背景：
In this post, we'll briefly compare LangChain and LlamaIndex and look at the key features of each solution so that you can choose which is the best suited for your next LLM-powered app.
在这篇文章中，我们将简要比较LangChain和LlamaIndex，并探讨每种解决方案的关键特性，以便您可以选择最适合您下一个基于大型语言模型（LLM）的应用程序的解决方案。
Introduction 引言
When it comes to developing applications powered by language models, two frameworks stand out: LangChain and LlamaIndex. Both frameworks offer unique features and capabilities that cater to different use cases. In this post, we'll look at the difference between LlamaIndex and LangChain so that you can determine which framework is the best fit for your specific use case.
在开发由语言模型驱动的应用程序时，有两个框架尤为突出：LangChain 和 LlamaIndex。这两个框架都提供了独特的功能和特性，以满足不同的用例需求。在本文中，我们将探讨 LlamaIndex 和 LangChain 之间的差异，以便您能够确定哪个框架最适合您的特定用例。
Both LangChain and LlamaIndex use Python and JavaScript, so you'll need to be somewhat familiar with the syntax.
LangChain 和 LlamaIndex 都使用 Python 和 JavaScript，因此您需要对这些语言的语法有一定的了解。
LangChain 简介 Introduction to LangChain
From the official docs: 从官方文档来看：
LangChain is a framework for developing applications powered by language models.
LangChain 是一个用于开发由语言模型驱动的应用程序的框架。
Simply put, LangChain is a framework that enables the development of data-aware and agentic applications. It provides a set of components and off-the-shelf chains that make it easy to work with LLMs (such as GPT). Whether you are a beginner or an advanced user, LangChain is suitable for simple prototyping and production apps.
简单来说，LangChain 是一个框架，它使得开发具有数据感知和代理特性的应用程序成为可能。它提供了一组组件和现成的链（chains），使得与大型语言模型（如 GPT）一起工作变得容易。无论您是初学者还是高级用户，LangChain 都适用于简单的原型制作和生产应用程序。
LangChain Components 组件
LangChain exposes high-level APIs (or components) to work with LLMs by abstracting most of the complexities. These components are relatively simple and easy to use.
LangChain 通过抽象化大部分复杂性，为与大型语言模型（LLMs）合作提供了高级 API（或组件）。这些组件相对简单且易于使用。
One such core component is LLMs which easily connect to LLM providers (OpenAI, Cohere, Hugging Face, etc.) allowing you to query easily as such:
其中一个核心组件是 LLMs，它轻松连接到 LLM 提供商（如 OpenAI、Cohere、Hugging Face 等），使您能够像这样轻松地查询：
python
const res = await llm.call("Tell me a joke");Off-the-shelf Chains 现成的链
LangChain includes off-the-shelf chains, so if you're looking to get started quickly, you can make use of the provided pre-built chains that can help you accomplish specific tasks. These chains can be customized or used as a base for building new apps.
LangChain 包含了现成的链（Chains），因此如果您希望快速入门，可以利用提供的预构建链来帮助您完成特定任务。这些链可以定制，也可以作为构建新应用程序的基础。
One such chain is the SqlDatabaseChain, which quickly allows you to connect a SQL Database and then generate a response from a provided LLM as seen below:
其中一个这样的链是 SqlDatabaseChain，它允许您快速连接 SQL 数据库，然后如下所示，从提供的 LLM 中生成响应：
python
const chain = new SqlDatabaseChain({
llm: new OpenAI({ temperature: 0 }),
database: db,
sqlOutputKey: "sql",
});
const res = await chain.call({ query: "How many tracks are there?" });
/* Expected result:
* {
* result: ' There are 3503 tracks.',
* sql: ' SELECT COUNT(*) FROM "Track";'
* }
*/
console.log(res);LlamaIndex 简介 Introduction to LlamaIndex
LlamaIndex, (previously known as GPT Index), is a data framework specifically designed for LLM apps. Its primary focus is on ingesting, structuring, and accessing private or domain-specific data. LlamaIndex offers a set of tools that facilitate the integration of private data into LLMs.
LlamaIndex（以前称为 GPT Index）是一个专门为 LLM 应用程序设计的数据框架。它的主要关注点是摄取、结构化以及访问私有或特定领域的数据。LlamaIndex 提供了一组工具，这些工具有助于将私有数据集成到 LLM 中。
Data connectors 数据连接器
LlamaIndex allows you to ingest data from various sources, including APIs, PDFs, SQL databases, and more. These connectors enable seamless integration of data into the LLM application.
LlamaIndex 允许您从各种来源摄取数据，包括 API、PDF、SQL 数据库等。这些连接器使得数据能够无缝集成到 LLM 应用程序中。
Data indexes 数据索引
LlamaIndex structures the ingested data into intermediate representations that are optimized for LLM consumption. This ensures efficient and performant access to the data.
LlamaIndex 将摄取的数据结构化为中间表示形式，这些表示形式针对 LLM 的使用进行了优化。这确保了数据的高效和性能良好的访问。
Engines 引擎
LlamaIndex provides different engines for natural language access to the data. These include query engines for knowledge retrieval, chat engines for conversational interactions, and data agents that augment LLM-powered knowledge workers.
LlamaIndex 提供了不同的引擎来以自然语言方式访问数据。这些引擎包括用于知识检索的查询引擎、用于会话交互的聊天引擎，以及增强由 LLM 驱动的知识工作者的数据代理。
The big question 核心问题
So which one should you choose? 那么你应该选择哪一个呢？
LangChain is ideal if you are looking for a broader framework to bring multiple tools together. LangChain is also suitable for building intelligent agents capable of performing multiple tasks simultaneously.
如果你正在寻找一个更广泛的框架来整合多个工具，那么 LangChain 是理想的选择。LangChain 也适合构建能够同时执行多个任务的智能代理。
On the other hand, if your main goal is smart search and retrieval , LlamaIndex is a great choice. It excels in indexing and retrieval for LLMs, making it a powerful tool for deep exploration of data.
另一方面，如果你的主要目标是智能搜索和检索，LlamaIndex 是一个很好的选择。它在为 LLM 进行索引和检索方面表现出色，使其成为深入探索数据的强大工具。
LlamaIndex can be integrated into LangChain to improve and optimize its retrieval capabilities.
LlamaIndex 可以集成到 LangChain 中，以改进和优化其检索能力。
Conclusion 结论
LangChain and LlamaIndex are both valuable and popular frameworks for developing apps powered by language models. LangChain offers a broader range of capabilities and tool integration while LlamaIndex specializes in deep indexing and retrieval for LLMs making it very efficient and fast at this task. Consider your specific use case and requirements to determine which solution aligns best with your specific needs.
LangChain 和 LlamaIndex 都是开发和支持语言模型的应用程序的有价值和流行的框架。LangChain 提供了更广泛的功能和工具集成，而 LlamaIndex 专注于为 LLM 提供深度索引和检索，使其在这项任务上非常高效和快速。请考虑您的具体用例和需求，以确定哪种解决方案最符合您的特定需求。
