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ChatCSV / indexes /query_engine.py

Update indexes/query_engine.py

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from typing import Dict, List, Any, Optional
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class CSVQueryEngine:

    def __init__(self, index_manager, llm):
        """Initialize with index manager and language model."""
        self.index_manager = index_manager
        self.llm = llm

    def _prepare_context(self, query: str, csv_ids: List[str]) -> str:
        """Prepare context from relevant CSV files."""
        context_parts = []
        
        for csv_id in csv_ids:
            # Get metadata
            if csv_id not in self.index_manager.indexes:
                continue
                
            metadata = self.index_manager.indexes[csv_id]["metadata"]
            file_path = self.index_manager.indexes[csv_id]["path"]
            
            # Add CSV metadata
            context_parts.append(f"CSV File: {metadata['filename']}")
            context_parts.append(f"Columns: {', '.join(metadata['columns'])}")
            context_parts.append(f"Row Count: {metadata['row_count']}")
            
            # Add sample data
            try:
                df = pd.read_csv(file_path)
                context_parts.append("\nSample Data:")
                context_parts.append(df.head(5).to_string())
                
                # Add some basic statistics for numeric columns
                context_parts.append("\nNumeric Column Statistics:")
                numeric_cols = df.select_dtypes(include=['number']).columns
                for col in numeric_cols:
                    stats = df[col].describe()
                    context_parts.append(f"{col} - mean: {stats['mean']:.2f}, min: {stats['min']:.2f}, max: {stats['max']:.2f}")
                
                # Add categorical column information
                categorical_cols = df.select_dtypes(include=['object', 'category']).columns
                if len(categorical_cols) > 0:
                    context_parts.append("\nCategorical Column Information:")
                    for col in categorical_cols:
                        value_counts = df[col].value_counts().head(5)
                        context_parts.append(f"{col} - unique values: {df[col].nunique()}, top values: {', '.join(value_counts.index.astype(str))}")
                
                # Add date information if present
                date_cols = []
                for col in df.columns:
                    try:
                        if pd.api.types.is_datetime64_dtype(df[col]) or pd.to_datetime(df[col], errors='coerce').notna().all():
                            date_cols.append(col)
                    except:
                        pass
                
                if date_cols:
                    context_parts.append("\nDate Column Information:")
                    for col in date_cols:
                        if not pd.api.types.is_datetime64_dtype(df[col]):
                            df[col] = pd.to_datetime(df[col], errors='coerce')
                        context_parts.append(f"{col} - range: {df[col].min()} to {df[col].max()}")
                
            except Exception as e:
                context_parts.append(f"Error reading CSV: {str(e)}")
        
        return "\n\n".join(context_parts)
    
    def _generate_prompt(self, query: str, context: str) -> str:
        """Generate a prompt for the LLM."""
        return f"""You are an AI assistant specialized in analyzing CSV data.
        Your goal is to help users understand their data and extract insights.
        
        Below is information about CSV files that might help answer the query:
        
        {context}
        
        User Query: {query}
        
        Please provide a comprehensive and accurate answer based on the data.
        If calculations are needed, explain your process.
        If the data doesn't contain information to answer the query, say so clearly.
        
        Answer:"""

    def query(self, query_text: str) -> Dict[str, Any]:
        """Process a natural language query across CSV files."""
        # Find relevant CSV files
        relevant_csvs = self.index_manager.find_relevant_csvs(query_text)
        
        if not relevant_csvs:
            return {
                "answer": "No relevant CSV files found for your query.",
                "sources": []
            }
        
        # Check for direct statistical queries
        direct_answer = self._handle_statistical_query(query_text, relevant_csvs)
        if direct_answer:
            return {
                "answer": direct_answer,
                "sources": self._get_sources(relevant_csvs)
            }
        
        # If not a direct statistical query, use the regular approach
        context = self._prepare_context(query_text, relevant_csvs)
        prompt = self._generate_prompt(query_text, context)
        response = self.llm.complete(prompt)
        
        return {
            "answer": response.text,
            "sources": self._get_sources(relevant_csvs)
        }
    
    def _get_sources(self, csv_ids: List[str]) -> List[Dict[str, str]]:
        """Get source information for the response."""
        sources = []
        
        for csv_id in csv_ids:
            if csv_id not in self.index_manager.indexes:
                continue
                
            metadata = self.index_manager.indexes[csv_id]["metadata"]
            sources.append({
                "csv": metadata["filename"],
                "columns": ", ".join(metadata["columns"][:5]) + ("..." if len(metadata["columns"]) > 5 else "")
            })
        
        return sources
    
    
    def _handle_statistical_query(self, query: str, csv_ids: List[str]) -> Optional[str]:
        """Handle direct statistical queries without using the LLM."""
        query_lower = query.lower()
        
        # Detect query type
        is_avg_query = "average" in query_lower or "mean" in query_lower or "avg" in query_lower
        is_max_query = "maximum" in query_lower or "max" in query_lower
        is_min_query = "minimum" in query_lower or "min" in query_lower
        is_count_query = "count" in query_lower or "how many" in query_lower
        is_unique_query = "unique" in query_lower or "distinct" in query_lower
        
        if not (is_avg_query or is_max_query or is_min_query or is_count_query or is_unique_query):
            return None  # Not a statistical query
        
        # Extract potential column names from query
        query_words = set(query_lower.replace("?", "").replace(",", "").split())
        
        for csv_id in csv_ids:
            if csv_id not in self.index_manager.indexes:
                continue
                
            file_path = self.index_manager.indexes[csv_id]["path"]
            metadata = self.index_manager.indexes[csv_id]["metadata"]
            
            try:
                df = pd.read_csv(file_path)
                
                # Find relevant columns based on query
                target_columns = []
                for col in df.columns:
                    col_lower = col.lower()
                    # Check if column name appears in query
                    if any(word in col_lower for word in query_words) or col_lower in query_lower:
                        target_columns.append(col)
                
                # If no direct matches but query mentions specific types of data
                if not target_columns:
                    if any(word in query_lower for word in ["age", "old", "young"]):
                        age_cols = [col for col in df.columns if "age" in col.lower()]
                        if age_cols:
                            target_columns = age_cols
                    elif any(word in query_lower for word in ["class", "category", "type", "grade"]):
                        class_cols = [col for col in df.columns if any(term in col.lower() 
                                    for term in ["class", "category", "type", "grade"])]
                        if class_cols:
                            target_columns = class_cols
                    elif any(word in query_lower for word in ["income", "salary", "money", "price", "cost"]):
                        income_cols = [col for col in df.columns if any(term in col.lower() 
                                    for term in ["income", "salary", "wage", "earnings", "price", "cost"])]
                        if income_cols:
                            target_columns = income_cols
                    elif any(word in query_lower for word in ["date", "time", "year", "month", "day"]):
                        date_cols = []
                        for col in df.columns:
                            try:
                                if pd.api.types.is_datetime64_dtype(df[col]) or pd.to_datetime(df[col], errors='coerce').notna().all():
                                    date_cols.append(col)
                            except:
                                pass
                        if date_cols:
                            target_columns = date_cols
                
                # If still no matches, use all columns for count/unique queries,
                # or numeric columns for other statistical queries
                if not target_columns:
                    if is_count_query or is_unique_query:
                        target_columns = df.columns.tolist()
                    else:
                        target_columns = df.select_dtypes(include=['number']).columns.tolist()
                
                # Perform the requested calculation
                results = []
                for col in target_columns:
                    if is_avg_query:
                        if pd.api.types.is_numeric_dtype(df[col]):
                            value = df[col].mean()
                            results.append(f"The average {col} is {value:.2f}")
                    elif is_max_query:
                        if pd.api.types.is_numeric_dtype(df[col]):
                            value = df[col].max()
                            results.append(f"The maximum {col} is {value}")
                        else:
                            # For non-numeric columns, show the maximum in alphabetical order
                            value = df[col].max()
                            results.append(f"The maximum (alphabetically) {col} is '{value}'")
                    elif is_min_query:
                        if pd.api.types.is_numeric_dtype(df[col]):
                            value = df[col].min()
                            results.append(f"The minimum {col} is {value}")
                        else:
                            # For non-numeric columns, show the minimum in alphabetical order
                            value = df[col].min()
                            results.append(f"The minimum (alphabetically) {col} is '{value}'")
                    elif is_count_query:
                        value = len(df)
                        results.append(f"The total count of rows is {value}")
                    elif is_unique_query:
                        value = df[col].nunique()
                        unique_values = df[col].unique()
                        unique_str = ", ".join(str(x) for x in unique_values[:5])
                        if len(unique_values) > 5:
                            unique_str += f", ... and {len(unique_values) - 5} more"
                        results.append(f"There are {value} unique values in {col}: {unique_str}")
                
                if results:
                    return "\n".join(results)
                    
            except Exception as e:
                print(f"Error processing CSV for statistical query: {e}")
        
        return None  # No results found
    
    
    def _handle_statistical_query1(self, query: str, csv_ids: List[str]) -> Optional[str]:
        """Handle direct statistical queries without using the LLM."""
        query_lower = query.lower()
        
        # Detect query type
        is_avg_query = "average" in query_lower or "mean" in query_lower or "avg" in query_lower
        is_max_query = "maximum" in query_lower or "max" in query_lower
        is_min_query = "minimum" in query_lower or "min" in query_lower
        is_count_query = "count" in query_lower or "how many" in query_lower
        
        if not (is_avg_query or is_max_query or is_min_query or is_count_query):
            return None  # Not a statistical query
        
        # Extract potential column names from query
        query_words = set(query_lower.replace("?", "").replace(",", "").split())
        
        for csv_id in csv_ids:
            if csv_id not in self.index_manager.indexes:
                continue
                
            file_path = self.index_manager.indexes[csv_id]["path"]
            metadata = self.index_manager.indexes[csv_id]["metadata"]
            
            try:
                df = pd.read_csv(file_path)
                
                # Find relevant columns based on query
                target_columns = []
                for col in df.columns:
                    col_lower = col.lower()
                    # Check if column name appears in query
                    if any(word in col_lower for word in query_words):
                        target_columns.append(col)
                
                # If no direct matches, try to infer from common column names
                if not target_columns:
                    if "age" in query_lower:
                        age_cols = [col for col in df.columns if "age" in col.lower()]
                        if age_cols:
                            target_columns = age_cols
                    elif "income" in query_lower or "salary" in query_lower:
                        income_cols = [col for col in df.columns if any(term in col.lower() 
                                       for term in ["income", "salary", "wage", "earnings"])]
                        if income_cols:
                            target_columns = income_cols
                    # Add more common column inferences as needed
                
                # If still no matches, use all numeric columns
                if not target_columns:
                    target_columns = df.select_dtypes(include=['number']).columns.tolist()
                
                # Perform the requested calculation
                results = []
                for col in target_columns:
                    if not pd.api.types.is_numeric_dtype(df[col]):
                        continue
                        
                    if is_avg_query:
                        value = df[col].mean()
                        results.append(f"The average {col} is {value:.2f}")
                    elif is_max_query:
                        value = df[col].max()
                        results.append(f"The maximum {col} is {value}")
                    elif is_min_query:
                        value = df[col].min()
                        results.append(f"The minimum {col} is {value}")
                    elif is_count_query:
                        value = len(df)
                        results.append(f"The total count of {col} is {value}")
                
                if results:
                    return "\n".join(results)
                    
            except Exception as e:
                print(f"Error processing CSV for statistical query: {e}")
        
        return None  # No results found

	from typing import Dict, List, Any, Optional
	import pandas as pd
	from sklearn.metrics.pairwise import cosine_similarity
	import numpy as np

	class CSVQueryEngine:

	def __init__(self, index_manager, llm):
	"""Initialize with index manager and language model."""
	self.index_manager = index_manager
	self.llm = llm

	def _prepare_context(self, query: str, csv_ids: List[str]) -> str:
	"""Prepare context from relevant CSV files."""
	context_parts = []

	for csv_id in csv_ids:
	# Get metadata
	if csv_id not in self.index_manager.indexes:
	continue

	metadata = self.index_manager.indexes[csv_id]["metadata"]
	file_path = self.index_manager.indexes[csv_id]["path"]

	# Add CSV metadata
	context_parts.append(f"CSV File: {metadata['filename']}")
	context_parts.append(f"Columns: {', '.join(metadata['columns'])}")
	context_parts.append(f"Row Count: {metadata['row_count']}")

	# Add sample data
	try:
	df = pd.read_csv(file_path)
	context_parts.append("\nSample Data:")
	context_parts.append(df.head(5).to_string())

	# Add some basic statistics for numeric columns
	context_parts.append("\nNumeric Column Statistics:")
	numeric_cols = df.select_dtypes(include=['number']).columns
	for col in numeric_cols:
	stats = df[col].describe()
	context_parts.append(f"{col} - mean: {stats['mean']:.2f}, min: {stats['min']:.2f}, max: {stats['max']:.2f}")

	# Add categorical column information
	categorical_cols = df.select_dtypes(include=['object', 'category']).columns
	if len(categorical_cols) > 0:
	context_parts.append("\nCategorical Column Information:")
	for col in categorical_cols:
	value_counts = df[col].value_counts().head(5)
	context_parts.append(f"{col} - unique values: {df[col].nunique()}, top values: {', '.join(value_counts.index.astype(str))}")

	# Add date information if present
	date_cols = []
	for col in df.columns:
	try:
	if pd.api.types.is_datetime64_dtype(df[col]) or pd.to_datetime(df[col], errors='coerce').notna().all():
	date_cols.append(col)
	except:
	pass

	if date_cols:
	context_parts.append("\nDate Column Information:")
	for col in date_cols:
	if not pd.api.types.is_datetime64_dtype(df[col]):
	df[col] = pd.to_datetime(df[col], errors='coerce')
	context_parts.append(f"{col} - range: {df[col].min()} to {df[col].max()}")

	except Exception as e:
	context_parts.append(f"Error reading CSV: {str(e)}")

	return "\n\n".join(context_parts)

	def _generate_prompt(self, query: str, context: str) -> str:
	"""Generate a prompt for the LLM."""
	return f"""You are an AI assistant specialized in analyzing CSV data.
	Your goal is to help users understand their data and extract insights.

	Below is information about CSV files that might help answer the query:

	{context}

	User Query: {query}

	Please provide a comprehensive and accurate answer based on the data.
	If calculations are needed, explain your process.
	If the data doesn't contain information to answer the query, say so clearly.

	Answer:"""

	def query(self, query_text: str) -> Dict[str, Any]:
	"""Process a natural language query across CSV files."""
	# Find relevant CSV files
	relevant_csvs = self.index_manager.find_relevant_csvs(query_text)

	if not relevant_csvs:
	return {
	"answer": "No relevant CSV files found for your query.",
	"sources": []
	}

	# Check for direct statistical queries
	direct_answer = self._handle_statistical_query(query_text, relevant_csvs)
	if direct_answer:
	return {
	"answer": direct_answer,
	"sources": self._get_sources(relevant_csvs)
	}

	# If not a direct statistical query, use the regular approach
	context = self._prepare_context(query_text, relevant_csvs)
	prompt = self._generate_prompt(query_text, context)
	response = self.llm.complete(prompt)

	return {
	"answer": response.text,
	"sources": self._get_sources(relevant_csvs)
	}

	def _get_sources(self, csv_ids: List[str]) -> List[Dict[str, str]]:
	"""Get source information for the response."""
	sources = []

	for csv_id in csv_ids:
	if csv_id not in self.index_manager.indexes:
	continue

	metadata = self.index_manager.indexes[csv_id]["metadata"]
	sources.append({
	"csv": metadata["filename"],
	"columns": ", ".join(metadata["columns"][:5]) + ("..." if len(metadata["columns"]) > 5 else "")
	})

	return sources


	def _handle_statistical_query(self, query: str, csv_ids: List[str]) -> Optional[str]:
	"""Handle direct statistical queries without using the LLM."""
	query_lower = query.lower()

	# Detect query type
	is_avg_query = "average" in query_lower or "mean" in query_lower or "avg" in query_lower
	is_max_query = "maximum" in query_lower or "max" in query_lower
	is_min_query = "minimum" in query_lower or "min" in query_lower
	is_count_query = "count" in query_lower or "how many" in query_lower
	is_unique_query = "unique" in query_lower or "distinct" in query_lower

	if not (is_avg_query or is_max_query or is_min_query or is_count_query or is_unique_query):
	return None # Not a statistical query

	# Extract potential column names from query
	query_words = set(query_lower.replace("?", "").replace(",", "").split())

	for csv_id in csv_ids:
	if csv_id not in self.index_manager.indexes:
	continue

	file_path = self.index_manager.indexes[csv_id]["path"]
	metadata = self.index_manager.indexes[csv_id]["metadata"]

	try:
	df = pd.read_csv(file_path)

	# Find relevant columns based on query
	target_columns = []
	for col in df.columns:
	col_lower = col.lower()
	# Check if column name appears in query
	if any(word in col_lower for word in query_words) or col_lower in query_lower:
	target_columns.append(col)

	# If no direct matches but query mentions specific types of data
	if not target_columns:
	if any(word in query_lower for word in ["age", "old", "young"]):
	age_cols = [col for col in df.columns if "age" in col.lower()]
	if age_cols:
	target_columns = age_cols
	elif any(word in query_lower for word in ["class", "category", "type", "grade"]):
	class_cols = [col for col in df.columns if any(term in col.lower()
	for term in ["class", "category", "type", "grade"])]
	if class_cols:
	target_columns = class_cols
	elif any(word in query_lower for word in ["income", "salary", "money", "price", "cost"]):
	income_cols = [col for col in df.columns if any(term in col.lower()
	for term in ["income", "salary", "wage", "earnings", "price", "cost"])]
	if income_cols:
	target_columns = income_cols
	elif any(word in query_lower for word in ["date", "time", "year", "month", "day"]):
	date_cols = []
	for col in df.columns:
	try:
	if pd.api.types.is_datetime64_dtype(df[col]) or pd.to_datetime(df[col], errors='coerce').notna().all():
	date_cols.append(col)
	except:
	pass
	if date_cols:
	target_columns = date_cols

	# If still no matches, use all columns for count/unique queries,
	# or numeric columns for other statistical queries
	if not target_columns:
	if is_count_query or is_unique_query:
	target_columns = df.columns.tolist()
	else:
	target_columns = df.select_dtypes(include=['number']).columns.tolist()

	# Perform the requested calculation
	results = []
	for col in target_columns:
	if is_avg_query:
	if pd.api.types.is_numeric_dtype(df[col]):
	value = df[col].mean()
	results.append(f"The average {col} is {value:.2f}")
	elif is_max_query:
	if pd.api.types.is_numeric_dtype(df[col]):
	value = df[col].max()
	results.append(f"The maximum {col} is {value}")
	else:
	# For non-numeric columns, show the maximum in alphabetical order
	value = df[col].max()
	results.append(f"The maximum (alphabetically) {col} is '{value}'")
	elif is_min_query:
	if pd.api.types.is_numeric_dtype(df[col]):
	value = df[col].min()
	results.append(f"The minimum {col} is {value}")
	else:
	# For non-numeric columns, show the minimum in alphabetical order
	value = df[col].min()
	results.append(f"The minimum (alphabetically) {col} is '{value}'")
	elif is_count_query:
	value = len(df)
	results.append(f"The total count of rows is {value}")
	elif is_unique_query:
	value = df[col].nunique()
	unique_values = df[col].unique()
	unique_str = ", ".join(str(x) for x in unique_values[:5])
	if len(unique_values) > 5:
	unique_str += f", ... and {len(unique_values) - 5} more"
	results.append(f"There are {value} unique values in {col}: {unique_str}")

	if results:
	return "\n".join(results)

	except Exception as e:
	print(f"Error processing CSV for statistical query: {e}")

	return None # No results found


	def _handle_statistical_query1(self, query: str, csv_ids: List[str]) -> Optional[str]:
	"""Handle direct statistical queries without using the LLM."""
	query_lower = query.lower()

	# Detect query type
	is_avg_query = "average" in query_lower or "mean" in query_lower or "avg" in query_lower
	is_max_query = "maximum" in query_lower or "max" in query_lower
	is_min_query = "minimum" in query_lower or "min" in query_lower
	is_count_query = "count" in query_lower or "how many" in query_lower

	if not (is_avg_query or is_max_query or is_min_query or is_count_query):
	return None # Not a statistical query

	# Extract potential column names from query
	query_words = set(query_lower.replace("?", "").replace(",", "").split())

	for csv_id in csv_ids:
	if csv_id not in self.index_manager.indexes:
	continue

	file_path = self.index_manager.indexes[csv_id]["path"]
	metadata = self.index_manager.indexes[csv_id]["metadata"]

	try:
	df = pd.read_csv(file_path)

	# Find relevant columns based on query
	target_columns = []
	for col in df.columns:
	col_lower = col.lower()
	# Check if column name appears in query
	if any(word in col_lower for word in query_words):
	target_columns.append(col)

	# If no direct matches, try to infer from common column names
	if not target_columns:
	if "age" in query_lower:
	age_cols = [col for col in df.columns if "age" in col.lower()]
	if age_cols:
	target_columns = age_cols
	elif "income" in query_lower or "salary" in query_lower:
	income_cols = [col for col in df.columns if any(term in col.lower()
	for term in ["income", "salary", "wage", "earnings"])]
	if income_cols:
	target_columns = income_cols
	# Add more common column inferences as needed

	# If still no matches, use all numeric columns
	if not target_columns:
	target_columns = df.select_dtypes(include=['number']).columns.tolist()

	# Perform the requested calculation
	results = []
	for col in target_columns:
	if not pd.api.types.is_numeric_dtype(df[col]):
	continue

	if is_avg_query:
	value = df[col].mean()
	results.append(f"The average {col} is {value:.2f}")
	elif is_max_query:
	value = df[col].max()
	results.append(f"The maximum {col} is {value}")
	elif is_min_query:
	value = df[col].min()
	results.append(f"The minimum {col} is {value}")
	elif is_count_query:
	value = len(df)
	results.append(f"The total count of {col} is {value}")

	if results:
	return "\n".join(results)

	except Exception as e:
	print(f"Error processing CSV for statistical query: {e}")

	return None # No results found