#!/usr/bin/env python3
"""
Interactive Hybrid RAG Demo
Easy-to-use command-line interface for querying your documents.
NO MCP SERVER REQUIRED - just run this script!
"""
import sys
from pathlib import Path
# Add project root to path for imports
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
import yaml
from typing import Dict, Any
from langchain_community.vectorstores import Chroma
from langchain_ollama import OllamaEmbeddings, OllamaLLM
from langchain_core.prompts import ChatPromptTemplate
from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain
from src.hybrid_rag import (
DocumentLoaderUtility,
configure_logging,
create_document_type_aware_retriever
)
from src.hybrid_rag.query_preprocessor import QueryPreprocessor
class InteractiveRAG:
"""Interactive RAG system with easy command-line interface."""
def __init__(self):
"""Initialize the RAG system."""
print("=" * 70)
print("🚀 HYBRID RAG SYSTEM - INTERACTIVE DEMO")
print("=" * 70)
print("\nInitializing system...\n")
# Configure logging
configure_logging()
# Load configuration
config_path = Path(__file__).parent.parent.parent / "config" / "config.yaml"
with open(config_path, "r") as f:
self.config = yaml.safe_load(f)
print("✅ Configuration loaded")
# Initialize query preprocessor for product ID mapping
print("🔧 Loading product ID mapping...")
self.query_preprocessor = QueryPreprocessor()
print("✅ Query preprocessor ready (product ID expansion enabled)")
# Initialize components
self._load_documents()
self._initialize_ollama()
self._create_vector_store()
self._create_retriever()
self._create_qa_chain()
print("\n" + "=" * 70)
print("✅ SYSTEM READY - You can now ask questions!")
print("=" * 70)
def _load_documents(self):
"""Load documents from data directory."""
data_dir = self.config['data']['directory']
data_path = Path(__file__).parent.parent.parent / data_dir
print(f"📂 Loading documents from: {data_path}")
loader = DocumentLoaderUtility(str(data_path), config=self.config)
self.documents = loader.load_documents()
if not self.documents:
print(f"\n⚠️ No documents found in '{data_path}'")
print(f"⚠️ Supported formats: {', '.join(loader.get_supported_formats())}")
sys.exit(1)
# Count file types
sources = [doc.metadata.get('source', '') for doc in self.documents]
unique_files = set([Path(s).name for s in sources if s])
print(f"✅ Loaded {len(self.documents)} chunks from {len(unique_files)} files")
def _initialize_ollama(self):
"""Connect to Ollama and initialize models."""
try:
ollama_url = self.config['ollama']['base_url']
self.embedding_model = self.config['ollama']['embedding_model']
self.llm_model = self.config['ollama']['llm_model']
self.embeddings = OllamaEmbeddings(
model=self.embedding_model,
base_url=ollama_url
)
self.llm = OllamaLLM(
model=self.llm_model,
base_url=ollama_url
)
print(f"✅ Connected to Ollama at {ollama_url}")
print(f" • Embedding model: {self.embedding_model}")
print(f" • LLM model: {self.llm_model}")
except Exception as e:
print(f"❌ Error connecting to Ollama: {e}")
print("\n💡 Make sure Ollama is running:")
print(" 1. Start Ollama: ollama serve")
print(" 2. Pull models:")
print(f" ollama pull {self.embedding_model}")
print(f" ollama pull {self.llm_model}")
sys.exit(1)
def _create_vector_store(self):
"""Create or load vector store."""
persist_dir = Path(__file__).parent.parent / self.config['vector_store']['persist_directory']
print(f"🔧 Creating vector store (this may take a few minutes)...")
self.vectorstore = Chroma.from_documents(
self.documents,
self.embeddings,
persist_directory=str(persist_dir)
)
print(f"✅ Vector store created with {len(self.documents)} embeddings")
def _create_retriever(self):
"""Create hybrid retriever."""
print("🔧 Creating hybrid retriever...")
self.retriever = create_document_type_aware_retriever(
documents=self.documents,
vectorstore=self.vectorstore,
config=self.config
)
print("✅ Hybrid retriever ready (semantic + keyword search)")
def _create_qa_chain(self):
"""Create QA chain."""
prompt = ChatPromptTemplate.from_template("""
You are an expert assistant. Answer the user's question based on the provided context.
If the context doesn't contain enough information, say so clearly.
Keep your answer concise and relevant.
<context>
{context}
</context>
Question: {input}
Answer:""")
document_chain = create_stuff_documents_chain(self.llm, prompt)
self.qa_chain = create_retrieval_chain(self.retriever, document_chain)
print("✅ QA chain constructed")
def query(self, question: str, show_sources: bool = True):
"""
Ask a question and get an answer.
Args:
question: The question to ask
show_sources: Whether to show source documents
Returns:
Dictionary with 'answer' and 'context'
"""
try:
# Expand query with product ID mappings
expanded_question = self.query_preprocessor.expand_query(question)
# Show expansion if it happened
if expanded_question != question:
print(f"🔍 Expanded query with product IDs: {expanded_question[:100]}...")
response = self.qa_chain.invoke({"input": expanded_question})
if show_sources:
print("\n📚 Sources:")
sources_seen = set()
for i, doc in enumerate(response.get('context', [])[:5], 1):
source = doc.metadata.get('source', 'unknown')
source_file = Path(source).name if source != 'unknown' else 'unknown'
if source_file not in sources_seen:
sources_seen.add(source_file)
print(f" [{i}] {source_file}")
return response
except Exception as e:
print(f"\n❌ Error processing query: {e}")
return None
def interactive_mode(self):
"""Run interactive question-answering loop."""
print("\n💬 INTERACTIVE MODE")
print(" • Type your questions and press Enter")
print(" • Type 'exit' or 'quit' to stop")
print(" • Type 'help' for example questions")
print(" • Type 'stats' for system statistics")
print()
while True:
try:
# Get user input
question = input("❓ Your question: ").strip()
if not question:
continue
# Handle commands
if question.lower() in ['exit', 'quit', 'q']:
print("\n👋 Goodbye!")
break
elif question.lower() == 'help':
self._show_help()
continue
elif question.lower() == 'stats':
self._show_stats()
continue
# Process question
print("\n🤔 Thinking...")
response = self.query(question, show_sources=True)
if response:
print(f"\n💡 Answer:\n{response['answer']}\n")
print("-" * 70)
except KeyboardInterrupt:
print("\n\n👋 Goodbye!")
break
except Exception as e:
print(f"\n❌ Error: {e}\n")
def _show_help(self):
"""Show example questions."""
print("\n📖 EXAMPLE QUESTIONS:")
print()
print("General:")
print(" • What information is available in these documents?")
print(" • Summarize the main topics covered")
print()
print("Specific (adjust based on your data):")
print(" • What OLED TVs are available?")
print(" • Which products are low in stock?")
print(" • Show me the largest orders in November")
print(" • What are common warranty claim types?")
print(" • Which supplier has the best ratings?")
print(" • What customer feedback mentions delivery?")
print()
def _show_stats(self):
"""Show system statistics."""
print("\n📊 SYSTEM STATISTICS:")
print()
# Document statistics
sources = [doc.metadata.get('source', '') for doc in self.documents]
unique_files = set([Path(s).name for s in sources if s])
file_types = {}
for source in sources:
if source:
ext = Path(source).suffix
file_types[ext] = file_types.get(ext, 0) + 1
print(f"Documents:")
print(f" • Total chunks: {len(self.documents)}")
print(f" • Unique files: {len(unique_files)}")
print(f" • File types:")
for ext, count in sorted(file_types.items()):
print(f" - {ext}: {count} chunks")
print()
print(f"Models:")
print(f" • Embedding: {self.embedding_model}")
print(f" • LLM: {self.llm_model}")
print()
print(f"Configuration:")
print(f" • Vector search k: {self.config['retrieval']['vector_search_k']}")
print(f" • Keyword search k: {self.config['retrieval']['keyword_search_k']}")
print(f" • CSV weight: {self.config['retrieval']['csv_weight']}")
print(f" • Text weight: {self.config['retrieval']['text_weight']}")
print()
def main():
"""Main entry point."""
# Parse command line arguments
import argparse
parser = argparse.ArgumentParser(
description="Interactive Hybrid RAG Demo",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Interactive mode (default)
python interactive_demo.py
# Ask a single question
python interactive_demo.py --query "What OLED TVs are available?"
# Ask without showing sources
python interactive_demo.py --query "Show me products" --no-sources
"""
)
parser.add_argument(
'--query', '-q',
type=str,
help='Ask a single question (non-interactive mode)'
)
parser.add_argument(
'--no-sources',
action='store_true',
help='Hide source documents in output'
)
args = parser.parse_args()
# Initialize system
try:
rag = InteractiveRAG()
except Exception as e:
print(f"\n❌ Failed to initialize system: {e}")
sys.exit(1)
# Single query mode
if args.query:
print(f"\n❓ Question: {args.query}")
response = rag.query(args.query, show_sources=not args.no_sources)
if response:
print(f"\n💡 Answer:\n{response['answer']}")
sys.exit(0)
# Interactive mode
rag.interactive_mode()
if __name__ == "__main__":
main()
