The capabilities of Large Language Models (LLMs) are enhanced by Retrieval-Augmented Generation (RAG). Thus, RAG comes up with a super powerful tec...
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Let me know if you want a blog on a specific framework!!
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Langchain
Basically, everyone is trying to build their own RAG pipeline for their specific use case and then market them as a side gig... And I believe this abundance create analysis paralysis and as a result — creating another new RAG tool instead of selecting existing one 😅
In fact, I worked with llamaindex and in result needed to (re)implement some of their TS APIs because they were lacking at the moment. So all in all, implementing your own dedicated and specialized RAG tool is not such a bad idea actually (and not such a big deal too, depending on use case).
LangChain and GPTIndex started this way when ChatGPT was released first. And now they've grown bigger.
+1
You're right.
This factor decides a lot! But it's still great to use the pre-built tools to save time. If they are missing a specific feature, do contribute to that as they are open-source. 😉
Great post! I don't how many times i will return to reread it to check them out..
save it for later.. Ehehe 😉
Nice and detailed article. However, LlamaIndex and JinaAI are not RAG frameworks. They serve their purpose in a RAG or AI project pipeline/stack.
yes, but they help developers build production RAG pipelines. And this is much needed! So, I put them in the RAG framework. However, they are ultimately a RAG framework.
Thanks for including us!
Just to clarify, “LLM-App” is actually a set of ready-to-run AI pipelines built on top of Pathway’s core engine (rather than a standalone framework). It’s Docker-friendly, uses YAML-based configuration to define sources and pipeline logic, and stays continuously in sync with SharePoint, S3, databases, etc. We also have built-in indexing (vector/hybrid/full-text) for real-time search and RAG use cases.
If you’re curious about how it compares to other RAG solutions, feel free to check our in-depth comparison at pathway.com/rag-frameworks. Let us know if you have any questions—we’re always happy to help!
Thank you Saksham for sharing this with us. But every framework is different and there are pros and cons in all of them. 😉
This post provides an excellent introduction to RAG (Retrieval-Augmented Generation) frameworks and highlights their importance in enhancing LLM capabilities. The simplified explanation using a toy analogy makes it accessible even for beginners, while the step-by-step breakdown of RAG’s workflow effectively demonstrates its functionality.
The list of top open-source RAG frameworks is a valuable resource, starting with LLMWare.ai, which stands out due to its enterprise-friendly features like LLM orchestration, document processing, vector database integration, and custom fine-tuning. Its scalability and security make it particularly appealing for businesses looking to deploy AI-powered applications.
Thank you, Hassan!
Also, LLMWare.ai is one of the best RAG Frameworks. You can try it and let me know the feedback.
Awesome work man. I can see some really new ones here. 🔥 That table recap is nice too.
Thank you, Anmol. (and thank you for always helping me)
I thought a summarized table would be great as this blog was a little longer to read. I'm glad that you liked it! 🙈
Amazing listicle !
I'm glad you liked it!
check out github.com/FalkorDB/GraphRAG-SDK/t...
Graph Rag.. That's cool!!
Amazing, keep it up
Thank you!! I hope you enjoyed reading it. 😉
Wow mentioned so many projects
Very long listicle
Yusss. A detailed one!
Amazing!
I hope you had a great read!
thanks bro
My pleasure!!!
very good
Yuss. Thanks!!!