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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,6 +1,3 @@ # Building a Custom AI Chatbot with Spring Boot, React, and LangChain4j **Speaker:** Marcus Hellberg @@ -25,15 +22,12 @@ Incorporating LLMs into Java applications requires several tools and components - **Context Window (RAM):** Controls how much context is retained and processed during interactions.  --- ## AI for Engineers: APIs and Libraries - **LangChain4j**: A Java-based AI library facilitating LLM integrations. - **Hilla**: A full-stack framework for building modern web applications, combining Spring Boot on the server side and TypeScript on the client side. It simplifies the development of reactive web apps. --- ## Agent Autonomy Levels @@ -46,8 +40,7 @@ This diagram from the presentation illustrates the various components like Tools - Most business apps will fall into this category. 3. **Fully Autonomous**: Fully capable of making decisions without human intervention.  --- @@ -64,9 +57,7 @@ This graph illustrates the spectrum of agent autonomy, ranging from chatbots to - **Definition**: A token is a chunk of text (ranging from a character to a word) used for processing by the AI. - **Token Count**: The token count is typically about 25% greater than the word count.  --- ## RAG (Retrieval Augmented Generation) @@ -84,15 +75,9 @@ This diagram highlights the structure of a context window, showcasing how system --- ## LangChain4j Specifics - **LangChain Interface**: Works similarly to Spring Data JPA, allowing developers to query LLMs. - **Reactive Nature**: LangChain allows real-time token streaming during responses. ### Key Components 1. **System Prompt**: Defines a new AI identity for a session. 2. **Config**: Provides access to the necessary data and configuration parameters. -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,98 @@ # Building a Custom AI Chatbot with Spring Boot, React, and LangChain4j **Speaker:** Marcus Hellberg **YouTube Link:** [Watch the video](https://www.youtube.com/watch?v=1dXKlRs5pbY) **GitHub Repository:** [java-ai-playground](https://github.com/marcushellberg/java-ai-playground) --- ## Key Concepts and Insights ### Vaading Perspective - **LLMs (Large Language Models)** are generic, but your business is unique. ### Tools for Java Developers to Build AI-Powered Applications Incorporating LLMs into Java applications requires several tools and components to manage resources efficiently: - **Tools (Programs):** Manage various AI processing functions. - **Vector Store (Disk):** Stores embeddings on disk or other storage mediums for retrieval. - **CPU (Processing):** Powers the execution of LLMs. - **Search Tools (Ethernet):** Helps retrieve relevant information. - **Other LLMs:** Can be integrated to provide more advanced functionalities. - **Context Window (RAM):** Controls how much context is retained and processed during interactions.  This diagram from the presentation illustrates the various components like Tools, Vector Store, CPU, and how they interact with the Context Window and Search Tools for optimal LLM use in a Java environment. --- ## AI for Engineers: APIs and Libraries - **LangChain4j**: A Java-based AI library facilitating LLM integrations. - **Hilla**: Exciting new features introduced here. --- ## Agent Autonomy Levels 1. **Chatbot**: - Knows a lot of things but lacks specific domain expertise. 2. **Copilot**: - Acts on your behalf in predefined scenarios. - Monitors and assists users but doesn’t control the entire workflow. - Most business apps will fall into this category. 3. **Fully Autonomous**: Fully capable of making decisions without human intervention.  This graph illustrates the spectrum of agent autonomy, ranging from chatbots to fully autonomous agents, with **Copilot** being a middle ground. --- ## Context Window - A **context window** is essential for determining the amount of information processed by the LLM in one session. - Components include: - **System Prompt**: Sets the behavior or identity of the AI. - **History**: Previous interactions. - **Prompt**: The user's input. - **Relevant Information**: Critical to the specific task at hand. - **Response Space**: Ensuring enough space is left for the AI's response. ### Tokens - **Definition**: A token is a chunk of text (ranging from a character to a word) used for processing by the AI. - **Token Count**: The token count is typically about 25% greater than the word count.  This diagram highlights the structure of a context window, showcasing how system prompts, history, user prompts, and relevant information are stored while ensuring room for responses. --- ## RAG (Retrieval Augmented Generation) - **Definition**: A method where the AI retrieves specific case-related information before generating responses. - **LLMs Know Two Things**: 1. Content they’re trained on. 2. Specific information retrieved via **RAG**. --- ## Embedding Model - **Purpose**: Transforms text into vectors for processing. - **Vector Store**: Essential for storing and retrieving relevant vectorized information. --- ## LangChain4j Specifics - **LangChain Interface**: Works similarly to Spring Data JPA, allowing developers to query LLMs. - **Reactive Nature**: LangChain allows real-time token streaming during responses. ### Key Components 1. **System Prompt**: Defines a new AI identity for a session. 2. **Config**: Provides access to the necessary data and configuration parameters. ### Code Snippet: LangChain4j Configuration ```java! LangChain4jConfig.java