
With the increasing shift towards digitization, businesses and organizations constantly explore new ways to improve their operations and customer experience. A leading approach in this space involves the development of advanced customer support bots, internal knowledge graphs, and Q&A systems.
Retrieval Augmented Generation (RAG) applications are becoming more prevalent to make these more efficient. RAG’s ability to blend pre-trained models with proprietary data is a game-changer. However, ensuring that these applications are used safely without compromising data integrity is crucial. In this blog, we discuss the critical elements of RAG and introduce a way to manage RAG workflows with security and governance.
Retrieval Augmented Generation, commonly known as RAG, is a cutting-edge architecture in artificial intelligence that blends the capabilities of large-scale neural language models with information retrieval systems. Instead of generating responses based solely on pre-trained knowledge, RAG retrieves relevant documents or data fragments from vast datasets. Then, it utilizes these fragments as context for the subsequent text generation. This allows RAG to tap into specific, up-to-date, or domain-specific knowledge, making it particularly effective for applications such as chatbots and Q&A systems that require real-time information access and customization based on external data.
Javelin LLM Gateway is an Enterprise-grade LLM (Large Language Model) Gateway that enables enterprises to apply policy controls, adhere to governance measures, and enforce comprehensive security guardrails, including data leak prevention, to ensure safe and compliant model use. Javelin is tailored to empower Operational teams, enabling them to manage LLM-enabled applications and oversee their model access effectively. This blog shows how we can effectively harden RAG workflows with security, governance, and privacy controls.
Setting up a Retrieval Augmented Generation (RAG) system involves combining a retrieval mechanism with a generation model. Below is a high-level overview of the process:
Security Tip: This is the phase where it is generally easy for sensitive or secure documents to make their way into your RAG Document Dataset inadvertently. Two key issues need to be managed:
Javelin is an LLM Gateway best suited to simply provide a single model access point from within your RAG workflows. Routes to various models may be securely provisioned on the gateway — this prevents your RAG application from having to understand, maintain, and keep track of models. This central model management also makes it easy to quickly select newer models as they become available or switch to different embedding models from different applications.
It’s easy to install the Javelin Python SDK:
Now, you can easily create one or more routes. Each route represents an endpoint that can be centrally managed with security and governance guardrails:
Once you have provisioned one or more routes, replace the model URLs in your apps and instead have them use Javelin.
Love Langchain? We love it, too! Here is an easy way (~1 line of code) to do this from your Langchain-enabled apps:
Store these vector representations in a vector database, allowing for efficient nearest-neighbor lookups.
Security Tip: Three key security issues need to be carefully managed in this phase:
Secure Model Access With Javelin — with Javelin, your model access is always secure. Since all LLM access goes through Javelin, you have complete visibility of the applications using various models, how much they use them, and what data is being passed to models.
Budget Guardrails: You can even set up cost guardrails to control budgets for large document RAG jobs!
Security Tip: While security for fine-tuning is a complete article in itself, the key things to keep in mind at this point are:
You can use Javelin in your model fine-tuning workflows. Just enable archiving on your Javelin routes, and all model interactions are automatically enabled:
By enabling all data collection in a central location, you drastically reduce your Security footprint and reduce data sprawl. This is highly important from a governance and responsible model usage standpoint. You can also use Javelin Archives for compliance and audits!
Prevent Sensitive Data Leaks — it is easy to provide security guardrails that inspect, redact, mask, or anonymize sensitive data before it ends in vector space. It is easy to restrict applications that generate embeddings versus applications that encode questions. You create two routes, one for the application that encodes questions and another for the application that generates embeddings. By passing requests through Javelin, you can set policies and automatic guardrails that prevent PII and PHI passthrough:
Securing Document Retrievals — RAG workflows are exposed to the most sensitive and proprietary enterprise and user data. Often, this data is internal to enterprises and has intellectual property or other corporate secrets. Encoding your workflows with purpose-built security like Javelin is critical to secure this access.
Once you are satisfied with the performance of your RAG workflow and the accuracy of your Q&A through this workflow, you are now ready to move your app for production use (either for internal users or external customers)
For RAG applications, Javelin’s Gateway Routes can be efficiently created in Javelin for various models. Setting up models like Llama2–70b-Chat, PaLm 2, or Mistral is a breeze.
Here is a Langchain example,
Javelin gives you a central point for production controls around model use:
With Javelin, you have full transparency over model access, usage, and real-time monitoring.
Following these steps, one can set up a RAG system tailored to specific requirements, ensuring efficient information retrieval and high-quality response generation.
The power of Javelin lies in its capacity to streamline governance, enforce security, and set policies for accessing various model APIs. This centralized approach empowers organizations, allowing them to distribute Routes to Models, which, in turn, democratizes access to LLMs while maintaining the integrity and security of the system.
The fusion of RAG applications with the Javelin AI platform promises ease of taking generative AI applications to production with security, confidence, and efficiency. The potential is immense, and the potential of customer support and Q&A systems can be tapped responsibly.
Javelin is an enterprise-grade LLM gateway. It is built to be lightning-fast 🚀 and highly secure for dealing with your most sensitive data 🔒. It is built on a zero-trust security architecture 🛡️ for use in even the most regulated industries.
We have built petabyte-scale Internet systems, so we understand Data and Security. Do you have a question about designing your RAG or just want to talk security? We would love to chat!