.embedDocument(Arrays.asList(document));Document vecDocument= documents.get(0);// 向量化知识String embeddingString = JSON.toJSONString(vecDocument.getEmbedding()).replaceAll("\\[", "{").replaceAll("\\]", "}");return embeddingString;...
description="Part of the document that the text comes from", type="string or list[string]" ), ] document_content_description = "Major sections of the document" llm = OpenAI(temperature=0) retriever = SelfQueryRetriever.from_llm(llm, vectordb, document_content_description, metadata_fields_info...
longString = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else" print(dynamic_prompt.format(input=longString)) 另外官方也提供了根据最大边际相关性、文法重叠、语义相似性来选择示例。