use LlamaIndex & LangChain together |tutorial:8
GITHUB: https://github.com/ronidas39/llamaind...
TELEGRAM: https://t.me/ttyoutubediscussion
*00:00:20* - Introduction
What's up, guys? This is Ronnie. Welcome back to our Channel, Total Technology Zone. This is tutorial 8, and today's topic is how to use LlamaIndex and LangChain together. This is part of the Lang LlamaIndex series. I'll show you how to combine LangChain with LlamaIndex. This tutorial was inspired by requests asking if LlamaIndex and LangChain can work together.
*00:01:01* - Channel Promotion
Before we start, I have a request. If you're new to my channel, please subscribe, hit the like button, share our videos with your family and friends, and hit the bell notification icon so you don't miss any updates. If you're an existing viewer but haven't subscribed yet, please do so. You will learn a lot. For those interested in large language model development or becoming an AI engineer, check out my LangChain tutorial playlist. I have 102 videos in that series, covering everything from scratch.
*00:02:00* - Coding Begins
Let's start coding. We'll use the LLM module from LangChain and integrate it with LlamaIndex. First, import the necessary modules from LlamaIndex and LangChain.
*00:03:22* - Setting Up LLMs
We'll set up the LLM using LangChain's `langchain.openai` module. We'll load a document from a directory, create a vector index, and set up the query engine.
*00:05:22* - Loading Documents
We'll load the document using `SimpleDirectoryReader` from LlamaIndex and check if it's loaded correctly by printing the content.
*00:06:43* - Creating Index and Query Engine
We'll create an index using `VectorStoreIndex.from_documents` and set up the query engine. Next, we'll create a response variable to handle queries.
*00:07:50* - Querying and Response
We'll ask a sample question about the differences between merge sort and quicksort, and the LLM will generate the answer.
*00:08:57* - Additional Queries
We'll test additional questions, such as how Dijkstra's algorithm ensures the shortest path in a weighted graph.
*00:10:03* - Conclusion
This tutorial demonstrates how to use LlamaIndex with LangChain. You can integrate them in different ways, such as using the integration framework from LlamaIndex and the LLM from LangChain or vice versa. This flexibility allows for various use cases.
*00:11:52* - Final Request
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