I'm currently working on developing a chatbot powered by a Large Language Model (LLM), and I want it to provide responses based on my own documents. I understand that using a fine-tuned model on my documents might not yield direct responses, so I'm exploring the concept of Retrieval-Augmented Generation (RAG) to enhance its performance.
In my research, I've come across two tools, Langchain and LlamaIndex, that seem to facilitate RAG. However, I'm struggling to understand the main differences between them. I've noticed that some tutorials and resources use both tools simultaneously, and I'm curious about why one might choose to use one over the other or when it makes sense to use them together.
Could someone please provide insights into the key distinctions between Langchain and LlamaIndex for RAG, and when it is beneficial to use one tool over the other or combine them in chatbot development?
You'll be fine with just LangChain, however, LlamaIndex is optimized for indexing, and retrieving data.
To answer your question, it's important we go over the following terms:
Retrieval-Augmented Generation (or RAG) is an architecture used to help large language models like GPT-4 provide better responses by using relevant information from additional sources and reducing the chances that an LLM will leak sensitive data, or ‘hallucinate’ incorrect or misleading information.
Vector Embeddings are numerical vector representations of data. They are not only limited to text but can also represent images, videos, and other types of data. They are usually created using an embedding model such as OpenAI's text-embedding-ada-002 (see here for more information)
Let me start off by saying that it's not either LangChain or LlamaIndex. As you mentioned in your question, both tools can be used together to enhance your RAG application.
You can think of LangChain as a framework rather than a tool. It provides a lot of tools right out of the box that enable you to interact with LLMs. Key LangChain components include chains. Chains allow the chaining of components together, meaning you could use a PromptTemplate and a LLMChain to:
Here's a quick example:
...
prompt = PromptTemplate(template=template, input_variables=["questions"])
chain = LLMChain(
llm=llm,
prompt=prompt
)
chain.run(query)
You can read more about LangChain components here.
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. It offers a set of tools that facilitate the integration of custom data into LLMs.
Based on my experience with LlamaIndex, it is an ideal solution if you're looking to work with vector embeddings. Using its many available plugins you could load (or ingest) data from many sources easily, and generate vector embeddings using an embedding model.
One key feature of LlamaIndex is that it is optimized for index querying. After the data is ingested, an index is created. This index represents your vectorized data and can be easily queried like so:
...
query_engine = index.as_query_engine()
response = query_engine.query("Stackoverflow is Awesome.")
LlamaIndex abstracts this but it is essentially taking your query "Stackoverflow is Awesome." and comparing it with the most relevant information from your vectorized data (or index) which is then provided as context to the LLM.
It should be clear to you now why you might choose one or both technologies for your specific use case. If your app requires indexing and retrieval capabilities, and while you'll be just fine using LangChain (as it can handle that as well) I recommend integrating with LlamaIndex since it is optimized for that task and it is generally easier to ingest data using all the plugins and data connectors. Otherwise, if you just need to work with LLMs stick with only LangChain.
If you'd like to read more, I cover both LangChain and LlamaIndex on my blog. Here's a post looking at LangChain and LlamaIndex.
Note: I am the author of this post.