Update foundational examples to use "user" role

Use system_instruction on LLM service constructors instead of adding
system messages to LLMContext. Messages added to context now use
"user" role.

examples/foundational/20c-persistent-context-anthropic.py

@@ -26,7 +26,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService, AnthropicLLMSettings
 from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
@@ -38,7 +38,6 @@ load_dotenv(override=True)
 
 
 BASE_FILENAME = "/tmp/pipecat_conversation_"
-tts = None
 
 
 async def fetch_weather_from_api(params: FunctionCallParams):
@@ -82,7 +81,6 @@ async def save_conversation(params: FunctionCallParams):
 
 
 async def load_conversation(params: FunctionCallParams):
-    global tts
     filename = params.arguments["filename"]
     logger.debug(f"loading conversation from {filename}")
     try:
@@ -96,18 +94,7 @@ async def load_conversation(params: FunctionCallParams):
         await params.result_callback({"success": False, "error": str(e)})
 
 
-# Test message munging ...
-messages = [
-    {
-        "role": "system",
-        "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a succinct, creative and helpful way. Prefer responses that are one sentence long unless you are asked for a longer or more detailed response.",
-    },
-    {"role": "user", "content": "Start the call by saying the word 'hello'. Say only that word."},
-    # {"role": "user", "content": ""},
-    # {"role": "assistant", "content": []},
-    # {"role": "user", "content": "Tell me"},
-    # {"role": "user", "content": "a joke"},
-]
+system_instruction = "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a succinct, creative and helpful way. Prefer responses that are one sentence long unless you are asked for a longer or more detailed response."
 
 weather_function = FunctionSchema(
     name="get_current_weather",
@@ -183,8 +170,6 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    global tts
-
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
@@ -194,7 +179,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ),
     )
 
-    llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+    llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        settings=AnthropicLLMSettings(system_instruction=system_instruction),
+    )
 
     # you can either register a single function for all function calls, or specific functions
     # llm.register_function(None, fetch_weather_from_api)
@@ -203,7 +191,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
     llm.register_function("load_conversation", load_conversation)
 
-    context = LLMContext(messages, tools)
+    context = LLMContext(tools=tools)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -234,6 +222,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
+        context.add_message(
+            {
+                "role": "user",
+                "content": "Start the call by saying the word 'hello'. Say only that word.",
+            }
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")