Upgrade your LLama-Index RAG pipeline with NeuralDB
Give your LLama-Index pipeline a ThirdAI boost with fine-tunable retrieval, Reinforcement Learning with Human Feedback (RLHF), LLM Firewall, and all sorts of low-latency NLP utilities, without overloading any GPU cycles.
The LLama-Index’s orchestration tools allow you to customize a variety of documents and uncommon file type parsers effectively. As a result, LLama-Index is becoming popular for building RAG pipelines for prototyping, testing, and deploying Generative AI applications.
In this post, we will show how to use ThirdAI’s platform in any existing LLama-Index pipeline to scale and customize your RAG stack for deployment and production.
The Hike from Demo to Production: Building a basic demo RAG pipeline is very easy with several reasonable choices. However, scaling up a RAG demo pipeline to cater to millions or billions of text chunks makes almost all RAG pipelines prohibitively slow and expensive. Poor latency of RAG at scale makes it infeasible for real-time applications. Furthermore, taking a RAG pipeline to production requires many carefully crafted customizations. These customizations could range from catering to domain-specialized questions of broad interest to PII redaction, sentiment or toxicity detection, and many more.
One-Line Integration with ThirdAI
One of the standout features of integrating LLama-Index with ThirdAI is its simplicity. By making just a single line change to your existing pipeline, you can seamlessly integrate ThirdAI and start benefiting from new capabilities immediately. To experience this integration firsthand, check out our demo here and see how ThirdAI and LLama-Index together can transform your data retrieval processes.
Four Major Immediate Upgrades to your RAG pipeline
Integrating ThirdAI into your RAG pipeline immediately unlocks the following:
- Ultra-Low Latency RAG at Scale: Enjoy unprecedented efficiency for RAG. ThirdAI’s engine can index 10 million raw text files in a matter of 4 minutes on a laptop-grade CPU (benchmarked on an M1 Mac) while reaching OpenAI-level accuracy. The search latency is in the single-digit milliseconds on the same device. Read the case study here.
- Hyper-Customized Retrieval with RLHF: Never worry about your RAG engine making mistakes on critical queries. You can hyper-customize and personalize your search results using supervised learning and reinforcement learning with our associate and upvote feature. Boost your retrieval accuracies to levels previously impossible with any RAG pipeline. Read the case study here and an open RAG accuracy challenge here.
- Real-time NLP including LLM Firewall: Gain access to ThirdAI’s real-time NLP capabilities, some of the fastest available. From PII or NER detection to complex NLP classifications for identifying security vulnerabilities, toxicity, privacy risks, and more, all with less than 10ms latency on local cores. Read about them here and here.
- Keep your GPUs for something else: And yes, as always, you never need any GPUs for running any of ThirdAI’s capabilities. So, free up your GPUs from text processing or retrieval tasks. Keep your precious GPU cycles for other applications that cannot function without them.
Links
- Get a ThirdAI Trial License: Link
- Learn more about ThirdAI: https://www.thirdai.com
- Full Demo Notebook: https://github.com/ThirdAILabs/Demos/blob/main/neural_db/llama_index.ipynb
