RAG with LlamaParse from LlamaIndex and LangChain

Summary

This webpage discusses the use of LlamaParse from LlamaIndex and LangChain for parsing PDFs and creating a clean and better quality document to feed into a language model, along with the use of other tools such as Fastembed, Qdrant, Groq, and Chainlit.

Abstract

The webpage titled "RAG with LlamaParse from LlamaIndex and LangChain" discusses the use of LlamaParse, a tool introduced by LlamaIndex, for parsing PDFs and creating a clean and better quality document to feed into a language model. The author suggests that this process can be useful for those who want to create a clean and high-quality document from a PDF. The author also mentions the use of other tools such as Fastembed, Qdrant, Groq, and Chainlit in this process. The author provides a link to their Github repository, which contains the code for the process described in the webpage. The author also provides links to their previous blog posts, which explain some of the tools used in this process. The author encourages readers to follow their Github repository for updates and to leave feedback and comments.

Bullet points

The webpage discusses the use of LlamaParse from LlamaIndex and LangChain for parsing PDFs and creating a clean and better quality document to feed into a language model.
The author suggests that this process can be useful for those who want to create a clean and high-quality document from a PDF.
The author mentions the use of other tools such as Fastembed, Qdrant, Groq, and Chainlit in this process.
The author provides a link to their Github repository, which contains the code for the process described in the webpage.
The author encourages readers to follow their Github repository for updates and to leave feedback and comments.

RAG with LlamaParse from LlamaIndex and LangChain

Uses Qdrant and Mixtral via Groq API🔥

👨🏾‍💻 GitHub ⭐️| 🐦 Twitter | 📹 YouTube | 👔LinkedIn | ☕️Ko-fi

Image from Author

LlamaIndex is a great data framework and recently they introduced LlamaParse where we can parse PDF in a simplest way possible. Wouldn’t it be cool, if we can create a clean and better quality document out of PDF to feed into the LLM ? If your answer is yes, you landed in the right post 😎

It would be good for you to understand things if you first go through my previous blog post.

If you are a follower you know I don’t try to replicate things in different places, if you are new, now you know. I have a Github repo which has code of what I am going to list here.

What is the flow in the code ? Here it is.

In ingest.pyfile, I have explained how we can use LlamaParse to parse documents ( get clean documents ) and how to make that document available for LangChain. After that, I have explained how to create embeddings which are stored in the qdrant cloud.

In app.py file, I have explained how to create a simple Chainlit app to have conversation with your document. Use models from Groq for super fast Inference.

Now, you know how to create a parser, do some extra steps to use that with Langchain and use that parsed document to create embeddings and store it in Qdrant. And finally use models from Groq for fast Inference with Chainlit.

👨🏾‍💻 GitHub ⭐️| 🐦 Twitter | 📹 YouTube | 👔LinkedIn | ☕️Ko-fi

RAG with LlamaParse from LlamaIndex and LangChain

GitHub - sudarshan-koirala/RAG-chat-with-documents: Chainlit app for advanced RAG. Uses llamaparse…

Chainlit app for advanced RAG. Uses llamaparse, langchain, qdrant and models from groq. …

Recommended YouTube playlists: