obsidianmd-parser 0.2.0

A Python library for parsing Obsidian Markdown (.md) files and vaults.

obsidianmd-parser

A Python package for parsing Obsidian Markdown vaults and notes, with support for Obsidian's built-in markdown format and Dataview queries.

Features

• Complete Vault Parsing: Load and parse entire Obsidian vaults
• Note Object Model: Work with notes as Python objects with attributes and methods
• Obsidian Markdown Support:
  • Wikilinks ([[links]] and [[links|aliases]])
  • Tags (#tag, #nested/tag)
  • Task lists with status tracking
  • Obsidian callouts
• Relationship Tracking: Analyze backlinks and relationships between notes
• Dataview Support:
  • Parse Dataview queries from notes
  • Evaluate Dataview queries programmatically
• Search Capabilities:
  • Exact search for notes
  • Similarity search using various algorithms
• Code Block Handling: Correctly excludes parsing within code blocks

Installation

pip install obsidianmd-parser

Quick Start

from obsidian_parser import Vault

# Load a vault
vault = Vault("path/to/your/obsidian/vault")

# Find notes by exact name
note = vault.get_note("My Note")

# Search notes by similarity
similar_notes = vault.find_notes("machine learning", case_sensitive=False)

# Access note properties
print(note.title)
print(note.tags)
print(note.wikilinks)
print(note.tasks)

# Work with relationships
backlinks = note.get_backlinks(vault=vault)
related = note.get_forward_links(vault=vault)
most_linked = note.get_most_linked()

Core API

Vault

The Vault class represents an entire Obsidian vault:

# lazy_load = notes are parsed only when accessed (default: True)
vault = Vault("path/to/vault", lazy_load=True)

# Search and retrieval
note = vault.get_note("Note Title")
notes = vault.find_similar_notes("search query", threshold=0.5)

# Vault analysis
note_graph = vault.get_note_graph()                 # Produces a note graph tuple object
dataview_usage = vault.analyze_dataview_usage()     # Get vault statistics for dataview queries
broken_links = vault.find_broken_links()            # Finds all broken links in the vault

Note

The Note class represents an individual note:

# Access note metadata
note.title          # Note title
note.path          # File path
note.content       # Raw markdown content
note.frontmatter   # Parsed YAML frontmatter

# Access parsed elements
note.tags          # List of tags in the note
note.wikilinks     # List of wikilinks (forward)
note.tasks         # List of tasks
note.callouts      # List of callouts

# Access raw frontmatter
raw = note.frontmatter  # Dict-like object with raw values

# Get cleaned frontmatter (removes wikilinks, formats dates)
cleaned = note.frontmatter.clean()

# Custom date formatting
cleaned = note.frontmatter.clean(date_format='DD-MM-YYYY')
cleaned = note.frontmatter.clean(date_format='%B %d, %Y')  # "March 24, 2025"

# Relationships
vault=Vault('path/to/vault')
note.get_backlinks(vault)       # Notes that link to this note
note.get_forward_links(vault)   # Notes this note links to
note.get_related_notes()        # Related notes by various metrics
note.get_link_context("Target") # Get the context for a piece of text in your note 
note.get_link_context(          # E.g. context for a wikilink.
  target=note.wikilinks[0].display_text, 
  context_chars=40)

Dataview Support

Parse and evaluate Dataview queries:

# Parse Dataview queries from a note
queries = note.dataview_queries

query = queries[0]
query.evaluate(vault, note)

# Evaluate a Dataview query in notes or sections
print(note.get_evaluated_view(vault))

note_section = notes.sections[10]

print(note_section.get_evaluated_view(vault))

Advanced Usage

Custom Search

# Configure similarity search
results = vault.search(
    query="machine learning",
    limit=10
    threshold=0.6
)

Vault Analysis

# Build an note index dataframe of the vault
vault_index = vault.build_index()

# Build and analyze vault graph
graph = vault.get_note_graph()

# Find broken links
broken_links = vault.find_broken_links()

# Relationship analysis
relationship_stats = vault.analyze_relationships()          # Builds a Relationship Analyzer object
stats_report = relationship_stats.build_statistics_report()
df = relationship_stats.export_to_dataframe()               # Pandas dataframe object
relationship_stats.find_hub_notes(                          # Find notes with lots of connections (default = 10)
  min_connections=50
) 
orphaned_notes = relationship_stats.find_orphaned_notes()   # Find orphaned notes (no backlinks)

Working with Parsed Elements

# Access specific elements
for link in note.wikilinks:
    print(f"Link to: {link.target}, alias: {link.alias}")

for task in note.tasks:
    if task.status == " ":
        print(f"TODO: {task.text}")

for tag in note.tags:
    print(f"Tag: #{tag.name}")

Requirements

• Python 3.12+ (earlier versions may be supported but not yet tested)
• Dependencies are automatically installed with pip

Contributing

Contributions are welcome! The project is hosted on Codeberg:

https://codeberg.org/paddyd/obsidian-parser

Please feel free to submit issues and pull requests.

License

MIT

Changelog

0.2.0 (2025-01-13)

• Added Frontmatter.clean() method for cleaning frontmatter values
• Frontmatter now returns a dict-like object instead of plain dict
• Improved wikilink parsing in frontmatter values

0.1.0 (Initial Release)

• Core vault and note parsing functionality
• Obsidian markdown format support
• Dataview query parsing and evaluation
• Search capabilities (exact and similarity)
• Relationship tracking and graph building