144 lines
7.1 KiB
Markdown
144 lines
7.1 KiB
Markdown
# Knowledge Builder MVP — Product Spec
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*Defined by D J — 2026-02-15 | Revised 2026-02-15 (v3: AgentZero self-processing + NotebookLM UI)*
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## What It Is
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A deployment tool that spins up pre-configured AgentZero containers and hands them source material to self-process. The Builder does NOT process data itself — it launches the expert and tells it what to learn. Each deployed agent includes a NotebookLM-style frontend for ongoing knowledge evolution.
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## Architecture
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### Layer 1: Builder UI (the "factory") — Lightweight Launcher
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Our onboarding application. Collects config and deploys containers. Does NOT download or process any data.
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**Onboarding Workflow:**
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1. Create a new project (name, description, persona/domain)
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2. Add data sources — paste YouTube URLs, upload files, add web pages
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3. Configure AI backend: Claude API key, OpenAI key, or local llama.cpp/Ollama endpoint
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4. Configure agent persona: name, system prompt, behavior guidelines
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5. **Deploy:** Spins up an AgentZero container, passes source list + config
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6. AgentZero takes over — downloads, processes, and learns from the sources autonomously
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7. User gets a running expert agent with a link to access it
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**Data sources supported (passed to AgentZero, NOT processed by Builder):**
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- YouTube videos (single URL)
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- YouTube channels (bulk)
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- PDFs (uploaded files copied into container)
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- Text files
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- Web pages / URLs
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- Any other parseable format
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### Layer 2: AgentZero Container (the "expert") — Self-Processing
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Each deployed container:
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- **Receives source list from Builder** at deploy time
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- **Downloads and processes its own data** — YouTube (yt-dlp), PDFs, web scraping, transcription
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- **Builds its own knowledge base** — chunks, embeds, stores in its local RAG
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- **Has full agent capabilities** — code execution, terminal, web search, tool creation
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- **Knows how to consume all data formats** — Builder ensures this via system prompt + pre-installed tools
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- **Self-contained** — runs independently once deployed
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### Layer 3: NotebookLM-Style Frontend (inside container)
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A separate web UI inside each container for ongoing knowledge management:
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- **Source Manager** — add new sources (YouTube URLs, upload PDFs, paste text, web URLs)
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- **Knowledge Browser** — see what the agent knows, browse indexed content, view source citations
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- **Chat Interface** — ask questions, get answers grounded in the knowledge base
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- **Audio Overview** — generate podcast-style summaries (stretch goal)
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- **Processing Status** — see what's being ingested, progress, errors
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- **Refinement Tools** — correct the agent, add context, mark important sections
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This is how users continuously evolve their expert after initial deployment.
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## Deployment Flow
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```
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1. Builder UI collects: source list + LLM config + persona
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2. Builder spins up AgentZero container (docker run)
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3. Builder injects into container:
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a. Source manifest (URLs, file paths) — NOT processed data
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b. System prompt with persona + data processing instructions
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c. LLM config (API keys, endpoints)
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d. Pre-installed tools for: yt-dlp, PDF parsing, web scraping, Whisper transcription
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e. NotebookLM frontend (served on a separate port or path)
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4. AgentZero boot sequence:
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a. Reads source manifest
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b. Downloads/processes each source autonomously
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c. Chunks and embeds into local RAG
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d. Reports status via NotebookLM frontend
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5. Container is ready — user accesses:
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- AgentZero chat UI (port 50001)
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- NotebookLM knowledge manager (port 50002 or /notebook path)
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```
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## What Builder Must Ensure at Deploy Time
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The Builder is responsible for making sure each AgentZero instance can consume all data formats:
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- **yt-dlp** installed in container for YouTube downloads
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- **Whisper** available (local model or remote endpoint to GPU box)
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- **PDF parser** installed (pdftotext, pdf-parse, or similar)
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- **Web scraper** available (requests + beautifulsoup or similar)
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- **Embedding model** configured (Ollama nomic-embed-text or similar)
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- **Vector DB** running inside container (ChromaDB or similar)
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- **System prompt** includes instructions for processing the source manifest on first boot
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- **Processing tools** as AgentZero custom tools in python/tools/
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## NotebookLM Frontend Requirements
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**Tech Stack:** Next.js 15 + Tailwind v4 + ShadCN UI + Lucide + TypeScript (matches Builder)
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**Pages/Features:**
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1. **Dashboard** — overview of knowledge base (source count, chunk count, last updated)
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2. **Sources** — list all sources, add new ones, see processing status, remove sources
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3. **Chat** — conversational interface with source citations in responses
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4. **Browse** — explore indexed content, search within knowledge base
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5. **Settings** — LLM config, embedding model, persona settings
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**Key Behaviors:**
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- Adding a new source triggers AgentZero to process it (same pipeline as initial deploy)
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- Chat responses include citations linking back to source chunks
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- Processing is async — user sees real-time status updates
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## Tech Stack
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- **Builder UI:** Next.js 15 + Tailwind v4 + Framer Motion + ShadCN UI + Lucide + TypeScript
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- **NotebookLM Frontend:** Same stack (bundled into container)
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- **Generated Containers:** AgentZero (Python, Docker)
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- **Transcription:** Faster Whisper (inside container or remote GPU endpoint)
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- **Embeddings:** nomic-embed-text via Ollama (inside container or remote)
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- **Vector DB:** ChromaDB (inside container)
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- **Container Runtime:** Docker
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## Infrastructure
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- Builder runs on VM (192.168.86.45:3001)
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- Docker on VM for spinning up AgentZero containers
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- GPU box (192.168.86.40) available as remote Whisper endpoint
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- Generated containers are portable — run anywhere with Docker
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## What We Already Have
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- ✅ Builder UI scaffolded (Next.js 16, running at :3001, systemd service)
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- ✅ Onboarding flow (project create, source input)
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- ✅ Docker installed on VM
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- ✅ AgentZero Docker image available
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- ✅ yt-dlp working
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- ✅ Faster Whisper on GPU box
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- ✅ ChromaDB infrastructure
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- ✅ Ollama + nomic-embed-text
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## What Needs Building
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- [ ] **Refactor Builder** — remove all processing logic, make it a pure launcher
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- [ ] **Container deployment engine** — spin up AgentZero, inject config + source manifest
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- [ ] **AgentZero boot processor** — custom tool that reads source manifest and processes on startup
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- [ ] **Data format tools for AgentZero** — YouTube downloader, PDF parser, web scraper, transcription
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- [ ] **NotebookLM frontend** — full knowledge management UI (sources, chat, browse, settings)
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- [ ] **Bundle NotebookLM frontend into container** — served alongside AgentZero
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- [ ] **Container management UI in Builder** — list running agents, start/stop, access links
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- [ ] **Unit tests** — pipeline tools, API routes, deployment engine
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- [ ] **E2E tests** — full flow: create project → add sources → deploy → verify agent processes data → chat works
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## Pricing Target
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| Tier | Price | Includes |
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|------|-------|----------|
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| Free | $0 | 1 agent, 50 docs, local LLM only |
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| Personal | $12/mo | 5 agents, 500 docs, cloud LLM |
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| Pro | $25/mo | Unlimited agents, API access, priority processing |
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| Self-Hosted | Free (open core) | Full feature set, BYOLLM |
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| Enterprise | Custom | SSO, audit logs, dedicated support |
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