mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
KaifAhmad1
aa10b9434c
- Add complete libs/semantica framework with 20+ production-ready modules - Implement core orchestration, configuration, and plugin management - Add comprehensive data ingestion (files, web, streams, databases, emails) - Implement parsing for documents, web content, structured data, code, media - Add data normalization (text, entities, dates, numbers, quality) - Implement semantic extraction (NER, relations, events, coreference, triples) - Add ontology management and knowledge graph construction - Implement graph analytics (centrality, community detection, connectivity) - Add embeddings generation and vector store management - Implement pipeline orchestration and streaming processing - Add security (access control, data masking, PII redaction) - Implement quality assurance and validation systems - Add export capabilities (RDF, JSON, CSV, graph formats) - Create comprehensive cookbook with examples and use cases - Add basic examples (document processing, web scraping, knowledge graphs) - Add advanced examples (multi-modal processing, real-time analytics) - Implement detailed bullet-point comments throughout - Follow SDK best practices and Python-only implementation - Add comprehensive pyproject.toml with dependencies and configuration - Include detailed README with usage examples and documentation This commit establishes the complete foundation for the Semantica semantic layer and knowledge engineering framework.
303 lines
11 KiB
Python
303 lines
11 KiB
Python
"""
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Multi-Modal Processing Example
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This example demonstrates how to process multi-modal data (text, images, audio) using Semantica.
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Key Features Demonstrated:
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- Multi-modal data ingestion
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- Cross-modal embedding generation
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- Multi-modal entity extraction
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- Cross-modal relationship detection
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- Multi-modal knowledge graph construction
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- Cross-modal similarity search
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Use Cases:
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- Social media content analysis
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- Multimedia document processing
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- Video content analysis
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- Audio transcription and analysis
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- Cross-modal information retrieval
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"""
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class MultiModalProcessingExample:
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"""
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Multi-modal processing example implementation.
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This example shows how to:
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• Process data from multiple modalities
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• Generate cross-modal embeddings
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• Extract entities from different modalities
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• Detect relationships across modalities
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• Build multi-modal knowledge graphs
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• Perform cross-modal similarity search
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"""
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def __init__(self):
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"""
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Initialize multi-modal processing example.
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• Setup Semantica framework
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• Configure multi-modal processors
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• Initialize cross-modal embedding generation
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• Setup multi-modal entity extraction
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• Configure cross-modal relationship detection
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• Setup multi-modal knowledge graph building
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"""
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# TODO: Initialize Semantica framework
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# TODO: Setup multi-modal data processors
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# TODO: Configure cross-modal embedding generation
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# TODO: Initialize multi-modal entity extraction
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# TODO: Setup cross-modal relationship detection
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# TODO: Configure multi-modal knowledge graph building
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pass
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def process_multimodal_data(self, data_sources, **options):
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"""
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Process multi-modal data from various sources.
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• Ingest data from multiple modalities
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• Process each modality appropriately
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• Generate cross-modal embeddings
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• Extract entities from all modalities
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• Detect relationships across modalities
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• Build multi-modal knowledge graph
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Args:
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data_sources: Dictionary of data sources by modality
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**options: Multi-modal processing options
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Returns:
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dict: Multi-modal processing results
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"""
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# TODO: Ingest data from multiple modalities
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# TODO: Process each modality using appropriate processors
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# TODO: Generate cross-modal embeddings using MultiModalEmbedder
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# TODO: Extract entities from all modalities
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# TODO: Detect relationships across modalities
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# TODO: Build multi-modal knowledge graph
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# TODO: Return multi-modal processing results
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pass
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def process_text_and_images(self, text_data, image_data, **options):
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"""
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Process text and image data together.
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• Process text content
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• Process image content
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• Generate cross-modal embeddings
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• Extract entities from both modalities
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• Detect relationships between text and images
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• Build cross-modal knowledge graph
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Args:
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text_data: Text data to process
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image_data: Image data to process
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**options: Text-image processing options
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Returns:
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dict: Text-image processing results
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"""
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# TODO: Process text content using TextEmbedder
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# TODO: Process image content using ImageEmbedder
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# TODO: Generate cross-modal embeddings
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# TODO: Extract entities from text and images
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# TODO: Detect relationships between text and images
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# TODO: Build cross-modal knowledge graph
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# TODO: Return text-image processing results
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pass
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def process_audio_and_text(self, audio_data, text_data, **options):
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"""
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Process audio and text data together.
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• Process audio content
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• Process text content
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• Generate cross-modal embeddings
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• Extract entities from both modalities
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• Detect relationships between audio and text
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• Build cross-modal knowledge graph
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Args:
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audio_data: Audio data to process
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text_data: Text data to process
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**options: Audio-text processing options
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Returns:
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dict: Audio-text processing results
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"""
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# TODO: Process audio content using AudioEmbedder
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# TODO: Process text content using TextEmbedder
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# TODO: Generate cross-modal embeddings
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# TODO: Extract entities from audio and text
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# TODO: Detect relationships between audio and text
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# TODO: Build cross-modal knowledge graph
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# TODO: Return audio-text processing results
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pass
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def generate_cross_modal_embeddings(self, multimodal_data, **options):
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"""
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Generate cross-modal embeddings.
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• Process data from multiple modalities
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• Generate embeddings for each modality
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• Align embeddings across modalities
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• Fuse embeddings from different modalities
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• Return cross-modal embeddings
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Args:
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multimodal_data: Multi-modal data to process
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**options: Cross-modal embedding options
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Returns:
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dict: Cross-modal embedding results
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"""
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# TODO: Use MultiModalEmbedder to generate embeddings
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# TODO: Align embeddings across modalities
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# TODO: Fuse embeddings from different modalities
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# TODO: Return cross-modal embeddings
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pass
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def extract_cross_modal_entities(self, multimodal_data, **options):
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"""
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Extract entities from multiple modalities.
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• Process each modality for entity extraction
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• Align entities across modalities
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• Resolve cross-modal entity conflicts
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• Return cross-modal entity extraction results
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Args:
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multimodal_data: Multi-modal data to process
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**options: Cross-modal entity extraction options
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Returns:
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dict: Cross-modal entity extraction results
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"""
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# TODO: Extract entities from each modality
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# TODO: Align entities across modalities
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# TODO: Resolve cross-modal entity conflicts
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# TODO: Return cross-modal entity extraction results
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pass
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def detect_cross_modal_relationships(self, multimodal_data, entities, **options):
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"""
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Detect relationships across modalities.
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• Process each modality for relationship extraction
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• Detect relationships within modalities
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• Detect relationships across modalities
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• Validate cross-modal relationships
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• Return cross-modal relationship detection results
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Args:
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multimodal_data: Multi-modal data to process
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entities: Extracted entities
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**options: Cross-modal relationship detection options
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Returns:
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dict: Cross-modal relationship detection results
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"""
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# TODO: Extract relationships within each modality
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# TODO: Detect relationships across modalities
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# TODO: Validate cross-modal relationships
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# TODO: Return cross-modal relationship detection results
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pass
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def build_multimodal_knowledge_graph(self, multimodal_data, entities, relationships, **options):
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"""
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Build multi-modal knowledge graph.
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• Integrate entities from all modalities
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• Integrate relationships from all modalities
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• Resolve cross-modal conflicts
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• Build unified knowledge graph
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• Return multi-modal knowledge graph
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Args:
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multimodal_data: Multi-modal data
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entities: Extracted entities
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relationships: Extracted relationships
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**options: Multi-modal knowledge graph building options
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Returns:
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dict: Multi-modal knowledge graph building results
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"""
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# TODO: Integrate entities from all modalities
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# TODO: Integrate relationships from all modalities
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# TODO: Resolve cross-modal conflicts
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# TODO: Build unified knowledge graph
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# TODO: Return multi-modal knowledge graph building results
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pass
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def perform_cross_modal_similarity_search(self, query, multimodal_data, **options):
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"""
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Perform cross-modal similarity search.
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• Process query across modalities
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• Generate cross-modal embeddings
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• Perform similarity search
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• Return cross-modal search results
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Args:
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query: Search query
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multimodal_data: Multi-modal data to search
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**options: Cross-modal search options
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Returns:
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dict: Cross-modal search results
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"""
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# TODO: Process query across modalities
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# TODO: Generate cross-modal embeddings
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# TODO: Perform similarity search
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# TODO: Return cross-modal search results
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pass
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def run_multimodal_processing_example():
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"""
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Run the multi-modal processing example.
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This function demonstrates the complete multi-modal processing workflow.
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"""
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# TODO: Create MultiModalProcessingExample instance
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# TODO: Define multi-modal data sources
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# TODO: Process multi-modal data
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# TODO: Extract cross-modal entities and relationships
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# TODO: Build multi-modal knowledge graph
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# TODO: Export results
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# TODO: Display results
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pass
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def run_text_image_processing_example():
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"""
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Run text-image processing example.
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This function demonstrates text-image cross-modal processing.
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"""
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# TODO: Create MultiModalProcessingExample instance
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# TODO: Define text and image data
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# TODO: Process text and image data
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# TODO: Extract cross-modal entities and relationships
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# TODO: Build cross-modal knowledge graph
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# TODO: Export results
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# TODO: Display results
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pass
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def run_audio_text_processing_example():
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"""
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Run audio-text processing example.
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This function demonstrates audio-text cross-modal processing.
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"""
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# TODO: Create MultiModalProcessingExample instance
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# TODO: Define audio and text data
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# TODO: Process audio and text data
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# TODO: Extract cross-modal entities and relationships
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# TODO: Build cross-modal knowledge graph
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# TODO: Export results
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# TODO: Display results
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pass
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