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diff --git a/tool-use-example.py b/tool-use-example.py
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+"""
+LM Studio Tool Use Demo: Wikipedia Querying Chatbot
+Demonstrates how an LM Studio model can query Wikipedia
+"""
+
+# Standard library imports
+import itertools
+import json
+import shutil
+import sys
+import threading
+import time
+import urllib.parse
+import urllib.request
+
+# Third-party imports
+from openai import OpenAI
+
+# Initialize LM Studio client
+client = OpenAI(base_url="http://0.0.0.0:1234/v1", api_key="lm-studio")
+MODEL = "LaylaLLM:2"
+
+
+def fetch_wikipedia_content(search_query: str) -> dict:
+    """Fetches wikipedia content for a given search_query"""
+    try:
+        # Search for most relevant article
+        search_url = "https://en.wikipedia.org/w/api.php"
+        search_params = {
+            "action": "query",
+            "format": "json",
+            "list": "search",
+            "srsearch": search_query,
+            "srlimit": 1,
+        }
+
+        url = f"{search_url}?{urllib.parse.urlencode(search_params)}"
+        with urllib.request.urlopen(url) as response:
+            search_data = json.loads(response.read().decode())
+
+        if not search_data["query"]["search"]:
+            return {
+                "status": "error",
+                "message": f"No Wikipedia article found for '{search_query}'",
+            }
+
+        # Get the normalized title from search results
+        normalized_title = search_data["query"]["search"][0]["title"]
+
+        # Now fetch the actual content with the normalized title
+        content_params = {
+            "action": "query",
+            "format": "json",
+            "titles": normalized_title,
+            "prop": "extracts",
+            "exintro": "true",
+            "explaintext": "true",
+            "redirects": 1,
+        }
+
+        url = f"{search_url}?{urllib.parse.urlencode(content_params)}"
+        with urllib.request.urlopen(url) as response:
+            data = json.loads(response.read().decode())
+
+        pages = data["query"]["pages"]
+        page_id = list(pages.keys())[0]
+
+        if page_id == "-1":
+            return {
+                "status": "error",
+                "message": f"No Wikipedia article found for '{search_query}'",
+            }
+
+        content = pages[page_id]["extract"].strip()
+        return {
+            "status": "success",
+            "content": content,
+            "title": pages[page_id]["title"],
+        }
+
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+
+# Define tool for LM Studio
+WIKI_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "fetch_wikipedia_content",
+        "description": (
+            "Search Wikipedia and fetch the introduction of the most relevant article. "
+            "Always use this if the user is asking for something that is likely on wikipedia. "
+            "If the user has a typo in their search query, correct it before searching."
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "search_query": {
+                    "type": "string",
+                    "description": "Search query for finding the Wikipedia article",
+                },
+            },
+            "required": ["search_query"],
+        },
+    },
+}
+
+
+# Class for displaying the state of model processing
+class Spinner:
+    def __init__(self, message="Processing..."):
+        self.spinner = itertools.cycle(["-", "/", "|", "\\"])
+        self.busy = False
+        self.delay = 0.1
+        self.message = message
+        self.thread = None
+
+    def write(self, text):
+        sys.stdout.write(text)
+        sys.stdout.flush()
+
+    def _spin(self):
+        while self.busy:
+            self.write(f"\r{self.message} {next(self.spinner)}")
+            time.sleep(self.delay)
+        self.write("\r\033[K")  # Clear the line
+
+    def __enter__(self):
+        self.busy = True
+        self.thread = threading.Thread(target=self._spin)
+        self.thread.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.busy = False
+        time.sleep(self.delay)
+        if self.thread:
+            self.thread.join()
+        self.write("\r")  # Move cursor to beginning of line
+
+
+def chat_loop():
+    """
+    Main chat loop that processes user input and handles tool calls.
+    """
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are an assistant that can retrieve Wikipedia articles. "
+                "When asked about a topic, you can retrieve Wikipedia articles "
+                "and cite information from them."
+            ),
+        }
+    ]
+
+    print(
+        "Assistant: "
+        "Hi! I can access Wikipedia to help answer your questions about history, "
+        "science, people, places, or concepts - or we can just chat about "
+        "anything else!"
+    )
+    print("(Type 'quit' to exit)")
+
+    while True:
+        user_input = input("\nYou: ").strip()
+        if user_input.lower() == "quit":
+            break
+
+        messages.append({"role": "user", "content": user_input})
+        try:
+            with Spinner("Thinking..."):
+                response = client.chat.completions.create(
+                    model=MODEL,
+                    messages=messages,
+                    tools=[WIKI_TOOL],
+                )
+
+            if response.choices[0].message.tool_calls:
+                # Handle all tool calls
+                tool_calls = response.choices[0].message.tool_calls
+
+                # Add all tool calls to messages
+                messages.append(
+                    {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "id": tool_call.id,
+                                "type": tool_call.type,
+                                "function": tool_call.function,
+                            }
+                            for tool_call in tool_calls
+                        ],
+                    }
+                )
+
+                # Process each tool call and add results
+                for tool_call in tool_calls:
+                    args = json.loads(tool_call.function.arguments)
+                    result = fetch_wikipedia_content(args["search_query"])
+
+                    # Print the Wikipedia content in a formatted way
+                    terminal_width = shutil.get_terminal_size().columns
+                    print("\n" + "=" * terminal_width)
+                    if result["status"] == "success":
+                        print(f"\nWikipedia article: {result['title']}")
+                        print("-" * terminal_width)
+                        print(result["content"])
+                    else:
+                        print(
+                            f"\nError fetching Wikipedia content: {result['message']}"
+                        )
+                    print("=" * terminal_width + "\n")
+
+                    messages.append(
+                        {
+                            "role": "tool",
+                            "content": json.dumps(result),
+                            "tool_call_id": tool_call.id,
+                        }
+                    )
+
+                # Stream the post-tool-call response
+                print("\nAssistant:", end=" ", flush=True)
+                stream_response = client.chat.completions.create(
+                    model=MODEL, messages=messages, stream=True
+                )
+                collected_content = ""
+                for chunk in stream_response:
+                    if chunk.choices[0].delta.content:
+                        content = chunk.choices[0].delta.content
+                        print(content, end="", flush=True)
+                        collected_content += content
+                print()  # New line after streaming completes
+                messages.append(
+                    {
+                        "role": "assistant",
+                        "content": collected_content,
+                    }
+                )
+            else:
+                # Handle regular response
+                print("\nAssistant:", response.choices[0].message.content)
+                messages.append(
+                    {
+                        "role": "assistant",
+                        "content": response.choices[0].message.content,
+                    }
+                )
+
+        except Exception as e:
+            print(
+                f"\nError chatting with the LM Studio server!\n\n"
+                f"Please ensure:\n"
+                f"1. LM Studio server is running at 0.0.0.0:1234 (hostname:port)\n"
+                f"2. Model '{MODEL}' is downloaded\n"
+                f"3. Model '{MODEL}' is loaded, or that just-in-time model loading is enabled\n\n"
+                f"Error details: {str(e)}\n"
+                "See https://lmstudio.ai/docs/basics/server for more information"
+            )
+            exit(1)
+
+
+if __name__ == "__main__":
+    chat_loop()
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