AI: LangChain vs LlamaIndex

 LangChain vs LlamaIndex: Choose the Best Framework for Your AI Applications

LlamaIndex is a powerful tool for data indexing and retrieval, designed to enhance information accessibility. It streamlines the process of efficiently indexing data, making it easier to locate and retrieve relevant information. 

By focusing on effective data retrieval, LlamaIndex ensures that users can access the information they need quickly and accurately. LlamaIndex is particularly adept at indexing and storing data into embeddings, which significantly improves the relevance and precision of data retrieval.

run-llama/llama_index: LlamaIndex is a data framework for your LLM applications @GitHub

LangChain, on the other hand, is a versatile framework designed to empower developers to create a wide range of language model-powered applications. 

The modular architecture of LangChain enables developers to efficiently design customized solutions for various use cases. 

It provides interfaces for prompt management, interaction with language models, and chain management. It also includes memory management to remember previous interactions. LangChain excels at chatbot applications, generating text, answering queries, and language translations.

AI summary (Google)

LlamaIndex and LangChain are both tools that work with AI systems, but they have different strengths and are best suited for different use cases: 

• LlamaIndex

  Best for: 

  • Data access: LlamaIndex is a good choice for applications that need to access data efficiently and accurately, such as internal search systems, knowledge management, and enterprise solutions. 
  • Data integration: LlamaIndex is a bridge between data and language models, and is good for integrating specific, structured data. 
  • Workflows: LlamaIndex workflows are event-driven, which makes it easier to follow the flow of actions and interactions. 
• LangChain

  Best for: 

  • Complex interaction: LangChain is good for applications that require complex interaction and content generation, such as customer support, code documentation, and NLP tasks. 
  • Model interaction: LangChain's model interaction module lets it interact with any language model, managing inputs and extracting information from outputs. 

You can also use LlamaIndex as an "on-demand" data query tool within a LangChain agent.

chatbot - Differences between Langchain & LlamaIndex - Stack Overflow

Docs

Building an LLM Application - LlamaIndex

Vector Stores - LlamaIndex

By default, LlamaIndex uses a simple in-memory vector store that's great for quick experimentation. They can be persisted to (and loaded from) disk (or vector database)

ts.llamaindex.ai

LlamaIndex.TS is the JS/TS version of LlamaIndex,
the framework for building agentic generative AI applications connected to your data.

Retrieval Augmented Generation (RAG) Tutorial | LlamaIndex.TS

run-llama/llama_index: LlamaIndex is a data framework for your LLM applications @GitHub

Courses:

Course: Build RAG Applications with LlamaIndex and JavaScript [NEW] | Udemy Business

Course: LangChain & LLMs - Build Autonomous AI Tools Masterclass | Udemy Business

JS/TS

langchain - npm

llamaindex - npm

AI: Graph RAG (Microsoft)

Intro to GraphRAG - YouTube by Microsoft "Reactor" (learning)

a technique that enhances document analysis and question-and-answer performance by leveraging large language models (LLMs) to create knowledge graphs. We'll explore how GraphRAG builds upon Retrieval-Augmented Generation (RAG) by using knowledge graphs instead of vector similarity for retrieval. You'll learn how to set up the environment, prepare data, and implement GraphRAG using LangChain, with practical code examples. Additionally, we'll explore some advanced features and customization options available in LangChain to optimize and tailor GraphRAG to your specific needs.

GitHub - microsoft/RAG_Hack: Hack Together: RAG Hack | Register, Learn, Hack

Event Series | Microsoft Reactor 

RAGHack 2024

Retrieval augmented generation in Azure AI Studio - Azure AI Studio | Microsoft Learn

RAG is a pattern that uses your data with an LLM to generate answers specific to your data. When a user asks a question, the data store is searched based on user input. The user question is then combined with the matching results and sent to the LLM using a prompt (explicit instructions to an AI or machine learning model) to generate the desired answer. This can be illustrated as follows.

RAG uses your data to generate answers to the user question. For RAG to work well, we need to find a way to search and send your data in an easy and cost efficient manner to the LLMs. This is achieved by using an index. An index is a data store that allows you to search data efficiently.

How to build and consume vector indexes in Azure AI Studio - Azure AI Studio | Microsoft Learn