Update examples, wherever possible, to use LLMContext and associated machinery instead of OpenAILLMContext and associated machinery.

With all these examples updated, we no longer need dedicated examples illustrating `LLMContext`, so they're removed.

Here’s where we *don’t* yet use `LLMContext` and associated machinery:
- Realtime services: OpenAI Realtime, Gemini Live, and AWS Nova Sonic (support coming soon)
- `GoogleLLMOpenAIBetaService` (it’s deprecated, so we didn’t bother adding support)
- `LLMLogObserver` (support coming soon)
- `GatedOpenAILLMContextAggregator` (support coming soon)
- `LangchainProcessor` (support coming soon)
- `Mem0MemoryService` (support coming soon)
- Examples that use LLM-specific tools definitions as opposed to `ToolsSchema` (these will be updated soon)
- Examples that rely `GoogleLLMContext.upgrade_to_google` (TBD what to do with these)

Examples that use `LLMLogObserver`:
- 30-

Examples that use `GatedOpenAILLMContextAggregator`:
- 22-

Examples that use `LangchainProcessor`:
- 07b-

Examples that use `Mem0MemoryService`:
- 37-

Examples that need updating to use `ToolsSchema`:
- 15-
- 15a-
- 20a-
- 20c-
- 20d-
- 22b-
- 22c-
- 33-
- 36-

Examples that use `GoogleLLMContext.upgrade_to_google`:
- 22d-
- 25-

examples/foundational/02-llm-say-one-thing.py

@@ -9,14 +9,11 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMContextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -63,7 +60,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        await task.queue_frames([OpenAILLMContextFrame(OpenAILLMContext(messages)), EndFrame()])
+        await task.queue_frames([LLMContextFrame(LLMContext(messages)), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)