In-depth analysis: key components and functions in the AI ​​LLM framework

This article deeply explores the high-level architecture of an artificial intelligence framework, analyzing its internal components and their functions in the overall system. This AI framework aims to make it easier to combine traditional software with large language models (LLMs).

Its core purpose is to provide developers with a set of tools to help them smoothly integrate artificial intelligence into software already used in the company. This innovative strategy has created a software platform for us that can run many AI applications and intelligent agents at the same time, thereby realizing more high-end and complex solutions.

1. Application examples of AI framework

In order to have a deeper understanding of the capabilities of this framework, here are some application examples that can be developed using this framework:

• AI Sales Assistant: This is a tool that can automatically search for potential customers, analyze their business needs, and draft proposals for the sales team. Such an AI assistant will find effective ways to establish contact with target customers and open the first step of sales.
• AI Real Estate Research Assistant: This tool can continuously monitor new listings in the real estate market and screen qualified listings based on specified criteria. In addition, it can design communication strategies, collect more information about a certain property, and provide assistance to users in all aspects of home buying.
• AI Zhihu Discussion Summary ApplicationThis intelligent application should be able to analyze discussions on Zhihu , Extract conclusions, tasks, and next steps that need to be taken.

2. AI framework module

The AI ​​framework should provide developers with a set of different modules, including contract definitions, interfaces, and implementations of common abstractions.

This solution should be a solid foundation upon which you can build your own solution, using proven patterns, adding your own implementations of individual modules, or using community-prepared modules .

• The Hints and Chaining Module is responsible for building hints, i.e. programs written for language models, and chains of calls to these hints, which are executed one after the other in sequence. This module should make it possible to implement various techniques used in Language Models (LM) and Large Language Models (LLM). It should also be able to combine prompts with models and create prompt groups that provide a single functionality across multiple LLM models.
• Model Module is responsible for processing and connecting the LLM model to the software, making it available to other parts of the system.
• Communication Module is responsible for handling and adding new communication channels with users, whether in the form of chats in one of the messaging programs or as APIs and webhooks for integration with other systems (webhook) form.
• Tools module is responsible for providing functionality to add tools used by AI applications, such as reading the content of a website from a link, reading a PDF file, searching for information online, or sending an email Ability.
• Memory module should be responsible for memory management and allow additional memory function implementations to be added to AI applications to store the current state, data and currently executing tasks.
• Knowledge Base ModuleThis module should be responsible for managing access rights and allowing the addition of new sources of organizational knowledge, such as information about processes, documentation, guidance, and all electronically captured information in the organization.
• Routing moduleThis module should be responsible for routing external information from the communication module to the appropriate AI application. Its role is to determine the user's intent and launch the correct application. If the application has been started previously and has not completed the operation, it should resume and pass data from the communication module.
• AI Application ModuleThis module should allow the addition of specialized AI applications that are focused on performing specific tasks, such as automating or partially automating processes. An example solution might be a Slack or Teams chat summary application. Such an application might include one or more prompts linked together, using tools, memory, and leveraging information from a knowledge base.
• AI Agent ModuleThis module should contain more advanced application versions that can autonomously talk to the LLM model and perform assigned tasks automatically or semi-automatically.
• Accountability and Transparency ModuleThe Accountability and Transparency Module records all interactions between users and AI systems. It tracks queries, responses, timestamps, and authorship to differentiate between human-generated and AI-generated content. These logs provide visibility into autonomous actions taken by the AI ​​and messages between the model and the software.
• User Module In addition to basic user management functions, this module should also maintain user account mapping across integrated systems from different modules.
• Permission moduleThis module should store user permission information and control user access to resources, ensuring that they can only access appropriate resources and applications.

3. AI framework component architecture

In order to better demonstrate the interaction between different modules in the AI ​​framework architecture, the following is an overview of a component diagram:

This diagram shows the relationship between the key components of the framework:

• Prompts and Chained Modules: Building prompts for AI models, And concatenate multiple prompts through chain calls to implement more complex logic.
• Memory module: Memory management through memory abstraction. The knowledge base module provides access to knowledge sources.
• Tool module: Provides tools that can be used by AI applications and agents.
• Routing module: Direct queries to the appropriate AI application. Applications are managed in the AI ​​application module.
• Communication Module: Handles communication channels like chat.

This component architecture shows how different modules work together to make it possible to build complex AI solutions. The modular design allows functionality to be easily expanded by adding new components.

4. Module dynamic example

In order to illustrate the collaboration between AI framework modules, let’s analyze a typical information processing path in the system:

• The user sends a query using the chat function through the communication module.
• The routing module analyzes the content and determines the appropriate AI application from the application module.
• The application obtains the necessary data from the storage module to restore the conversation context.
• Next, it uses the command module to build the appropriate commands and passes them to the AI ​​model from the model module.
• If necessary, it will execute the tools in the tool module, such as searching for information online.
• Finally, it returns a response to the user through the communication module.
• Important information will be stored in the storage module to continue the conversation.

Thanks to this way of functioning, framework modules should be able to collaborate with each other to enable AI applications and agents to implement complex scenarios.

5. Summary

The AI ​​framework should provide comprehensive tools for building modern AI-based systems. Its flexible, modular architecture should allow for easy expansion of functionality and integration with an organization's existing software. Thanks to AI frameworks, programmers should be able to quickly design and implement a variety of innovative solutions using language models. With ready-made modules, they should be able to focus on business logic and application functionality. This makes it possible for AI frameworks to significantly accelerate the digital transformation of many organizations.

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