# How to Perform a Basic Extraction with Graphify: CLI and Python API Guide > Learn to perform a basic extraction with Graphify using its CLI or Python API. Generate structured knowledge graphs from your code effortlessly with this comprehensive guide. - Repository: [Graphify Labs/graphify](https://github.com/Graphify-Labs/graphify) - Tags: how-to-guide - Published: 2026-07-16 --- **To perform a basic extraction with Graphify, run `graphify extract ` from the CLI after installing with `uv tool install graphifyy`, or use the Python API `from graphify.ingest import ingest` followed by `ingest(path="your_project/")` to generate a structured knowledge graph from source code and documentation.** Graphify is an open-source knowledge graph generator that transforms source code repositories into queryable structured data. The Graphify-Labs/graphify repository provides both a command-line interface and a Python API for extracting entities and relationships from code files and non-code assets like markdown and PDFs. ## Install Graphify Before performing a basic extraction, install the tool using either `uv` or `pipx`: ```bash uv tool install graphifyy # or pipx install graphifyy ``` ## Method 1: Command-Line Extraction The fastest way to perform a basic extraction with Graphify is through the CLI. Navigate to your project directory and run: ```bash graphify extract . ``` This command initiates the file discovery process, walking the directory to build a manifest of files to process according to the logic in [`graphify/cli.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/cli.py). ## Understanding the Generated Output After extraction completes, Graphify writes results to the `graphify-out/` directory: - [`graph.json`](https://github.com/Graphify-Labs/graphify/blob/main/graph.json) contains the raw graph data with nodes and edges - [`GRAPH_REPORT.md`](https://github.com/Graphify-Labs/graphify/blob/main/GRAPH_REPORT.md) presents a human-readable report including god nodes, community clusters, and confidence tags (`EXTRACTED` vs. `INFERRED`) View the interactive graph visualization: ```bash graphify-open graphify-out/graph.html ``` Inspect the markdown report: ```bash cat graphify-out/GRAPH_REPORT.md ``` ## Method 2: Programmatic Extraction with Python For integration into existing workflows, perform a basic extraction using the Python API. Import the `ingest` function from `graphify.ingest`: ```python from graphify.ingest import ingest # Ingest a single file or entire directory graph = ingest(path="my_project/") graph.save("graphify-out/graph.json") print(graph.summary()) # High-level overview of extracted entities ``` The `ingest` function handles the complete pipeline: file discovery, parsing, and graph construction. ## Method 3: Direct Language-Specific Extractors For fine-grained control over how specific programming languages are parsed, invoke the dedicated extractors directly from [`graphify/extract.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/extract.py): ```python from graphify.extract import extract_python from pathlib import Path result = extract_python(Path("example.py")) print(result["functions"]) # List of discovered functions print(result["classes"]) # List of discovered classes ``` Available extractors include `extract_python`, `extract_js`, `extract_java`, and others, each implementing tree-sitter based AST parsing. ## The Extraction Pipeline Architecture Understanding how Graphify processes files helps optimize your extraction workflows. ### File Discovery and Classification Graphify begins by walking the target directory to identify source files. Files matching recognized code extensions are routed to tree-sitter parsers, while markdown, PDFs, and images are queued for LLM-based processing. ### Language-Specific Parsing In [`graphify/extract.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/extract.py), each language-specific function parses the file's AST via tree-sitter. The extractor walks the AST to identify **nodes** (classes, functions, modules) and **edges** (calls, imports, inheritance), emitting dictionaries that describe discovered entities. ### Non-Code Asset Extraction Files without tree-sitter parsers are handled by `extract_files_direct` in [`graphify/llm.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/llm.py). This function uses language model inference to extract headings, code blocks, and referenced assets from documentation and binary files. ### Graph Construction and Deduplication The [`graphify/build.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/build.py) module merges individual extraction results into a unified knowledge graph. The deduplication logic in [`graphify/dedup.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/dedup.py) ensures unique entities before persisting the final graph to [`graphify-out/graph.json`](https://github.com/Graphify-Labs/graphify/blob/main/graphify-out/graph.json). ## Querying Your Knowledge Graph After you perform a basic extraction with Graphify, explore relationships without grepping source files: ```bash # Ask natural language questions graphify query "How does the authentication module work?" # Find paths between entities graphify path module_A module_B ``` ## Summary - **Install Graphify** using `uv tool install graphifyy` or `pipx install graphifyy` to get the CLI and Python library - **Run basic extraction** with `graphify extract ` from the command line or `ingest(path="")` from Python - **Access outputs** in `graphify-out/` including [`graph.json`](https://github.com/Graphify-Labs/graphify/blob/main/graph.json) for raw data and [`GRAPH_REPORT.md`](https://github.com/Graphify-Labs/graphify/blob/main/GRAPH_REPORT.md) for human-readable summaries - **Leverage language-specific extractors** like `extract_python` in [`graphify/extract.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/extract.py) for custom parsing workflows - **Query results** using `graphify query` or `graphify path` commands to explore codebase relationships ## Frequently Asked Questions ### What file types does Graphify support for extraction? Graphify supports programming languages through tree-sitter grammars (Python, JavaScript, Java, etc.) via [`graphify/extract.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/extract.py), and non-code assets (markdown, PDFs, images) through LLM-based extraction in [`graphify/llm.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/llm.py). The tool automatically classifies files during the discovery phase and routes them to the appropriate processor. ### How do I extract only specific file types? Use the Python API to filter paths before ingestion, or invoke language-specific extractors directly from [`graphify/extract.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/extract.py) (e.g., `extract_python`, `extract_js`) for targeted processing. The CLI currently processes all recognized files in the provided directory. ### What is the difference between EXTRACTED and INFERRED tags in GRAPH_REPORT.md? Tags marked **EXTRACTED** indicate relationships derived directly from static code analysis via tree-sitter parsing in [`graphify/extract.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/extract.py). **INFERRED** tags represent relationships generated by the LLM extractor (`extract_files_direct` in [`graphify/llm.py`](https://github.com/Graphify-Labs/graphify/blob/main/graphify/llm.py)) for non-code assets or ambiguous code patterns. ### Can I perform extraction without installing the CLI tool? Yes. Import the extraction functions directly in Python: `from graphify.ingest import ingest` for full pipeline processing, or `from graphify.extract import extract_python` (and similar functions) for specific languages. This approach requires the graphify package installed via pip rather than as a standalone tool.