Merge pull request #3926 from pipecat-ai/mb/update-deps-2026-03-05

Update dependency version ranges for flexibility

examples/foundational/07-interruptible-cartesia-http.py

@@ -6,6 +6,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -52,60 +53,64 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    async with aiohttp.ClientSession() as session:
+        stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
 
-    tts = CartesiaHttpTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            aiohttp_session=session,
+        )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        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.",
-    )
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            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()),
-    )
+        context = LLMContext()
+        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        )
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                user_aggregator,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                assistant_aggregator,  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+        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(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            context.add_message(
+                {"role": "system", "content": "Please introduce yourself to the user."}
+            )
+            await task.queue_frames([LLMRunFrame()])
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
+            await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

examples/foundational/07c-interruptible-deepgram-sagemaker.py

@@ -110,7 +110,7 @@ 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": "system", "content": "Please introduce yourself to the user."})
+        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/foundational/07w-interruptible-fal.py

@@ -7,6 +7,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -53,62 +54,66 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = FalSTTService(
-        api_key=os.getenv("FAL_KEY"),
-    )
+    async with aiohttp.ClientSession() as session:
+        stt = FalSTTService(
+            api_key=os.getenv("FAL_KEY"),
+            aiohttp_session=session,
+        )
 
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        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.",
-    )
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            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()),
-    )
+        context = LLMContext()
+        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        )
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                user_aggregator,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                assistant_aggregator,  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+        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(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            context.add_message(
+                {"role": "system", "content": "Please introduce yourself to the user."}
+            )
+            await task.queue_frames([LLMRunFrame()])
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
+            await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):