https://github.com/ya0002/obsidian-assist
Make Zettelkasten-style note-taking the foundation of interactions with Large Language Models (LLMs).
https://github.com/ya0002/obsidian-assist
docling graph-based knowledge-graph large-language-models llm llm-privacy obsidian obsidian-assist obsidian-md ollama second-brain zettlekasten
Last synced: about 2 months ago
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Make Zettelkasten-style note-taking the foundation of interactions with Large Language Models (LLMs).
- Host: GitHub
- URL: https://github.com/ya0002/obsidian-assist
- Owner: ya0002
- Created: 2025-05-08T16:16:18.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-19T18:56:05.000Z (5 months ago)
- Last Synced: 2025-06-08T13:01:52.447Z (4 months ago)
- Topics: docling, graph-based, knowledge-graph, large-language-models, llm, llm-privacy, obsidian, obsidian-assist, obsidian-md, ollama, second-brain, zettlekasten
- Language: Python
- Homepage:
- Size: 4.59 MB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Obsidian Assist
**Note:** *This project is experimental and primarily a hobby endeavor.*
**`obsidian-assist`** aims to make **Zettelkasten-style note-taking** the foundation of interactions with Large Language Models (LLMs). By using an **Obsidian vault as the backend**, users retain full control over the relationships between notes, enabling them to explicitly define, edit, and guide context-building for LLM responses.
Obsidian is often used as a "second brain"—this tool aspires to be a way to **interact with that second brain**, allowing for intelligent and personalized dialogue grounded in the user’s own knowledge graph.
## Overview
This tool supports two primary use cases:
---
### 1. Automatic Conversion of Documents into Obsidian Notes
Automatically convert PDFs, PowerPoint presentations, and Markdown files into structured, connected notes within Obsidian. This process leverages **Docling** and **Large Language Models (LLMs)**:
- **Docling** is used to convert PDFs and PPTs into Markdown format.
- The resulting Markdown files are then segmented and analyzed using LLMs and Vector Databases.
- Each segment is transformed into an individual, interlinked note to support a connected note-taking workflow in Obsidian.
##### Tutorial
[](https://www.youtube.com/embed/JVaCnP9epLs?si=B0j5eKWLuZHntn3L)
---
### 2. Analysis and Interaction with an Obsidian Vault
Interact with and analyze the contents of an Obsidian vault through two distinct methods:
- **Cosine Similarity Search**
A generic semantic search method using cosine distance to retrieve contextually relevant notes.
- **Graph-Based Contextual Search**
Utilizes user-defined relationships (graph edges) between notes to build contextual understanding. This method offers three interaction modes:
1. **Auto Node Traversal**
Start chatting directly — the system identifies the most relevant node to your query and traverses its neighboring nodes to build context.
2. **Fixed Start Node**
Specify a starting node — responses are centered around this predefined note.
3. **Start-to-End Path Search**
Specify both a start and end node — the shortest path between them is used to build the context for the response.
##### Tutorial
[](https://www.youtube.com/embed/wP2JzcK-qpg?si=3dMe23_F4nh3SXOk)
---
## Supported LLM Handlers
1. Ollama
2. Gemini
3. Huggingface
## Running with Docker
### Create a .env file with credentials in the same directory
```
CHROMA_DB_PATH=/app/ChromaDB
HF_TOKEN=your_actual_token
markdown_chunk_collection_vdb=md_chunk_vdb
markdown_notes_collection_vdb=md_notes_vdb
GEMINI_KEY=your_gemini_api_key
OLLAMA_HOST=http://host.docker.internal:11434
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_DRIVER_CAPABILITIES=compute,utility
```
### For GPU
Make sure you have the NVIDIA Container Toolkit installed on your host machine. You can verify this by checking if the `nvidia-smi` command works in your terminal.
```docker
### build:
docker compose build --no-cache
### start:
docker compose up
```
### For CPU
```docker
### build:
docker compose -f docker-compose.cpu.yml build --no-cache
### start:
docker compose -f docker-compose.cpu.yml up
```
## Mount paths to access your files
### Linux/Mac Downloads directory
```${HOME:-/home}/Downloads:/home/Downloads```
### Windows Downloads directory
For windows, the image is mounted to the Downloads folder
```${USERPROFILE:-C:\Users}/Downloads:/windows/Downloads```
You provide the source file like
`/windows/Downloads/some.md OR some.pdf OR some.pptx`
Or you can provide a directory full of PDFs/PPTs/MDs
`/windows/Downloads/some_dir`
Your vault path would be like
`/windows/Downloads/obsidian__vault_dir`
### For macOS, which may have Downloads in a different location
```${HOME}/Downloads:/mac/Downloads```
## Running with conda
Make sure to set the environment variables.
Create an environment
```
$ conda create -n obsdidian python=3.11
```
Install requirements.txt
```
$ conda activate obsdidian
$ pip install --no-cache-dir --no-deps -r requirements.txt
```
Run the app
```
$ python app.py
```