# Semantic Syntax Generator for Groggy - Comprehensive Plan

## 🎯 **Executive Summary**

Create a **Meta API Graph** system that converts semantic intents into correct groggy syntax by treating the API itself as a navigable graph where object types are nodes and methods are edges. This enables LLM agents to discover and use correct syntax patterns through semantic reasoning rather than memorized examples.

## 🧠 **Core Concept: API as a Graph**

### **The Meta API Graph Model**
```
Graph → [add_node] → Graph
Graph → [nodes] → NodesAccessor  
NodesAccessor → [filter] → NodesAccessor
NodesAccessor → [table] → GraphTable
NodesAccessor → [subgraph] → Subgraph
GraphTable → [rename] → GraphTable
GraphTable → [to_dict] → Dict
Subgraph → [centrality] → Dict
```

**Key Insight**: Every API operation is a **transformation** from one object state to another. The challenge is finding the optimal path through this transformation space to achieve a semantic goal.

## 🏗️ **System Architecture**

### **Component Overview**
```
┌─────────────────┐    ┌──────────────────┐    ┌────────────────┐
│   Semantic      │    │   Meta API       │    │   Code         │
│   Intent        │───▶│   Navigator      │───▶│   Generator    │
│   Parser        │    │                  │    │                │
└─────────────────┘    └──────────────────┘    └────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌──────────────────┐    ┌────────────────┐
│"filter nodes    │    │ Graph→filter→    │    │ g.nodes.filter │
│ by score > 0.8" │    │ subgraph→analyze │    │ ('score','>',  │
│                 │    │                  │    │  0.8).subgraph │
└─────────────────┘    └──────────────────┘    └────────────────┘
```

---

## 📊 **Component 1: Meta API Graph Construction**

### **API Graph Data Structure**
```python
@dataclass
class APINode:
    """Represents an object type in the API"""
    type_name: str              # "Graph", "NodesAccessor", "GraphTable"
    description: str            # Human-readable description
    capabilities: List[str]     # What this type can do semantically
    attributes: Dict[str, str]  # Available attributes and their types
    common_use_cases: List[str] # Typical usage patterns

@dataclass  
class APIEdge:
    """Represents a method/transformation in the API"""
    method_name: str            # "filter", "table", "centrality"
    source_type: str           # Input object type
    target_type: str           # Output object type
    parameters: List[Parameter] # Method parameters with types and defaults
    semantic_tags: List[str]   # ["filtering", "analysis", "transformation"]
    examples: List[str]        # Usage examples
    complexity: float          # Estimated complexity/cost (0.0-1.0)
    frequency: float           # How often this method is used (0.0-1.0)

class MetaAPIGraph:
    """The complete API graph with semantic navigation"""
    def __init__(self):
        self.nodes: Dict[str, APINode] = {}
        self.edges: List[APIEdge] = []
        self.semantic_index: Dict[str, List[APIEdge]] = {}
        self.similarity_matrix: np.ndarray = None
    
    def find_paths(self, 
                   start_type: str, 
                   semantic_goal: str, 
                   constraints: Dict = None) -> List[List[APIEdge]]:
        """Find all valid paths to achieve a semantic goal"""
        pass
    
    def rank_paths(self, 
                   paths: List[List[APIEdge]], 
                   context: Dict) -> List[Tuple[List[APIEdge], float]]:
        """Rank paths by relevance, simplicity, and context fit"""
        pass
```

### **API Graph Construction Algorithm**
```python
def build_groggy_api_graph() -> MetaAPIGraph:
    """Automatically construct the API graph from groggy codebase"""
    graph = MetaAPIGraph()
    
    # 1. Extract object types from Python API
    object_types = extract_api_classes("python-groggy/python/groggy/")
    
    # 2. Analyze method signatures and return types
    for cls in object_types:
        node = APINode(
            type_name=cls.__name__,
            description=extract_docstring_summary(cls),
            capabilities=extract_semantic_capabilities(cls),
            attributes=extract_class_attributes(cls)
        )
        graph.add_node(node)
        
        # Extract methods as edges
        for method in cls.__dict__.values():
            if callable(method) and not method.__name__.startswith('_'):
                edge = analyze_method_signature(method, cls.__name__)
                graph.add_edge(edge)
    
    # 3. Build semantic index
    graph.build_semantic_index()
    
    # 4. Compute similarity matrix using embeddings
    graph.compute_semantic_similarities()
    
    return graph

def extract_semantic_capabilities(cls) -> List[str]:
    """Extract what this class can do semantically"""
    capabilities = []
    
    # Analyze method names for semantic patterns
    methods = [m for m in dir(cls) if not m.startswith('_')]
    
    if any('filter' in m for m in methods):
        capabilities.append("filtering")
    if any('add' in m for m in methods):
        capabilities.append("creation")
    if any(m in ['centrality', 'degree', 'neighbors'] for m in methods):
        capabilities.append("analysis")
    if any(m in ['table', 'to_dict', 'export'] for m in methods):
        capabilities.append("data_export")
    if any(m in ['subgraph', 'neighborhood'] for m in methods):
        capabilities.append("subgraph_operations")
    
    return capabilities
```

---

## 🧩 **Component 2: Semantic Intent Parser**

### **Intent Classification System**
```python
class SemanticIntentParser:
    """Parse natural language intents into structured queries"""
    
    def __init__(self):
        self.intent_classifier = self.load_intent_model()
        self.entity_extractor = self.load_entity_model()
        self.constraint_parser = ConstraintParser()
    
    def parse(self, natural_language: str) -> SemanticIntent:
        """Convert natural language to structured intent"""
        
        # 1. Classify the primary intent
        primary_intent = self.classify_intent(natural_language)
        
        # 2. Extract entities (what to operate on)
        entities = self.extract_entities(natural_language)
        
        # 3. Parse constraints and conditions
        constraints = self.constraint_parser.parse(natural_language)
        
        # 4. Determine desired output format
        output_format = self.infer_output_format(natural_language)
        
        return SemanticIntent(
            primary_intent=primary_intent,
            entities=entities,
            constraints=constraints,
            output_format=output_format,
            raw_text=natural_language
        )

@dataclass
class SemanticIntent:
    primary_intent: str         # "filter", "analyze", "create", "transform"
    entities: List[str]         # ["nodes", "edges", "attributes"]
    constraints: List[Constraint] # [{"attr": "score", "op": ">", "value": 0.8}]
    output_format: str          # "table", "dict", "subgraph", "visualization"
    raw_text: str              # Original input
    
    def to_query(self) -> Dict:
        """Convert to API graph query format"""
        return {
            "intent": self.primary_intent,
            "target_entities": self.entities,
            "filters": [c.to_dict() for c in self.constraints],
            "desired_output": self.output_format
        }

# Intent classification patterns
INTENT_PATTERNS = {
    "filter": [
        r"filter .* by",
        r"get .* where",
        r"find .* with", 
        r"select .* that",
        r".* greater than",
        r".* less than"
    ],
    "analyze": [
        r"calculate .*",
        r"compute .*",
        r"analyze .*",
        r"centrality",
        r"clustering",
        r"degree",
        r"importance"
    ],
    "create": [
        r"add .*",
        r"create .*",
        r"build .*",
        r"generate .*"
    ],
    "transform": [
        r"convert .* to",
        r"export .* as",
        r"transform .*",
        r"format .* as"
    ],
    "explore": [
        r"show me .*",
        r"what are .*",
        r"list .*",
        r"display .*"
    ]
}
```

### **Advanced Constraint Parsing**
```python
class ConstraintParser:
    """Parse complex constraints from natural language"""
    
    def parse(self, text: str) -> List[Constraint]:
        constraints = []
        
        # Numerical constraints
        numerical_patterns = [
            r"(\w+)\s*(>|>=|<|<=|==|!=)\s*([0-9.]+)",
            r"(\w+)\s+greater than\s+([0-9.]+)",
            r"(\w+)\s+less than\s+([0-9.]+)",
            r"(\w+)\s+equals?\s+([0-9.]+)"
        ]
        
        # Categorical constraints  
        categorical_patterns = [
            r"(\w+)\s+is\s+(['\"]?\w+['\"]?)",
            r"(\w+)\s+equals?\s+(['\"]?\w+['\"]?)",
            r"type\s+(\w+)",
            r"category\s+(\w+)"
        ]
        
        # Range constraints
        range_patterns = [
            r"(\w+)\s+between\s+([0-9.]+)\s+and\s+([0-9.]+)",
            r"([0-9.]+)\s*<\s*(\w+)\s*<\s*([0-9.]+)"
        ]
        
        # Apply all patterns and build constraints
        for pattern_group in [numerical_patterns, categorical_patterns, range_patterns]:
            for pattern in pattern_group:
                matches = re.finditer(pattern, text, re.IGNORECASE)
                for match in matches:
                    constraint = self.build_constraint_from_match(match, pattern)
                    if constraint:
                        constraints.append(constraint)
        
        return constraints
    
    def build_constraint_from_match(self, match, pattern) -> Optional[Constraint]:
        """Build a constraint object from regex match"""
        # Implementation depends on pattern type
        pass

@dataclass
class Constraint:
    attribute: str
    operator: str  # ">", "==", "in", "between", etc.
    value: Any
    value_type: str  # "numeric", "categorical", "range"
    
    def to_groggy_filter(self) -> str:
        """Convert to groggy filter syntax"""
        if self.operator in [">", ">=", "<", "<=", "==", "!="]:
            return f"filter('{self.attribute}', '{self.operator}', {self.value})"
        elif self.operator == "in":
            return f"filter('{self.attribute}', 'in', {list(self.value)})"
        elif self.operator == "between":
            low, high = self.value
            return f"filter('{self.attribute}', '>=', {low}).filter('{self.attribute}', '<=', {high})"
        else:
            raise ValueError(f"Unsupported operator: {self.operator}")
```

---

## 🗺️ **Component 3: API Graph Navigator**

### **Path Finding Algorithm**
```python
class APINavigator:
    """Navigate the API graph to find optimal transformation paths"""
    
    def __init__(self, api_graph: MetaAPIGraph):
        self.graph = api_graph
        self.semantic_embeddings = self.load_semantic_embeddings()
    
    def find_transformation_paths(self, 
                                 intent: SemanticIntent,
                                 start_type: str = "Graph",
                                 max_depth: int = 5) -> List[TransformationPath]:
        """Find all valid paths to achieve the semantic intent"""
        
        target_capabilities = self.intent_to_capabilities(intent)
        valid_paths = []
        
        # BFS through API graph with semantic scoring
        queue = [(start_type, [], 0.0)]  # (current_type, path, score)
        visited = set()
        
        while queue:
            current_type, path, score = queue.pop(0)
            
            if len(path) > max_depth:
                continue
                
            if current_type in visited and len(path) > 2:
                continue
            visited.add(current_type)
            
            # Check if current state can satisfy the intent
            if self.can_satisfy_intent(current_type, intent):
                valid_paths.append(TransformationPath(
                    path=path,
                    final_type=current_type,
                    semantic_score=score,
                    estimated_complexity=self.estimate_complexity(path)
                ))
            
            # Explore next steps
            for edge in self.graph.get_edges_from_type(current_type):
                semantic_score = self.compute_semantic_score(edge, target_capabilities)
                new_score = score + semantic_score
                new_path = path + [edge]
                
                queue.append((edge.target_type, new_path, new_score))
        
        # Rank and return best paths
        return self.rank_paths(valid_paths, intent)
    
    def intent_to_capabilities(self, intent: SemanticIntent) -> List[str]:
        """Convert semantic intent to required API capabilities"""
        capabilities = []
        
        intent_mapping = {
            "filter": ["filtering"],
            "analyze": ["analysis", "computation"],
            "create": ["creation", "modification"],
            "transform": ["data_export", "format_conversion"],
            "explore": ["data_access", "visualization"]
        }
        
        capabilities.extend(intent_mapping.get(intent.primary_intent, []))
        
        # Add entity-specific capabilities
        if "nodes" in intent.entities:
            capabilities.append("node_operations")
        if "edges" in intent.entities:
            capabilities.append("edge_operations")
        if intent.output_format == "table":
            capabilities.append("tabular_export")
        
        return capabilities
    
    def compute_semantic_score(self, edge: APIEdge, target_capabilities: List[str]) -> float:
        """Score how well an edge matches the target capabilities"""
        edge_tags = set(edge.semantic_tags)
        target_tags = set(target_capabilities)
        
        # Jaccard similarity between edge tags and target capabilities
        intersection = len(edge_tags & target_tags)
        union = len(edge_tags | target_tags)
        
        if union == 0:
            return 0.0
        
        semantic_score = intersection / union
        
        # Boost score for common operations
        frequency_boost = edge.frequency * 0.2
        
        # Penalty for complexity
        complexity_penalty = edge.complexity * 0.1
        
        return semantic_score + frequency_boost - complexity_penalty

@dataclass
class TransformationPath:
    path: List[APIEdge]
    final_type: str
    semantic_score: float
    estimated_complexity: float
    
    def to_code(self, variable_name: str = "g") -> str:
        """Generate code from the transformation path"""
        code = variable_name
        
        for edge in self.path:
            method_call = self.edge_to_method_call(edge)
            code += f".{method_call}"
        
        return code
    
    def edge_to_method_call(self, edge: APIEdge) -> str:
        """Convert an API edge to a method call string"""
        # This would need to be sophisticated enough to fill in parameters
        # based on the semantic intent
        pass
```

---

## 🔧 **Component 4: Context-Aware Code Generation**

### **Intelligent Parameter Inference**
```python
class CodeGenerator:
    """Generate executable code from transformation paths"""
    
    def __init__(self, api_graph: MetaAPIGraph):
        self.graph = api_graph
        self.parameter_inferrer = ParameterInferrer()
        self.template_engine = CodeTemplateEngine()
    
    def generate_code(self, 
                     path: TransformationPath, 
                     intent: SemanticIntent,
                     context: Dict = None) -> GeneratedCode:
        """Generate complete, executable code"""
        
        code_blocks = []
        variable_stack = ["g"]  # Track variable names through transformations
        
        for i, edge in enumerate(path.path):
            # Infer parameters for this method call
            params = self.parameter_inferrer.infer_parameters(
                edge=edge,
                intent=intent,
                context=context,
                position_in_path=i
            )
            
            # Generate method call
            current_var = variable_stack[-1]
            method_call = self.build_method_call(edge, params, current_var)
            
            # Decide if we need a new variable
            if self.should_create_variable(edge, i, len(path.path)):
                new_var = f"result_{i}"
                code_blocks.append(f"{new_var} = {method_call}")
                variable_stack.append(new_var)
            else:
                if i == len(path.path) - 1:  # Last operation
                    code_blocks.append(f"result = {method_call}")
                else:
                    # Chain the operation
                    variable_stack[-1] = method_call
        
        # Add any post-processing based on intent
        post_processing = self.generate_post_processing(intent, path.final_type)
        code_blocks.extend(post_processing)
        
        return GeneratedCode(
            main_code="\n".join(code_blocks),
            imports=self.get_required_imports(path),
            explanation=self.generate_explanation(path, intent),
            alternatives=self.generate_alternatives(path, intent)
        )

class ParameterInferrer:
    """Intelligently infer method parameters from context"""
    
    def infer_parameters(self, 
                        edge: APIEdge, 
                        intent: SemanticIntent,
                        context: Dict,
                        position_in_path: int) -> Dict[str, Any]:
        """Infer the best parameters for a method call"""
        
        params = {}
        
        # Process each parameter of the method
        for param in edge.parameters:
            if param.name in ["self"]:  # Skip self parameter
                continue
                
            # Try different inference strategies
            value = None
            
            # 1. Direct mapping from constraints
            if param.semantic_role == "filter_attribute":
                value = self.infer_from_constraints(param, intent.constraints)
            
            # 2. Context-based inference
            elif param.semantic_role == "column_name":
                value = self.infer_column_name(param, context)
            
            # 3. Default value inference
            elif param.has_default:
                value = param.default_value
            
            # 4. Type-based inference
            else:
                value = self.infer_from_type(param, intent, context)
            
            if value is not None:
                params[param.name] = value
        
        return params
    
    def infer_from_constraints(self, param, constraints) -> Any:
        """Infer parameter value from semantic constraints"""
        for constraint in constraints:
            if param.semantic_role == "filter_attribute":
                return constraint.attribute
            elif param.semantic_role == "filter_operator":
                return constraint.operator
            elif param.semantic_role == "filter_value":
                return constraint.value
        return None

@dataclass
class GeneratedCode:
    main_code: str
    imports: List[str]
    explanation: str
    alternatives: List[str]
    
    def to_executable(self) -> str:
        """Create complete executable code block"""
        lines = []
        
        if self.imports:
            lines.extend(self.imports)
            lines.append("")
        
        lines.append(self.main_code)
        
        return "\n".join(lines)
    
    def to_markdown(self) -> str:
        """Format as markdown with explanation"""
        md = f"""## Generated Code

```python
{self.to_executable()}
```

## Explanation
{self.explanation}

## Alternative Approaches
"""
        for alt in self.alternatives:
            md += f"- {alt}\n"
        
        return md
```

---

## 🌐 **Component 5: MCP Server Implementation**

### **MCP Server for LLM Integration**
```python
from mcp.server import Server
from mcp.types import Tool, TextContent

class GroggySemanticServer:
    """MCP server exposing semantic syntax generation"""
    
    def __init__(self):
        self.server = Server("groggy-semantic")
        self.api_graph = build_groggy_api_graph()
        self.intent_parser = SemanticIntentParser()
        self.navigator = APINavigator(self.api_graph)
        self.code_generator = CodeGenerator(self.api_graph)
        
        self.setup_tools()
    
    def setup_tools(self):
        """Register MCP tools"""
        
        @self.server.tool()
        async def generate_groggy_syntax(
            intent: str,
            context: str = "",
            max_complexity: float = 0.8,
            num_alternatives: int = 3
        ) -> str:
            """Generate groggy syntax from semantic intent
            
            Args:
                intent: Natural language description of what to do
                context: Additional context about the data/graph
                max_complexity: Maximum complexity threshold (0.0-1.0)
                num_alternatives: Number of alternative approaches to generate
            """
            try:
                # Parse the semantic intent
                parsed_intent = self.intent_parser.parse(intent)
                
                # Find transformation paths
                paths = self.navigator.find_transformation_paths(
                    intent=parsed_intent,
                    start_type="Graph"
                )
                
                # Filter by complexity
                paths = [p for p in paths if p.estimated_complexity <= max_complexity]
                
                if not paths:
                    return "No suitable transformation paths found. Try simplifying the request."
                
                # Generate code for the best path
                best_path = paths[0]
                context_dict = self.parse_context(context)
                
                generated = self.code_generator.generate_code(
                    path=best_path,
                    intent=parsed_intent,
                    context=context_dict
                )
                
                # Add alternatives
                alternatives = []
                for path in paths[1:num_alternatives]:
                    alt_code = self.code_generator.generate_code(
                        path=path,
                        intent=parsed_intent,
                        context=context_dict
                    )
                    alternatives.append(alt_code.main_code)
                
                generated.alternatives = alternatives
                
                return generated.to_markdown()
                
            except Exception as e:
                return f"Error generating syntax: {str(e)}"
        
        @self.server.tool()
        async def validate_groggy_syntax(
            code: str,
            suggest_improvements: bool = True
        ) -> str:
            """Validate and improve groggy syntax
            
            Args:
                code: Python code using groggy
                suggest_improvements: Whether to suggest improvements
            """
            try:
                validator = GroggyCodeValidator(self.api_graph)
                result = validator.validate(code)
                
                if suggest_improvements:
                    improvements = validator.suggest_improvements(code)
                    result.suggestions = improvements
                
                return result.to_report()
                
            except Exception as e:
                return f"Error validating syntax: {str(e)}"
        
        @self.server.tool()
        async def explore_groggy_api(
            object_type: str = "Graph",
            capability: str = "",
            max_depth: int = 3
        ) -> str:
            """Explore available operations in groggy API
            
            Args:
                object_type: Starting object type to explore from
                capability: Semantic capability to focus on
                max_depth: Maximum exploration depth
            """
            try:
                explorer = APIExplorer(self.api_graph)
                result = explorer.explore_from_type(
                    start_type=object_type,
                    capability_filter=capability,
                    max_depth=max_depth
                )
                
                return result.to_markdown()
                
            except Exception as e:
                return f"Error exploring API: {str(e)}"

    def parse_context(self, context_str: str) -> Dict:
        """Parse context string into structured context"""
        context = {}
        
        # Extract common context patterns
        patterns = {
            r"graph has (\d+) nodes": "node_count",
            r"graph has (\d+) edges": "edge_count", 
            r"node attributes: ([^,]+)": "node_attributes",
            r"edge attributes: ([^,]+)": "edge_attributes",
            r"graph type: (\w+)": "graph_type"
        }
        
        for pattern, key in patterns.items():
            match = re.search(pattern, context_str, re.IGNORECASE)
            if match:
                context[key] = match.group(1)
        
        return context

# Server startup
async def main():
    server = GroggySemanticServer()
    await server.server.run()

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())
```

---

## 🧪 **Component 6: Training Data Generation**

### **Synthetic Training Data Pipeline**
```python
class TrainingDataGenerator:
    """Generate training data for the semantic intent parser"""
    
    def __init__(self, api_graph: MetaAPIGraph):
        self.api_graph = api_graph
        self.intent_templates = self.load_intent_templates()
        self.synthetic_graph_generator = SyntheticGraphGenerator()
    
    def generate_training_dataset(self, num_samples: int = 10000) -> Dataset:
        """Generate comprehensive training dataset"""
        
        samples = []
        
        # Generate samples for each intent type
        intent_types = ["filter", "analyze", "create", "transform", "explore"]
        samples_per_intent = num_samples // len(intent_types)
        
        for intent_type in intent_types:
            intent_samples = self.generate_intent_samples(
                intent_type=intent_type,
                num_samples=samples_per_intent
            )
            samples.extend(intent_samples)
        
        return Dataset.from_list(samples)
    
    def generate_intent_samples(self, intent_type: str, num_samples: int) -> List[Dict]:
        """Generate samples for a specific intent type"""
        samples = []
        
        templates = self.intent_templates[intent_type]
        
        for _ in range(num_samples):
            # Choose random template
            template = random.choice(templates)
            
            # Generate synthetic graph context
            graph_context = self.synthetic_graph_generator.generate()
            
            # Fill template with context
            natural_language = self.fill_template(template, graph_context)
            
            # Generate corresponding groggy code
            groggy_code = self.generate_corresponding_code(
                intent_type=intent_type,
                context=graph_context,
                natural_language=natural_language
            )
            
            samples.append({
                "natural_language": natural_language,
                "intent_type": intent_type,
                "groggy_code": groggy_code,
                "context": graph_context,
                "semantic_tags": self.extract_semantic_tags(natural_language)
            })
        
        return samples

# Intent templates for different operations
INTENT_TEMPLATES = {
    "filter": [
        "Find all {entity_type} with {attribute} {operator} {value}",
        "Get {entity_type} where {attribute} is {operator} {value}",
        "Filter {entity_type} by {attribute} {operator} {value}",
        "Show me {entity_type} that have {attribute} {operator} {value}"
    ],
    "analyze": [
        "Calculate {metric} for the graph",
        "Compute {metric} of all {entity_type}",
        "Analyze the {metric} distribution",
        "What is the {metric} of {entity_type}?"
    ],
    "create": [
        "Add a {entity_type} with {attributes}",
        "Create a new {entity_type} that {description}",
        "Insert {entity_type} with the following properties: {attributes}"
    ],
    "transform": [
        "Convert the {entity_type} to a {output_format}",
        "Export {entity_type} as {output_format}",
        "Transform the data into {output_format} format"
    ],
    "explore": [
        "Show me the {entity_type}",
        "What {entity_type} are in the graph?",
        "List all {attribute} values for {entity_type}",
        "Display the {entity_type} data"
    ]
}
```

---

## 🎛️ **Component 7: Real-time Learning System**

### **Usage Pattern Learning**
```python
class UsagePatternLearner:
    """Learn from actual usage patterns to improve suggestions"""
    
    def __init__(self):
        self.usage_database = UsageDatabase()
        self.pattern_analyzer = PatternAnalyzer()
        self.feedback_processor = FeedbackProcessor()
    
    async def record_usage(self, 
                          natural_language: str,
                          generated_code: str,
                          user_feedback: str,
                          execution_success: bool):
        """Record usage pattern for learning"""
        
        usage_record = UsageRecord(
            timestamp=datetime.now(),
            intent=natural_language,
            generated_code=generated_code,
            feedback=user_feedback,
            success=execution_success,
            user_id=self.get_user_id()
        )
        
        await self.usage_database.store(usage_record)
        
        # Update patterns in real-time
        await self.update_patterns(usage_record)
    
    async def update_patterns(self, record: UsageRecord):
        """Update internal patterns based on new usage"""
        
        # Extract patterns from successful usage
        if record.success:
            intent_pattern = self.pattern_analyzer.extract_intent_pattern(record.intent)
            code_pattern = self.pattern_analyzer.extract_code_pattern(record.generated_code)
            
            # Update intent-to-code mappings
            await self.usage_database.update_pattern_frequency(
                intent_pattern=intent_pattern,
                code_pattern=code_pattern,
                success_weight=1.0
            )
        
        # Learn from failures
        else:
            failure_pattern = self.pattern_analyzer.extract_failure_pattern(record)
            await self.usage_database.record_failure_pattern(failure_pattern)
    
    async def get_improved_suggestions(self, intent: str) -> List[str]:
        """Get suggestions improved by learned patterns"""
        
        # Find similar historical intents
        similar_intents = await self.usage_database.find_similar_intents(
            intent=intent,
            similarity_threshold=0.8
        )
        
        # Extract successful patterns
        successful_patterns = [
            record.generated_code for record in similar_intents 
            if record.success
        ]
        
        # Rank by success rate and frequency
        ranked_suggestions = self.rank_by_success_metrics(successful_patterns)
        
        return ranked_suggestions[:5]  # Top 5 suggestions

class UsageDatabase:
    """Database for storing and querying usage patterns"""
    
    def __init__(self):
        self.vector_store = ChromaDB()  # For semantic similarity
        self.sql_store = SQLiteDatabase()  # For structured queries
    
    async def find_similar_intents(self, intent: str, similarity_threshold: float) -> List[UsageRecord]:
        """Find historically similar intents using vector similarity"""
        
        # Get intent embedding
        intent_embedding = self.get_embedding(intent)
        
        # Search vector store
        similar_records = self.vector_store.similarity_search(
            query_vector=intent_embedding,
            threshold=similarity_threshold,
            limit=50
        )
        
        return [self.sql_store.get_record(record_id) for record_id in similar_records]
```

---

## 🚀 **Implementation Roadmap**

### **Phase 1: Foundation (Weeks 1-4)**
```yaml
Week 1: API Graph Construction
  - Extract groggy API structure automatically
  - Build initial Meta API Graph
  - Create basic semantic tagging

Week 2: Intent Parser Development  
  - Implement basic intent classification
  - Build constraint parsing system
  - Create entity extraction pipeline

Week 3: Path Finding Algorithm
  - Implement BFS path finding in API graph
  - Add semantic scoring system
  - Create path ranking algorithms

Week 4: Basic Code Generation
  - Build template-based code generator
  - Implement parameter inference
  - Create basic MCP server
```

### **Phase 2: Intelligence (Weeks 5-8)**
```yaml
Week 5: Advanced Semantic Understanding
  - Add embedding-based similarity
  - Implement context-aware parameter inference
  - Enhance constraint parsing with ML

Week 6: Training Data Generation
  - Build synthetic data pipeline
  - Generate comprehensive training dataset
  - Train custom intent classification model

Week 7: Real-time Learning
  - Implement usage pattern recording
  - Build feedback processing system
  - Create pattern learning algorithms

Week 8: MCP Server Enhancement
  - Add advanced validation tools
  - Implement API exploration features
  - Create debugging and explanation tools
```

### **Phase 3: Polish & Production (Weeks 9-12)**
```yaml
Week 9: Performance Optimization
  - Optimize path finding algorithms
  - Cache common patterns
  - Improve response times

Week 10: Comprehensive Testing
  - Test with complex real-world scenarios
  - Validate across different graph types
  - Performance benchmarking

Week 11: Documentation & Examples
  - Create comprehensive usage guide
  - Build example notebooks
  - Document MCP server API

Week 12: Production Deployment
  - Set up monitoring and logging
  - Deploy MCP server
  - Create usage analytics dashboard
```

---

## 🎯 **Expected Outcomes & Success Metrics**

### **Quantitative Metrics**
- **Syntax Accuracy**: >90% of generated code executes without errors
- **Semantic Relevance**: >85% of generated code achieves user intent
- **Path Efficiency**: Average path length <4 method calls for common tasks
- **Response Time**: <500ms for code generation
- **Coverage**: Handle >95% of documented groggy operations

### **Qualitative Improvements**
- **LLM Agent Adoption**: Agents can use groggy effectively without prior training
- **User Experience**: Natural language interface reduces learning curve
- **Code Quality**: Generated code follows best practices and is readable
- **Discoverability**: Users can explore API capabilities through semantic queries

### **Real-world Validation**
```python
# Example successful transformations:

"Find users with high scores and create a social network subgraph"
→ g.nodes.filter('type', '==', 'user').filter('score', '>', 0.8).subgraph()

"Calculate centrality for important nodes and export as table"  
→ g.nodes.filter('importance', '>', 0.5).assign_centrality('betweenness').table()

"Get neighborhood around node X and analyze clustering"
→ g.neighborhood('X', radius=2).clustering_coefficient()

"Create a mapping from node IDs to categories for edge annotation"
→ g.nodes.table()[['id', 'category']].to_dict(keys='id')
```

---

## 🏁 **Conclusion**

This semantic syntax generator would revolutionize how LLM agents interact with domain-specific libraries by:

1. **Treating APIs as navigable graphs** - Making library exploration intuitive
2. **Converting semantic intents to syntax** - Bridging natural language and code
3. **Learning from usage patterns** - Continuously improving suggestions
4. **Providing multiple pathways** - Offering alternatives like pandas flexibility

The system would make groggy as accessible to LLM agents as pandas/numpy, enabling sophisticated graph analytics through natural language interfaces while maintaining the performance and expressiveness of the underlying Rust implementation.

**Key Innovation**: By modeling the API itself as a graph, we create a **meta-representation** that enables semantic navigation through the library's capabilities, transforming how agents discover and compose operations.