RAG 原理与实战：让大模型拥有知识

RAG 架构

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

loader = TextLoader("knowledge.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)

from langchain.document_loaders import TextLoader

from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain.embeddings import OpenAIEmbeddings

from langchain.vectorstores import Chroma

loader = TextLoader("knowledge.txt")

documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)

docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

vectorstore = Chroma.from_documents(docs, embeddings)

三大组件：文档处理 → 向量检索 → 上下文增强

进阶：HyDE · Re-ranking · Self-RAG

RAG 是目前解决 LLM 幻觉问题最实用的方案之一。