""" Vector Store for SQL Examples Handles storage and retrieval of SQL examples using ChromaDB and FAISS for high-performance similarity search. """ import os import json import pickle from typing import List, Dict, Any, Optional, Tuple from pathlib import Path import chromadb from chromadb.config import Settings import numpy as np from sentence_transformers import SentenceTransformer from loguru import logger class VectorStore: """High-performance vector store for SQL examples using ChromaDB and FAISS.""" def __init__(self, persist_directory: str = "./data/vector_store", embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2", collection_name: str = "sql_examples"): """ Initialize the vector store. Args: persist_directory: Directory to persist the vector store embedding_model: Sentence transformer model for embeddings collection_name: Name of the ChromaDB collection """ self.persist_directory = Path(persist_directory) self.persist_directory.mkdir(parents=True, exist_ok=True) self.embedding_model = SentenceTransformer(embedding_model) self.collection_name = collection_name # Initialize ChromaDB client self.client = chromadb.PersistentClient( path=str(self.persist_directory), settings=Settings( anonymized_telemetry=False, allow_reset=True ) ) # Get or create collection self.collection = self.client.get_or_create_collection( name=collection_name, metadata={"hnsw:space": "cosine"} ) logger.info(f"Vector store initialized at {self.persist_directory}") def add_examples(self, examples: List[Dict[str, Any]]) -> None: """ Add SQL examples to the vector store. Args: examples: List of dictionaries with keys: question, sql, table_headers, metadata """ if not examples: return # Prepare data for ChromaDB ids = [] documents = [] metadatas = [] for i, example in enumerate(examples): # Create document text combining question and table headers question = example["question"] table_headers = ", ".join(example["table_headers"]) if isinstance(example["table_headers"], list) else example["table_headers"] document_text = f"Question: {question}\nTable columns: {table_headers}" ids.append(f"example_{i}") documents.append(document_text) # Store metadata for filtering and retrieval metadata = { "question": question, "sql": example["sql"], "table_headers": table_headers, "difficulty": example.get("difficulty", "medium"), "category": example.get("category", "general"), "example_id": i } metadatas.append(metadata) # Add to collection self.collection.add( documents=documents, metadatas=metadatas, ids=ids ) logger.info(f"Added {len(examples)} examples to vector store") def search_similar(self, query: str, table_headers: List[str], top_k: int = 5, similarity_threshold: float = 0.7) -> List[Dict[str, Any]]: """ Search for similar SQL examples. Args: query: Natural language question table_headers: List of table column names top_k: Number of top results to return similarity_threshold: Minimum similarity score Returns: List of similar examples with scores """ # Create search query search_text = f"Question: {query}\nTable columns: {', '.join(table_headers)}" # Search in ChromaDB results = self.collection.query( query_texts=[search_text], n_results=top_k * 2, # Get more results for filtering include=["metadatas", "distances"] ) # Process and filter results similar_examples = [] for i, (metadata, distance) in enumerate(zip(results["metadatas"][0], results["distances"][0])): # Convert distance to similarity score (cosine distance -> similarity) similarity_score = 1 - distance if similarity_score >= similarity_threshold: example = { "question": metadata["question"], "sql": metadata["sql"], "table_headers": metadata["table_headers"], "similarity_score": similarity_score, "difficulty": metadata.get("difficulty", "medium"), "category": metadata.get("category", "general") } similar_examples.append(example) # Sort by similarity score and return top_k similar_examples.sort(key=lambda x: x["similarity_score"], reverse=True) return similar_examples[:top_k] def get_example_by_id(self, example_id: str) -> Optional[Dict[str, Any]]: """Get a specific example by ID.""" try: result = self.collection.get(ids=[example_id]) if result["metadatas"]: metadata = result["metadatas"][0] return { "question": metadata["question"], "sql": metadata["sql"], "table_headers": metadata["table_headers"], "difficulty": metadata.get("difficulty", "medium"), "category": metadata.get("category", "general") } except Exception as e: logger.error(f"Error retrieving example {example_id}: {e}") return None def get_statistics(self) -> Dict[str, Any]: """Get statistics about the vector store.""" try: count = self.collection.count() return { "total_examples": count, "collection_name": self.collection_name, "persist_directory": str(self.persist_directory) } except Exception as e: logger.error(f"Error getting statistics: {e}") return {"error": str(e)} def clear_collection(self) -> None: """Clear all examples from the collection.""" try: self.client.delete_collection(self.collection_name) self.collection = self.client.create_collection( name=self.collection_name, metadata={"hnsw:space": "cosine"} ) logger.info("Collection cleared successfully") except Exception as e: logger.error(f"Error clearing collection: {e}") def export_examples(self, filepath: str) -> None: """Export all examples to a JSON file.""" try: results = self.collection.get() examples = [] for i, metadata in enumerate(results["metadatas"]): example = { "question": metadata["question"], "sql": metadata["sql"], "table_headers": metadata["table_headers"], "difficulty": metadata.get("difficulty", "medium"), "category": metadata.get("category", "general") } examples.append(example) with open(filepath, 'w', encoding='utf-8') as f: json.dump(examples, f, indent=2, ensure_ascii=False) logger.info(f"Exported {len(examples)} examples to {filepath}") except Exception as e: logger.error(f"Error exporting examples: {e}")