Feature o1 branch

examples/openai/o1-03_function-calling.py

@@ -0,0 +1,394 @@
+#!/usr/bin/env python
+import os
+import json
+import re
+import argparse
+from dotenv import load_dotenv
+from openai import OpenAI, AzureOpenAI
+from javelin_sdk import JavelinClient, JavelinConfig, RouteNotFoundError
+
+# Load environment variables once at the start
+load_dotenv()
+
+# ---------------------------
+# OpenAI – Unified Endpoint Examples
+# ---------------------------
+def init_openai_client():
+    api_key = os.getenv("OPENAI_API_KEY")
+    return OpenAI(api_key=api_key)
+
+def init_javelin_client(openai_client, route_name="openai_univ"):
+    javelin_api_key = os.getenv("JAVELIN_API_KEY")
+    config = JavelinConfig(javelin_api_key=javelin_api_key)
+    client = JavelinClient(config)
+    client.register_openai(openai_client, route_name=route_name)
+    return client
+
+def openai_function_call_non_stream():
+    print("\n==== Running OpenAI Non-Streaming Function Calling Example ====")
+    client = init_openai_client()
+    init_javelin_client(client)
+    response = client.chat.completions.create(
+        model="o3-mini",  # Latest o1 model
+        messages=[
+            {"role": "user", "content": "What is the current weather in New York?"}
+        ],
+        functions=[
+            {
+                "name": "get_current_weather",
+                "description": "Retrieves current weather information",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "City and state (e.g., New York, NY)"
+                        },
+                        "unit": {
+                            "type": "string",
+                            "enum": ["celsius", "fahrenheit"]
+                        }
+                    },
+                    "required": ["location"]
+                }
+            }
+        ],
+        function_call="auto"
+    )
+    print("OpenAI Non-Streaming Response:")
+    print(response.model_dump_json(indent=2))
+
+def openai_function_call_stream():
+    print("\n==== Running OpenAI Streaming Function Calling Example ====")
+    client = init_openai_client()
+    init_javelin_client(client)
+    stream = client.chat.completions.create(
+        model="o3-mini",
+        messages=[
+            {"role": "user", "content": "Tell me a fun fact and then call a function."}
+        ],
+        functions=[
+            {
+                "name": "tell_fun_fact",
+                "description": "Returns a fun fact",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "fact": {
+                            "type": "string",
+                            "description": "A fun fact about the topic"
+                        }
+                    },
+                    "required": ["fact"]
+                }
+            }
+        ],
+        function_call="auto",
+        stream=True
+    )
+    collected = []
+    print("OpenAI Streaming Response:")
+    for chunk in stream:
+        delta = chunk.choices[0].delta
+        print(chunk)
+        if hasattr(delta, "content") and delta.content:
+            collected.append(delta.content)
+    print("".join(collected))
+
+def openai_structured_output_call_generic():
+    print("\n==== Running OpenAI Structured Output Function Calling Example ====")
+    openai_client = init_openai_client()
+    init_javelin_client(openai_client)
+    messages = [
+        {
+            "role": "system",
+            "content": "You are an assistant that always responds in valid JSON format without any additional text."
+        },
+        {
+            "role": "user",
+            "content": (
+                "Provide a generic example of structured data output in JSON format. "
+                "The JSON should include the keys: 'id', 'name', 'description', "
+                "and 'attributes' (which should be a nested object with arbitrary key-value pairs)."
+            )
+        }
+    ]
+
+    response = openai_client.chat.completions.create(
+        model="o3-mini",  # can use o1 model as well
+        messages=messages,
+    )
+
+    print("Structured Output (JSON) Response:")
+    print(response.model_dump_json(indent=2))
+
+    try:
+        reply_content = response.choices[0].message.content
+    except (IndexError, AttributeError) as e:
+        print("Error extracting message content:", e)
+        reply_content = ""
+
+    try:
+        json_output = json.loads(reply_content)
+        print("\nParsed JSON Output:")
+        print(json.dumps(json_output, indent=2))
+    except Exception as e:
+        print("\nFailed to parse JSON output. Error:", e)
+        print("Raw content:", reply_content)
+
+# ---------------------------
+# Azure OpenAI – Unified Endpoint Examples
+# ---------------------------
+def init_azure_client():
+    azure_api_key = os.getenv("AZURE_OPENAI_API_KEY")
+    return AzureOpenAI(
+        api_version="2023-07-01-preview",
+        azure_endpoint="https://javelinpreview.openai.azure.com",
+        api_key=azure_api_key
+    )
+
+def init_javelin_client_azure(azure_client, route_name="azureopenai_univ"):
+    javelin_api_key = os.getenv("JAVELIN_API_KEY")
+    config = JavelinConfig(javelin_api_key=javelin_api_key)
+    client = JavelinClient(config)
+    client.register_azureopenai(azure_client, route_name=route_name)
+    return client
+
+def azure_function_call_non_stream():
+    print("\n==== Running Azure OpenAI Non-Streaming Function Calling Example ====")
+    azure_client = init_azure_client()
+    init_javelin_client_azure(azure_client)
+    response = azure_client.chat.completions.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "user", "content": "Schedule a meeting at 10 AM tomorrow."}
+        ],
+        functions=[
+            {
+                "name": "schedule_meeting",
+                "description": "Schedules a meeting in the calendar",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "time": {"type": "string", "description": "Meeting time (ISO format)"},
+                        "date": {"type": "string", "description": "Meeting date (YYYY-MM-DD)"}
+                    },
+                    "required": ["time", "date"]
+                }
+            }
+        ],
+        function_call="auto"
+    )
+    print("Azure OpenAI Non-Streaming Response:")
+    print(response.to_json())
+
+def azure_function_call_stream():
+    print("\n==== Running Azure OpenAI Streaming Function Calling Example ====")
+    azure_client = init_azure_client()
+    init_javelin_client_azure(azure_client)
+    stream = azure_client.chat.completions.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "user", "content": "Schedule a meeting at 10 AM tomorrow."}
+        ],
+        functions=[
+            {
+                "name": "schedule_meeting",
+                "description": "Schedules a meeting in the calendar",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "time": {"type": "string", "description": "Meeting time (ISO format)"},
+                        "date": {"type": "string", "description": "Meeting date (YYYY-MM-DD)"}
+                    },
+                    "required": ["time", "date"]
+                }
+            }
+        ],
+        function_call="auto",
+        stream=True
+    )
+    print("Azure OpenAI Streaming Response:")
+    for chunk in stream:
+        print(chunk)
+
+def extract_json_from_markdown(text: str) -> str:
+    """
+    Extracts JSON content from a markdown code block if present.
+    Removes leading and trailing triple backticks.
+    """
+    match = re.search(r"```(?:json)?\s*(\{.*\})\s*```", text, re.DOTALL)
+    if match:
+        return match.group(1)
+    return text.strip()
+
+def azure_structured_output_call():
+    print("\n==== Running Azure OpenAI Structured Output Function Calling Example ====")
+    azure_client = init_azure_client()
+    init_javelin_client_azure(azure_client)
+    messages = [
+        {
+            "role": "system",
+            "content": "You are an assistant that always responds in valid JSON format without any additional text."
+        },
+        {
+            "role": "user",
+            "content": (
+                "Provide structured data in JSON format. "
+                "The JSON should contain the following keys: 'id' (integer), 'title' (string), "
+                "'description' (string), and 'metadata' (a nested object with arbitrary key-value pairs)."
+            )
+        }
+    ]
+
+    response = azure_client.chat.completions.create(
+        model="gpt-4o",
+        messages=messages
+    )
+
+    print("Structured Output (JSON) Response:")
+    print(response.to_json())
+
+    try:
+        reply_content = response.choices[0].message.content
+        reply_content_clean = extract_json_from_markdown(reply_content)
+        json_output = json.loads(reply_content_clean)
+        print("\nParsed JSON Output:")
+        print(json.dumps(json_output, indent=2))
+    except Exception as e:
+        print("\nFailed to parse JSON output. Error:", e)
+        print("Raw content:", reply_content)
+
+# ---------------------------
+# OpenAI – Regular Route Endpoint Examples
+# ---------------------------
+def openai_regular_non_stream():
+    print("\n==== Running OpenAI Regular Route Non-Streaming Function Calling Example ====")
+    javelin_api_key = os.getenv('JAVELIN_API_KEY')
+    llm_api_key = os.getenv("OPENAI_API_KEY")
+    if not javelin_api_key or not llm_api_key:
+        raise ValueError("Both JAVELIN_API_KEY and OPENAI_API_KEY must be set.")
+    print("OpenAI LLM API Key:", llm_api_key)
+    config = JavelinConfig(
+        base_url="https://api-dev.javelin.live",
+        javelin_api_key=javelin_api_key,
+        llm_api_key=llm_api_key,
+    )
+    client = JavelinClient(config)
+    print("Successfully connected to Javelin Client for OpenAI")
+
+    query_data = {
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant that translates English to French."},
+            {"role": "user", "content": "AI has the power to transform humanity and make the world a better place."},
+        ]
+    }
+
+    try:
+        response = client.query_route("openai", query_data)
+        print("Response from OpenAI Regular Endpoint:")
+        print(response)
+    except RouteNotFoundError:
+        print("Route 'openai' Not Found")
+    except Exception as e:
+        print("Error querying OpenAI endpoint:", e)
+
+def openai_regular_stream():
+    print("\n==== Running OpenAI Regular Route Streaming Function Calling Example ====")
+    javelin_api_key = os.getenv('JAVELIN_API_KEY')
+    llm_api_key = os.getenv("OPENAI_API_KEY")
+    if not javelin_api_key or not llm_api_key:
+        raise ValueError("Both JAVELIN_API_KEY and OPENAI_API_KEY must be set.")
+    config = JavelinConfig(
+        base_url="https://api-dev.javelin.live",
+        javelin_api_key=javelin_api_key,
+        llm_api_key=llm_api_key,
+    )
+    client = JavelinClient(config)
+    print("Successfully connected to Javelin Client for OpenAI")
+
+    query_data = {
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant that translates English to French."},
+            {"role": "user", "content": "AI has the power to transform humanity and make the world a better place."},
+        ],
+        "functions": [
+            {
+                "name": "translate_text",
+                "description": "Translates English text to French",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "text": {
+                            "type": "string",
+                            "description": "Text to translate"
+                        }
+                    },
+                    "required": ["text"]
+                }
+            }
+        ],
+        "function_call": "auto",
+        "stream": True
+    }
+
+    try:
+        response = client.query_route("openai", query_data)
+        print("Response from OpenAI Regular Endpoint (Streaming):")
+        if query_data.get("stream"):
+            for chunk in response:
+                print(chunk)
+        else:
+            print(response)
+    except RouteNotFoundError as e:
+        print(f"Route 'openai' not found: {str(e)}")
+    except Exception as e:
+        print(f"Error occurred while getting response: {str(e)}")
+
+
+# ---------------------------
+# Main function and argument parsing
+# ---------------------------
+def main():
+    parser = argparse.ArgumentParser(description="Run Unified Endpoint Examples")
+    parser.add_argument(
+        "--example",
+        type=str,
+        default="all",
+        choices=[
+            "all", "openai_non_stream", "openai_stream", "openai_structured",
+            "azure_non_stream", "azure_stream", "azure_structured",
+            "openai_regular_non_stream", "openai_regular_stream"
+        ],
+        help="The example to run (or 'all' to run every example)"
+    )
+    args = parser.parse_args()
+
+    if args.example == "all":
+        openai_function_call_non_stream()
+        openai_function_call_stream()
+        openai_structured_output_call_generic()
+        azure_function_call_non_stream()
+        azure_function_call_stream()
+        azure_structured_output_call()
+        openai_regular_non_stream()
+        openai_regular_stream()
+    elif args.example == "openai_non_stream":
+        openai_function_call_non_stream()
+    elif args.example == "openai_stream":
+        openai_function_call_stream()
+    elif args.example == "openai_structured":
+        openai_structured_output_call_generic()
+    elif args.example == "azure_non_stream":
+        azure_function_call_non_stream()
+    elif args.example == "azure_stream":
+        azure_function_call_stream()
+    elif args.example == "azure_structured":
+        azure_structured_output_call()
+    elif args.example == "openai_regular_non_stream":
+        openai_regular_non_stream()
+    elif args.example == "openai_regular_stream":
+        openai_regular_stream()
+
+if __name__ == "__main__":
+    main()