Our 4-Tool Stack + Strategy for Building Enterprise Applications on LLMs – AI&YOU#53
Stat/Fact of the Week: The global LLM market is projected to grow from $1.59 billion in 2023 to $259.8 billion in 2030, a CAGR of 79.8% during 2023-2030 (Springs)
Large language models (LLMs) have emerged as a key to building intelligent enterprise applications. However, harnessing the power of these language models requires a robust and efficient LLM application stack.
At Skim AI, our LLM app stack enables us to create powerful applications with advanced natural language interaction capabilities. Our stack comprises carefully selected tools and frameworks, like LLM APIs, LangChain, and vector databases.
In this week’s edition of AI&YOU, we are highlighting our 4-tool stack and strategy for building enterprise LLM applications through our published blogs:
Our 4-tool stack and strategy for building enterprise applications on LLMs – AI&YOU #53
With our enterprise LLM stack, developers can seamlessly integrate domain-specific data, fine-tune models, create efficient data pipelines for retrieving contextual data, and more.
This empowers businesses to build applications that understand and respond to user queries with unprecedented accuracy and context-awareness.
At the same time, one of the main techniques to go along with this stack is to utilize existing tools and frameworks provided by the various components. This allows developers to focus on building applications rather than creating tools from scratch, saving valuable time and effort.
Tool 1: An LLM API Like GPT, Claude, Llama, or Mistral
At the core of your LLM application stack should be an LLM API. LLM APIs provide a way to integrate powerful language models into your applications without the need to train or host the models yourself. They act as a bridge between your software and the complex algorithms that power language models, enabling you to add advanced natural language processing capabilities to your applications with minimal effort.
One of the key advantages of using an LLM API is the ability to leverage state-of-the-art language models that have been trained on vast amounts of data. These models, such as GPT, Claude, Mistral, and Llama, are capable of understanding and generating human-like text with remarkable accuracy and fluency.
By making API calls to these models, you can quickly add a wide range of capabilities, including text generation, sentiment analysis, question-answering, and much more to your applications.
Factors to Consider When Choosing an LLM API
When choosing an LLM API for your stack, there are several factors to consider:
Performance and accuracy: Ensure that the API can handle your workload and provide reliable results.
Customization and flexibility: Consider whether you need to fine-tune the model for your specific use case or integrate it with other components of your stack.
Scalability: If you anticipate high volumes of requests, make sure the API can scale accordingly.
Support and community: Evaluate the level of support and the size of the community around the API, as this can impact the long-term viability of your application.
At the heart of most LLM APIs are deep neural networks, typically based on transformer architectures, that are trained on massive amounts of text data. These models are accessed via an API interface, which handles tasks like authentication, request routing, and response formatting. LLM APIs also often include additional components for data processing, such as tokenization and normalization, as well as tools for fine-tuning and customization.
Tool 2: LangChain
After selecting an LLM API for your LLM application stack, the next component to consider is LangChain. LangChain is a powerful framework designed to simplify the process of building applications on top of large language models. It provides a standardized interface for interacting with various LLM APIs, making it easier to integrate them into your LLM tech stack.
One of the key benefits of using LangChain is its modular architecture. LangChain consists of several components, such as prompts, chains, agents, and memory, which can be combined to create complex workflows. This modularity allows you to build applications that can handle a wide range of tasks, from simple question-answering to more advanced use cases like content generation and data analysis, enabling natural language interaction with your domain-specific data.
LangChain’s Various Tools and Support
LangChain also provides a variety of tools and utilities that streamline working with LLMs. For instance, it offers tools for working with embeddings, which are numerical representations of text used for tasks like semantic search and clustering. Additionally, LangChain includes utilities for managing prompts, which are the input strings used to guide the behavior of language models.
Another crucial feature of LangChain is its support for vector databases. By integrating with vector databases like Chroma (which is what we use), LangChain enables you to build applications that can efficiently store and retrieve large amounts of data. This integration allows you to create knowledge-intensive applications that can draw upon a wide range of information sources, enhancing the retrieval of contextual data for your LLM app stack.
LangChain is a vital component of any enterprise LLM application stack. Its modular design, powerful tools, and active community make it an indispensable tool for building sophisticated language-based applications.
Tool 3: A Vector Database Like Chroma
In addition to an LLM API and LangChain, another essential component of your LLM app stack is a vector database. Vector databases are specialized data stores optimized for storing and searching high-dimensional vectors, such as embeddings generated by large language models. By integrating a vector database into your LLM tech stack, you can enable fast, efficient retrieval of relevant data based on semantic similarity.
Chroma is a popular open-source choice for a vector database in LLM application stacks, and we use it here at Skim AI. It is designed to work seamlessly with LangChain and other components of your stack, providing a robust and scalable solution for storing and retrieving embeddings.
One of the key advantages of using Chroma is its ability to handle large volumes of data efficiently. Chroma uses advanced indexing techniques to enable fast similarity search, even on massive datasets. This makes it an ideal choice for applications that need to store and search through large amounts of textual data, such as document repositories, knowledge bases, and content management systems.
Chroma also offers advanced features like filtering and metadata support. You can store additional metadata alongside your embeddings, such as document IDs, timestamps, or custom attributes. This metadata can be used to filter search results, enabling more precise and targeted retrieval of contextual data.
Integrating Chroma Into Your Enterprise LLM Stack
Integrating Chroma into your LLM app stack is straightforward, thanks to its compatibility with LangChain and other popular tools and frameworks. LangChain provides built-in support for Chroma, making it easy to store and retrieve embeddings generated by your language models. This integration allows you to build powerful retrieval mechanisms that can quickly surface relevant information based on natural language interaction.
Using a vector database like Chroma in conjunction with LLMs opens up new possibilities for building intelligent, context-aware applications. By leveraging the power of embeddings and similarity search, you can create applications that can understand and respond to user queries with unprecedented accuracy and relevance.
When combined with LangChain and an LLM API, Chroma forms a powerful foundation for building intelligent, data-driven applications that can transform the way we interact with enterprise data and domain-specific information.
Tool 4: crewAI for Multi-Agent Systems
At Skim AI, we know that the future of AI workflows is agentic, which is why a multi-agent system is important for any enterprise today.
crewAI is another powerful tool that you can add to your LLM app stack to enhance the capabilities of your applications. crewAI is a framework that enables you to create multi-agent systems, where multiple AI agents work together to accomplish complex tasks.
At its core, crewAI is designed to facilitate collaboration between multiple AI agents, each with its own specific role and expertise. These agents can communicate and coordinate with each other to break down complex problems into smaller, more manageable subtasks.
Leveraging the Power of Specialization
One of the key advantages of using crewAI in your LLM tech stack is its ability to leverage the power of specialization. By assigning specific roles and tasks to different agents, you can create a system that is more efficient and effective than a single, monolithic AI model. Each agent can be trained and optimized for its particular task, allowing it to perform at a higher level than a general-purpose model, and enabling more targeted retrieval of contextual data from your domain-specific datasets.
Using multi-agent systems like crewAI in combination with other components of your LLM tech stack can help you to unlock new possibilities for building intelligent, multi-agent systems that can handle complex real-world tasks. By leveraging the power of specialization and collaboration, you can create applications that are more efficient, effective, and user-friendly than traditional single-model approaches.
Unlocking the Power of LLMs with the Right Application Stack
This stack allows you to seamlessly integrate domain-specific data, enable efficient retrieval of contextual information, and build sophisticated workflows that can tackle complex real-world challenges. By leveraging the power of these tools and frameworks, you can push the boundaries of what is possible with language-based AI applications and create truly intelligent systems that can transform the way your enterprise interacts with data and technology.
Top 5 LLM API Integration Strategies for Your Enterprise
This week, we also explored the top 5 LLM API integration strategies for your enterprise.
From modular integration to continuous monitoring and optimization, these strategies are designed to ensure smooth implementation, optimal performance, and long-term success.
Modular integration involves breaking down the LLM API integration process into smaller, manageable modules that can be implemented incrementally. This approach allows for a phased implementation, easier troubleshooting, and more flexible updates and enhancements.
An API gateway acts as a single entry point for all API requests, managing authentication, rate limiting, and request routing. It provides centralized authentication, rate limiting, and valuable insights into API usage and performance.
Microservices architecture involves breaking down a monolithic application into smaller, loosely coupled services that can be developed, deployed, and scaled independently. It enables independent development, granular scalability, and increased agility and flexibility.
Customizing and fine-tuning LLM APIs involves adapting them to better suit specific industry, domain, or application requirements. This improves accuracy, relevance of outputs, and allows aligning with terminology, styles, and formats.
Continuous monitoring and optimization involve tracking performance metrics, assessing output quality/relevance, and making iterative improvements. This allows proactively identifying issues, adapting to changing requirements, and continuously improving value from LLM API integrations.
As the field of LLM technology continues to evolve at a rapid pace, enterprises that invest in robust, scalable, and adaptable integration strategies will be well-positioned to unlock the full potential of these transformative tools.
Top 5 Open Source LLMs for Your Enterprise
Open-source large language models (LLMs) have emerged as a powerful tool for enterprises in 2024.
One of the key advantages of using open-source LLMs is the flexibility and customization they offer. Moreover, open-source LLMs provide a cost-effective alternative to developing and maintaining proprietary models. By leveraging the collective efforts of the AI community, enterprises can access state-of-the-art language models without the need for extensive investments in research and development.
Llama 3 by Meta: Llama 3 is a cutting-edge open-source large language model with two size variants (8B and 70B parameters), each offering Base and Instruct models. It excels in various NLP tasks, is deployment-friendly, and adheres to responsible AI practices.
Claude 3 by Anthropic: Claude 3 comes in three variants (Haiku, Sonnet, Opus) optimized for different use cases. It demonstrates impressive performance across cognitive tasks like reasoning, expert knowledge, and language fluency, outperforming models like GPT-4.
Grok by xAI: Grok, developed by Elon Musk’s xAI, specializes in text summarization and comprehension. Its latest iteration, Grok-1.5, introduces long context understanding, advanced reasoning, and strong coding/math capabilities.
BERT by Google: BERT pioneered bidirectional language understanding and excels in tasks like text classification, sentiment analysis, and question answering. Its pre-training allows it to generate human-like text and provide contextually relevant responses.
Mistral Large by Mistral AI: Mistral Large, with 314B parameters, shines in complex reasoning tasks and specialized applications. It offers multilingual support, instruction-following, and function-calling abilities, enhancing its versatility.
As the open-source AI community continues to push the boundaries of what is possible with language models, enterprises that embrace these powerful tools will be well-positioned to stay ahead of the curve and achieve long-term success.
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