canonmap 0.2.4

CanonMap - A Python library for entity canonicalization and mapping with enhanced configuration and response models

CanonMap

CanonMap is a Python library for generating and managing canonical entity artifacts from various data sources. It provides a streamlined interface for processing data files and generating standardized artifacts that can be used for entity matching and data integration.

Features

• Flexible Input Support: Process data from:

  • CSV/JSON files
  • Directories of data files
  • Pandas DataFrames
  • Python dictionaries

• Artifact Generation:

  • Generate canonical entity lists
  • Create database schemas (supports multiple database types)
  • Generate semantic embeddings for entities
  • Clean and standardize field names
  • Process metadata fields

• Database Support:

  • DuckDB (default)
  • SQLite
  • BigQuery
  • MariaDB
  • MySQL
  • PostgreSQL

• Enhanced Configuration:

  • Separate configuration for artifacts and embeddings
  • Advanced GCP integration with bucket management
  • Flexible sync strategies for cloud storage
  • Comprehensive error handling and logging

Installation

Lightweight Installation (Core Features Only)

pip install canonmap

Full Installation (Including Embedding Support)

pip install canonmap[embedding]

Note: The lightweight installation includes all core features (GCP integration, file processing, schema generation) but excludes embedding functionality. If you need semantic embeddings, use the full installation with [embedding] extras.

Quick Start

from canonmap import (
    CanonMap,
    CanonMapGCPConfig,
    CanonMapCustomGCSConfig,
    CanonMapArtifactsConfig,
    CanonMapEmbeddingConfig,
    ArtifactGenerationRequest,
    EntityField,
    SemanticField
)

# 1. Set up base GCP configuration
base_gcp = CanonMapGCPConfig(
    gcp_service_account_json_path="path/to/service_account.json",
    troubleshooting=False
)

# 2. Configure GCS for artifacts and embeddings
artifacts_gcs = CanonMapCustomGCSConfig(
    gcp_config=base_gcp,
    bucket_name="your-artifacts-bucket",
    bucket_prefix="artifacts/",
    auto_create_bucket=True,
    sync_strategy="refresh"
)

embedding_gcs = CanonMapCustomGCSConfig(
    gcp_config=base_gcp,
    bucket_name="your-models-bucket",
    bucket_prefix="models/",
    auto_create_bucket=True,
    sync_strategy="refresh"
)

# 3. Create application-specific configs
artifacts_config = CanonMapArtifactsConfig(
    artifacts_local_path="./artifacts",
    gcs_config=artifacts_gcs
)

embedding_config = CanonMapEmbeddingConfig(
    embedding_model_hf_name="sentence-transformers/all-MiniLM-L12-v2",
    embedding_model_local_path="./models/sentence-transformers/all-MiniLM-L12-v2",
    gcs_config=embedding_gcs
)

# 4. Initialize CanonMap
canonmap = CanonMap(
    artifacts_config=artifacts_config,
    embedding_config=embedding_config,
    verbose=True,
    api_mode=False
)

# 5. Configure artifact generation
request = ArtifactGenerationRequest(
    input_path="path/to/your/data.csv",
    source_name="my_source",
    table_name="my_table",
    entity_fields=[
        EntityField(table_name="my_table", field_name="name"),
        EntityField(table_name="my_table", field_name="id")
    ],
    semantic_fields=[
        SemanticField(table_name="my_table", field_name="description"),
        SemanticField(table_name="my_table", field_name="notes")
    ],
    generate_schema=True,
    generate_embeddings=True,
    generate_semantic_texts=True
)

# 6. Generate artifacts
response = canonmap.generate_artifacts(request)
print(f"Generated {len(response.generated_artifacts)} artifacts")

Artifact Generation Example

from canonmap import (
    CanonMap,
    CanonMapArtifactsConfig,
    CanonMapEmbeddingConfig,
    CanonMapGCPConfig,
    CanonMapCustomGCSConfig,
    ArtifactGenerationRequest,
    EntityField,
    SemanticField,
    ArtifactGenerationResponse
)

# Set up configurations
base_gcp = CanonMapGCPConfig(
    gcp_service_account_json_path="path/to/service_account.json"
)

artifacts_gcs = CanonMapCustomGCSConfig(
    gcp_config=base_gcp,
    bucket_name="my-bucket",
    bucket_prefix="artifacts/",
    auto_create_bucket=True
)

embedding_gcs = CanonMapCustomGCSConfig(
    gcp_config=base_gcp,
    bucket_name="my-bucket", 
    bucket_prefix="models/",
    auto_create_bucket=True
)

artifacts_config = CanonMapArtifactsConfig(
    artifacts_local_path="./artifacts",
    gcs_config=artifacts_gcs
)

embedding_config = CanonMapEmbeddingConfig(
    embedding_model_hf_name="sentence-transformers/all-MiniLM-L12-v2",
    embedding_model_local_path="./models",
    gcs_config=embedding_gcs
)

# Initialize CanonMap
cm = CanonMap(
    artifacts_config=artifacts_config,
    embedding_config=embedding_config,
    verbose=True
)

# Create generation request
gen_req = ArtifactGenerationRequest(
    input_path="input",
    source_name="football_data",
    entity_fields=[
        EntityField(table_name="passing", field_name="player"),
        EntityField(table_name="rushing", field_name="rusher_name"),
    ],
    semantic_fields=[
        SemanticField(table_name="passing", field_name="description"),
        SemanticField(table_name="rushing", field_name="notes"),
    ],
    generate_schema=True,
    save_processed_data=True,
    generate_semantic_texts=True
)

# Generate artifacts
resp: ArtifactGenerationResponse = cm.generate_artifacts(gen_req)

# Access response details
print(f"Status: {resp.status}")
print(f"Generated {len(resp.generated_artifacts)} artifacts")
print(f"Processing time: {resp.processing_stats.processing_time_seconds:.2f} seconds")

Entity Mapping Example

from canonmap import (
    CanonMap,
    EntityMappingRequest,
    TableFieldFilter,
    EntityMappingResponse
)

# Initialize CanonMap (reusing configs from above)
cm = CanonMap(
    artifacts_config=artifacts_config,
    embedding_config=embedding_config
)

# Create mapping request
mapping_request = EntityMappingRequest(
    entities=["tim brady", "jake alan"],
    filters=[
        TableFieldFilter(table_name="passing", table_fields=["player"])
    ],
    num_results=3,
)

# Map entities
resp: EntityMappingResponse = cm.map_entities(mapping_request)

# Access mapping results
print(f"Processed {resp.total_entities_processed} entities")
print(f"Found {resp.total_matches_found} matches")

for mapping in resp.mappings:
    print(f"\nEntity: {mapping.entity}")
    for match in mapping.matches:
        print(f"  Match: {match.matched_entity} (Score: {match.score:.3f})")

Configuration Options

CanonMapGCPConfig

Base GCP configuration with service account and troubleshooting settings:

• gcp_service_account_json_path: Path to GCP service account JSON file
• troubleshooting: Enable detailed logging and validation

CanonMapCustomGCSConfig

Bucket-specific configuration extending the base GCP config:

• gcp_config: Base GCP configuration
• bucket_name: GCS bucket name
• bucket_prefix: Optional prefix for bucket operations
• auto_create_bucket: Automatically create bucket if it doesn't exist
• auto_create_bucket_prefix: Automatically create prefix directory
• sync_strategy: Sync strategy ("none", "missing", "overwrite", "refresh")

CanonMapArtifactsConfig

Configuration for artifact storage and management:

• artifacts_local_path: Local directory for artifacts
• gcs_config: GCS configuration for artifact storage
• troubleshooting: Enable troubleshooting mode

CanonMapEmbeddingConfig

Configuration for embedding model management:

• embedding_model_hf_name: HuggingFace model name
• embedding_model_local_path: Local path for model storage
• gcs_config: GCS configuration for model storage
• troubleshooting: Enable troubleshooting mode

ArtifactGenerationRequest

Comprehensive configuration for artifact generation:

• Input/Output:

  • input_path: Path to data file/directory or DataFrame/dict
  • source_name: Logical source name
  • table_name: Logical table name

• Directory Processing:

  • recursive: Process subdirectories
  • file_pattern: File matching pattern (e.g., "*.csv")
  • table_name_from_file: Use filename as table name

• Entity Processing:

  • entity_fields: List of fields to treat as entities
  • semantic_fields: List of fields to extract as individual semantic text files
  • use_other_fields_as_metadata: Include non-entity fields as metadata

• Generation Options:

  • generate_canonical_entities: Generate entity list
  • generate_schema: Generate database schema
  • generate_embeddings: Generate semantic embeddings
  • generate_semantic_texts: Generate semantic text files from semantic_fields
  • save_processed_data: Save cleaned data
  • schema_database_type: Target database type
  • clean_field_names: Standardize field names

Response Models

ArtifactGenerationResponse

Comprehensive response containing:

• status: Success/failure status
• message: Human-readable message
• generated_artifacts: List of generated artifacts with metadata
• processing_stats: Detailed processing statistics
• errors: List of errors encountered
• warnings: List of warnings
• gcp_upload_info: GCP upload details
• Convenience paths for common artifacts

EntityMappingResponse

Detailed mapping results including:

• status: Success/failure status
• mappings: List of entity mappings with matches
• total_entities_processed: Number of entities processed
• total_matches_found: Total number of matches found
• processing_stats: Performance metrics
• configuration_summary: Request configuration summary
• errors: List of errors encountered
• warnings: List of warnings

API Mode

For API deployments, initialize CanonMap with api_mode=True:

canonmap = CanonMap(
    artifacts_config=artifacts_config,
    embedding_config=embedding_config,
    verbose=True,
    api_mode=True  # Enables API-specific optimizations
)

Output

The generate_artifacts() method returns an ArtifactGenerationResponse containing:

• Generated artifacts with metadata
• Processing statistics and timing information
• Error and warning information
• GCP upload details (if applicable)
• Convenience paths to common artifacts

Semantic Text Files

When semantic_fields is specified, CanonMap creates zip files containing individual text files for each non-null semantic field value:

• Single table: {source}_{table}_semantic_texts.zip
• Multiple tables: {source}_semantic_texts.zip (combined)
• File naming: {table_name}_row_{row_index}_{field_name}.txt
• Content: Raw text content from the specified semantic fields

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.