工具使用

-> % lms log stream
Streaming logs from LM Studio

timestamp: 11/13/2024, 9:35:15 AM
type: llm.prediction.input
modelIdentifier: gemma-2-2b-it
modelPath: lmstudio-community/gemma-2-2b-it-GGUF/gemma-2-2b-it-Q4_K_M.gguf
input: "<start_of_turn>system
You are a tool-calling AI. You can request calls to available tools with this EXACT format:
[TOOL_REQUEST]{"name": "tool_name", "arguments": {"param1": "value1"}}[END_TOOL_REQUEST]

AVAILABLE TOOLS:
{
  "type": "toolArray",
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_delivery_date",
        "description": "Get the delivery date for a customer's order",
        "parameters": {
          "type": "object",
          "properties": {
            "order_id": {
              "type": "string"
            }
          },
          "required": [
            "order_id"
          ]
        }
      }
    }
  ]
}

RULES:
- Only use tools from AVAILABLE TOOLS
- Include all required arguments
- Use one [TOOL_REQUEST] block per tool
- Never use [TOOL_RESULT]
- If you decide to call one or more tools, there should be no other text in your message

Examples:
"Check Paris weather"
[TOOL_REQUEST]{"name": "get_weather", "arguments": {"location": "Paris"}}[END_TOOL_REQUEST]

"Send email to John about meeting and open browser"
[TOOL_REQUEST]{"name": "send_email", "arguments": {"to": "John", "subject": "meeting"}}[END_TOOL_REQUEST]
[TOOL_REQUEST]{"name": "open_browser", "arguments": {}}[END_TOOL_REQUEST]

Respond conversationally if no matching tools exist.<end_of_turn>
<start_of_turn>user
Get me delivery date for order 123<end_of_turn>
<start_of_turn>model
"

[TOOL_REQUEST]{"name": "get_delivery_date", "arguments": {"order_id": "123"}}[END_TOOL_REQUEST]

from datetime import datetime, timedelta
import json
import random
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url="https://127.0.0.1:1234/v1", api_key="lm-studio")
model = "lmstudio-community/qwen2.5-7b-instruct"


def get_delivery_date(order_id: str) -> datetime:
    # Generate a random delivery date between today and 14 days from now
    # in a real-world scenario, this function would query a database or API
    today = datetime.now()
    random_days = random.randint(1, 14)
    delivery_date = today + timedelta(days=random_days)
    print(
        f"\nget_delivery_date function returns delivery date:\n\n{delivery_date}",
        flush=True,
    )
    return delivery_date


tools = [
    {
        "type": "function",
        "function": {
            "name": "get_delivery_date",
            "description": "Get the delivery date for a customer's order. Call this whenever you need to know the delivery date, for example when a customer asks 'Where is my package'",
            "parameters": {
                "type": "object",
                "properties": {
                    "order_id": {
                        "type": "string",
                        "description": "The customer's order ID.",
                    },
                },
                "required": ["order_id"],
                "additionalProperties": False,
            },
        },
    }
]

messages = [
    {
        "role": "system",
        "content": "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
    },
    {
        "role": "user",
        "content": "Give me the delivery date and time for order number 1017",
    },
]

# LM Studio
response = client.chat.completions.create(
    model=model,
    messages=messages,
    tools=tools,
)

print("\nModel response requesting tool call:\n", flush=True)
print(response, flush=True)

# Extract the arguments for get_delivery_date
# Note this code assumes we have already determined that the model generated a function call.
tool_call = response.choices[0].message.tool_calls[0]
arguments = json.loads(tool_call.function.arguments)

order_id = arguments.get("order_id")

# Call the get_delivery_date function with the extracted order_id
delivery_date = get_delivery_date(order_id)

assistant_tool_call_request_message = {
    "role": "assistant",
    "tool_calls": [
        {
            "id": response.choices[0].message.tool_calls[0].id,
            "type": response.choices[0].message.tool_calls[0].type,
            "function": response.choices[0].message.tool_calls[0].function,
        }
    ],
}

# Create a message containing the result of the function call
function_call_result_message = {
    "role": "tool",
    "content": json.dumps(
        {
            "order_id": order_id,
            "delivery_date": delivery_date.strftime("%Y-%m-%d %H:%M:%S"),
        }
    ),
    "tool_call_id": response.choices[0].message.tool_calls[0].id,
}

# Prepare the chat completion call payload
completion_messages_payload = [
    messages[0],
    messages[1],
    assistant_tool_call_request_message,
    function_call_result_message,
]

# Call the OpenAI API's chat completions endpoint to send the tool call result back to the model
# LM Studio
response = client.chat.completions.create(
    model=model,
    messages=completion_messages_payload,
)

print("\nFinal model response with knowledge of the tool call result:\n", flush=True)
print(response.choices[0].message.content, flush=True)

import json
from urllib.parse import urlparse
import webbrowser
from datetime import datetime
import os
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url="https://127.0.0.1:1234/v1", api_key="lm-studio")
model = "lmstudio-community/qwen2.5-7b-instruct"


def is_valid_url(url: str) -> bool:
        
    try:
        result = urlparse(url)
        return bool(result.netloc)  # Returns True if there's a valid network location
    except Exception:
        return False


def open_safe_url(url: str) -> dict:
    # List of allowed domains (expand as needed)
    SAFE_DOMAINS = {
        "lmstudio.ai",
        "github.com",
        "google.com",
        "wikipedia.org",
        "weather.com",
        "stackoverflow.com",
        "python.org",
        "docs.python.org",
    }

    try:
        # Add http:// if no scheme is present
        if not url.startswith(('http://', 'https://')):
            url = 'http://' + url

        # Validate URL format
        if not is_valid_url(url):
            return {"status": "error", "message": f"Invalid URL format: {url}"}

        # Parse the URL and check domain
        parsed_url = urlparse(url)
        domain = parsed_url.netloc.lower()
        base_domain = ".".join(domain.split(".")[-2:])

        if base_domain in SAFE_DOMAINS:
            webbrowser.open(url)
            return {"status": "success", "message": f"Opened {url} in browser"}
        else:
            return {
                "status": "error",
                "message": f"Domain {domain} not in allowed list",
            }
    except Exception as e:
        return {"status": "error", "message": str(e)}


def get_current_time() -> dict:
    """Get the current system time with timezone information"""
    try:
        current_time = datetime.now()
        timezone = datetime.now().astimezone().tzinfo
        formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S %Z")
        return {
            "status": "success",
            "time": formatted_time,
            "timezone": str(timezone),
            "timestamp": current_time.timestamp(),
        }
    except Exception as e:
        return {"status": "error", "message": str(e)}


def analyze_directory(path: str = ".") -> dict:
    """Count and categorize files in a directory"""
    try:
        stats = {
            "total_files": 0,
            "total_dirs": 0,
            "file_types": {},
            "total_size_bytes": 0,
        }

        for entry in os.scandir(path):
            if entry.is_file():
                stats["total_files"] += 1
                ext = os.path.splitext(entry.name)[1].lower() or "no_extension"
                stats["file_types"][ext] = stats["file_types"].get(ext, 0) + 1
                stats["total_size_bytes"] += entry.stat().st_size
            elif entry.is_dir():
                stats["total_dirs"] += 1
                # Add size of directory contents
                for root, _, files in os.walk(entry.path):
                    for file in files:
                        try:
                            stats["total_size_bytes"] += os.path.getsize(os.path.join(root, file))
                        except (OSError, FileNotFoundError):
                            continue

        return {"status": "success", "stats": stats, "path": os.path.abspath(path)}
    except Exception as e:
        return {"status": "error", "message": str(e)}


tools = [
    {
        "type": "function",
        "function": {
            "name": "open_safe_url",
            "description": "Open a URL in the browser if it's deemed safe",
            "parameters": {
                "type": "object",
                "properties": {
                    "url": {
                        "type": "string",
                        "description": "The URL to open",
                    },
                },
                "required": ["url"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "Get the current system time with timezone information",
            "parameters": {
                "type": "object",
                "properties": {},
                "required": [],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "analyze_directory",
            "description": "Analyze the contents of a directory, counting files and folders",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {
                        "type": "string",
                        "description": "The directory path to analyze. Defaults to current directory if not specified.",
                    },
                },
                "required": [],
            },
        },
    },
]


def process_tool_calls(response, messages):
    """Process multiple tool calls and return the final response and updated messages"""
    # Get all tool calls from the response
    tool_calls = response.choices[0].message.tool_calls

    # Create the assistant message with tool calls
    assistant_tool_call_message = {
        "role": "assistant",
        "tool_calls": [
            {
                "id": tool_call.id,
                "type": tool_call.type,
                "function": tool_call.function,
            }
            for tool_call in tool_calls
        ],
    }

    # Add the assistant's tool call message to the history
    messages.append(assistant_tool_call_message)

    # Process each tool call and collect results
    tool_results = []
    for tool_call in tool_calls:
        # For functions with no arguments, use empty dict
        arguments = (
            json.loads(tool_call.function.arguments)
            if tool_call.function.arguments.strip()
            else {}
        )

        # Determine which function to call based on the tool call name
        if tool_call.function.name == "open_safe_url":
            result = open_safe_url(arguments["url"])
        elif tool_call.function.name == "get_current_time":
            result = get_current_time()
        elif tool_call.function.name == "analyze_directory":
            path = arguments.get("path", ".")
            result = analyze_directory(path)
        else:
            # llm tried to call a function that doesn't exist, skip
            continue

        # Add the result message
        tool_result_message = {
            "role": "tool",
            "content": json.dumps(result),
            "tool_call_id": tool_call.id,
        }
        tool_results.append(tool_result_message)
        messages.append(tool_result_message)

    # Get the final response
    final_response = client.chat.completions.create(
        model=model,
        messages=messages,
    )

    return final_response


def chat():
    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant that can open safe web links, tell the current time, and analyze directory contents. Use these capabilities whenever they might be helpful.",
        }
    ]

    print(
        "Assistant: Hello! I can help you open safe web links, tell you the current time, and analyze directory contents. What would you like me to do?"
    )
    print("(Type 'quit' to exit)")

    while True:
        # Get user input
        user_input = input("\nYou: ").strip()

        # Check for quit command
        if user_input.lower() == "quit":
            print("Assistant: Goodbye!")
            break

        # Add user message to conversation
        messages.append({"role": "user", "content": user_input})

        try:
            # Get initial response
            response = client.chat.completions.create(
                model=model,
                messages=messages,
                tools=tools,
            )

            # Check if the response includes tool calls
            if response.choices[0].message.tool_calls:
                # Process all tool calls and get final response
                final_response = process_tool_calls(response, messages)
                print("\nAssistant:", final_response.choices[0].message.content)

                # Add assistant's final response to messages
                messages.append(
                    {
                        "role": "assistant",
                        "content": final_response.choices[0].message.content,
                    }
                )
            else:
                # If no tool call, just print the response
                print("\nAssistant:", response.choices[0].message.content)

                # Add assistant's response to messages
                messages.append(
                    {
                        "role": "assistant",
                        "content": response.choices[0].message.content,
                    }
                )

        except Exception as e:
            print(f"\nAn error occurred: {str(e)}")
            exit(1)


if __name__ == "__main__":
    chat()

from openai import OpenAI
import time

client = OpenAI(base_url="http://127.0.0.1:1234/v1", api_key="lm-studio")
MODEL = "lmstudio-community/qwen2.5-7b-instruct"

TIME_TOOL = {
    "type": "function",
    "function": {
        "name": "get_current_time",
        "description": "Get the current time, only if asked",
        "parameters": {"type": "object", "properties": {}},
    },
}

def get_current_time():
    return {"time": time.strftime("%H:%M:%S")}

def process_stream(stream, add_assistant_label=True):
    """Handle streaming responses from the API"""
    collected_text = ""
    tool_calls = []
    first_chunk = True

    for chunk in stream:
        delta = chunk.choices[0].delta

        # Handle regular text output
        if delta.content:
            if first_chunk:
                print()
                if add_assistant_label:
                    print("Assistant:", end=" ", flush=True)
                first_chunk = False
            print(delta.content, end="", flush=True)
            collected_text += delta.content

        # Handle tool calls
        elif delta.tool_calls:
            for tc in delta.tool_calls:
                if len(tool_calls) <= tc.index:
                    tool_calls.append({
                        "id": "", "type": "function",
                        "function": {"name": "", "arguments": ""}
                    })
                tool_calls[tc.index] = {
                    "id": (tool_calls[tc.index]["id"] + (tc.id or "")),
                    "type": "function",
                    "function": {
                        "name": (tool_calls[tc.index]["function"]["name"] + (tc.function.name or "")),
                        "arguments": (tool_calls[tc.index]["function"]["arguments"] + (tc.function.arguments or ""))
                    }
                }
    return collected_text, tool_calls

def chat_loop():
    messages = []
    print("Assistant: Hi! I am an AI agent empowered with the ability to tell the current time (Type 'quit' to exit)")

    while True:
        user_input = input("\nYou: ").strip()
        if user_input.lower() == "quit":
            break

        messages.append({"role": "user", "content": user_input})

        # Get initial response
        response_text, tool_calls = process_stream(
            client.chat.completions.create(
                model=MODEL,
                messages=messages,
                tools=[TIME_TOOL],
                stream=True,
                temperature=0.2
            )
        )

        if not tool_calls:
            print()

        text_in_first_response = len(response_text) > 0
        if text_in_first_response:
            messages.append({"role": "assistant", "content": response_text})

        # Handle tool calls if any
        if tool_calls:
            tool_name = tool_calls[0]["function"]["name"]
            print()
            if not text_in_first_response:
                print("Assistant:", end=" ", flush=True)
            print(f"**Calling Tool: {tool_name}**")
            messages.append({"role": "assistant", "tool_calls": tool_calls})

            # Execute tool calls
            for tool_call in tool_calls:
                if tool_call["function"]["name"] == "get_current_time":
                    result = get_current_time()
                    messages.append({
                        "role": "tool",
                        "content": str(result),
                        "tool_call_id": tool_call["id"]
                    })

            # Get final response after tool execution
            final_response, _ = process_stream(
                client.chat.completions.create(
                    model=MODEL, 
                    messages=messages, 
                    stream=True
                ),
                add_assistant_label=False
            )

            if final_response:
                print()
                messages.append({"role": "assistant", "content": final_response})

if __name__ == "__main__":
    chat_loop()