[fix] big moving around, probably made lot more bugs.

1 files changed, 0 insertions, 178 deletions

diff --git a/src/agents/02-sandbox/main.py b/src/agents/02-sandbox/main.py
deleted file mode 100644
index 24866f3..0000000
--- a/src/agents/02-sandbox/main.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import re
-import json
-import datetime
-import os
-from typing import List, Dict, Any, Callable
-
-from langchain_core.messages import SystemMessage, HumanMessage, BaseMessage
-from langchain.tools import tool
-from langchain_ollama import ChatOllama
-from langchain_core.tools import render_text_description
-
-# --- 1. Tool Definitions ---
-
-@tool
-def validate_user(user_id: int, addresses: List[str]) -> bool:
-    """Validate user using historical addresses.
-
-    Args:
-        user_id (int): the user ID.
-        addresses (List[str]): Previous addresses as a list of strings.
-    """
-    # In a real scenario, this would involve some logic.
-    # For now, it just returns True as in the original script.
-    return True
-
-def get_tools() -> List[Callable]:
-    """Returns a list of available tools."""
-    return [validate_user]
-
-# --- 2. Prompt Engineering ---
-
-def create_system_prompt(tools: List[Callable]) -> str:
-    """Creates a system prompt with tool descriptions."""
-    tool_description = render_text_description(tools)
-    # Using triple quotes for the f-string to handle quotes inside easily.
-    return f'''# Tools
-
-    You may call one or more functions to assist with the user query.
-
-    You are provided with function signatures within <tools></tools> XML tags:
-    <tools>
-    {tool_description}
-    </tools>
-
-    For each function call, first think about what you need to do and write it down in a <think></think> block. Then, return a JSON object with the function name and arguments within <tool_call></tool_call> XML tags.
-    **IMPORTANT**: The JSON object must be a valid JSON, with all keys and string values enclosed in double quotes.
-
-    Example of a valid tool call:
-    <tool_call>
-    {{"name": "validate_user", "arguments": {{"user_id": 123, "addresses": ["123 Fake St", "456 Main St"]}}}}
-    </tool_call>'''
-
-# --- 3. LLM Response Parsing ---
-
-def parse_llm_response(content: str) -> Dict[str, Any]:
-    """Parses the LLM's response to extract reasoning and tool calls."""
-    think_match = re.search(r"<think>(.*?)</think>", content, re.DOTALL)
-    reasoning = think_match.group(1).strip() if think_match else None
-
-    tool_call_match = re.search(r"<tool_call>(.*?)</tool_call>", content, re.DOTALL)
-    tool_call_str = tool_call_match.group(1).strip() if tool_call_match else None
-
-    tool_call = None
-    parsing_error = None
-    if tool_call_str:
-        try:
-            # The model sometimes returns a markdown code block.
-            if tool_call_str.startswith("```json"):
-                tool_call_str = tool_call_str[7:-4]
-            tool_call = json.loads(tool_call_str)
-        except json.JSONDecodeError as e:
-            parsing_error = f"Failed to parse tool_call JSON: {e}"
-
-    return {
-        "reasoning": reasoning,
-        "tool_call": tool_call,
-        "parsing_error": parsing_error,
-    }
-
-# --- 4. Agent Core Logic ---
-
-def execute_tool_call(tool_call: Dict[str, Any], tools: List[Callable]) -> Dict[str, Any]:
-    """Executes a tool call and returns the result."""
-    if not tool_call:
-        return {"error": "No tool call provided."}
-
-    tool_name = tool_call.get("name")
-    tool_args = tool_call.get("arguments")
-
-    tool_to_call = next((t for t in tools if t.name == tool_name), None)
-
-    if not tool_to_call:
-        return {"error": f"Tool '{tool_name}' not found."}
-
-    try:
-        result = tool_to_call.invoke(tool_args)
-        return {"result": result}
-    except Exception as e:
-        return {"error": f"Error calling tool '{tool_name}': {e}"}
-
-def save_log(log_data: Dict[str, Any]):
-    """Saves the log data to a JSON file."""
-    # Get the directory of the current script
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    # Go up three levels to the project root and then into the `output` directory
-    output_dir = os.path.join(script_dir, "..", "..", "..", "output")
-    
-    os.makedirs(output_dir, exist_ok=True)
-    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-    # get the script name
-    script_name = os.path.basename(__file__).split('.')[0]
-    file_path = os.path.join(output_dir, f"{script_name}_{timestamp}.json")
-    with open(file_path, "w") as f:
-        json.dump(log_data, f, indent=2)
-    print(f"Log saved to {os.path.normpath(file_path)}")
-
-
-def main():
-    """Main function to run the agent."""
-    # --- Configuration ---
-    model_config = {
-        "model": "qwen3:1.7b",
-        "temperature": 0,
-    }
-    human_message_content = "Could you validate user 123? They previously lived at 123 Fake St in Boston MA and 234 Pretend Boulevard in Houston TX. Think before you call any tools"
-
-    # --- Initialization ---
-    log_data = {"run_config": {"model_config": model_config}}
-    tools = get_tools()
-    llm = ChatOllama(**model_config)
-    system_prompt = create_system_prompt(tools)
-    
-    messages: List[BaseMessage] = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(content=human_message_content),
-    ]
-
-    # --- Log initial data ---
-    log_data["messages"] = [json.loads(msg.model_dump_json()) for msg in messages]
-
-
-    # --- LLM Invocation ---
-    response = llm.invoke(messages)
-    log_data["raw_response"] = json.loads(response.model_dump_json())
-
-    # --- Parsing ---
-    parsed_response = parse_llm_response(response.content)
-    log_data.update(parsed_response)
-
-    # --- Tool Execution ---
-    tool_result = None
-    if parsed_response["tool_call"]:
-        tool_result = execute_tool_call(parsed_response["tool_call"], tools)
-        log_data["tool_result"] = tool_result
-    
-    # --- Print to console (to replicate original behavior) ---
-    print("--- Extracted Reasoning ---")
-    print(log_data.get("reasoning"))
-    print("\n" + "="*40 + "\n")
-    print("--- Extracted Tool Call ---")
-    print(log_data.get("tool_call"))
-    if tool_result:
-        print("\n" + "="*40 + "\n")
-        if "result" in tool_result:
-            print(f"--- Calling Tool: {log_data['tool_call']['name']} ---")
-            print("--- Tool Call Result ---")
-            print(tool_result["result"])
-        else:
-            print("--- Tool Call Error ---")
-            print(tool_result["error"])
-    print("\n" + "="*40 + "\n")
-
-    # --- Save comprehensive log ---
-    save_log(log_data)
-
-
-if __name__ == "__main__":
-    main()