Enterprise-Ready Generative AI Applications
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Generative AI has transformed the way enterprises interact with data, but creating enterprise - grade applications demands exceptional security and performance. In this blog, we explore how to build Retrieval-Augmented Generation (RAG) applications using the Apolo platform, leveraging its on-premise capabilities and industry-leading tools.
Whether you're querying financial data or building a chatbot for enterprise documentation, Apolo streamlines the process, allowing developers to focus on innovation.
Enterprise-ready solutions prioritize:
Security: Complete data privacy - nothing leaves your environment, ensuring full control.
Performance: Comparable with leading AI models like OpenAI, Anthropic, and Meta models.
Apolo’s platform combines these pillars with the flexibility to run Llama 3.1 models (ranging from 8B to 70B parameters), making it an ideal choice for building scalable, secure, and high-performing generative AI applications.
Retrieval-Augmented Generation (RAG) enhances the quality of generative AI applications by combining powerful LLMs with structured retrieval systems. Here’s how it works:
Generative LLM: Generates responses by interpreting input text.
Embedding LLM: Converts text into numerical embeddings for efficient similarity searches.
Re-ranker LLM: Scores and ranks retrieved data for relevance.
Additionally, RAG applications require:
Retrieval Database: For efficient storage and querying of embeddings (e.g., PostgreSQL with PGVector).
Data Moat: Continuous improvement through user feedback, stored and analyzed in tools like Argilla.
The Apolo platform simplifies these complexities by offering:
Apolo CLI: Streamline operations via command-line management.
Apolo Storage: Secure and scalable data storage.
Apolo Jobs: GPU-powered infrastructure for high-performance model operations.
Apolo UI: A user-friendly interface for visualizing workflows.
The two case studies - Apolo Documentation Chatbot and Canada Budget Chatbot - demonstrate the versatility and power of RAG architectures on the Apolo platform.
By combining:
Secure, on-premise infrastructure.
High-performance generative AI models.
Advanced retrieval mechanisms powered by PostgreSQL, vector embeddings, and LLMs.
Feedback-driven iterative improvements with Argilla.
The Apolo platform enables the development of enterprise-ready generative AI applications.
Enterprise-grade capabilities: Apolo ensures data security and high performance, meeting the stringent demands of enterprise use cases.
Customizable architectures: The modular RAG setup can be tailored for different domains, from technical documentation to financial analysis.
Iterative refinement: Feedback loops drive continuous improvement, enhancing both the user experience and system accuracy.
Whether you’re building a chatbot to navigate complex corporate documentation or to provide insights from voluminous data like budgets, the Apolo platform offers a seamless path to creating scalable, efficient, and secure generative AI applications.
If you’re interested in exploring this further, feel free to contact us (start@apolo.us) for a demo or check out the code on GitHub.
