Emerging Architectures for LLM Applications
May 21, 2024 | by Enceladus Ventures
Large Language Models (LLMs) have emerged as a transformative technology, offering a powerful new tool for building software. However, due to their novelty and unique behavior, developers often face challenges in harnessing their full potential. In this article, we present a reference architecture for the emerging LLM app stack, providing insights into common systems, tools, and design patterns used by AI startups and tech companies. While this stack is still evolving, we hope it serves as a valuable resource for developers navigating the world of LLMs.
LLMs represent a paradigm shift in software development, enabling developers to create sophisticated AI applications with unprecedented ease and speed. This reference architecture is based on insights gathered from conversations with AI startup founders and engineers, as well as our own observations of industry trends.
The Stack
The LLM app stack comprises several key components, each playing a crucial role in the development and deployment of LLM-based applications. These components include:
Data Pipelines: Tools like Databricks and Airflow are commonly used for data preprocessing and transformation, preparing contextual data for input into LLMs.
Embedding Models: OpenAI's text-embedding-ada-002 model is widely used for generating embeddings of textual data. Additionally, Hugging Face's Sentence Transformers library provides an open-source alternative for creating embeddings tailored to specific use cases.
Vector Database: Pinecone is a popular choice for storing and efficiently retrieving embeddings, offering scalability and performance for large-scale applications. Other options include open-source systems like Weaviate and Vespa, as well as local libraries like Chroma and Faiss.
Playground: Platforms such as nat.dev and Humanloop provide environments for experimenting with LLMs and fine-tuning models for specific tasks.
Orchestration: Frameworks like LangChain and LlamaIndex streamline the process of prompt construction, retrieval, and execution, abstracting away complexity and facilitating rapid development of LLM applications.
APIs/Plugins: APIs and plugins, including those provided by OpenAI and Hugging Face, enable seamless integration of LLMs into existing workflows and applications.
LLM Cache: Caching solutions like Redis improve application performance by storing frequently accessed LLM outputs and reducing latency.
Design Pattern: In-Context Learning
The core idea behind in-context learning is to leverage LLMs off the shelf, controlling their behavior through intelligent prompting and conditioning on contextual data. This approach enables developers to avoid the complexities of fine-tuning models while achieving high levels of accuracy and efficiency.
Data Preprocessing/Embedding
In the data preprocessing stage, contextual data is transformed into embeddings using pre-trained models. These embeddings are then stored in a vector database for efficient retrieval during inference.
Prompt Construction/Retrieval
When a user submits a query, the application constructs prompts consisting of prompt templates, few-shot examples, and relevant contextual data retrieved from the vector database. Orchestration frameworks play a key role in automating this process and generating optimized prompts for LLM inference.
Prompt Execution/Inference
The compiled prompts are submitted to pre-trained LLMs for inference, with operational systems like logging and caching ensuring smooth and efficient execution. While proprietary model APIs like those offered by OpenAI are commonly used, open-source models are also gaining traction, particularly in high-volume use cases.
Looking Ahead
As the field of LLMs continues to evolve, we anticipate further advancements in both technology and architecture. The emergence of AI agent frameworks represents a promising development, offering new capabilities for reasoning, planning, and learning from experience. While still in the early stages, these frameworks have the potential to revolutionize the LLM app stack and unlock new possibilities for AI-driven applications.
Conclusion
The emergence of LLMs has ushered in a new era of software development, enabling developers to create innovative applications with unprecedented speed and efficiency. By understanding the key components and design patterns of the LLM app stack, developers can harness the full potential of this transformative technology and drive the next wave of AI innovation.
At Enceladus Ventures, we're committed to staying at the forefront of LLM development and supporting startups in harnessing the power of these cutting-edge technologies. Through our expertise in product development and startup investment strategies, we aim to empower entrepreneurs to build groundbreaking LLM applications that drive positive change across industries.
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