[Misc] refactor examples series - Chat Completion Client With Tools (#16829)

Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>

examples/online_serving/openai_chat_completion_client_with_tools.py

@@ -17,6 +17,7 @@ vllm serve --model NousResearch/Hermes-2-Pro-Llama-3-8B \
             --enable-auto-tool-choice --tool-call-parser hermes
 """
 import json
+from typing import Any
 
 from openai import OpenAI
 
@@ -24,15 +25,6 @@ from openai import OpenAI
 openai_api_key = "EMPTY"
 openai_api_base = "http://localhost:8000/v1"
 
-client = OpenAI(
-    # defaults to os.environ.get("OPENAI_API_KEY")
-    api_key=openai_api_key,
-    base_url=openai_api_base,
-)
-
-models = client.models.list()
-model = models.data[0].id
-
 tools = [{
     "type": "function",
     "function": {
@@ -78,86 +70,123 @@ messages = [{
     "Can you tell me what the temperate will be in Dallas, in fahrenheit?"
 }]
 
-chat_completion = client.chat.completions.create(messages=messages,
-                                                 model=model,
-                                                 tools=tools)
-
-print("Chat completion results:")
-print(chat_completion)
-print("\n\n")
-
-tool_calls_stream = client.chat.completions.create(messages=messages,
-                                                   model=model,
-                                                   tools=tools,
-                                                   stream=True)
-
-chunks = []
-for chunk in tool_calls_stream:
-    chunks.append(chunk)
-    if chunk.choices[0].delta.tool_calls:
-        print(chunk.choices[0].delta.tool_calls[0])
-    else:
-        print(chunk.choices[0].delta)
-
-arguments = []
-tool_call_idx = -1
-for chunk in chunks:
-
-    if chunk.choices[0].delta.tool_calls:
-        tool_call = chunk.choices[0].delta.tool_calls[0]
-
-        if tool_call.index != tool_call_idx:
-            if tool_call_idx >= 0:
-                print(
-                    f"streamed tool call arguments: {arguments[tool_call_idx]}"
-                )
-            tool_call_idx = chunk.choices[0].delta.tool_calls[0].index
-            arguments.append("")
-        if tool_call.id:
-            print(f"streamed tool call id: {tool_call.id} ")
-
-        if tool_call.function:
-            if tool_call.function.name:
-                print(f"streamed tool call name: {tool_call.function.name}")
-
-            if tool_call.function.arguments:
-                arguments[tool_call_idx] += tool_call.function.arguments
-
-if len(arguments):
-    print(f"streamed tool call arguments: {arguments[-1]}")
-
-print("\n\n")
-
-messages.append({
-    "role": "assistant",
-    "tool_calls": chat_completion.choices[0].message.tool_calls
-})
 
-
-# Now, simulate a tool call
 def get_current_weather(city: str, state: str, unit: 'str'):
     return ("The weather in Dallas, Texas is 85 degrees fahrenheit. It is "
             "partly cloudly, with highs in the 90's.")
 
 
-available_tools = {"get_current_weather": get_current_weather}
-
-completion_tool_calls = chat_completion.choices[0].message.tool_calls
-for call in completion_tool_calls:
-    tool_to_call = available_tools[call.function.name]
-    args = json.loads(call.function.arguments)
-    result = tool_to_call(**args)
-    print(result)
+def handle_tool_calls_stream(
+    client: OpenAI,
+    messages: list[dict[str, str]],
+    model: str,
+    tools: list[dict[str, Any]],
+) -> list[Any]:
+    tool_calls_stream = client.chat.completions.create(messages=messages,
+                                                       model=model,
+                                                       tools=tools,
+                                                       stream=True)
+    chunks = []
+    print("chunks: ")
+    for chunk in tool_calls_stream:
+        chunks.append(chunk)
+        if chunk.choices[0].delta.tool_calls:
+            print(chunk.choices[0].delta.tool_calls[0])
+        else:
+            print(chunk.choices[0].delta)
+    return chunks
+
+
+def handle_tool_calls_arguments(chunks: list[Any]) -> list[str]:
+    arguments = []
+    tool_call_idx = -1
+    print("arguments: ")
+    for chunk in chunks:
+        if chunk.choices[0].delta.tool_calls:
+            tool_call = chunk.choices[0].delta.tool_calls[0]
+            if tool_call.index != tool_call_idx:
+                if tool_call_idx >= 0:
+                    print(f"streamed tool call arguments: "
+                          f"{arguments[tool_call_idx]}")
+                tool_call_idx = chunk.choices[0].delta.tool_calls[0].index
+                arguments.append("")
+            if tool_call.id:
+                print(f"streamed tool call id: {tool_call.id} ")
+
+            if tool_call.function:
+                if tool_call.function.name:
+                    print(
+                        f"streamed tool call name: {tool_call.function.name}")
+
+                if tool_call.function.arguments:
+                    arguments[tool_call_idx] += tool_call.function.arguments
+
+    return arguments
+
+
+def main():
+    # Initialize OpenAI client
+    client = OpenAI(
+        # defaults to os.environ.get("OPENAI_API_KEY")
+        api_key=openai_api_key,
+        base_url=openai_api_base,
+    )
+
+    # Get available models and select one
+    models = client.models.list()
+    model = models.data[0].id
+
+    chat_completion = client.chat.completions.create(messages=messages,
+                                                     model=model,
+                                                     tools=tools)
+
+    print("-" * 70)
+    print("Chat completion results:")
+    print(chat_completion)
+    print("-" * 70)
+
+    # Stream tool calls
+    chunks = handle_tool_calls_stream(client, messages, model, tools)
+    print("-" * 70)
+
+    # Handle arguments from streamed tool calls
+    arguments = handle_tool_calls_arguments(chunks)
+
+    if len(arguments):
+        print(f"streamed tool call arguments: {arguments[-1]}\n")
+
+    print("-" * 70)
+
+    # Add tool call results to the conversation
     messages.append({
-        "role": "tool",
-        "content": result,
-        "tool_call_id": call.id,
-        "name": call.function.name
+        "role": "assistant",
+        "tool_calls": chat_completion.choices[0].message.tool_calls
     })
 
-chat_completion_2 = client.chat.completions.create(messages=messages,
-                                                   model=model,
-                                                   tools=tools,
-                                                   stream=False)
-print("\n\n")
-print(chat_completion_2)
+    # Now, simulate a tool call
+    available_tools = {"get_current_weather": get_current_weather}
+
+    completion_tool_calls = chat_completion.choices[0].message.tool_calls
+    for call in completion_tool_calls:
+        tool_to_call = available_tools[call.function.name]
+        args = json.loads(call.function.arguments)
+        result = tool_to_call(**args)
+        print("tool_to_call result: ", result)
+        messages.append({
+            "role": "tool",
+            "content": result,
+            "tool_call_id": call.id,
+            "name": call.function.name
+        })
+
+    chat_completion_2 = client.chat.completions.create(messages=messages,
+                                                       model=model,
+                                                       tools=tools,
+                                                       stream=False)
+    print("Chat completion2 results:")
+    print(chat_completion_2)
+    print("-" * 70)
+
+
+if __name__ == "__main__":
+    main()