feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'

src/main.py

@@ -1,64 +1,41 @@
 """
-Main entry point for the FAQ bot.
+Command‑line interface for the FAQ bot.
 """
 
 import argparse
 import os
-import json
-from src.embedding import embed_text
-from src.database import ChromaDB
-from src.mcp_tools import generate_answer
 
-def load_sample_faq() -> list:
-    """
-    Load a small sample FAQ dataset.
-    """
-    return [
-        {"id": "0", "text": "What is the return policy? Our return policy allows returns within 30 days of purchase."},
-        {"id": "1", "text": "How do I track my order? You can track your order using the tracking link sent to your email."},
-        {"id": "2", "text": "What payment methods are accepted? We accept Visa, MasterCard, and PayPal."},
-        {"id": "3", "text": "Do you ship internationally? Yes, we ship to most countries worldwide."},
-        {"id": "4", "text": "How can I contact customer support? You can contact us via email at support@example.com."},
-    ]
+from .bot import FAQBot
 
 def main():
-    parser = argparse.ArgumentParser(description="FAQ Bot using ChromaDB and MCP-tools")
-    parser.add_argument("--question", type=str, help="Your question to ask the bot")
+    parser = argparse.ArgumentParser(description="FAQ Bot CLI")
+    parser.add_argument(
+        "--persist-dir",
+        type=str,
+        default="chromadb_persist",
+        help="Directory to persist ChromaDB data",
+    )
+    parser.add_argument(
+        "--openai-key",
+        type=str,
+        default=os.getenv("OPENAI_API_KEY"),
+        help="OpenAI API key (optional)",
+    )
     args = parser.parse_args()
 
-    if not args.question:
-        print("Please provide a question using --question")
-        return
+    bot = FAQBot(persist_dir=args.persist_dir, openai_api_key=args.openai_key)
 
-    # Initialize database
-    db = ChromaDB()
-
-    # If the collection is empty, load sample data
-    if db.is_empty():
-        print("Database empty. Loading sample FAQ data...")
-        sample_data = load_sample_faq()
-        for item in sample_data:
-            text = item["text"]
-            doc_id = item.get("id")
-            embedding = embed_text(text)
-            db.add_document(text, embedding, doc_id=doc_id)
-        print("Sample data loaded.")
-
-    # Generate embedding for the question
-    question_embedding = embed_text(args.question)
-
-    # Query the database for relevant documents
-    results = db.query(question_embedding, k=5)
-
-    # Extract context documents
-    context = [res["document"] for res in results]
-
-    # Generate answer using MCP-tools
-    answer = generate_answer(context, args.question)
-
-    # Output the answer
-    print("\nAnswer:")
-    print(answer)
+    print("FAQ Bot is ready. Type your question (Ctrl+C to exit).")
+    while True:
+        try:
+            question = input("\n> ")
+            if not question.strip():
+                continue
+            answer = bot.ask(question)
+            print(f"\nAnswer: {answer}")
+        except (KeyboardInterrupt, EOFError):
+            print("\nGoodbye!")
+            break
 
 if __name__ == "__main__":
     main()