Kade Heyborne
48c6ddc066
- Complete planning documentation for 5-phase development - UI design specifications and integration - Domain architecture and directory templates - Technical specifications and requirements - Knowledge incorporation strategies - Dana language reference and integration notes
43 lines
3.7 KiB
Markdown
43 lines
3.7 KiB
Markdown
# User Flow Example: Media Processing and Agent Customization
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This document illustrates a critical user journey within the Advanced Second Brain PKM system, demonstrating the seamless integration of media processing, knowledge extraction, and the advanced capability of agent engineering and cross-domain querying.
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## Scenario: Integrating a New Video Lecture and Customizing the Agent
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The following steps detail how a user interacts with the system to ingest new media, extract insights, modify an agent's behavior, and execute a complex, cross-domain query.
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### Step 1: Data Ingestion via Background Agent
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The process begins with the user introducing new knowledge into the system.
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1. **User Action**: The user acquires a new video lecture on neuroscience and places the file into the designated local directory for that domain, specifically within the `Neuroscience/Media` folder. This action leverages the system's core principle of **Local Data Sovereignty**.
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2. **System Action**: The system's background **"Media Scraper Agent"** automatically detects the new file.
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3. **System Action**: The agent initiates a transcription process, generating an interactive transcript file that is placed alongside the video. This transcript is synchronized with the video timeline, preparing the media for advanced analysis.
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### Step 2: Knowledge Extraction in Knowledge Browser Mode
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The user then moves to the application interface to engage with the newly ingested data.
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1. **User Action**: The user navigates to the **Neuroscience Domain** using the Global Navigation Sidebar.
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2. **User Action**: Within the **Knowledge Browser Mode**, the user selects the video file from **Pane 1: The Drawer**.
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3. **System Action**: The video is displayed in **Pane 2: Content Viewer**, accompanied by the synchronized, interactive transcript.
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4. **User Action**: To extract key insights, the user selects a "Fabric" pattern, such as "Extract Ideas," and clicks the corresponding button in **Pane 3: Insight/Fabric**.
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5. **System Action**: The Neuroscience Agent processes the transcript, and the right pane is populated with structured, extracted bullet points, representing the key insights from the lecture.
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### Step 3: Cross-Domain Agent Customization in Agent Studio Mode
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Recognizing a connection between the new content and another domain, the user customizes the agent's logic.
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1. **User Action**: The user switches to the **Agent Studio Mode** to access the agent's source code.
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2. **User Action**: In the **Middle Panel (The Dana Editor)**, the user modifies the Dana agent's code. The modification explicitly instructs the Neuroscience Agent to seek connections to concepts like "neural networks" within the **CompSci Domain**'s knowledge base.
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3. **User Action**: The user immediately tests the modified agent logic in the **Bottom Panel (The REPL & Logs)** using a command such as `dana> agent.query("test context")`, confirming the new cross-domain search capability.
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### Step 4: Complex Query via Global Orchestrator Chat
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Finally, the user leverages the system's multi-agent architecture to synthesize knowledge across domains.
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1. **User Action**: The user navigates to the **Global Orchestrator Chat**.
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2. **User Action**: In the **Domain Scope Selector**, the user explicitly checks the boxes for both **"Neuroscience"** and **"CompSci"**.
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3. **User Action**: The user inputs a complex, cross-domain query: "How does the lecture I just watched relate to current LLM architecture?"
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4. **System Action**: The Orchestrator Agent intelligently calls both the newly customized Neuroscience Agent and the CompSci Agent, synthesizes their respective findings, and delivers a comprehensive, integrated answer to the user.
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