refactor: Extract agent logic into separate Agent class and simplify agentic_search.py

README.md

@@ -56,9 +56,19 @@ Event working with tools, when used with `agentic_search.py` worked, up to a poi
 
 2.  **Run the agent script:**
     ```bash
-    python agentic_search.py
+    # Run with direct prompt
+    python agentic_search.py --model "qwen3:32b" prompt "Your prompt here"
+
+    # Run with prompt from stdin
+    echo "Your prompt" | python agentic_search.py prompt -
+
+    # Run with custom server and API key
+    python agentic_search.py \
+        --model "hf.co/unsloth/Qwen3-30B-A3B-GGUF:Q5_K_M" \
+        --server "https://api.example.com/v1" \
+        --api-key "your-key" \
+        prompt "Your prompt"
     ```
-    This will execute the predefined query in the script, run the agent, print progress dots (`.`) for each response chunk, and finally output the full structured response and the extracted content.
 
 ## Dependencies
 

agent.py

@@ -0,0 +1,68 @@
+import json
+from dataclasses import dataclass
+from typing import Optional, List, Dict, Any
+from rich.console import Console
+from qwen_agent.agents import Assistant
+
+@dataclass
+class Agent:
+    model: str
+    server: str
+    api_key: str
+    max_tokens: int = 30000
+    enable_thinking: bool = True
+    tools: Optional[List[Dict[str, Any]]] = None
+    console: Console = Console()
+
+    def __post_init__(self):
+        if self.tools is None:
+            self.tools = [
+                {'mcpServers': {
+                    'time': {
+                        'command': 'uvx',
+                        'args': ['mcp-server-time', '--local-timezone=Europe/London']
+                    },
+                    "fetch": {
+                        "command": "uvx",
+                        "args": ["mcp-server-fetch"]
+                    },
+                    "ddg-search": {
+                        "command": "npx",
+                        "args": ["-y", "duckduckgo-mcp-server"]
+                    },
+                }},
+                'code_interpreter',
+            ]
+
+    def run(self, prompt: str) -> None:
+        """Run the agent with the given prompt"""
+        llm_cfg = {
+            'model': self.model,
+            'model_server': self.server,
+            'api_key': self.api_key,
+        }
+
+        # Define Agent
+        bot = Assistant(llm=llm_cfg, function_list=self.tools)
+
+        # Streaming generation
+        messages = [{'role': 'user', 'content': prompt}]
+
+        final_responses = None
+        try:
+            with self.console.status("[bold blue]Thinking...", spinner="dots") as status:
+                for responses in bot.run(messages=messages, 
+                                       enable_thinking=self.enable_thinking, 
+                                       max_tokens=self.max_tokens):
+                    final_responses = responses.pop()
+        except Exception as e:
+            self.console.print(f"[bold red]An error occurred during agent execution:[/] {e}")
+
+        # Pretty-print the final response object
+        if final_responses:
+            self.console.print("\n[bold green]--- Full Response Object ---[/]")
+            self.console.print(json.dumps(final_responses, indent=2))
+            self.console.print("\n[bold green]--- Extracted Content ---[/]")
+            self.console.print(final_responses.get('content', 'No content found in response.'))
+        else:
+            self.console.print("[bold red]No final response received from the agent.[/]")

agentic_search.py

@@ -1,71 +1,46 @@
-import json
+import sys
-from rich.console import Console
+import argparse
-from rich.spinner import Spinner
+from agent import Agent
-from qwen_agent.agents import Assistant
 
-# Define LLM
+def setup_argparse():
-llm_cfg = {
+    parser = argparse.ArgumentParser(description='Qwen3 Agent CLI')
-        'model': 'hf.co/unsloth/Qwen3-30B-A3B-GGUF:Q5_K_M',
+    parser.add_argument('--model', default='qwen3:32b',
-    # 'model': 'qwen3:32b',
+                       help='Model identifier (default: qwen3:32b)')
+    parser.add_argument('--server', default='http://localhost:11434/v1',
+                       help='Model server URL (default: http://localhost:11434/v1)')
+    parser.add_argument('--api-key', default='EMPTY',
+                       help='API key for the model server (default: EMPTY)')
+    
+    subparsers = parser.add_subparsers(dest='command', help='Available commands')
+    
+    # Prompt command
+    prompt_parser = subparsers.add_parser('prompt', help='Run agent with a prompt')
+    prompt_parser.add_argument('text', nargs='?', default='-',
+                             help='Prompt text or "-" for stdin (default: -)')
+    
+    return parser
 
-    # Use a custom endpoint compatible with OpenAI API:
+def read_prompt(text: str) -> str:
-    'model_server': 'http://localhost:11434/v1',  # api_base
+    """Read prompt from argument or stdin if text is '-'"""
-    'api_key': 'EMPTY',
+    if text == '-':
+        return sys.stdin.read().strip()
+    return text
 
-    # Other parameters:
-    # 'generate_cfg': {
-    #         # Add: When the response content is `<think>this is the thought</think>this is the answer;
-    #         # Do not add: When the response has been separated by reasoning_content and content.
-    #         'thought_in_content': True,
-    #     },
-}
 
-# Define Tools
+def main():
-tools = [
+    parser = setup_argparse()
-    {'mcpServers': {  # You can specify the MCP configuration file
+    args = parser.parse_args()
-        'time': {
+    
-            'command': 'uvx',
+    if args.command == 'prompt':
-            'args': ['mcp-server-time', '--local-timezone=Europe/London']
+        prompt_text = read_prompt(args.text)
-        },
+        agent = Agent(
-        "fetch": {
+            model=args.model,
-            "command": "uvx",
+            server=args.server,
-            "args": ["mcp-server-fetch"]
+            api_key=args.api_key
-        },
+        )
-          "ddg-search": {
+        agent.run(prompt_text)
-          "command": "npx",
+    else:
-          "args": ["-y", "duckduckgo-mcp-server"]
+        parser.print_help()
-        },
-    }
-    },
-    'code_interpreter',  # Built-in tools
-]
 
-# Define Agent
+if __name__ == '__main__':
-bot = Assistant(llm=llm_cfg, function_list=tools)
+    main()
-console = Console()
-
-# Streaming generation
-messages = [{'role': 'user',
-             'content':
-                 """"
-                 - ***Research** Parsing CLI commands and options in python code.
-                 -- **Analyze** Clean separation of concerns between parsing commands and options and execution of the commands.
-                 -- **Answer** What is the best way to parse CLI commands and options in python code?"""}]
-
-final_responses = None
-# Consider adding error handling around bot.run
-try:
-    with console.status("[bold blue]Thinking...", spinner="dots") as status:
-        for responses in bot.run(messages=messages, enable_thinking=True, max_tokens=30000):
-            final_responses = responses.pop()
-except Exception as e:
-    console.print(f"[bold red]An error occurred during agent execution:[/] {e}")
-
-# Pretty-print the final response object
-if final_responses:
-    console.print("\n[bold green]--- Full Response Object ---[/]")
-    console.print(json.dumps(final_responses, indent=2))
-    console.print("\n[bold green]--- Extracted Content ---[/]")
-    console.print(final_responses.get('content', 'No content found in response.'))
-else:
-    console.print("[bold red]No final response received from the agent.[/]")