from langchain import FAISS from langchain.document_loaders import WebBaseLoader from langchain.embeddings import HuggingFaceEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter loader = WebBaseLoader("https://in.m.jd.com/help/app/register_info.html") data = loader.load() te...
Document Loaders:各种格式文件的加载器(pdf、html、word...),当使用loader加载器读取到数据源后,数据源需要转换成 Document 对象后,后续才能进行使用。 Document transformers: 对文档的常用操作,如:split, filter, translate, extract metadata等等,比如文档分割或者下面的向量化可以在一定程度上解决token长度限制 Text ...
输入 text_to_translate = """Imagine buying a brand-new sports car: fast, shiny, and full of...
st.markdown(f""" <p style="text-indent:2em">{result['message']}</p> """, unsafe_allow_html=True) else: st.write('在提交过程中出现了错误') langchain_utils # # # 1.从Http Multi File文件流加载文件到服务器,这一步在API已完成 # # 2.从服务器路径加载文件到向量数据库 import os....
1020 "html-to-text": "^9.0.5", 1021 "ignore": "^5.2.0", 1022 "ioredis": "^5.3.2", 1023 "jsdom": "*", 1024 "mammoth": "^1.6.0", 1025 "mongodb": "^5.2.0", 1026 "node-llama-cpp": "*", 1027 "notion-to-md": "^3.1.0", 1028 "officeparser": "^4.0...
Examples include summarization of long pieces of text and question/answering over specific data sources. 🤖 Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface ...
分块(chunking)是将大块文本分解成小段的过程。分块可以帮助我们优化从向量数据库被召回的内容的准确性。LangChain 也提供了许多文本分割工具,对于这个的示例,可以使用 CharacterTextSplitter来进行分割。设置片段大小 chunk_size 为 500,并且设置重叠token数量 chunk_overlap 为 50,以确保文本块之间的连贯性。
to_string(), path) .await .expect("Failed to create PandocLoader"); let docs = loader .load() .await .unwrap() .map(|d| d.unwrap()) .collect::<Vec<_>>() .await; } HTML use futures_util::StreamExt; use url::Url; async fn main() { let path = "./src/document_loaders/...
Development: Build your applications using LangChain's open-sourcebuilding blocks,components, andthird-party integrations. UseLangGraphto build stateful agents with first-class streaming and human-in-the-loop support. Productionization: UseLangSmithto inspect, monitor and evaluate your chains, so that...
const response = await completePrompt(promptWithQuestion);console.log(response.choices[0].text); 得到的结果如下: Question: What was the high temperature in SF yesterday in Fahrenheit?Thought: I can try searching the answerAction: searchAction Input: "high temperature san francisco yesterday fahrenhe...