Events2Join

RAG quickstart with Ray


RAG quickstart with Ray, LangChain, and HuggingFace

In this post, you'll learn how to quickly deploy a complete RAG application on Google Kubernetes Engine (GKE), and Cloud SQL for PostgreSQL and pgvector.

Julie Amundson on LinkedIn: RAG quickstart with Ray, LangChain ...

- Cloudflare R2 for storage. - Neon for serverless Postgres. - unstructured.io for super simple RAG chunking. + OpenAI & Anthropic APIs (Haiku ...

ray-rag.md - huggingface/blog - GitHub

Sample script to finetune RAG using Ray for distributed retrieval. · Add parent directory to python path to access lightning_base.py ·../" · ${PYTHONPATH}" · Start ...

Jason Soo Hoo's Post - LinkedIn

RAG quickstart with Ray, LangChain, and HuggingFace | Google Cloud Blog.

RAG quickstart with Ray, LangChain, and HuggingFace - CCoE

We have developed a quickstart solution and reference architecture for RAG applications built on top of GKE, Cloud SQL, and open-source frameworks Ray, ...

Build RAG-based large language model applications with Ray on ...

Large Language Models (LLMs) have changed the way we interact with information. A base LLM is only aware of the information it was trained ...

RAG quickstart with Ray, LangChain, and HuggingFace | daily.dev

Learn how to deploy a complete RAG application on Google Kubernetes Engine (GKE) and Cloud SQL for PostgreSQL and pgvector using Ray, LangChain, ...

README.md - ray-project/llm-applications - GitHub

... rag-based-llm-applications-part-1; GitHub repository: https://github.com/ray ... Start serving (+fine-tuning) OSS LLMs with Anyscale Endpoints ($1/M ...

RAG quickstart with Ray, LangChain, and HuggingFace | daily.dev

Learn how to deploy a complete RAG application on Google Kubernetes Engine (GKE), and Cloud SQL for PostgreSQL and pgvector using Ray, LangChain, ...

Scaling RAG and Embedding Computations with Ray and Pinecone

Developing a retrieval augmented generation (RAG) based LLM application can be hard and data intensive. It requires many different ...

I am just getting started with RAG. Need advice. - Reddit

Properly chunk code cells, table structures, and so on. We use ray to do parallel processing and then do the rest of the flow with many other ...

End to end training of RAG retriever with RAY - Ray

Hi, thanks a lot for your quick reply. In my suggestion, we do not need to do any gradient flow to the RAY Actors. We only need to run a RAY ...

Building RAG-based LLM Applications for Production - Anyscale

Load data. We're going to start by loading the Ray documentation from the website to a local directory:.

Bay.Area.AI: Build RAG-based large language model applications ...

Build RAG-based large language model applications with Ray and KubeRay on Kubernetes Large Language Models (LLMs) have changed the way we ...

Building an Image Question Answering System with RAG and LLM

We then parallelize the insertion process on the embedded text dataset. We use the .count() function to kick-start the process, given that Ray's ...

Retrieval Augmented Generation with Huggingface Transformers ...

7ray start --head 8 9# A sample finetuning run, you need to specify ... If you plan to try RAG+Ray integration out, please feel free to ...

ray on X: " #RaySummit Training Session Alert: "RAG Applications ...

RaySummit Training Session Alert: "RAG Applications - From Quickstart to Scalable RAG"! Learn to implement and scale RAG applications, ...

Build and Scale a Powerful Query Engine with LlamaIndex and Ray

On the other hand, the Ray blogs provide a Quick Start guide, a User ... Supercharge your LlamaIndex RAG Pipeline with UpTrain Evaluations.

Quickstart — Ray 2.38.0

Apply user-defined functions (UDFs) to transform datasets. Ray executes transformations in parallel for performance. from typing import Dict import numpy ...

Best Practices in Retrieval Augmented Generation - Gradient Flow

On a speculative note, hyperparameter optimization tools like Ray Tune could enable the systematic refinement of RAG systems. ... Start WritingGet ...