Include examples in type checking

Remove `examples/` from the `pyrightconfig.json` ignore list and fix
the resulting type errors across all example files. Common fixes:

- Required API keys: `os.getenv("X")` -> `os.environ["X"]` so the
  return type is `str` rather than `str | None`, and misconfiguration
  fails fast.
- Narrow `LLMContextMessage` union members with `isinstance(..., dict)`
  before dict-style access.
- `assert isinstance(params.llm, ...)` before calling service-specific
  methods that aren't on the base `LLMService`.
- Guard optional frame fields (e.g. `LLMSearchResponseFrame.search_result`)
  before use.

examples/rag/rag-gemini-grounding-metadata.py

@@ -95,10 +95,10 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
 
     tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+        api_key=os.environ["CARTESIA_API_KEY"],
         settings=CartesiaTTSService.Settings(
             voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         ),
@@ -106,7 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     # Initialize the Gemini Multimodal Live model
     llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
+        api_key=os.environ["GOOGLE_API_KEY"],
         settings=GoogleLLMService.Settings(
             system_instruction=system_instruction,
         ),

examples/rag/rag-gemini.py

@@ -52,7 +52,7 @@ import os
 import time
 
 from dotenv import load_dotenv
-from google import genai
+from google import genai  # pyright: ignore[reportAttributeAccessIssue]
 from loguru import logger
 
 from pipecat.adapters.schemas.function_schema import FunctionSchema
@@ -179,10 +179,10 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
 
     tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+        api_key=os.environ["CARTESIA_API_KEY"],
         settings=CartesiaTTSService.Settings(
             voice="f9836c6e-a0bd-460e-9d3c-f7299fa60f94",  # Southern Lady
         ),
@@ -197,7 +197,7 @@ Your response will be turned into speech so use only simple words and punctuatio
 """
 
     llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
+        api_key=os.environ["GOOGLE_API_KEY"],
         settings=GoogleLLMService.Settings(
             model=VOICE_MODEL,
             system_instruction=system_prompt,

examples/rag/rag-mem0.py

@@ -133,11 +133,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     logger.info(f"Starting bot")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
 
     # Initialize text-to-speech service
     tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        api_key=os.environ["ELEVENLABS_API_KEY"],
         settings=ElevenLabsTTSService.Settings(
             voice="pNInz6obpgDQGcFmaJgB",
         ),
@@ -196,7 +196,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     # Initialize LLM service
     llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
+        api_key=os.environ["OPENAI_API_KEY"],
         settings=OpenAILLMService.Settings(
             system_instruction="""You are a personal assistant. You can remember things about the person you are talking to.
                         Some Guidelines: