to_string()) parser.parse(output) Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!') 具体参见:Output parsers | ️ Langchain Chain链组件 更复杂的应用需要将LLM串联起来--要么相互串联,要么与其他组件串联。这时就需要用到Chain链组件。 LangChain为这种 "...
reticulate：：py_run_string('print("Hello, world!") ')1.2.3.如果用户喜欢的话，可以采用Python变量设置OpenAI API密钥。因为已经在一个R环境变量中有了它，通常使用R来设置OpenAI API密钥。用户可以使用reticulate的r_to_py()函数将任何R变量保存为python友好的格式，包括环境变量：复制 api_key_for_py <-...
(name="bad_string", description="This a poorly formatted user input string"), ResponseSchema(name="good_string", description="This is your response, a reformatted response") ] output_parser = StructuredOutputParser.from_response_schemas(response_schemas) format_instructions = output_parser.get_...
<< FORMATTING >> Return a markdown code snippet with a JSON object formatted to look like: { "destination": string \ name of the prompt to use or "DEFAULT" "next_inputs": string \ a potentially modified version of the original input } REMEMBER: "destination" MUST be one of the ...
from langchain.document_loaders import OnlinePDFLoader from langchain.vectorstores import Chroma from langchain.embeddings import GPT4AllEmbeddings from langchain import PromptTemplate from langchain.llms import Ollama from langchain.callbacks.manager import CallbackManager from langchain.callbacks....
fromlangchain_core.documentsimportDocumentdefprint_colored_pii(string):colored_string=re.sub(r"(<[^>]*>)",lambdam:"\033[31m"+m.group(1)+"\033[0m",string)print(colored_string)document_content="""Date: October 19, 2021 Witness: John Doe ...
{function_name} 源代码: {source_code} 解释: """ class FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel): """一个自定义提示模板,以函数名作为输入,并格式化提示模板以提供函数的源代码。 """ @validator("input_variables") def validate_input_variables(cls, v): """验证输入变量是否...
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。
比如,chunk_overlap=0时, 第一个 document 为 aaaaaa,第二个为 bbbbbb;当 chunk_overlap=2 时,第一个 document 为 aaaaaa,第二个为 aabbbbbb。 不过,这个也不是绝对的,要看所使用的那个文本分割模型内部的具体算法。 文本分割器可以参考这个文档:https://python.langchain.com/en/latest/modules/indexes/...
import { OpenAI } from "langchain/llms/openai"; import { loadSummarizationChain, AnalyzeDocumentChain } from "langchain/chains"; import * as fs from "fs"; // In this example, we use the `AnalyzeDocumentChain` to summarize a large text document. const text = fs.readFileSync("state_of...