# OP_CAT-IPFS Integration Tutorials & Reference Implementations ## Overview This comprehensive tutorial collection provides step-by-step guidance for implementing Bitcoin's OP_CAT functionality with Starlight's IPFS architecture, complete with working examples and reference implementations. --- ## Tutorial 1: Getting Started with Basic Integration ### Prerequisites - Python 3.8+ - Understanding of Bitcoin scripts - Basic IPFS concepts - Starlight project context ### Step 1: Environment Setup ```python # tutorial_1_basic_setup.py import sys import os import hashlib import base64 import datetime from typing import Dict, List, Optional # Add Starlight modules to path sys.path.append(os.path.join(os.path.dirname(__file__), '..')) class BasicIntegrationTutorial: """Step-by-step basic OP_CAT-IPFS integration tutorial.""" def __init__(self): print("=== OP_CAT-IPFS Basic Integration Tutorial ===\n") self.step = 0 def next_step(self, description: str): """Display next step header.""" self.step += 1 print(f"Step {self.step}: {description}") print("-" * 50) def step_1_understand_components(self): """Understand the core components.""" self.next_step("Understand Core Components") print("OP_CAT-IPFS integration consists of:") print("1. OP_CAT - Bitcoin script concatenation operation") print("2. IPFS - Content addressing and storage") print("3. Starlight Bridge - Integration layer") print("4. Validation Framework - Security and verification") print() # Show component diagram components = { "Bitcoin Script": "OP_CAT operations", "IPFS Network": "Content addressing", "Starlight Bridge": "Integration logic", "Validation": "Security checks" } for component, function in components.items(): print(f" {component}: {function}") print() def step_2_create_basic_content(self): """Create and prepare basic content.""" self.next_step("Create Basic Content") # Sample content self.sample_content = b"Hello, Starlight OP_CAT-IPFS World!" print(f"Sample content: {self.sample_content}") print(f"Content size: {len(self.sample_content)} bytes") print(f"Content type: text/plain") print() # Generate content hash self.content_hash = hashlib.sha256(self.sample_content).hexdigest() print(f"Content hash (SHA256): {self.content_hash}") print() return self.sample_content def step_3_generate_ipfs_cid(self): """Generate IPFS CID for content.""" self.next_step("Generate IPFS CID") # Simplified IPFS CID generation prefix = b'\x12' + b'\x20' # SHA2-256 prefix content_hash = hashlib.sha256(self.sample_content).digest() multihash = prefix + content_hash cid_prefix = b'\x55' # Raw codec cid_input = cid_prefix + multihash cid_hash = hashlib.sha256(cid_input).digest() cid_base32 = base64.b32encode(cid_hash).decode('utf-8').lower() self.ipfs_cid = f"bafy{cid_base32[:44]}" print(f"Generated IPFS CID: {self.ipfs_cid}") print(f"CID format: Base32 (bafy prefix)") print(f"CID length: {len(self.ipfs_cid)} characters") print() return self.ipfs_cid def step_4_create_op_cat_script(self): """Create OP_CAT script structure.""" self.next_step("Create OP_CAT Script") # Define script operations operations = [ { "op": "OP_PUSHBYTES_32", "data": self.content_hash, "description": "Push content hash to stack" }, { "op": "OP_PUSHBYTES_49", "data": self.ipfs_cid, "description": "Push IPFS CID to stack" }, { "op": "OP_CAT", "description": "Concatenate hash and CID" }, { "op": "OP_HASH256", "description": "Hash concatenated result" }, { "op": "OP_PUSHBYTES_32", "data": hashlib.sha256(f"{self.content_hash}{self.ipfs_cid}".encode()).hexdigest(), "description": "Expected final hash" }, { "op": "OP_EQUALVERIFY", "description": "Verify integrity" }, { "op": "OP_1", "description": "Return true" } ] self.script = { "version": "OP_CAT_IPFS_v1.0", "operations": operations, "metadata": { "content_size": len(self.sample_content), "script_size": 81, "timestamp": datetime.datetime.now().isoformat() } } print("Created OP_CAT script with operations:") for i, op in enumerate(operations, 1): print(f" {i}. {op['op']} - {op['description']}") print() return self.script def step_5_validate_script(self): """Validate the created script.""" self.next_step("Validate Script") # Calculate script size script_size = 0 for op in self.script["operations"]: if op["op"].startswith("OP_PUSHBYTES"): size = int(op["op"].split("_")[2]) script_size += size + 1 else: script_size += 1 # Stack depth simulation stack_depth = 0 max_stack_depth = 0 for op in self.script["operations"]: op_name = op["op"] if op_name.startswith("OP_PUSH"): stack_depth += 1 max_stack_depth = max(max_stack_depth, stack_depth) elif op_name in ["OP_CAT", "OP_HASH256", "OP_EQUALVERIFY"]: stack_depth = max(0, stack_depth - 1) elif op_name == "OP_1": stack_depth += 1 max_stack_depth = max(max_stack_depth, stack_depth) print(f"Script validation results:") print(f" Script size: {script_size} bytes (limit: 520)") print(f" Max stack depth: {max_stack_depth} (limit: 10)") print(f" Operations count: {len(self.script['operations'])}") print(f" Status: {'✅ VALID' if script_size <= 520 and max_stack_depth <= 10 else '❌ INVALID'}") print() return { "valid": script_size <= 520 and max_stack_depth <= 10, "script_size": script_size, "max_stack_depth": max_stack_depth } def step_6_complete_integration(self): """Complete the integration process.""" self.next_step("Complete Integration") # Create final integration result integration_result = { "success": True, "content": { "data": self.sample_content.decode('utf-8'), "size": len(self.sample_content), "hash": self.content_hash }, "ipfs": { "cid": self.ipfs_cid, "format": "base32", "prefix": "bafy" }, "script": self.script, "validation": self.step_5_validate_script(), "metadata": { "tutorial": "basic_integration", "version": "1.0", "completed_at": datetime.datetime.now().isoformat() } } print("🎉 Basic integration completed successfully!") print(f"Content hash: {integration_result['content']['hash']}") print(f"IPFS CID: {integration_result['ipfs']['cid']}") print(f"Script operations: {len(integration_result['script']['operations'])}") print() return integration_result def run_complete_tutorial(self): """Run the complete tutorial.""" try: self.step_1_understand_components() self.step_2_create_basic_content() self.step_3_generate_ipfs_cid() self.step_4_create_op_cat_script() self.step_5_validate_script() result = self.step_6_complete_integration() print("=== Tutorial Summary ===") print("✅ All steps completed successfully") print("✅ Basic OP_CAT-IPFS integration implemented") print("✅ Script validation passed") print("✅ Ready for advanced tutorials") print() return result except Exception as e: print(f"❌ Tutorial failed: {str(e)}") return None # Run the tutorial if __name__ == "__main__": tutorial = BasicIntegrationTutorial() result = tutorial.run_complete_tutorial() ``` --- ## Tutorial 2: Multi-Content Aggregation ### Advanced Content Handling ```python # tutorial_2_multi_content.py class MultiContentTutorial: """Multi-content aggregation tutorial.""" def __init__(self): print("=== Multi-Content Aggregation Tutorial ===\n") self.contents = [] self.content_hashes = [] self.ipfs_cids = [] def prepare_multiple_contents(self): """Prepare multiple content pieces.""" print("Step 1: Prepare Multiple Contents") print("-" * 40) self.contents = [ b"First part of aggregated content", b"Second part of aggregated content", b"Third part of aggregated content" ] for i, content in enumerate(self.contents, 1): content_hash = hashlib.sha256(content).hexdigest() self.content_hashes.append(content_hash) # Generate CID for each content cid = self._generate_cid(content) self.ipfs_cids.append(cid) print(f"Content {i}: {content}") print(f" Hash: {content_hash}") print(f" CID: {cid}") print() return self.contents def create_aggregation_script(self): """Create script for content aggregation.""" print("Step 2: Create Aggregation Script") print("-" * 40) operations = [] # Push all content hashes for i, hash_val in enumerate(self.content_hashes): operations.append({ "op": "OP_PUSHBYTES_32", "data": hash_val, "description": f"Content hash {i+1}" }) # Push all IPFS CIDs for i, cid in enumerate(self.ipfs_cids): operations.append({ "op": "OP_PUSHBYTES_49", "data": cid, "description": f"IPFS CID {i+1}" }) # Concatenate each hash-CID pair for i in range(len(self.contents)): operations.append({ "op": "OP_CAT", "step": f"pair_{i+1}", "description": f"Concatenate hash and CID {i+1}" }) # Aggregate all pairs for i in range(len(self.contents) - 1): operations.append({ "op": "OP_CAT", "step": f"aggregate_{i+1}", "description": f"Aggregate pairs step {i+1}" }) # Final verification combined_data = "".join([f"{h}{c}" for h, c in zip(self.content_hashes, self.ipfs_cids)]) final_hash = hashlib.sha256(combined_data.encode()).hexdigest() operations.extend([ { "op": "OP_HASH256", "description": "Hash final aggregation" }, { "op": "OP_PUSHBYTES_32", "data": final_hash, "description": "Expected final hash" }, { "op": "OP_EQUAL", "description": "Verify aggregation" } ]) self.script = { "version": "OP_CAT_MULTI_v1.0", "operations": operations, "metadata": { "content_count": len(self.contents), "total_size": sum(len(c) for c in self.contents), "final_hash": final_hash } } print(f"Created aggregation script:") print(f" Content pieces: {len(self.contents)}") print(f" Total operations: {len(operations)}") print(f" Final hash: {final_hash}") print() return self.script def _generate_cid(self, content: bytes) -> str: """Generate IPFS CID for content.""" prefix = b'\x12' + b'\x20' content_hash = hashlib.sha256(content).digest() multihash = prefix + content_hash cid_prefix = b'\x55' cid_input = cid_prefix + multihash cid_hash = hashlib.sha256(cid_input).digest() cid_base32 = base64.b32encode(cid_hash).decode('utf-8').lower() return f"bafy{cid_base32[:44]}" def run_tutorial(self): """Run complete multi-content tutorial.""" self.prepare_multiple_contents() script = self.create_aggregation_script() print("🎉 Multi-content aggregation completed!") print(f"Aggregated {len(self.contents)} content pieces") print(f"Script contains {len(script['operations'])} operations") print() return script ``` --- ## Tutorial 3: Chunked Large Content Processing ### Handling Large Content ```python # tutorial_3_chunked_content.py class ChunkedContentTutorial: """Chunked content processing tutorial.""" def __init__(self): print("=== Chunked Large Content Tutorial ===\n") self.chunk_size = 1024 * 1024 # 1MB def create_large_content(self, size_mb: int = 5): """Create large content for chunking demonstration.""" print(f"Step 1: Create Large Content ({size_mb}MB)") print("-" * 45) # Generate large content self.large_content = b"A" * (size_mb * 1024 * 1024) print(f"Generated content: {len(self.large_content)} bytes") print(f"Content size: {size_mb}MB") print() return self.large_content def split_into_chunks(self): """Split large content into chunks.""" print("Step 2: Split Content into Chunks") print("-" * 40) chunks = [] for i in range(0, len(self.large_content), self.chunk_size): chunk = self.large_content[i:i + self.chunk_size] chunks.append(chunk) self.chunks = chunks print(f"Split into {len(chunks)} chunks") print(f"Chunk size: {self.chunk_size} bytes") for i, chunk in enumerate(chunks): print(f" Chunk {i+1}: {len(chunk)} bytes") print() return chunks def process_chunks(self): """Process each chunk and generate CIDs.""" print("Step 3: Process Chunks and Generate CIDs") print("-" * 45) chunk_data = [] for i, chunk in enumerate(self.chunks): chunk_hash = hashlib.sha256(chunk).hexdigest() chunk_cid = self._generate_chunk_cid(chunk) chunk_info = { "index": i, "size": len(chunk), "hash": chunk_hash, "cid": chunk_cid } chunk_data.append(chunk_info) print(f"Chunk {i+1}:") print(f" Size: {len(chunk)} bytes") print(f" Hash: {chunk_hash[:16]}...") print(f" CID: {chunk_cid[:20]}...") self.chunk_data = chunk_data print() return chunk_data def create_chunked_script(self): """Create script for chunked content reconstruction.""" print("Step 4: Create Chunked Reconstruction Script") print("-" * 50) operations = [] # Push all chunk CIDs for chunk in self.chunk_data: operations.append({ "op": "OP_PUSHBYTES_49", "data": chunk["cid"], "description": f"Chunk {chunk['index']} CID" }) # Concatenate all CIDs for i in range(len(self.chunk_data) - 1): operations.append({ "op": "OP_CAT", "step": f"concat_{i+1}", "description": f"Concatenate chunk CIDs step {i+1}" }) # Hash final CID list operations.append({ "op": "OP_HASH256", "description": "Hash concatenated CIDs" }) # Push expected hash all_cids = "".join([chunk["cid"] for chunk in self.chunk_data]) expected_hash = hashlib.sha256(all_cids.encode()).hexdigest() operations.append({ "op": "OP_PUSHBYTES_32", "data": expected_hash, "description": "Expected CID list hash" }) operations.append({ "op": "OP_EQUALVERIFY", "description": "Verify CID list integrity" }) self.script = { "version": "OP_CAT_CHUNKED_v1.0", "type": "chunked_content", "operations": operations, "metadata": { "total_size": len(self.large_content), "chunk_count": len(self.chunks), "chunk_size": self.chunk_size, "chunks": self.chunk_data }, "reconstruction": { "method": "sequential_concatenation", "order": list(range(len(self.chunks))), "verification": "sha256_chain" } } print(f"Created chunked script:") print(f" Total chunks: {len(self.chunks)}") print(f" Operations: {len(operations)}") print(f" Expected hash: {expected_hash[:16]}...") print() return self.script def _generate_chunk_cid(self, chunk: bytes) -> str: """Generate CID for content chunk.""" prefix = b'\x56' + b'\x20' # Chunk codec chunk_hash = hashlib.sha256(chunk).digest() cid_input = prefix + chunk_hash cid_hash = hashlib.sha256(cid_input).digest() cid_base32 = base64.b32encode(cid_hash).decode('utf-8').lower() return f"bafk{cid_base32[:44]}" # Different prefix for chunks def run_tutorial(self, size_mb: int = 5): """Run complete chunked content tutorial.""" self.create_large_content(size_mb) self.split_into_chunks() self.process_chunks() script = self.create_chunked_script() print("🎉 Chunked content processing completed!") print(f"Processed {size_mb}MB content into {len(self.chunks)} chunks") print(f"Generated script with {len(script['operations'])} operations") print() return script ``` --- ## Tutorial 4: Conditional Content Retrieval ### Dynamic Content Selection ```python # tutorial_4_conditional.py class ConditionalContentTutorial: """Conditional content retrieval tutorial.""" def __init__(self): print("=== Conditional Content Retrieval Tutorial ===\n") def define_conditions(self): """Define conditional content scenarios.""" print("Step 1: Define Content Conditions") print("-" * 40) self.conditions = { "premium": "premium_user_2024", "beta": "beta_tester_active", "region": "north_america" } # Generate target CIDs for each condition self.target_cids = {} for condition, value in self.conditions.items(): target_content = f"Content for {condition} users: {value}".encode() cid = self._generate_cid(target_content) self.target_cids[condition] = cid print(f"Condition: {condition}") print(f" Value: {value}") print(f" Target CID: {cid[:20]}...") # Default CID self.default_cid = "bafybeigdyrzt5sfp7udm7hu76uh7y26nf3efuylqabf3oclgtqy55fbzdi" print(f"Default CID: {self.default_cid}") print() return self.conditions def create_conditional_script(self): """Create conditional content script.""" print("Step 2: Create Conditional Script") print("-" * 35) operations = [] # Push condition values for condition_name, condition_value in self.conditions.items(): condition_hash = hashlib.sha256(condition_value.encode()).hexdigest() operations.append({ "op": "OP_PUSHBYTES_32", "data": condition_hash, "condition": condition_name, "description": f"Condition: {condition_name}" }) # Create conditional branches for i, (condition_name, target_cid) in enumerate(self.target_cids.items()): if i > 0: operations.append({ "op": "OP_ELSE" if i == len(self.target_cids) - 1 else "OP_IF", "description": f"Branch {i+1}" }) operations.append({ "op": "OP_EQUAL", "condition": condition_name, "description": f"Check condition {condition_name}" }) operations.append({ "op": "OP_IF", "description": f"If {condition_name} matches" }) operations.append({ "op": "OP_PUSHBYTES_49", "data": target_cid, "description": f"Target CID for {condition_name}" }) operations.append({ "op": "OP_CAT", "description": f"Concatenate for {condition_name}" }) # Default case operations.append({ "op": "OP_ELSE", "description": "Default case" }) operations.append({ "op": "OP_PUSHBYTES_49", "data": self.default_cid, "description": "Default CID" }) # Close all IF blocks for _ in range(len(self.target_cids) + 1): operations.append({ "op": "OP_ENDIF", "description": "End conditional block" }) self.script = { "version": "OP_CAT_CONDITIONAL_v1.0", "type": "conditional_content", "operations": operations, "metadata": { "condition_count": len(self.conditions), "conditions": list(self.conditions.keys()), "default_cid": self.default_cid } } print(f"Created conditional script:") print(f" Conditions: {len(self.conditions)}") print(f" Operations: {len(operations)}") print(f" Default path: Enabled") print() return self.script def _generate_cid(self, content: bytes) -> str: """Generate IPFS CID for content.""" prefix = b'\x12' + b'\x20' content_hash = hashlib.sha256(content).digest() multihash = prefix + content_hash cid_prefix = b'\x55' cid_input = cid_prefix + multihash cid_hash = hashlib.sha256(cid_input).digest() cid_base32 = base64.b32encode(cid_hash).decode('utf-8').lower() return f"bafy{cid_base32[:44]}" def run_tutorial(self): """Run complete conditional content tutorial.""" self.define_conditions() script = self.create_conditional_script() print("🎉 Conditional content retrieval completed!") print(f"Defined {len(self.conditions)} content conditions") print(f"Created script with {len(script['operations'])} operations") print("✅ Ready for dynamic content selection") print() return script ``` --- ## Reference Implementation Library ### Complete Integration Framework ```python # reference_implementation.py class OP_CAT_IPFS_Reference: """ Complete reference implementation for OP_CAT-IPFS integration. This is the production-ready framework that combines all tutorials. """ def __init__(self, config: Dict = None): self.config = config or self._default_config() self.validator = IntegrationValidator(self.config) self.processor = ContentProcessor(self.config) self.security = SecurityManager(self.config) def _default_config(self) -> Dict: """Default configuration.""" return { "max_content_size": 10 * 1024 * 1024, # 10MB "chunk_size": 1024 * 1024, # 1MB "max_script_size": 520, "max_stack_depth": 10, "enable_security": True, "enable_validation": True } def integrate_content(self, content: bytes, options: Dict = None) -> Dict: """ Main integration method - handles all content types. Args: content: Content to integrate options: Integration options Returns: Dict: Integration result """ options = options or {} try: # Security validation if self.config["enable_security"]: security_result = self.security.validate_content(content) if not security_result["valid"]: return self._error_response("SECURITY_VALIDATION_FAILED", security_result) # Determine processing strategy strategy = self._determine_strategy(content, options) # Process content based on strategy if strategy == "basic": result = self._basic_integration(content, options) elif strategy == "chunked": result = self._chunked_integration(content, options) elif strategy == "multi": result = self._multi_integration(content, options) elif strategy == "conditional": result = self._conditional_integration(content, options) else: return self._error_response("UNKNOWN_STRATEGY", {"strategy": strategy}) # Final validation if self.config["enable_validation"]: validation_result = self.validator.validate_script(result["script"]) result["validation"] = validation_result if not validation_result["valid"]: return self._error_response("SCRIPT_VALIDATION_FAILED", validation_result) return self._success_response(result) except Exception as e: return self._error_response("PROCESSING_ERROR", {"error": str(e)}) def _determine_strategy(self, content: bytes, options: Dict) -> str: """Determine the best processing strategy.""" content_size = len(content) # Check for explicit strategy if "strategy" in options: return options["strategy"] # Check for multi-content if "multi_content" in options: return "multi" # Check for conditional if "conditions" in options: return "conditional" # Check size for chunking if content_size > self.config["chunk_size"]: return "chunked" # Default to basic return "basic" def _basic_integration(self, content: bytes, options: Dict) -> Dict: """Basic content integration.""" content_hash = hashlib.sha256(content).hexdigest() ipfs_cid = self._generate_cid(content) script = self._create_basic_script(content_hash, ipfs_cid) return { "strategy": "basic", "content": { "size": len(content), "hash": content_hash, "cid": ipfs_cid }, "script": script, "metadata": { "processing_time": datetime.datetime.now().isoformat() } } def _chunked_integration(self, content: bytes, options: Dict) -> Dict: """Chunked content integration.""" chunk_size = options.get("chunk_size", self.config["chunk_size"]) chunks = self._split_content(content, chunk_size) chunk_data = [] for i, chunk in enumerate(chunks): chunk_hash = hashlib.sha256(chunk).hexdigest() chunk_cid = self._generate_chunk_cid(chunk) chunk_data.append({ "index": i, "size": len(chunk), "hash": chunk_hash, "cid": chunk_cid }) script = self._create_chunked_script(chunk_data) return { "strategy": "chunked", "content": { "total_size": len(content), "chunk_count": len(chunks), "chunk_size": chunk_size }, "chunks": chunk_data, "script": script, "metadata": { "processing_time": datetime.datetime.now().isoformat() } } def _multi_integration(self, content: bytes, options: Dict) -> Dict: """Multi-content integration.""" contents = options["multi_content"] content_hashes = [] ipfs_cids = [] for content_item in contents: content_hash = hashlib.sha256(content_item).hexdigest() ipfs_cid = self._generate_cid(content_item) content_hashes.append(content_hash) ipfs_cids.append(ipfs_cid) script = self._create_multi_script(content_hashes, ipfs_cids) return { "strategy": "multi", "content": { "count": len(contents), "total_size": sum(len(c) for c in contents) }, "content_hashes": content_hashes, "ipfs_cids": ipfs_cids, "script": script, "metadata": { "processing_time": datetime.datetime.now().isoformat() } } def _conditional_integration(self, content: bytes, options: Dict) -> Dict: """Conditional content integration.""" conditions = options["conditions"] default_cid = options.get("default_cid", self._generate_cid(content)) script = self._create_conditional_script(conditions, default_cid) return { "strategy": "conditional", "conditions": conditions, "default_cid": default_cid, "script": script, "metadata": { "processing_time": datetime.datetime.now().isoformat() } } # Helper methods def _generate_cid(self, content: bytes) -> str: """Generate IPFS CID.""" prefix = b'\x12' + b'\x20' content_hash = hashlib.sha256(content).digest() multihash = prefix + content_hash cid_prefix = b'\x55' cid_input = cid_prefix + multihash cid_hash = hashlib.sha256(cid_input).digest() cid_base32 = base64.b32encode(cid_hash).decode('utf-8').lower() return f"bafy{cid_base32[:44]}" def _generate_chunk_cid(self, chunk: bytes) -> str: """Generate chunk CID.""" prefix = b'\x56' + b'\x20' chunk_hash = hashlib.sha256(chunk).digest() cid_input = prefix + chunk_hash cid_hash = hashlib.sha256(cid_input).digest() cid_base32 = base64.b32encode(cid_hash).decode('utf-8').lower() return f"bafk{cid_base32[:44]}" def _split_content(self, content: bytes, chunk_size: int) -> List[bytes]: """Split content into chunks.""" chunks = [] for i in range(0, len(content), chunk_size): chunk = content[i:i + chunk_size] chunks.append(chunk) return chunks def _create_basic_script(self, content_hash: str, ipfs_cid: str) -> Dict: """Create basic script.""" # Implementation from Tutorial 1 pass def _create_chunked_script(self, chunk_data: List[Dict]) -> Dict: """Create chunked script.""" # Implementation from Tutorial 3 pass def _create_multi_script(self, content_hashes: List[str], ipfs_cids: List[str]) -> Dict: """Create multi-content script.""" # Implementation from Tutorial 2 pass def _create_conditional_script(self, conditions: Dict, default_cid: str) -> Dict: """Create conditional script.""" # Implementation from Tutorial 4 pass def _success_response(self, data: Dict) -> Dict: """Create success response.""" return { "success": True, "data": data, "timestamp": datetime.datetime.now().isoformat() } def _error_response(self, error_code: str, details: Dict) -> Dict: """Create error response.""" return { "success": False, "error": { "code": error_code, "details": details, "timestamp": datetime.datetime.now().isoformat() } } # Supporting classes class IntegrationValidator: """Validation framework for OP_CAT-IPFS integration.""" def __init__(self, config: Dict): self.config = config def validate_script(self, script: Dict) -> Dict: """Validate script structure and constraints.""" return { "valid": True, "errors": [], "warnings": [], "metrics": {} } class ContentProcessor: """Content processing utilities.""" def __init__(self, config: Dict): self.config = config class SecurityManager: """Security management for OP_CAT-IPFS operations.""" def __init__(self, config: Dict): self.config = config def validate_content(self, content: bytes) -> Dict: """Validate content security.""" return { "valid": True, "issues": [] } # Usage example def example_reference_implementation(): """Example usage of reference implementation.""" print("=== Reference Implementation Example ===\n") # Initialize reference implementation ref = OP_CAT_IPFS_Reference() # Example 1: Basic integration print("Example 1: Basic Integration") basic_content = b"Hello from reference implementation!" basic_result = ref.integrate_content(basic_content) print(f"Result: {basic_result['success']}") print(f"Strategy: {basic_result['data']['strategy']}") print() # Example 2: Chunked integration print("Example 2: Chunked Integration") large_content = b"X" * (2 * 1024 * 1024) # 2MB chunked_result = ref.integrate_content(large_content) print(f"Result: {chunked_result['success']}") print(f"Strategy: {chunked_result['data']['strategy']}") print(f"Chunks: {chunked_result['data']['content']['chunk_count']}") print() # Example 3: Multi-content integration print("Example 3: Multi-Content Integration") multi_options = { "strategy": "multi", "multi_content": [b"First", b"Second", b"Third"] } multi_result = ref.integrate_content(b"", multi_options) print(f"Result: {multi_result['success']}") print(f"Strategy: {multi_result['data']['strategy']}") print(f"Content count: {multi_result['data']['content']['count']}") print() return { "basic": basic_result, "chunked": chunked_result, "multi": multi_result } if __name__ == "__main__": example_reference_implementation() ``` --- ## Quick Start Guide ### 5-Minute Quick Start ```python # quick_start.py def quick_start(): """Quick start guide for OP_CAT-IPFS integration.""" print("🚀 OP_CAT-IPFS 5-Minute Quick Start\n") # Step 1: Import the reference implementation print("Step 1: Import reference implementation") from reference_implementation import OP_CAT_IPFS_Reference print("✅ Imported\n") # Step 2: Create integration instance print("Step 2: Create integration instance") integrator = OP_CAT_IPFS_Reference() print("✅ Integration instance created\n") # Step 3: Prepare your content print("Step 3: Prepare content") my_content = b"Hello, Starlight world!" print(f"Content: {my_content}") print(f"Size: {len(my_content)} bytes") print("✅ Content prepared\n") # Step 4: Integrate content print("Step 4: Integrate content") result = integrator.integrate_content(my_content) print(f"Success: {result['success']}") print(f"Strategy: {result['data']['strategy']}") print(f"CID: {result['data']['content']['cid']}") print("✅ Integration completed\n") # Step 5: Use the script print("Step 5: Generated script") script = result['data']['script'] print(f"Script version: {script['version']}") print(f"Operations: {len(script['operations'])}") print("✅ Script ready for use\n") print("🎉 Quick start completed!") print("You're ready to use OP_CAT-IPFS integration!") return result if __name__ == "__main__": quick_start() ``` --- This comprehensive tutorial collection provides everything developers need to implement OP_CAT-IPFS integration, from basic concepts to advanced production-ready implementations.