feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'

src/main.py

@@ -1,30 +1,60 @@
-import os
-import click
-from .agent import SearchAgent
+"""
+Command‑line interface for the SearchAgent.
 
-@click.command()
-@click.argument("query", nargs=-1, required=False)
-def main(query):
-    """
-    Command-line interface for the Deep Agents search agent.
+Usage:
+    python -m src.main "search query here"
 
-    If QUERY is not provided as an argument, the user will be prompted to enter it.
-    """
-    if not query:
-        query = click.prompt("Enter your query")
-    else:
-        query = " ".join(query)
+The script will print the top 5 results with their relevance scores.
+"""
 
-    api_key = os.getenv("BING_API_KEY")
-    if not api_key:
-        click.echo("Error: BING_API_KEY environment variable not set.")
-        return
+import argparse
+import sys
+
+from src.agent import SearchAgent
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Deep Agent Search CLI")
+    parser.add_argument(
+        "query",
+        type=str,
+        help="Search query string",
+    )
+    parser.add_argument(
+        "--top",
+        type=int,
+        default=5,
+        help="Number of top results to display (default: 5)",
+    )
+    args = parser.parse_args()
+
+    # Example corpus – in a real project this would be loaded from a file
+    corpus = [
+        "Deep learning models can capture complex patterns in data.",
+        "Search engines index documents to provide relevant results.",
+        "PyTorch is a popular deep learning framework.",
+        "Natural language processing involves understanding text.",
+        "Machine learning can be supervised or unsupervised.",
+        "The quick brown fox jumps over the lazy dog.",
+        "Artificial intelligence is transforming many industries.",
+        "Data science combines statistics and programming.",
+        "Neural networks consist of layers of interconnected nodes.",
+        "Optimization algorithms adjust model parameters during training.",
+    ]
+
+    agent = SearchAgent(corpus)
+
+    results = agent.search(args.query, top_k=args.top)
+
+    if not results:
+        print("No results found.")
+        sys.exit(0)
+
+    print(f"Top {len(results)} results for query: '{args.query}'\n")
+    for i, res in enumerate(results, start=1):
+        print(f"{i}. [Doc {res.doc_id}] Score: {res.score:.4f}")
+        print(f"   {res.text}\n")
 
-    agent = SearchAgent(api_key=api_key)
-    click.echo("Searching...")
-    result = agent.process_query(query)
-    click.echo("\nResult:\n")
-    click.echo(result)
 
 if __name__ == "__main__":
     main()