tricoder 1.1.2

TriVector Code Intelligence - Multi-view code relationship model with advanced semantic embeddings

TriCoder Code Intelligence

TriCoder learns high-quality symbol-level embeddings from codebases using three complementary views:

1. Graph View: Structural relationships via PPMI and SVD
2. Context View: Semantic context via Node2Vec random walks and Word2Vec
3. Typed View: Type information via type-token co-occurrence (optional)

Features

• Subtoken Semantic Graph: Captures fine-grained semantic relationships through subtoken analysis
• File & Module Hierarchy: Leverages file/directory structure for better clustering
• Static Call-Graph Expansion: Propagates call relationships to depth 2-3
• Type Semantic Expansion: Expands composite types into constructors and primitives
• Context Window Co-occurrence: Captures lexical context within ±5 lines
• Improved Negative Sampling: Biased sampling for better temperature calibration
• Hybrid Similarity Scoring: Length-penalized cosine similarity
• Iterative Embedding Smoothing: Diffusion-based smoothing for better clustering
• Query-Time Semantic Expansion: Expands queries with subtokens and types

Installation

Using Poetry (Recommended)

poetry install

Using pip

pip install .

Usage

1. Extract Symbols from Codebase

tricoder-extract --input-dir /path/to/codebase --output-nodes nodes.jsonl --output-edges edges.jsonl --output-types types.jsonl

2. Train Model

tricoder-train --nodes nodes.jsonl --edges edges.jsonl --types types.jsonl --out model_output

3. Query Model

# Single query
tricoder-query --model-dir model_output --symbol sym_0001 --top-k 10

# Interactive mode
tricoder-query --model-dir model_output --interactive

Advanced Options

Training Options

• --graph-dim: Graph view dimensionality (default: auto)
• --context-dim: Context view dimensionality (default: auto)
• --typed-dim: Typed view dimensionality (default: auto)
• --final-dim: Final fused embedding dimensionality (default: auto)
• --num-walks: Number of random walks per node (default: 10)
• --walk-length: Length of each random walk (default: 80)
• --train-ratio: Fraction of edges for training (default: 0.8)
• --random-state: Random seed for reproducibility (default: 42)

Extraction Options

• --include-dirs: Include only specific subdirectories
• --exclude-dirs: Exclude specific directories
• --no-gitignore: Disable .gitignore filtering

Requirements

• Python 3.8+
• numpy >= 1.21.0
• scipy >= 1.7.0
• scikit-learn >= 1.0.0
• gensim >= 4.0.0
• annoy >= 1.17.0
• click >= 8.0.0
• rich >= 13.0.0

License

TriCoder is available under a Non-Commercial License.

• ✅ Free for non-commercial use: Personal projects, education, research, open-source
• ❌ Commercial license required: Paid products, SaaS, commercial consulting, enterprise use

For commercial licensing inquiries, please contact: [e-mail]

See LICENSE for full terms and LICENSE_COMMERCIAL.md for commercial license information.

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