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Articles by StrOutput Parser
Python Workers are now generally available
Gyeongjae Choi,Dominik Picheta, and Hood Chatham We introduced Python Workers two years ago, providing a way to run Python applications in the Cloudflare Workers runtime. Our goal was to make it as simple to write Workers in Python as it is in TypeScript, and to make the ecosystem of Python packages and frameworks “just work”. Today, Python Workers are now generally available (GA). What does GA mean? It means Python is now a first-class, fully supported language on the Cloudflare Developer Platform.
[LLM/RAG/LangChain] RAG part5 tavily, streamlit 실습
!pip install langchain_openai langchain_chroma langchain_community load_dotenv faiss-cpu sentence-transformers pymupdf GitPython from dotenv import load_dotenv load_dotenv('./drive/MyDrive/api_keys.txt') True from langchain_community.tools.tavily_search import TavilySearchResults # tavily - 웹검색기능 tools = [TavilySearchResults(max_results=3)] /tmp/ipython-input-2518516040.py:3: LangChainDeprecationWarning: The class TavilySearchResults was deprecated in LangChain 0.3.25 and will be removed in...
本地大模型编程实战(29)查询图数据库NEO4J(2)-CSDN博客
火云牌神 于 2025-04-29 16:22:58 发布 阅读量1k 收藏 28 点赞数 16 分类专栏： AI编程实战 文章标签： neo4j python LLM langchain 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 上一篇文章 用大语言模型LLM查询图数据库NEO4J(1) 介绍了使用GraphQACypherChain查询NEO4J。用它实现简单快捷，但是不容易定制，在生产环境中可能会面临挑战。 本文将基于langgraph 框架，用LLM(大语言模型)查询图数据库NEO4J。它可以定义清晰复杂的工作流，能应对比较复杂的应用场景。 以下是即将实现的可视化LangGraph流程： 定义状态 我们将首先定义 LangGraph 应用程序的输入、输出和整体状态。 我们可以认为所谓的状态是：节点之间数据交换的数据格式。它们都继承自TypedDict。 from operator import add from typing import Annotated, List from...
Getting Started With LangChain for Beginners
Large language models (LLMs) like OpenAI’s GPT-4 and Hugging Face models are powerful, but using them effectively in applications requires more than just calling an API. LangChain is a framework that simplifies working with LLMs, enabling developers to create advanced AI applications with ease. In this article, we’ll cover: What is LangChain?
Building a PDF RAG Chatbot: Langchain, OpenAI, PGVector, RediStore, and Streamlit
Retrieval Augmented Generation (RAG) has addressed many challenges we face in the business world today. With RAG, we can integrate external data sources with large language models to enhance the capabilities of these models. These data sources can include databases, research papers, spreadsheets, etc. In this tutorial, I will walk you through the process of building a PDF-based RAG chatbot that utilizes the GPT model.
Enhancing Multimodal RAG Capabilities Using Docling
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Tutorial: Building SQL Agent with Langchain, OpenAI and DuckDB.
We will explain how to implement an SQL Agent using LangChain, OpenAI API, and DuckDB , and how to turn it into an application with Morph . This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions.
Mastering Multimodal RAG with Vertex AI & Gemini for Content
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Building a RAG System for AI Reasoning with DeepSeek R1 Distilled Model
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Creating a Medical Question-Answering Chatbot Using Open-Source BioMistral LLM, LangChain, Chroma’s Vector Storage, and RAG: A Step-by-Step Guide
In this tutorial, we’ll build a powerful, PDF-based question-answering chatbot tailored for medical or health-related content. We’ll leveRAGe the open-source BioMistral LLM and LangChain’s flexible data orchestration capabilities to process PDF documents into manageable text chunks. We’ll then encode these chunks using Hugging Face embeddings, capturing deep semantic relationships and storing them in a Chroma vector database for high-efficiency retrieval.
Supercharging AI Code Reviews: Our Journey with Mistral-Large-2411
In the realm of AI-powered code review systems, the quality of the underlying language model is crucial for providing actionable insights. This technical deep dive details our journey upgrading LlamaPReview (a fully automated PR review Github APP) from Mistral-Large-2407 to Mistral-Large-2411, focusing on the challenges we encountered and the solutions we engineered.
Mastering Query Answering with RAG: Overcoming Key Challenges in Large-Scale Meeting Data
In the digital age of information overload, extracting actionable insights from large datasets is more crucial than ever. Recently, I embarked on a journey to leverage Retrieval-Augmented Generation (RAG) to address a major challenge — delivering precise answers from a vast collection of meeting notes. This blog explores the obstacles, solutions, and achievements that turned my RAG-based query-answering system into a robust tool for extracting insights from unstructured meeting data.
LangChain Memory Component Deep Dive: Chain Components and Runnable Study
In building complex AI applications, effectively managing conversation history and context information is crucial. The LangChain framework provides various memory components, enabling developers to easily implement chatbots with memory functions. This article will delve into the memory components, Chain components, and Runnable interface in LangChain to help developers better understand and use these powerful tools.
Build a scalable, context-aware chatbot with Amazon DynamoDB, Amazon Bedrock, and LangChain | Amazon Web Services
Chatbots are expected to understand not only their immediate queries but also the context of their ongoing conversations. For developers, the challenge lies in creating a chatbot that can scale seamlessly while maintaining conversation history across multiple sessions. Amazon DynamoDB, Amazon Bedrock, and LangChain can provide a powerful combination for building robust, context-aware chatbots.
LLM Models and RAG Applications Step-by-Step - Part III - Searching and Injecting Context
Welcome to the third and final publication of our articles dedicated to the development of RAG applications based on LLM models. In this final article, we will see, based on our small example project, how we can find the most appropriate context for the question we want to send to our LLM model and for this we will make use of the vector search functionality included in IRIS.
A Comprehensive Guide to RAG-to-SQL on Google Cloud
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Parallel Chains in LangChain
In this guide, we'll delve into how LangChain facilitates parallel processing using a Meeting Summary Generator as a reference. Why Parallel Chains? Parallel chains allow multiple tasks to run concurrently, reducing overall execution time and improving resource utilization. This is especially beneficial when dealing with tasks that can operate independently, such as extracting different components from a dataset.
Guide to Gemini's Free AI Development Tools
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Implementing GraphReader with Neo4j and LangGraph
Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems Large Language Models (LLMs) are great at traditional NLP tasks like summarization and sentiment analysis but the stronger models also demonstrate promising reasoning abilities. LLM reasoning is often understood as the ability to tackle complex problems by formulating a plan, executing it, and assessing progress at each step.
A Visual Exploration of Semantic Text Chunking
Automatic information retrieval and summarization of large volumes of text has many useful applications. One of the most well developed is Retrieval Augmented Generation (RAG), which involves extraction of relevant chunks of text from a large corpus — typically via semantic search or some other filtering step — in response to a user question. Then, the chunks are interpreted or summarized by an LLM with the aim of providing a high quality, accurate answer.
Agentic Discovery
Self-Contained Environment: The agent in the study operates within a contained environment and does not integrate external APIs or libraries. Instead, it relies on reasoning modules that guide it to generate solutions to complex tasks. Self-Discovery Process: The self-discovery process indeed involves three key steps: selecting relevant reasoning modules, adapting those modules to fit the specific task, and then implementing a structured reasoning plan.
From Start to Finish
In our cozy office, the team is now ready to roll up their sleeves and dive into setting up their RAG-powered app. It's time to prepare the workspace and gather the right tools for our project. Here's how we do it: Jupyter Notebook: The Interactive Kitchen Think of Jupyter notebooks as an interactive kitchen, where we mix ingredients, test recipes, and troubleshoot any hiccups that come our way.
Debugging large code bases with ChromaDB and Langchain
Over the last week, I've been diving back into Langchain for an upcoming project. While working through some code, I hit an edge case that stumped me. My first instinct was to turn to Anthropic's Claude and OpenAI's GPT-4 for help, but their suggestions didn't quite cut it. Frustrated, I turned to the usual suspects - Google and StackOverflow - but came up empty-handed there too.
Retrieval-Augmented Generation on Paperspace + DigitalOcean: Help Your Models Give Their Best Answers
Modern large language models (LLMs) are very powerful and becoming more so by the day. However, their power is generic: they are good at answering questions, following instructions, etc., on public content on the internet, but can rapidly fall away when dealing with specialized subjects outside of what they were trained on. They either do not know what to output, or worse, fabricate something incorrect that can appear legitimate.
How to run Llama model locally on MacBook Pro and Function calling in LLM -Llama web search agent breakdown
Easily install Open source Large Language Models (LLM) locally on your Mac with Ollama.On a basic M1 Pro Macbook with 16GB memory, this configuration takes approximately 10 to 15 minutes to get going. The model itself starts up in less than ten seconds after the setup is finished. Go to >> https://ollama.com/download/mac and download the software for your OS 2.Open the Ollama application and Move to Applications 3.
Asserting Mosaic AI Agent Framework and Agent Analysis
Databricks introduced the general public preview of Mosaic AI Agent Framework & Agent Analysis alongside our Generative AI Cookbook on the Information + AI Summit 2024. These instruments are designed to assist builders construct and deploy high-quality Agentic and Retrieval Augmented Technology (RAG) functions inside the Databricks Information Intelligence Platform.
Build Your Own RAG App: A Step-by-Step Guide to Setup LLM locally using Ollama, Python, and ChromaDB
In an era where data privacy is paramount, setting up your own local language model (LLM) provides a crucial solution for companies and individuals alike. This tutorial is designed to guide you through the process of creating a custom chatbot using Ollama, Python 3, and ChromaDB, all hosted locally on your system.
An Implementation of Text-to-SQL with AI
As the evolvement of AI, we can earn more benefits from it in our work. Generating SQL from natrual language is one of the most important benefits among AI capability. We can find several fine tuned LLM models to do such work, for example, codellama by Meta, sql-coder by defog.ai. These models allow you to add the table definition in the prompt, then generate SQL according to the user query.
langchain+neo4j 实现图谱GraphRAG
整体思路参考如下 1.首先单独conda create 一个环境，用来安装依赖 pip install --upgrade --quiet langchain langchain-community langchain-openai langchain-experimental neo4j 2.首先是基础代码，这一步先查看当前模型的能力是否能够提取出文本中的实体和关系。（在这之前，笔者尝试了deepseek,glm4等api）其中glm-4 api10次大概有2次能够识别正确结果。 后切换为qwen api，初步代码如下，参考的是langchain + neo4j #!/usr/bin/python from langchain.docstore.document import Document from langchain.text_splitter import CharacterTextSplitter from langchain_community.document_loaders import TextLoader from...
使用 Llama 3 开源和 Elastic 构建 RAG
作者：Rishikesh Radhakrishnan 使用开源的 Llama 3 和 Elastic 构建 RAG Llama 3 是 Meta 最近推出的开源大型语言模型。这是 Llama 2 的后继者，根据已发布的指标，这是一个重大改进。与 Gemma 7B Instruct、Mistral 7B Instruct 等最近发布的一些模型相比，它具有良好的评估指标。该模型有两个变体，分别是 80 亿和 700 亿参数。值得注意的是，在撰写这篇博客时，Meta 仍在训练 400B+ 版本的 Llama 3。 Meta Llama 3 Instruct 模型性能。（来自 https://ai.meta.com/blog/meta-llama-3/） 上图显示了与其他模型相比，Llama3 在不同数据集上的性能数据。为了针对现实世界场景进行性能优化，Llama3 还在高质量的人工评估集上进行了评估。 跨多个类别和提示的人工评估的汇总结果（来自 https://ai.meta.com/blog/meta-llama-3/） 本博客将介绍使用两种方法实现的 RAG。...
Query your database with AI using LangChain and Gradio
I remember starting in the world of Machine Learning (ML) around eight years ago, doing Andrew Ng's courses on Coursera and jumping into websites like Kaggle to apply the concepts and solve Machine Learning challenges.
LangChain 0.2 - 基于 SQL 数据构建问答系统
本文翻译整理自：Build a Question/Answering system over SQL data https://python.langchain.com/v0.2/docs/tutorials/sql_qa/ 使 LLM 系统能够查询结构化数据与非结构化文本数据在性质上可能有所不同。后者通常生成可在向量数据库中搜索的文本，而结构化数据的方法通常是让 LLM 编写和执行 DSL（例如 SQL）中的查询。在本指南中，我们将介绍在数据库中的表格数据上创建问答系统的基本方法。我们将介绍使用链和代理的实现。这些系统将允许我们询问有关数据库中数据的问题并得到自然语言答案。两者之间的主要区别在于，我们的代理可以根据需要多次循环查询数据库以回答问题。 ⚠️ 安全说明⚠️ 构建 SQL 数据库的问答系统需要执行模型生成的 SQL 查询。这样做存在固有风险。请确保您的数据库连接权限始终尽可能缩小以满足您的链/代理的需求。这将减轻但不会消除构建模型驱动系统的风险。有关一般安全最佳实践的更多信息，请参阅此处。 架构 从高层次来看，这些系统的步骤如下： 将问题转换为 DSL...
AI--构建检索增强生成 (RAG) 应用程序
LLM 所实现的最强大的应用之一是复杂的问答 (Q&A) 聊天机器人。这些应用程序可以回答有关特定源信息的问题。这些应用程序使用一种称为检索增强生成 (RAG) 的技术。 典型的 RAG 应用程序有两个主要组件 索引：从源中提取数据并对其进行索引的管道。这通常在线下进行。 检索和生成：实际的 RAG 链，它在运行时接受用户查询并从索引中检索相关数据，然后将其传递给模型。 从原始数据到答案最常见的完整序列如下： 加载：首先我们需要加载数据。这是通过DocumentLoaders完成的。 拆分：文本拆分器将大块内容拆分Documents成小块内容。这对于索引数据和将数据传递到模型都很有用，因为大块内容更难搜索，并且不适合模型的有限上下文窗口。 存储：我们需要一个地方来存储和索引我们的分割，以便以后可以搜索它们。这通常使用VectorStore和Embeddings模型来完成 检索和生成 4. 检索：根据用户输入，使用检索器从存储中检索相关分割。 5.
利用英特尔 Gaudi 2 和至强 CPU 构建经济高效的企业级 RAG 应用
检索增强生成 (Retrieval Augmented Generation，RAG) 可将存储在外部数据库中的新鲜领域知识纳入大语言模型以增强其文本生成能力。其提供了一种将公司数据与训练期间语言模型学到的知识分开的方式，有助于我们在性能、准确性及安全隐私之间进行有效折衷。 通过本文，你将了解到英特尔如何通过企业 AI 开放平台 OPEA 开源项目帮助你开发和部署 RAG 应用。你还将通过真实的 RAG 使用案例了解英特尔 Gaudi 2 AI 加速器和至强 CPU 如何助力企业级应用性能的显著飞跃。 在深入了解细节之前，我们先要获取硬件。英特尔 Gaudi 2 专为加速数据中心和云上的深度学习训练和推理而设计。你可在 英特尔开发者云 (IDC) 上获取其公开实例，也可在本地部署它。IDC 是尝试 Gaudi 2 的最简单方法，如果你尚没有帐户，可以考虑注册一个帐户，订阅 “Premium”，然后申请相应的访问权限。 在软件方面，我们主要使用 LangChain 来构建我们的应用。LangChain 是一个开源框架，旨在简化 LLM AI...
Building Reliable Agent using Advanced Rag Techniques, LangGraph, and Cohere LLM
Q1. Is there Cohere API free to use? A. Yes, Cohere currently allows free rate limited API calls for research and prototyping here. Q2. What are the advantages of Tavily Search API? A. It is more optimized for searches with RAG and LLMs as compared to other conventional search APIs. Q3. What is the compatibility of LangGraph? A. LangGraph offers compatibility with existing LangChain agents, allowing developers to modify AgentExecutor internals more easily.
基于LangChain自查询检索器的RAG系统开发实战-51CTO.COM
想了解更多AIGC的内容，请访问： 51CTO AI.x社区 https://www.51cto.com/aigc/ 最近，我在浏览Max.com网站时想找一部电影看。通常，这个过程包括浏览系统呈现给我的各种列表，阅读一些相关描述，然后挑选一些看起来有趣的电影。如果我知道我想看的电影的片名或我喜欢的演员的名字，我通常只会点击搜索功能。否则，搜索就没有多大用处了。 现在，我突然想到了一个新的想法：为什么我不能用自然语言来查找一部电影，更多地基于电影的氛围或实质，而不仅仅是标题或演员呢?例如，为什么我不能启动Max、Netflix或Hulu等流媒体播放平台，并在搜索栏中键入类似于以下查询之一呢： 给我找一部长度不到2小时、以宠物为主角的英语戏剧电影。 推荐僵尸电影，但要确保它们很有趣。 我喜欢《瞬息全宇宙》。给我一部类似的电影，但场景、氛围或者人物性格更加阴暗、沉重一些。 这种方法的美妙之处超出了更自然的电影搜索方式，还保护了用户的隐私。该系统根本不会使用用户数据，不是挖掘用户的行为、喜欢和不喜欢来提供给推荐系统。唯一需要的就是一个查询。...
Create A Multimodal Assistant With Advanced RAG And Amazon Bedrock
Retrieval Augmented Generation (RAG) models have emerged as a promising approach to enhance the capabilities of language models by incorporating external knowledge from large text corpora. However, despite their impressive performance in various natural language processing tasks, RAG models still face several limitations that need to be addressed. Naive RAG models face limitations such as missing content, reasoning mismatch, and challenges in handling multimodal data.
Create a multimodal assistant with advanced RAG and Amazon Bedrock | Amazon Web Services
Retrieval Augmented Generation (RAG) models have emerged as a promising approach to enhance the capabilities of language models by incorporating external knowledge from large text corpora. However, despite their impressive performance in various natural language processing tasks, RAG models still face several limitations that need to be addressed. Naive RAG models face limitations such as missing content, reasoning mismatch, and challenges in handling multimodal data.
Langchain: Data Protection
In today's world, data privacy is very important, especially when working with large language models (LLMs) and sensitive information. Companies and individuals often need to use private data, such as personal identifiable information (PII), into their LLM applications. However, the risk of data leaks and privacy breaches is a constant threat, making it necessary to implement data protection measures.
Blog-Reading Chatbot with GPT-4o
In this tutorial, you will build a RAG system that combines blog content ingestion with the capabilities of semantic search. OpenAI’s GPT-4o LLM is powerful, but scaling its use requires us to supply context systematically. RAG enhances the LLM’s generation of answers by retrieving relevant documents to aid the question-answering process. This setup showcases the integration of advanced search and AI language processing to improve information retrieval and generation tasks.
手把手教你从0到1搭建一个AI Agent(智能体)
LLM为什么需要agent化 虽然大语言模型的能力很强大，但是Llm仅限于用于训练的知识，这些知识很快会过时，所以llm有以下缺点 幻觉 结果并不总是真实的 对时事的了解有限或一无所知 难以应对复杂推理和计算 例如：买高铁票 (虽然LLM完全理解了买票的行为，但是它本身并不知道“我”所处的城市，列车的时刻表，价格等等信息） 而基于大模型的Agent (LLM based Agent) 可以利用外部工具来克服以上缺点。 ReAct Agent ReAct Agent 论文 Standard IO(直接回答) -> COT(chain-of-thought)(思维链) -> Action-Only (Function calling) -> Reason + Action ReAct = Reasoning(推理) + Action(行动) Models：LLM Prompts：对Agent的指令、约束 Memory : 记录Action执行状态 & 缓存已知信息 Indexes : 用于结构化文档，以便和模型交互 Chains ：Langchain的核心（链） Agent from...
LangChain Chatbot Framework With Retrievers
Subscribe to all my content for free! In my previous post I shared some ideas on how LLMs have disrupted the chatbot development landscape and how via LLMs, AI was introduced to the different levels of chatbot development. Ranging from dialog flows, Natural Language Generation (NLG) and Natural Language Understanding (NLU). In the previous article, I also shared a working Python code example of the simplest implementation of the LangChain Chatbot Framework.
How do I reduce tokens without losing relevant information?
3 retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 500}) What’s the size of each of your chunk? Your k set to 500? If each chunk contains about 1k token, then in each model run your context cost 500k token in chat history and you may need 1M context window model to run it. Try a smaller k to overcome the token limit, and see what actually sent to LLM, it’s takes certain effort to test and tune it.
RAG app with GROQ with unlimited rate limit!
Today, we’re diving into the exciting world of LangChain RAG applications, a revolutionary approach to building intelligent and informative AI experiences. What is LangChain RAG? Imagine an LLM that can not only access its vast internal knowledge but can also tap into the specific data you provide. That’s the magic of Retrieval-Augmented Generation (RAG) with LangChain.
Evaluate RAGs Rigorously or Perish
Use RAGAs framework with hyperparameter optimisation to boost the quality of your RAG system. TL;DR If you develop a RAG system you need to choose between different design options. This is where the ragas library can help you by generating synthetic evaluation data with answers grounded in your documents. This makes possible the rigorous evaluation of a RAG system with the classic split between train/validation/test sets. As the result the quality of your RAG system will get a big boost.
Build Intelligent RAG For Multimodality and Complex Document Structure
The advent of Retrieval-Augmented Generation (RAG) models has been a significant milestone in the field of Natural Language Processing (NLP). These models combine the power of information retrieval with generative language models to produce answers that are not just accurate but also contextually enriched. However, as the digital universe expands beyond textual data, incorporating image understanding and hierarchical document structure analysis into RAG systems is becoming increasingly crucial.
How to Use LangChain to Build With LLMs - A Beginner's Guide
Large language models (LLMs) are incredibly powerful general reasoning tools that are useful in a wide range of situations. But working with LLMs presents challenges that are different from building traditional software: Calls tend to be slow, and streams generate output as it becomes available. Instead of structured input (something like JSON) with fixed parameters, they take unstructured and arbitrary natural language as input. They are capable of “understanding” subtleties of that language.
【AI大模型应用开发】【LangChain系列】9. 实用技巧：大模型流式输出在 OpenAI 和 LangChain 中的使用
· 欢迎 点赞 + 关注 👏，持续学习，持续干货输出。 · +v: jasper_8017 一起交流💬，一起进步💪。 公众号内文章一览 当大模型的返回文字非常多时，返回完整的结果会耗费比较长的时间。如果等待大模型形成完整的答案再展示给用户，明显会给用户不好的体验。所以，现在市面上大多数的AI应用，在给用户结果时，都是以流式输出的方式展示给用户的。所谓的流式输出，就是类似打字机式的方式，一个字或一个词的输出，给用户一种答案逐渐出现的动画效果。 今天我们来学习下如何流式输出大模型的返回结果。本文将涵盖 LangChain 的流式输出方式和 OpenAI 原生的流式输出方式。 0.
【LangChain系列】8. 重要组件：如何用LCEL中的RunnableParallel和Passthrough？
公众号内文章一览 经过几次的LangChain实战，发现其中最常用的两个封装类：RunnableParallel 和 RunnablePassthrough。这是LangChain独有的表达式语言(LCEL)中最主要的组件。本文我们来深入学习下这两个组件的原理与使用。 0. 前置推荐阅读 LangChain实战系列： · 【AI大模型应用开发】【LangChain系列】实战案例1：用LangChain写Python代码并执行来生成答案 · 【AI大模型应用开发】【LangChain系列】实战案例2：通过URL加载网页内容 - LangChain对爬虫功能的封装 · 【AI大模型应用开发】【LangChain系列】实战案例3：深入源码，WebResearchRetriever与RAG联合的力量 · 【AI大模型应用开发】【LangChain系列】实战案例4：再战RAG问答，提取在线网页数据，并返回生成答案的来源 LangChain表达式语言（LCEL）介绍： · 【AI大模型应用开发】【LangChain系列】4. 从Chain到LCEL：探索和实战LangChain的巧妙设计 1.
【AI大模型应用开发】【LangChain系列】实战案例4：再战RAG问答，提取在线网页数据，并返回生成答案的来源
· 欢迎 点赞 + 关注 👏，持续学习，持续干货输出。 · +v: jasper_8017 一起交流💬，一起进步💪。 公众号内文章一览 0. 背景 目前为止，我们已经系统学习了一下内容： （1）RAG的基本流程与实践 · 【AI大模型应用开发】3. RAG初探 - 动手实现一个最简单的RAG应用 （2）网页数据抓取 · 【AI大模型应用开发】【LangChain系列】实战案例2：通过URL加载网页内容 - LangChain对爬虫功能的封装 （3）langchain基本使用： · 【AI大模型应用开发】【LangChain系列】1. 全面学习LangChain输入输出I/O模块：理论介绍+实战示例+细节注释 · 【AI大模型应用开发】【LangChain系列】2. 一文全览LangChain数据连接模块：文档加载到RAG，理论实战+细节 · 【AI大模型应用开发】【LangChain系列】4.
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