rag_from_scratch_5_to_9.ipynb Repository files navigation README RAG From Scratch LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine
RAG From Scratch Retrieval augmented generation (RAG) comes is a general methodology for connecting LLMs with external data sources. These notebooks builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation. About...
· https://docs.llamaindex.ai/en/stable/examples/query_transformations/HyDEQueryTransformDemo/#failure-case-1-hyde-may-mislead-when-query-can-be-mis-interpreted-without-context · https://github.com/langchain-ai/rag-from-scratch Step-back prompting Step-back prompting 技术旨在提高 LLM 进行抽象推...
如果实践中大模型在输出json格式中稳定性不足,个人推荐使用outlines框架(github.com/outlines-dev),它实现确保json mode的结构化输出的底层逻辑可以理解为是一种“约束解码”,准确地说是底层基于有限状态机(FSM)的“约束格式”(对齐模版的正则表达式)的解码,其于生成过程中每一步都检测生成后的文本是否对齐该正则表达...
参考资料 https://github.com/langchain-ai/rag-from-scratch/ Routing | ️ Langchain python.langchain.com/do 发布于 2024-03-19 20:00・江苏 rag LLM(大型语言模型) 方法 赞同31 条评论 分享喜欢收藏申请转载 写...
1from langchain.utils.math import cosine_similarity 2from langchain_core.output_parsers import StrOutputParser 3from langchain_core.prompts import PromptTemplate 4from langchain_core.runnables import RunnableLambda, RunnablePassthrough 5from langchain_openai import ChatOpenAI, OpenAIEmbeddings ...
1from langchain_community.document_loadersimport WebBaseLoader2from langchain_text_splittersimport RecursiveCharacterTextSplitter34loader= WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")5docs= loader.load()67loader= WebBaseLoader("https://lilianweng.github.io/posts/2024-02-...
Balancing generative and retrieval capabilities: Traditional chatbots are either generative (creating responses from scratch) or retrieval-based (finding the best match from a set of predefined responses). RAG allows for a hybrid approach, where the generative model can create more nuanced and varied...
图2: splitter.get_nodes_from_documents[4] 函数的主要逻辑。图片由原文作者提供。 图2 中提到的 “sentences” 是一个 Python 列表,其中每个成员都是包含四个键值对的字典,各键的含义如下: sentence:当前句子 index:当前句子的序号 combined_sentence:用于构建滑动窗口(sliding window),包括 [index - self.buffe...
https://github.com/ganeshnehru/RAG-Multi-Modal-Generative-AI-Agent/tree/main router -> each agents. A multimodal RAG-based generative AI digital assistant that combines text generation, vision QA, and code generation. Generative-AI-Digital-Assistant-w-RAG (Agent-Nesh 🤖) ...