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👾 Element Labs, Inc. © 2026

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Product

Download the app Models LM LinkNew LM Studio Hub Beta Releases Changelog

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Developer Docs lmstudio-js lmstudio-python LM Studio CLI (lms)llms.txt llms-full.txt

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Download the app Models LM LinkNew LM Studio Hub Beta Releases Changelog

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Developer Docs lmstudio-js lmstudio-python LM Studio CLI (lms)llms.txt llms-full.txt

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enigma-rakuko/local-openbook • LM Studio Hub

local-openbook

Public

Forked from

local-openbook

Public

Forked from

nuprin/local-openbook

Stats

1 Download

1 star

Stats

1 Download

1 star

Last updated

Updated on July 3by

nuprin/local-openbook

Stats

1 Download

1 star

Stats

1 Download

1 star

Last updated

Updated on July 3by

enigma-rakuko

Project Files

dist

src

HELP_ME.md

4 KB

manifest.json

115 B

package-lock.json

68 KB

package.json

668 B

README.md

3 KB

tsconfig.json

331 B

README

enigma-rakuko

Project Files

dist

src

HELP_ME.md

4 KB

manifest.json

115 B

package-lock.json

68 KB

package.json

668 B

README.md

3 KB

tsconfig.json

331 B

README

Local OpenBook Plugin for LM Studio

A private, local alternative to Google's NotebookLM, running entirely within LM Studio.

Features

RAG (Retrieval Augmented Generation): Intelligent search across your uploaded documents using Cross-Encoder Reranking for high precision.
Multi-Modal Ingestion:
- Files: Drag & drop PDFs, TXTs, MDs (Native).
- Web: "Add source https://example.com" (extracts article text).
- YouTube: "Add source https://youtube.com/watch?v=..." (downloads & transcribes).
Podcast Generation: "Generate a podcast about this" creates a dialogue script between two AI hosts.

Prerequisites

How to Use

Configuration

Reranker Enabled: Toggles the advanced reranking step. Disable for speed, enable for accuracy.
Retrieval Limit: How many text chunks to retrieve (default: 5).

Known Limitations

Web/YouTube Source Indexing: Currently, content fetched from the web/YouTube is added directly to the chat context. It is not indexed for RAG retrieval unless you copy the text into a .txt file and drag it back into LM Studio.
Audio Generation: This plugin generates the script. You need an external TTS tool (or a future update) to convert the script to audio.

Local OpenBook Plugin for LM Studio

A private, local alternative to Google's NotebookLM, running entirely within LM Studio.

Features

RAG (Retrieval Augmented Generation): Intelligent search across your uploaded documents using Cross-Encoder Reranking for high precision.
Multi-Modal Ingestion:
- Files: Drag & drop PDFs, TXTs, MDs (Native).
- Web: "Add source https://example.com" (extracts article text).
- YouTube: "Add source https://youtube.com/watch?v=..." (downloads & transcribes).
Podcast Generation: "Generate a podcast about this" creates a dialogue script between two AI hosts.

Prerequisites

How to Use

Configuration

Reranker Enabled: Toggles the advanced reranking step. Disable for speed, enable for accuracy.
Retrieval Limit: How many text chunks to retrieve (default: 5).

Known Limitations

Web/YouTube Source Indexing: Currently, content fetched from the web/YouTube is added directly to the chat context. It is not indexed for RAG retrieval unless you copy the text into a .txt file and drag it back into LM Studio.
Audio Generation: This plugin generates the script. You need an external TTS tool (or a future update) to convert the script to audio.

LM Studio 0.3.x or higher.

Embedding Model: Download nomic-embed-text-v1.5.Q4_K_M.gguf (or similar) in LM Studio.

Local Whisper (Optional, for YouTube):

Windows: Download whisper.cpp release (e.g., whisper-bin-x64.zip) and extract it. Set whisperBinaryPath to main.exe.
Fedora/Linux: Install yt-dlp and whisper-cpp via your package manager:
Or build whisper.cpp from source to get the main binary.
In LM Studio, go to the Plugins tab → Local OpenBook → Settings.
Set whisperBinaryPath to the path of your whisper-cpp or main binary (e.g., /usr/bin/whisper-cpp). If it's in your PATH, the plugin will now try to find it automatically.

Start a Chat.

Drag & Drop your research papers or notes.

Add Web Sources (Optional):

User: "Add source https://en.wikipedia.org/wiki/Artificial_intelligence"
The plugin will fetch the text and add it to the chat.

Ask Questions: "What does the text say about X?"

The plugin will search your documents and cite sources.

Create a Podcast:

User: "Generate a podcast script about these documents."
The model will produce a dialogue script.

LM Studio 0.3.x or higher.

Embedding Model: Download nomic-embed-text-v1.5.Q4_K_M.gguf (or similar) in LM Studio.

Local Whisper (Optional, for YouTube):

Windows: Download whisper.cpp release (e.g., whisper-bin-x64.zip) and extract it. Set whisperBinaryPath to main.exe.
Fedora/Linux: Install yt-dlp and whisper-cpp via your package manager:
Or build whisper.cpp from source to get the main binary.
In LM Studio, go to the Plugins tab → Local OpenBook → Settings.
Set whisperBinaryPath to the path of your whisper-cpp or main binary (e.g., /usr/bin/whisper-cpp). If it's in your PATH, the plugin will now try to find it automatically.

Start a Chat.

Drag & Drop your research papers or notes.

Add Web Sources (Optional):

User: "Add source https://en.wikipedia.org/wiki/Artificial_intelligence"
The plugin will fetch the text and add it to the chat.

Ask Questions: "What does the text say about X?"

The plugin will search your documents and cite sources.

Create a Podcast:

User: "Generate a podcast script about these documents."
The model will produce a dialogue script.

sudo dnf install yt-dlp whisper-cpp

sudo dnf install yt-dlp whisper-cpp