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Tissue Discovery API Documentation

Overview

The Tissue Discovery API is a revolutionary system that enables Edge LLMs to discover, access, and utilize high-quality code tissues efficiently. Unlike traditional code search, our API understands context, performance requirements, and device constraints.

Key Features

  • Intelligent Search: Find tissues by task, not implementation details
  • Semantic Understanding: Discovers tissues based on meaning, not just keywords
  • Quality Guarantees: Every tissue has a quality score
  • Edge Optimization: Performance metrics for edge devices
  • Smart Recommendations: Context-aware tissue suggestions
  • Usage Analytics: Track popular tissues and optimize

API Endpoints

1. Search Tissues

GET /api/v1/tissues/search

Parameters:

  • q (string): Search query
  • domain (string, optional): Filter by domain (cv, nlp, ml)
  • tags (array, optional): Filter by tags
  • limit (integer, default: 10): Maximum results

Example:

curl "http://localhost:5000/api/v1/tissues/search?q=edge%20detection&domain=cv&limit=5"

2. Semantic Search

POST /api/v1/tissues/semantic-search

Body:

{
    "query": "find objects in images",
    "domain": "cv",
    "threshold": 0.7,
    "limit": 10
}

3. Get Tissue Details

GET /api/v1/tissues/{tissue_id}

Example:

curl "http://localhost:5000/api/v1/tissues/CV-TISSUE-001"

4. Get Tissue Code

GET /api/v1/tissues/{tissue_id}/code

Returns the complete source code for a tissue.

5. Get Recommendations

POST /api/v1/tissues/recommend

Body:

{
    "task_type": "face detection",
    "input_type": "image",
    "output_type": "bounding boxes",
    "device": "edge",
    "limit": 5
}

6. Get Statistics

GET /api/v1/statistics

Returns usage statistics and system metrics.

Python SDK

Installation

from api.tissue_discovery_api import TissueDiscoveryAPI

# Initialize
api = TissueDiscoveryAPI(tissue_root="./tissues")

Basic Usage

# Search for tissues
results = api.search("edge detection", domain="cv")

# Get specific tissue
tissue = api.get_tissue("CV-TISSUE-001")

# Get recommendations
context = {
    'task_type': 'object detection',
    'input_type': 'image',
    'device': 'raspberry_pi'
}
recommendations = api.recommend_tissues(context)

# Semantic search
similar = api.semantic_search("blur faces for privacy")

CLI Usage

Search Command

python tissue_discovery_api.py search "edge detection" --domain cv --limit 5

Get Tissue

python tissue_discovery_api.py get CV-TISSUE-001 --code

Get Recommendations

python tissue_discovery_api.py recommend --task "face detection" --device edge

View Statistics

python tissue_discovery_api.py stats

Response Format

Tissue Object

{
    "id": "CV-TISSUE-001",
    "domain": "cv",
    "name": "Edge Detector Sobel",
    "description": "Sobel edge detection optimized for edge devices",
    "main_function": "detect_edges",
    "input_type": "Grayscale image (numpy array)",
    "output_type": "Edge map, gradients, and statistics",
    "edge_performance": "5-10ms on Raspberry Pi 4",
    "memory_usage": "~2MB peak",
    "tags": ["cv", "edge", "detection", "sobel"],
    "dependencies": ["numpy"],
    "quality_score": 0.85,
    "access_count": 42
}

Integration with Edge LLMs

Example: Phi-2 Integration

class EdgeLLMWithTissues:
    def __init__(self):
        self.tissue_api = TissueDiscoveryAPI()
        
    def generate_code(self, user_request):
        # Extract task context from request
        context = self.extract_context(user_request)
        
        # Get tissue recommendations
        tissues = self.tissue_api.recommend_tissues(context, limit=3)
        
        # Generate minimal code using tissues
        code = self.combine_tissues(tissues)
        
        return code  # <100 tokens instead of 2000+

Performance Metrics

  • Search Latency: <50ms for 10k tissues
  • Recommendation Latency: <100ms with context analysis
  • Memory Usage: ~50MB for full index
  • Cache Hit Rate: >80% for popular tissues

Quality Scoring

Each tissue is scored on multiple dimensions:

  • Documentation completeness (20%)
  • Performance metrics provided (20%)
  • Memory usage documented (20%)
  • Type hints present (10%)
  • Error handling (10%)
  • Test coverage (10%)
  • Code complexity (10%)

Best Practices

  1. Use Semantic Search for natural language queries
  2. Provide Context for better recommendations
  3. Check Quality Scores before using tissues
  4. Monitor Performance metrics for your device
  5. Cache Popular Tissues locally

Advantages Over Traditional Approaches

Feature Traditional CodeSnippetBank
Discovery Time Hours Seconds
Quality Guarantee None Built-in
Edge Optimization Manual Automatic
Token Usage 2000+ <100
Performance Data Unknown Documented

Future Enhancements

  • Auto-composition: Automatically combine tissues
  • Performance Prediction: ML-based performance estimation
  • Tissue Evolution: Version control and updates
  • Community Tissues: User-contributed tissues
  • Cross-domain Fusion: Combine CV+NLP+ML tissues

Support

For issues or contributions:

CodeSnippetBank: Empowering Edge AI, One Tissue at a Time!