similarity-tool 0.1.3

A modular, hybrid, and customizable document similarity framework.

SimilarityTool

SimilarityTool is a high-performance, asynchronous Information Retrieval (IR) and re-ranking pipeline designed for accurate matching across large-scale, long-text professional corpora (e.g., curricula, job descriptions, CVs, and project portfolios). SimilarityTool follows a SSS approach, leaning on semantic, syntactic, and structured features to match documents based on core meaning, regardless of domain.

The framework implements a highly optimized Waterfall Architecture:

1. Abstractive Ingestion Pass: A local small language model processes long text chunks concurrently to strip fluff and isolate core meaning.
2. Semantic Encoding: Blends multilingual, structural, and domain-focused transformers into a highly descriptive, high-dimensional embedding.
3. Syntatic Encoding: Supports semantic encoding with n-gram and keyword encoding, taking a more syntatic approach.
4. Structured Encoding: Incorporate domain- and use case-specific structured features, adding a more structural perpsective to document matching.
5. Stage-1 Recall: Lightning-fast retrieval of candidates using a vectorized FAISS index.
6. Stage-2 Re-ranking: Evaluates retrieved candidates via multi-channel linear fusion containing point-to-point token syntactic analysis, attribute-level Tversky set overlaps, and deep token-interaction cross-encoding.

Configuration Setup

The framework is governed by two clean YAML files. Update your parameters inside your project directory configuration files:

1. Main Pipeline Configuration (configs/main_config.yaml)

semantic_engine:
  models:
    - name: "sentence-transformers/all-mpnet-base-v2"
      weight: 1.0
      device: "cuda"
    - name: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
      weight: 0.6
      device: "cuda"
    - name: "shawhin/distilroberta-ai-job-embeddings"
      weight: 1.5
      device: "cuda"

storage:
  db_path: "data/corpus.db"
  index_path: "data/corpus.index"
  vector_dimension: 1920  # Matched perfectly to Concatenated Model Vectors (768 + 384 + 768)

orchestrator:
  strategy: "concatenate"
  weights:
    semantic: 0.5   # Cross-Encoder definitive strength
    syntactic: 0.2  # Min-Max Normalized pool token matching 
    structured: 0.3 # Tversky criteria matching`

2. Domain-specific Schema Rules (configs/schema_config.yaml)

text_fields:
  - name: "text"
    semantic_weight: 0.7
    syntactic_weight: 0.3
  - name: "title"
    semantic_weight: 1.0
    syntactic_weight: 0.0

structured_collections:
  - name: "tasks"
    alpha: 1.0
    beta: 1.0
    weight: 0.5
  - name: "skills"
    alpha: 0.2
    beta: 2.5   # Heavy penalty for candidates missing requested skills
    weight: 0.3
  - name: "ai"
    alpha: 1.0
    beta: 1.0
    weight: 0.2

Pipeline Usage Guide

Batch Ingestion

Ingest vast datasets from a Pandas DataFrame.

import pandas as pd
from similarity_tool import SimilarityTool
from similarity_tool.utils import DataMapper

# 1. Initialize the tool system layers (boots LLM and embedding models)
tool = SimilarityTool(
    main_config="configs/main_config.yaml", 
    schema_config="configs/schema_config.yaml",
    use_llm_distillation=True
)

# 2. Ingestion example
raw_data = {
    "doc_id": ["id_843125", "id_941012"],
    "title": ["Senior Deep Learning Architect", "Full-Stack Dev"],
    "description": [
        "Massive long 3000-word corporate description containing boilerplate benefits...",
        "Looking for a web application developer specializing in React and Python..."
    ],
    "skills": ["Python,PyTorch,CUDA,Docker", "JavaScript,React,Postgres"],
    "tasks": ["architecture,deployment", "frontend,api"],
    "ai": ["LLMs"]
}
df = pd.DataFrame(raw_data)

# 3. Trigger optimized transactional batch ingestion
DataMapper.batch_ingest_dataframe(
    tool=tool,
    df=df,
    text_columns={"description": "full_text", "title": "job_title"},
    collection_columns={"skills_required": "skills", "core_tasks": "tasks", "ai": "ai"},
    id_column="doc_id",
    delimiter=",",
    batch_size=16 
)

Query Search (1:N)

Execute a query on a target document.

# Construct a target query mapping document matching schema attributes
query = {
    "text_fields": {
        "job_title": "AI Infrastructure Engineer",
        "full_text": "Deploying deep learning models at scale using PyTorch and tuning custom CUDA kernels."
    },
    "collections": {
        "skills": ["Python", "PyTorch", "CUDA"],
        "tasks": ["architecture", "deployment"]
    }
}

# Run the queryt 
# limit: FAISS candidate subset retrieval boundary (lower is quicker, but less broad of a search)
# top_k: Final returned target slice
results = tool.search(query, limit=50, top_k=3)

# Display results
for rank, match in enumerate(results, 1):
    print(f"Rank {rank}: Doc ID = {match['id']} | Total Score = {match['total_score']}")
    print(f"  └─ Sem Cross: {match['breakdown']['semantic_cross']} | Syn: {match['breakdown']['syntactic']} | Str: {match['breakdown']['structured']}\n")

N:N Composite Document Search

Find documents that match the combined profile of multiple query documents simultaneously.

queries = [
    {
        "text_fields": {"job_title": "AI Architect", "full_text": "Expertise optimizing distributed CUDA clusters."},
        "collections": {"skills": ["CUDA", "C++"], "tasks": ["infrastructure"]}
    },
    {
        "text_fields": {"job_title": "ML DevOps Engineer", "full_text": "Building orchestration templates via Docker and PyTorch."},
        "collections": {"skills": ["PyTorch", "Docker"], "tasks": ["deployment"]}
    }
]

# Find the best matches across the corpus that fit this combined query documents
fused_results = tool.search_composite(queries, limit=50, top_k=5)

for rank, match in enumerate(fused_results, 1):
    print(f"Composite Rank {rank}: Doc ID = {match['id']} | Unified Score = {match['total_score']}")

1:1 Document Comparison

doc_a = {
    "text_fields": {"job_title": "Data Scientist", "full_text": "Focusing on pandas and scikit-learn models."},
    "collections": {"skills": ["Python", "Scikit-Learn"], "tasks": ["modeling"]}
}

doc_b = {
    "text_fields": {"job_title": "ML Engineer", "full_text": "Building predictive scikit-learn setups in python."},
    "collections": {"skills": ["Python", "Scikit-Learn", "Docker"], "tasks": ["modeling", "devops"]}
}

comparison = tool.compare(doc_a, doc_b)

Hyperparameter Tuning and Hot-Swapping Configuration (Advanced)

Fine-tune structural weights, Tversky penalties, and any other paramters on the fly without re-instantiating the tool.

tool.update_config('orchestrator', 'weights', {'semantic': 0.8, 'syntactic': 0.1, 'structured': 0.1})
run_a = tool.search(query, limit=50, top_k=1)

tool.update_config(
    category='schema', 
    key='structured_collections', 
    value={'alpha': 0.2, 'beta': 3.5, 'weight': 0.9}, 
    target_name='skills'
)

tool.update_config('orchestrator', 'weights', {'semantic': 0.2, 'syntactic': 0.1, 'structured': 0.7})

run_b = tool.search(query, limit=50, top_k=1)