---
title: "Demo Apps"
description: "This page provides an overview of the various packaged demo applications designed to showcase the capabilities and integration patterns of the core system. These applications serve as practical exa..."
last_updated: "2026-05-07T04:45:14.995935+00:00"
canonical_url: "https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/section-7/demo-apps"
---
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This page provides an overview of the various packaged demo applications designed to showcase the capabilities and integration patterns of the core system. These applications serve as practical examples for developers and users to understand how different functionalities, such as memory management, AI interaction, and data processing, can be implemented in real-world scenarios. The demos cover a range of technologies and use cases, from interactive chat interfaces to specialized data processing tools and web applications.
The primary goal of these demo apps is to offer ready-to-use examples that highlight key features, demonstrate best practices, and provide a starting point for building custom solutions. They illustrate how to integrate with various AI models, manage conversational memory, and deploy applications across different environments.
## Overview of Demo Application Categories
The demo applications are categorized by their primary function or technology stack, providing diverse examples of how the core system can be leveraged. This section outlines the main types of demo apps available.
While specific code implementations are not detailed here due to the absence of source files, the descriptions provide a conceptual understanding of each demo's purpose and architecture.
### Chat-PDF Application
The Chat-PDF demo application illustrates how to build an interactive chat interface that can process and respond to queries based on the content of PDF documents. This application typically involves document ingestion, text extraction, embedding generation, and retrieval-augmented generation (RAG) to provide context-aware answers.
**Conceptual Architecture:**
The Chat-PDF application generally follows a RAG pattern, where user queries are used to retrieve relevant document chunks, which are then fed to a large language model (LLM) along with the query to generate a coherent response.
Sources: Based on topic description.
### Next.js App Variants
A series of demo applications built using the Next.js framework showcase how to integrate the core system's functionalities into modern web applications. These variants might demonstrate different UI patterns, data fetching strategies, or authentication mechanisms while interacting with the backend services.
**Key Integration Points:**
* **API Routes:** Utilizing Next.js API routes to handle backend interactions, such as sending user queries to an AI service or retrieving memory states.
* **Server-Side Rendering (SSR) / Static Site Generation (SSG):** Demonstrating how to pre-render content or fetch data on the server for improved performance and SEO.
* **Client-Side Interaction:** Managing user input, displaying AI responses, and handling real-time updates using React components.
**Conceptual Interaction Flow (Client-Server):**
```mermaid
sequenceDiagram
participant Client as Next.js Client
participant Server as Next.js API Route
participant AIService as AI Backend Service
participant MemoryDB as Memory Database
Client->>Server: User Input (e.g., /api/chat)
activate Server
Server->>AIService: Process Query (with memory_id)
activate AIService
AIService->>MemoryDB: Retrieve Memory (memory_id)
activate MemoryDB
MemoryDB-->>AIService: Memory State
deactivate MemoryDB
AIService-->>Server: AI Response & Updated Memory
deactivate AIService
Server->>MemoryDB: Store Updated Memory (memory_id)
activate MemoryDB
MemoryDB-->>Server: Confirmation
deactivate MemoryDB
Server-->>Client: AI Response
deactivate Server
```
Sources: Based on topic description.
### Mistral Streamlit Application
The Mistral Streamlit demo provides an example of using the Mistral AI model within a Streamlit application. Streamlit is an open-source app framework for Machine Learning and Data Science teams, enabling rapid creation of interactive web applications with Python. This demo would likely focus on demonstrating real-time interaction with the Mistral model, potentially for tasks like text generation, summarization, or question-answering.
**Features:**
* **Interactive UI:** A simple, intuitive interface built with Streamlit widgets for user input and displaying AI output.
* **Mistral Integration:** Direct calls to the Mistral AI model API for processing user requests.
* **Real-time Feedback:** Displaying AI responses as they are generated, enhancing user experience.
Sources: Based on topic description.
### Private AI Mode Demonstration
The Private AI Mode demo showcases how the system can operate in a privacy-preserving manner, ensuring that sensitive user data or proprietary information is not exposed to external AI services or stored in unsecured locations. This mode is crucial for enterprise and regulated environments.
**Key Aspects:**
* **Local Processing:** Utilizing local or on-premise AI models where applicable, or ensuring data anonymization before external transmission.
* **Data Isolation:** Demonstrating mechanisms for keeping conversational memory and user data within a defined security boundary.
* **Configurable Privacy Settings:** Highlighting how users or administrators can configure the level of data privacy.
Implementing a truly "private AI mode" requires careful consideration of data governance, model deployment, and security protocols beyond just the application layer. This demo focuses on illustrating the architectural patterns.
Sources: Based on topic description.
### Other Showcase App Configurations
Beyond the specific examples, the project includes various other configurations and smaller showcase applications. These might cover:
* **Different LLM Integrations:** Demos integrating with other popular LLMs (e.g., OpenAI, Llama, Gemini) to show flexibility.
* **Advanced Memory Strategies:** Showcasing different memory management techniques, such as long-term memory, short-term context windows, or specialized memory types (e.g., for code, facts).
* **Tool Use/Function Calling:** Applications demonstrating how the AI can interact with external tools or APIs to perform actions or retrieve specific information.
* **Multi-modal AI:** Demos that might involve processing and generating content across different modalities (e.g., text and images), if supported by the underlying AI models.
These configurations aim to provide a comprehensive view of the system's adaptability and potential use cases across a broad spectrum of AI-powered applications.
Sources: Based on topic description.
## Sitemap
See the full [sitemap](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/llms.txt) for all pages in this wiki.