from pathlib import Path
from typing import List, Optional, Dict, Any, Union, Set
from dataclasses import dataclass
from enum import Enum
import json
from pydantic import BaseModel, Field
class DocumentType(str, Enum):
TUTORIAL = "tutorial"
REFERENCE = "reference"
EXAMPLE = "example"
CONCEPT = "concept"
class CodeBlock(BaseModel):
language: str
content: str
line_start: int
line_end: int
class DocumentMetadata(BaseModel):
file_path: str
title: Optional[str] = None
headings: List[str] = Field(default_factory=list)
code_blocks: List[CodeBlock] = Field(default_factory=list)
links: List[str] = Field(default_factory=list)
word_count: int = 0
class DocumentClassification(BaseModel):
file_path: str
doc_type: DocumentType
confidence: float
reasoning: str
class DependencyRelation(BaseModel):
concept: str
prerequisites: List[str] = Field(default_factory=list)
confidence: float
evidence: str
class AnalyzedDocument(BaseModel):
metadata: DocumentMetadata
classification: DocumentClassification
content: str
dependencies: List[DependencyRelation] = Field(default_factory=list)
summary: str = ""
class DocumentChunk(BaseModel):
"""Represents a chunk with contextual information"""
id: str
content: str
contextual_content: str # Original content + document summary
metadata: Dict[str, Any]
class QueryResult(BaseModel):
"""Result from vector database query"""
chunk: DocumentChunk
score: float
class LearningModule(BaseModel):
"""A learning module covering a specific topic"""
title: str
tutorial_content: List[str] = Field(default_factory=list) # chunk IDs
concept_content: List[str] = Field(default_factory=list) # chunk IDs
example_content: List[str] = Field(default_factory=list) # chunk IDs
reference_content: List[str] = Field(default_factory=list) # chunk IDs
estimated_time: int = 0 # minutes
content_summary: str = ""
class LearningPath(BaseModel):
"""Complete learning path with ordered modules"""
modules: List[LearningModule]
difficulty_level: str = "intermediate"
total_time: int = 0
module_count: int = 0
discovery_reasoning: str = ""
ordering_reasoning: str = ""
content_gaps: List[str] = Field(default_factory=list)
class ContentSummary(BaseModel):
"""Summary of available documentation content"""
total_docs: int
doc_types: Dict[str, int] # {"tutorial": 5, "concept": 10, ...}
main_topics: List[str]
sample_titles: List[str]
class CodeExample(BaseModel):
"""A code example with explanation"""
title: str
code: str
language: str
explanation: str
difficulty_level: str
class Exercise(BaseModel):
"""A practical exercise for learners"""
title: str
description: str
instructions: List[str]
hints: List[str] = Field(default_factory=list)
expected_outcome: str
difficulty_level: str
class AssessmentQuestion(BaseModel):
"""An assessment question to test understanding"""
question: str
question_type: str # multiple_choice, true_false, short_answer, code_completion
options: List[str] = Field(default_factory=list) # For multiple choice
correct_answer: str
explanation: str
difficulty_level: str
class LearningObjective(BaseModel):
"""A specific learning objective"""
objective: str
bloom_level: str # remember, understand, apply, analyze, evaluate, create
measurable_outcome: str
class GeneratedContent(BaseModel):
"""Complete generated content for a learning module"""
module_title: str
learning_objectives: List[LearningObjective]
lesson_text: str
code_examples: List[CodeExample]
exercises: List[Exercise]
assessment_questions: List[AssessmentQuestion]
estimated_time: int
prerequisites: List[str] = Field(default_factory=list)
key_concepts: List[str] = Field(default_factory=list)
class ContentChunk(BaseModel):
"""Content chunk retrieved from vector DB"""
id: str
content: str
doc_type: str
title: str
metadata: Dict[str, Any]
