Choosing between LlamaIndex or LangChain
LlamaIndex is a framework designed to index and query LLMs based on your own custom data.
LlamaIndex Architecture 1
LangChain is another framework used to build tailored LLMs based on custom data sources.
Langchain Architecture 1
LlamaIndex vs LangChain: Choosing the Right Framework
LLamaIndex vs Langchain key factors 1
What are the Project requirements? For a primitive: index, query search and retrieve application, we can go for LlamaIndex. But in the case of applications where we need to integrate custom workflows, then LangChain is a better choice.
How simple and accessible is it to use? While LlamaIndex gives a simpler interface, LangChain requires a deeper understanding of NLP concepts and components.
How much customization do you want to make? LangChain’s has a modular design that allows for ease in customization and tool integration, However, LlamaIndex is more of a search and retrieval-based framework.
In short if you were in a dilemma of choosing between them, then consider asking yourself questions like: What are the Project requirements? How simple and accessible is it to use? How much customization do you want to make?
How do LlamaIndex and LangChain differ in their primary focus? LangChain’s main focus is the development & deployment of LLMs, along with the customization of LLMs using fine-tuning methods. However, LlamaIndex aims to provide an end-to-end ML workflow, along with data management & model evaluation.
Which platform is better for beginners in machine learning? LlamaIndex is more preferrable for beginners due to its simple and intuitive implementation. As opposed to this, LangChain requires more indepth understanding of LLMs and NLP concepts.
other references
- Retrieval Augmented Generation - RAG
- RAG usage on an internal knowledge base
- DSPy - ideas to improve RAG