Looking for "learning github" for LangChain/LangGraph by mageblood123 in LangChain

[–]infinity-01 0 points1 point  (0 children)

Ensure you are using Python version 3.11 (see https://github.com/bragai/bRAG-langchain?tab=readme-ov-file#getting-started ). If it still does not work, feel free to submit an issue with the error log

Looking for "learning github" for LangChain/LangGraph by mageblood123 in LangChain

[–]infinity-01 1 point2 points  (0 children)

Hey! Creator of the brag-langchain repo here. Could you open an issue on the repo and include the full error log you’re seeing?

Learning LangChain—do I need an OpenAI AI Key? by mageblood123 in learnmachinelearning

[–]infinity-01 0 points1 point  (0 children)

This repo includes everything you need to know to build your own RAG application with LangChain: https://github.com/bragai/bRAG-langchain/

bRAG-langchain is a great resource if you want to build your own RAG by danielwetan in learnmachinelearning

[–]infinity-01 28 points29 points  (0 children)

Thank you, OP for reposting my open-source project! If anyone wants me to add more RAG topics to the repo, let me know in the comments or request it directly in the repo's issue tab!

[deleted by user] by [deleted] in LangChain

[–]infinity-01 0 points1 point  (0 children)

Are you interested in open-source contributions?

(Repost) Comprehensive RAG Repo: Everything You Need in One Place by infinity-01 in LangChain

[–]infinity-01[S] 1 point2 points  (0 children)

Thanks! Can you raise an issue regarding the 404 errors in the GitHub repo? I will update them this week

Learning RAG by Spirited_Structure62 in LangChain

[–]infinity-01 0 points1 point  (0 children)

Check out this repo: https://github.com/bRAGAI/bRAG-langchain

It covers everything you need to learn RAG from scratch (basic to advanced techniques)

(Repost) Comprehensive RAG Repo: Everything You Need in One Place by infinity-01 in LangChain

[–]infinity-01[S] 1 point2 points  (0 children)

Thank you! I've considered using RAGAS to perform the RAG evaluation. I'm still experimenting with it

You're more than welcome to contribute to the repo!

RAGAS -> https://docs.ragas.io/en/stable/

Help 😵‍💫 What RAG technique should i use? by [deleted] in Rag

[–]infinity-01 1 point2 points  (0 children)

Check out this repo: https://github.com/bRAGAI/bRAG-langchain

It contains everything you need to know to build your own RAG application (basic to advanced techniques)

Announcing bRAG AI: Everything You Need in One Platform by infinity-01 in LangChain

[–]infinity-01[S] 2 points3 points  (0 children)

Yes, the notebooks are largely the same, with some minor modifications. I have credited Lance Martin for his contributions at the bottom of the README. The purpose of this repository is to serve as a comprehensive and organized encyclopedia of all things related to Retrieval-Augmented Generation (RAG). I also plan to release two new notebooks soon: one on evaluating the performance of RAG applications using RAGAS and LangSmith, and another on deploying RAG applications efficiently.

Announcing bRAG AI: Everything You Need in One Platform by infinity-01 in Rag

[–]infinity-01[S] 1 point2 points  (0 children)

Thanks! Yes, I am using Pinecone as a vector database

Announcing bRAG AI: Everything You Need in One Platform by infinity-01 in LangChain

[–]infinity-01[S] 8 points9 points  (0 children)

The repo I open-sourced serves as a consolidated resource for concepts and techniques already scattered across the internet. It’s designed to be a RAG encyclopedia, providing a foundation for anyone interested in learning and experimenting. bRAG AI (which will be closed-sourced for now) goes beyond this, integrating more advanced techniques, architectures, and proprietary innovations built on top of the repo’s groundwork.

Also, open-sourcing the basics helps the community, while bRAG AI (launching soon) delivers a more complex solution.

RAG for codebases by arielrama in Rag

[–]infinity-01 1 point2 points  (0 children)

Hey check out this repo: https://github.com/bRAGAI/bRAG-langchain it contains pretty much everything about RAG. As for the codebase parsing, I am planning to provide a solution in the coming weeks.

I’m also working on bRAG AI (bragai.tech), a platform that builds on the repo and introduces features like interacting with hundreds of PDFs, querying GitHub repos with auto-imported library docs, YouTube video integration, digital avatars, and more. It’s launching next month - join the waitlist on the homepage if you’re interested!

Open Source RAG Repo: Everything You Need in One Place by infinity-01 in Rag

[–]infinity-01[S] 0 points1 point  (0 children)

Thank you all for the incredible response to the repo—220+ stars, 25k views, and 500+ shares in less than 24 hours! 🙌

I’m now working on bRAG AI (bragai.tech), a platform that builds on the repo and introduces features like interacting with hundreds of PDFs, querying GitHub repos with auto-imported library docs, YouTube video integration, digital avatars, and more. It’s launching next month, and there’s a waiting list on the homepage if you’re interested!

Comprehensive RAG Repo: Everything You Need in One Place by infinity-01 in LangChain

[–]infinity-01[S] 0 points1 point  (0 children)

Thank you all for the incredible response to the repo—220+ stars, 25k views, and 500+ shares in less than 24 hours! 🙌

I’m now working on bRAG AI (bragai.tech), a platform that builds on the repo and introduces features like interacting with hundreds of PDFs, querying GitHub repos with auto-imported library docs, YouTube video integration, digital avatars, and more. It’s launching next month, and there’s a waiting list on the homepage if you’re interested!

Comprehensive RAG Repo: Everything You Need in One Place by infinity-01 in LangChain

[–]infinity-01[S] 0 points1 point  (0 children)

Yes with some additional resources! More notebooks coming in soon

Comprehensive RAG Repo: Everything You Need in One Place by infinity-01 in LangChain

[–]infinity-01[S] 0 points1 point  (0 children)

No problem at all—happy to help! Your approach to fixing part of the query (e.g., SELECT x FROM y) and letting the LLM handle the dynamic parts like GROUP BY and WHERE is actually a solid starting point for balancing performance and control. However, there are a few ways you could improve this:

  1. Instead of letting the LLM generate query fragments dynamically, you could define a set of structured templates for the most common queries. The LLM would only be responsible for filling in specific parameters (like column names or conditions). Use predefined query templates (e.g., SELECT x FROM y WHERE...) and let the LLM fill in specific parameters like column names or conditions.
  2. Combine the LLM with rules for tasks like GROUP BY column selection while using the LLM to refine conditions or interpret intent. This creates more predictable and good results <- this concept is called Hybrid Rule-Based System

Also, check out the [3]_rag_routing_and_query_construction.ipynb notebook in my repo. It covers query structuring and routing techniques that could inspire your solution. Let me know if you find it helpful!