tegwick
5143864a86
- Created extensible BaseQueryParadigm interface with standardized QueryResult format - Implemented QueryParadigmRegistry for paradigm discovery and management - Added 5 working paradigms: SQL, FTS, GraphQL, JSONPath, Natural Language - Documented 9 additional paradigms: QBE, Batch Manipulation, Visual Query Builder, REST API, NoSQL, UNIX Pipeline, XPath/XQuery, RAG, Data Transformation - Integrated full CLI interface: list, search, show, exec, categories commands - Added comprehensive test suite with 23 test cases covering all components - Auto-registration system enables easy addition of new paradigms - Organized paradigms by category (structural, textual, semantic, visual, procedural, network) and complexity (beginner, intermediate, advanced) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
"""
|
|
Retrieval-Augmented Generation (RAG) Paradigm - LLM + Vector Database.
|
|
"""
|
|
|
|
import time
|
|
from typing import Dict, Any, List, Optional
|
|
|
|
from ..base import BaseQueryParadigm, QueryResult
|
|
|
|
|
|
class RAGParadigm(BaseQueryParadigm):
|
|
"""RAG paradigm for LLM-powered semantic search and generation."""
|
|
|
|
@property
|
|
def name(self) -> str:
|
|
return "RAG (Retrieval-Augmented Generation)"
|
|
|
|
@property
|
|
def description(self) -> str:
|
|
return "Large Language Model retrieves relevant facts from vector database for enhanced responses"
|
|
|
|
@property
|
|
def category(self) -> str:
|
|
return "semantic"
|
|
|
|
@property
|
|
def complexity(self) -> str:
|
|
return "advanced"
|
|
|
|
def execute(self, query: str, config: Dict[str, Any] = None) -> QueryResult:
|
|
"""Execute RAG query (not yet implemented)."""
|
|
start_time = time.time()
|
|
execution_time = (time.time() - start_time) * 1000
|
|
|
|
return QueryResult(
|
|
paradigm=self.name,
|
|
query=query,
|
|
execution_time_ms=execution_time,
|
|
result_count=0,
|
|
results=[],
|
|
metadata={
|
|
"status": "not_implemented",
|
|
"implementation_issue": "TBD - to be created",
|
|
"description": "RAG combines semantic search with LLM generation for intelligent responses"
|
|
},
|
|
success=False,
|
|
error_message="RAG paradigm not yet implemented."
|
|
)
|
|
|
|
def get_examples(self) -> List[Dict[str, str]]:
|
|
"""Get example RAG queries."""
|
|
return [
|
|
{
|
|
"name": "Semantic question",
|
|
"description": "Ask questions about content semantically",
|
|
"query": "What are the main configuration options for the API?"
|
|
},
|
|
{
|
|
"name": "Summarization request",
|
|
"description": "Generate summaries of related content",
|
|
"query": "Summarize all documentation about authentication methods"
|
|
},
|
|
{
|
|
"name": "Code explanation",
|
|
"description": "Explain code patterns found in files",
|
|
"query": "Explain the error handling patterns used in the codebase"
|
|
},
|
|
{
|
|
"name": "Comparative analysis",
|
|
"description": "Compare different approaches in documentation",
|
|
"query": "Compare the database migration strategies mentioned in the docs"
|
|
}
|
|
]
|
|
|
|
def validate_query(self, query: str) -> tuple[bool, Optional[str]]:
|
|
"""Validate RAG query."""
|
|
if not query.strip():
|
|
return False, "RAG query cannot be empty"
|
|
|
|
if len(query.strip()) < 10:
|
|
return False, "RAG query should be a descriptive question or request"
|
|
|
|
return True, None
|
|
|
|
def get_syntax_help(self) -> str:
|
|
"""Get syntax help for RAG queries."""
|
|
return """RAG (Retrieval-Augmented Generation) Syntax:
|
|
|
|
RAG queries are natural language questions or requests that combine:
|
|
1. Semantic retrieval from vector database
|
|
2. LLM generation for comprehensive answers
|
|
|
|
Query Types:
|
|
- Questions: "What is...?", "How does...?", "Why...?"
|
|
- Summaries: "Summarize...", "Overview of..."
|
|
- Comparisons: "Compare...", "Differences between..."
|
|
- Analysis: "Analyze...", "Explain the pattern..."
|
|
|
|
Examples:
|
|
"What are the main API endpoints and their purposes?"
|
|
"Summarize the security best practices mentioned in the documentation"
|
|
"How do I configure the database connection?"
|
|
"Compare SQL vs NoSQL approaches discussed in the docs"
|
|
|
|
The system will:
|
|
1. Convert your query to vector embeddings
|
|
2. Retrieve relevant document chunks
|
|
3. Generate a comprehensive response using LLM
|
|
4. Provide source citations
|
|
""" |