tricoder 1.2.7

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

TriVector Embeddings for Smarter Code Search for AI Agents

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
• GPU Acceleration: Supports CUDA (NVIDIA) and MPS (Mac) for faster training
• Keyword Search: Search symbols by keywords and type tokens
• Graph Optimization: Filter out low-value nodes and edges to improve training efficiency

Installation

Using Poetry (Recommended)

poetry install

Using pip

pip install .

GPU Support (Optional)

For NVIDIA GPUs (CUDA):

pip install cupy-cuda12x

For Mac GPUs (MPS):

pip install torch

Usage

1. Extract Symbols from Codebase

# Basic extraction (Python files only)
tricoder extract --input-dir /path/to/codebase

# Extract specific file types
tricoder extract --input-dir /path/to/codebase --extensions "py,js,ts"

# Exclude specific keywords from extraction
tricoder extract --input-dir /path/to/codebase --exclude-keywords debug --exclude-keywords temp

# Custom output files
tricoder extract --input-dir /path/to/codebase --output-nodes my_nodes.jsonl --output-edges my_edges.jsonl

Extraction Options:

• --input-dir, --root, -r: Input directory to scan (default: current directory)
• --extensions, --ext: Comma-separated file extensions (default: py)
• --include-dirs, -i: Include only specific subdirectories (can specify multiple)
• --exclude-dirs, -e: Exclude directories (default: .venv, __pycache__, .git, node_modules, .pytest_cache)
• --exclude-keywords, --exclude: Exclude symbol names (appended to default excluded keywords)
• --output-nodes, -n: Output file for nodes (default: nodes.jsonl)
• --output-edges, -d: Output file for edges (default: edges.jsonl)
• --output-types, -t: Output file for types (default: types.jsonl)
• --no-gitignore: Disable .gitignore filtering (enabled by default)

2. Optimize Graph (Optional)

Reduce graph size by filtering low-value nodes and edges:

# Basic optimization (overwrites input files)
tricoder optimize

# Custom output files
tricoder optimize --output-nodes nodes_opt.jsonl --output-edges edges_opt.jsonl

# Customize thresholds
tricoder optimize --min-edge-weight 0.5 --remove-isolated --remove-generic

# Keep isolated nodes
tricoder optimize --keep-isolated

Optimization Options:

• --nodes, -n: Input nodes file (default: nodes.jsonl)
• --edges, -e: Input edges file (default: edges.jsonl)
• --types, -t: Input types file (default: types.jsonl, optional)
• --output-nodes, -N: Output nodes file (default: overwrites input)
• --output-edges, -E: Output edges file (default: overwrites input)
• --output-types, -T: Output types file (default: overwrites input)
• --min-edge-weight: Minimum edge weight to keep (default: 0.3)
• --remove-isolated: Remove nodes with no edges (default: True)
• --keep-isolated: Keep isolated nodes (overrides --remove-isolated)
• --remove-generic: Remove generic names (default: True)
• --keep-generic: Keep generic names (overrides --remove-generic)
• --exclude-keywords, --exclude: Additional keywords to exclude (can specify multiple)

3. Train Model

# Basic training
tricoder train --out model_output

# With GPU acceleration
tricoder train --out model_output --use-gpu

# Fast mode (faster training, slightly lower quality)
tricoder train --out model_output --fast

# Custom dimensions
tricoder train --out model_output --graph-dim 128 --context-dim 128 --final-dim 256

# Custom training parameters
tricoder train --out model_output --num-walks 20 --walk-length 100 --train-ratio 0.9

Training Options:

• --nodes, -n: Path to nodes.jsonl (default: nodes.jsonl)
• --edges, -e: Path to edges.jsonl (default: edges.jsonl)
• --types, -t: Path to types.jsonl (default: types.jsonl, optional)
• --out, -o: Output directory (required)
• --graph-dim: Graph view dimensionality (default: auto-calculated)
• --context-dim: Context view dimensionality (default: auto-calculated)
• --typed-dim: Typed view dimensionality (default: auto-calculated)
• --final-dim: Final fused embedding dimensionality (default: auto-calculated)
• --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)
• --fast: Enable fast mode (reduces parameters for faster training)
• --use-gpu: Enable GPU acceleration (CUDA or MPS, falls back to CPU if unavailable)

4. Query Model

# Query by symbol ID
tricoder query --model-dir model_output --symbol function_my_function_0001 --top-k 10

# Search by keywords
tricoder query --model-dir model_output --keywords "database connection" --top-k 10

# Multi-word phrases (use quotes)
tricoder query --model-dir model_output --keywords '"user authentication" login'

# Exclude specific keywords from results
tricoder query --model-dir model_output --keywords handler --exclude-keywords debug --exclude-keywords temp

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

Query Options:

• --model-dir, -m: Path to model directory (required)
• --symbol, -s: Symbol ID to query
• --keywords, -w: Keywords to search for (use quotes for multi-word: "my function")
• --top-k, -k: Number of results to return (default: 10)
• --exclude-keywords, --exclude: Additional keywords to exclude (appended to default excluded keywords)
• --interactive, -i: Interactive mode

5. Incremental Retraining

Retrain only on changed files since last training:

# Basic retraining (detects changed files automatically)
tricoder retrain --model-dir model_output --codebase-dir /path/to/codebase

# Force full retraining
tricoder retrain --model-dir model_output --codebase-dir /path/to/codebase --force

# Custom training parameters
tricoder retrain --model-dir model_output --codebase-dir /path/to/codebase --num-walks 20

Retrain Options:

• --model-dir, -m: Path to existing model directory (required)
• --codebase-dir, -c: Path to codebase root (default: current directory)
• --output-nodes, -n: Temporary nodes file (default: nodes_retrain.jsonl)
• --output-edges, -d: Temporary edges file (default: edges_retrain.jsonl)
• --output-types, -t: Temporary types file (default: types_retrain.jsonl)
• --graph-dim, --context-dim, --typed-dim, --final-dim: Override model dimensions
• --num-walks, --walk-length, --train-ratio, --random-state: Training parameters
• --force: Force full retraining even if no files changed

Examples

Complete Workflow

# 1. Extract symbols from codebase
tricoder extract --input-dir ./my_project --extensions "py,js"

# 2. (Optional) Optimize the graph
tricoder optimize --min-edge-weight 0.4

# 3. Train model with GPU acceleration
tricoder train --out ./models/my_project --use-gpu

# 4. Query for similar symbols
tricoder query --model-dir ./models/my_project --keywords "database" --top-k 5

# 5. After code changes, retrain incrementally
tricoder retrain --model-dir ./models/my_project --codebase-dir ./my_project

Keyword Search Examples

# Search for authentication-related code
tricoder query --model-dir model_output --keywords "auth login password"

# Search for specific function name
tricoder query --model-dir model_output --keywords '"process_payment"'

# Search excluding common keywords
tricoder query --model-dir model_output --keywords handler --exclude-keywords temp --exclude-keywords debug

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

Optional (for GPU acceleration):

• cupy-cuda12x >= 12.0.0 (for NVIDIA GPUs)
• torch >= 2.0.0 (for Mac GPUs or CUDA fallback)

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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