conversation save/load for openai, openai-realtime, and anthropic

examples/foundational/19-openai-realtime-beta.py

@@ -5,9 +5,7 @@
 #
 
 import asyncio
-import json
 import os
-import re
 import sys
 from datetime import datetime
 
@@ -50,46 +48,6 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
     )
 
 
-async def get_saved_conversation_filenames(
-    function_name, tool_call_id, args, llm, context, result_callback
-):
-    pattern = re.compile("example_19_\\d{8}_\\d{6}\\.json$")
-    matching_files = []
-
-    for filename in os.listdir("."):
-        if pattern.match(filename):
-            matching_files.append(filename)
-
-    await result_callback({"filenames": matching_files})
-
-
-async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"example_19_{timestamp}.json"
-    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
-    try:
-        with open(filename, "w") as file:
-            json.dump(context.messages, file, indent=4)
-        await result_callback({"success": True})
-    except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
-
-
-async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
-    async def _reset():
-        filename = args["filename"]
-        logger.debug(f"loading conversation from {filename}")
-        try:
-            with open(filename, "r") as file:
-                context.set_messages(json.load(file))
-                await llm.reset_conversation()
-                await llm._create_response()
-        except Exception as e:
-            await result_callback({"success": False, "error": str(e)})
-
-    asyncio.create_task(_reset())
-
-
 tools = [
     {
         "type": "function",
@@ -110,42 +68,7 @@ tools = [
             },
             "required": ["location", "format"],
         },
-    },
-    {
-        "type": "function",
-        "name": "save_conversation",
-        "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
-        "parameters": {
-            "type": "object",
-            "properties": {},
-            "required": [],
-        },
-    },
-    {
-        "type": "function",
-        "name": "get_saved_conversation_filenames",
-        "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a timestamp. Each file is conversation history that can be loaded into this session.",
-        "parameters": {
-            "type": "object",
-            "properties": {},
-            "required": [],
-        },
-    },
-    {
-        "type": "function",
-        "name": "load_conversation",
-        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "filename": {
-                    "type": "string",
-                    "description": "The filename of the conversation history to load.",
-                }
-            },
-            "required": ["filename"],
-        },
-    },
+    }
 ]
 
 
@@ -202,11 +125,8 @@ Remember, your responses should be short. Just one or two sentences, usually."""
         # you can either register a single function for all function calls, or specific functions
         # llm.register_function(None, fetch_weather_from_api)
         llm.register_function("get_current_weather", fetch_weather_from_api)
-        llm.register_function("save_conversation", save_conversation)
-        llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
-        llm.register_function("load_conversation", load_conversation)
 
-        context = OpenAILLMContext([], tools)
+        context = OpenAILLMContext([{"role": "user", "content": "Say hello!"}], tools)
         context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(