Rename example files to prepend parent folder name, preventing package shadowing

Example files like openai.py shadow installed packages when Python adds the
script directory to sys.path. Prepend the parent folder name to each example
file (e.g. openai.py -> function-calling-openai.py). Also split
thinking-and-mcp/ into separate mcp/ and thinking/ directories.

examples/update-settings/llm/llm-sarvam.py

@@ -0,0 +1,137 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+from typing import Any
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame, LLMUpdateSettingsFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.sarvam.llm import SarvamLLMService
+from pipecat.services.sarvam.stt import SarvamSTTService
+from pipecat.services.sarvam.tts import SarvamTTSService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+def _require_env(name: str) -> str:
+    value = os.getenv(name)
+    if not value:
+        raise ValueError(f"Environment variable `{name}` is required.")
+    return value
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting bot")
+
+    stt = SarvamSTTService(
+        settings=SarvamSTTService.Settings(model="saaras:v3"),
+        api_key=_require_env("SARVAM_API_KEY"),
+    )
+
+    tts = SarvamTTSService(
+        settings=SarvamTTSService.Settings(model="bulbul:v3"),
+        api_key=_require_env("SARVAM_API_KEY"),
+    )
+
+    llm = SarvamLLMService(
+        api_key=_require_env("SARVAM_API_KEY"),
+        settings=SarvamLLMService.Settings(model="sarvam-30b"),
+        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 creative and helpful way."
+        ),
+    )
+
+    context = LLMContext()
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_aggregator,
+            llm,
+            tts,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected")
+        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
+
+        await asyncio.sleep(10)
+        logger.info("Updating Sarvam LLM settings: temperature=0.1")
+        await task.queue_frame(
+            LLMUpdateSettingsFrame(delta=SarvamLLMService.Settings(temperature=0.1))
+        )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()