NotebookLM-Lite

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NotebookLM-Lite is an open-source NotebookLM-style AI knowledge workspace for local files, grounded RAG chat, notes, interactive Studio artifacts, and long-form podcast generation. It is built for people who like the Google NotebookLM workflow but want a hackable, self-hosted project with their own model endpoints, their own document pipeline, and a clear backend architecture.

NotebookLM-Lite is not affiliated with Google NotebookLM. The goal is to bring most of the NotebookLM learning and research experience into an open project: source-grounded answers, citations, mind maps, flashcards, quizzes, reports, data tables, infographics, podcast/audio overviews, notes, and future slide/video workflows.

Watch the HD demo video

The demo uses doc/L9.md as the sample source and shows model configuration, source upload, cited RAG chat, interactive Mind Map, Flashcards/Quiz, Data Table, Podcast, and the Video Overview placeholder. Model waiting time is shortened for README pacing.

Why NotebookLM-Lite

NotebookLM-style tools are becoming the default interface for working with long documents, research notes, study material, product manuals, papers, and course content. NotebookLM-Lite focuses on the same core loop:

1. Add sources to a notebook-like knowledge base.
2. Ask questions grounded in selected sources.
3. Save useful answers and notes back into the knowledge base.
4. Generate structured outputs such as maps, quizzes, reports, tables, infographics, and podcasts.
5. Keep every generated artifact downloadable and inspectable.

That makes the project useful as a NotebookLM alternative, a RAG notebook starter, an AI study assistant, a document Q&A system, and a backend reference for multi-step AI workflows.

Highlights

• NotebookLM-like three-panel workspace: sources on the left, grounded chat in the center, Studio artifacts and generated files on the right.
• Multi-format source ingestion: upload or paste sources such as PDF, DOCX, TXT, Markdown, HTML, CSV, JSON, and YAML.
• Document intelligence pipeline: Docling extracts document text, Chonkie chunks it, and SeekDB persists sources, chunks, notes, jobs, and artifacts.
• Hybrid RAG retrieval: SeekDB combines lexical BM25-style recall, optional embeddings, optional rerank, selected-source filtering, and citations.
• Streaming grounded chat: ask questions over selected sources, render Markdown answers, and keep citations attached to retrieved chunks.
• Notes as first-class knowledge: create notes, save chat answers as notes, and convert notes back into sources for later RAG.
• Interactive Studio artifacts: generate Mind Maps, FAQ, Flashcards/Quiz, Reports/Study Guides, Data Tables, SVG Infographics, and Podcast scripts from selected sources.
• NotebookLM-like study UX: flashcards flip, quizzes show feedback and scores, mind maps expand, tables render as tables, and artifacts can be downloaded as Markdown/JSON/SVG where applicable.
• Podcast and Audio Overview workflow: structured podcast scripts are generated with Pydantic validation and can be expanded toward controllable durations up to 30 minutes; speech generation is optional when a compatible audio model is configured.
• Unified model runtime: LiteLLM manages text generation, embeddings, rerank, and OpenAI-compatible speech profiles. OpenAI-compatible, Anthropic, Gemini/GenAI-style, and custom gateway deployments can be configured from config.yaml.
• Extensible backend boundaries: FastAPI owns resource APIs, while longer workflows are isolated for future LangGraph orchestration, Deep Research, Slide Deck, and Video Overview support.

Capability Map

Capability	Status	Notes
Source upload and pasted text	Available	File sources and text sources are stored in SeekDB.
RAG chat with citations	Available	Supports selected sources, Markdown answers, and streaming response UI.
Notes	Available	Create notes, save answers as notes, and convert notes into sources.
Mind Maps	Available	Structured tree output with expandable frontend viewer.
FAQ	Available	Structured FAQ generated from selected sources.
Flashcards and Quizzes	Available	Flip-card viewer plus quiz feedback, score, and retake.
Reports and Study Guides	Available	Structured summaries, sections, and takeaways.
Data Tables	Available	Rendered as real tables and downloadable as artifact data.
Infographics	Available	Structured SVG infographic generation and safe image rendering.
Podcast / Audio Overview	Available	Script generation is core; audio download is available when speech config is valid.
Runtime model configuration	Available	Separate text, embedding, rerank, speech, image, and edit profiles.
Deep Research	Placeholder	API/job boundary exists so future research output can be saved as a source.
Slide Deck / PPT	Adapter boundary	AIPPT integration boundary exists; full viewer/download flow is not enabled yet.
Video Overview	Placeholder	Studio card is present and shows an in-development notice.

Architecture

frontend/
  React + Vite workbench
  SourcePanel, ChatPanel, NotesPanel, StudioPanel, ArtifactViewer

backend/
  FastAPI routes for sources, chat, notes, artifacts, podcast, config
  Docling parser + Chonkie chunking
  SeekDB repository and vector-store adapter
  LiteLLM provider for text, structured output, embeddings, and rerank
  Podcast workflow with Pydantic structured output

Key backend choices:

• FastAPI for CRUD, list, download, runtime config, and status APIs.
• Docling for document parsing.
• Chonkie for chunking, with a simple fallback.
• SeekDB as the project knowledge repository.
• LiteLLM as the unified model access layer.
• Pydantic structured output for Studio artifacts and podcast scripts.

Getting Started

Requirements

• Python 3.10+
• Node.js 18+
• A text model endpoint configured in config.yaml
• Optional: embedding, rerank, and speech model endpoints

Backend

pip install -r requirements.txt
cp config_example.yaml config.yaml

Edit config.yaml and fill in the profiles you want to use:

• api.models.text_model: chat, RAG answers, Studio artifacts, podcast scripts
• api.models.embedding_model: optional vector retrieval
• api.models.rerank_model: optional rerank
• api.models.audio_model: optional speech/audio generation
• storage.seekdb_path: local knowledge database
• documents.chunking: Chonkie/simple chunking settings

Keep config.yaml local. It is ignored by git and should not contain committed keys.

Start the backend:

python -m backend.main

The API runs at http://localhost:8000.

Frontend

cd frontend
npm install
npm run dev

Open http://localhost:5173.

Usage

1. Open the workbench and check the settings panel.
2. Upload a source file or paste text in the Sources panel.
3. Select one or more sources.
4. Ask source-grounded questions in Chat.
5. Save useful answers as notes or sources.
6. Generate Studio artifacts:
   • Podcast
   • Mind Map
   • FAQ
   • Flashcards / Quiz
   • Report / Study Guide
   • Data Table
   • Infographic
7. Download generated artifacts as Markdown, JSON, SVG, transcript, or audio where supported.

Testing

Backend:

PYTHONPATH=. pytest -q
PYTHONPATH=. python -m compileall -q backend

Frontend:

cd frontend
npm run build
npm run test
npm run test:e2e -- --project=chromium

OpenSpec:

openspec validate notebooklm-text-closure --strict

Current Scope and Roadmap

NotebookLM-Lite already covers the core text-based NotebookLM loop: sources, cited chat, notes, Studio study artifacts, data tables, infographics, and podcast scripts/audio. The next major areas are:

• Full Slide Deck/PPT integration using the AIPPT adapter boundary.
• Real Video Overview generation instead of the current placeholder card.
• Stronger Deep Research workflows that can search, synthesize, and save research reports as sources.
• More export targets, including CSV/Sheets-style table export and richer artifact sharing.
• More source types, such as URLs, audio, images, YouTube, and Google Drive-style integrations.

License

Apache 2.0