From c6c14d588754ca800faf19fd18246e992a1f7e76 Mon Sep 17 00:00:00 2001 From: Mark Backman Date: Mon, 23 Mar 2026 09:12:31 -0400 Subject: [PATCH] Remove error-wrapping overrides and align class docstring The OpenAI SDK already preserves server error details in typed exceptions (APIStatusError subclasses), and BaseOpenAILLMService propagates errors via push_error(). The RuntimeError wrapper was erasing exception types that callers may want to handle explicitly. --- .../14y-function-calling-sarvam.py | 178 ++++++++++++++++++ scripts/evals/run-release-evals.py | 1 + src/pipecat/services/sarvam/llm.py | 93 +-------- 3 files changed, 184 insertions(+), 88 deletions(-) create mode 100644 examples/foundational/14y-function-calling-sarvam.py diff --git a/examples/foundational/14y-function-calling-sarvam.py b/examples/foundational/14y-function-calling-sarvam.py new file mode 100644 index 000000000..72d089778 --- /dev/null +++ b/examples/foundational/14y-function-calling-sarvam.py @@ -0,0 +1,178 @@ +# +# Copyright (c) 2024-2026, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import os + +from dotenv import load_dotenv +from loguru import logger + +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame +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.llm_service import FunctionCallParams +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) + + +async def fetch_weather_from_api(params: FunctionCallParams): + await params.result_callback({"conditions": "nice", "temperature": "75"}) + + +async def fetch_restaurant_recommendation(params: FunctionCallParams): + await params.result_callback({"name": "The Golden Dragon"}) + + +# We use lambdas to defer transport parameter creation until the transport +# type is selected at runtime. +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, + ), +} + + +async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): + logger.info(f"Starting bot") + + stt = SarvamSTTService( + api_key=os.getenv("SARVAM_API_KEY"), + settings=SarvamSTTService.Settings( + model="saarika:v2.5", + ), + ) + + tts = SarvamTTSService( + api_key=os.getenv("SARVAM_API_KEY"), + settings=SarvamTTSService.Settings( + model="bulbul:v2", + voice="manisha", + ), + ) + llm = SarvamLLMService( + api_key=os.getenv("SARVAM_API_KEY"), + settings=SarvamLLMService.Settings( + system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.", + ), + ) + + # You can also register a function_name of None to get all functions + # sent to the same callback with an additional function_name parameter. + llm.register_function("get_current_weather", fetch_weather_from_api) + llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation) + + @llm.event_handler("on_function_calls_started") + async def on_function_calls_started(service, function_calls): + await tts.queue_frame(TTSSpeakFrame("Let me check on that.")) + + weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the user's location.", + }, + }, + required=["location", "format"], + ) + restaurant_function = FunctionSchema( + name="get_restaurant_recommendation", + description="Get a restaurant recommendation", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + }, + required=["location"], + ) + tools = ToolsSchema(standard_tools=[weather_function, restaurant_function]) + + context = LLMContext(tools=tools) + 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(f"Client connected") + # Kick off the conversation. + context.add_message({"role": "user", "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() + + 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() diff --git a/scripts/evals/run-release-evals.py b/scripts/evals/run-release-evals.py index 19492ba62..dcb531cd6 100644 --- a/scripts/evals/run-release-evals.py +++ b/scripts/evals/run-release-evals.py @@ -186,6 +186,7 @@ TESTS_14 = [ ("14v-function-calling-openai.py", EVAL_WEATHER), ("14w-function-calling-mistral.py", EVAL_WEATHER), ("14x-function-calling-openpipe.py", EVAL_WEATHER), + ("14y-function-calling-sarvam.py", EVAL_WEATHER), ("14-function-calling-openai-responses.py", EVAL_WEATHER), ("14-function-calling-openai-responses.py", EVAL_WEATHER_AND_RESTAURANT), # Video diff --git a/src/pipecat/services/sarvam/llm.py b/src/pipecat/services/sarvam/llm.py index 9b237d11b..8945e0066 100644 --- a/src/pipecat/services/sarvam/llm.py +++ b/src/pipecat/services/sarvam/llm.py @@ -6,27 +6,19 @@ """Sarvam LLM service implementation using OpenAI-compatible interface.""" -import asyncio -import json from dataclasses import dataclass, field -from typing import Any, Awaitable, Literal, Mapping, Optional, TypeVar +from typing import Literal, Mapping, Optional -import httpx from loguru import logger -from openai import NOT_GIVEN, APITimeoutError, AsyncStream -from openai.types.chat import ChatCompletionChunk +from openai import NOT_GIVEN from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams -from pipecat.processors.aggregators.llm_context import LLMContext -from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai.base_llm import OpenAILLMSettings from pipecat.services.openai.llm import OpenAILLMService from pipecat.services.sarvam._sdk import sdk_headers from pipecat.services.settings import NOT_GIVEN as _NOT_GIVEN from pipecat.services.settings import _NotGiven, is_given -_T = TypeVar("_T") - @dataclass class SarvamLLMSettings(OpenAILLMSettings): @@ -44,15 +36,10 @@ class SarvamLLMSettings(OpenAILLMSettings): class SarvamLLMService(OpenAILLMService): - """Sarvam LLM service using Sarvam's OpenAI-compatible chat completions API. + """A service for interacting with Sarvam's API using the OpenAI-compatible interface. - This service extends ``OpenAILLMService`` while adding Sarvam-specific behavior: - - - model allow-list validation - - request shaping for Sarvam-compatible parameters - - Sarvam auth header wiring (``api-subscription-key``) - - SDK User-Agent propagation on every API call - - raw Sarvam server error passthrough + This service extends OpenAILLMService to connect to Sarvam's API endpoint while + maintaining full compatibility with OpenAI's interface and functionality. """ _SUPPORTED_MODELS = frozenset( @@ -153,44 +140,6 @@ class SarvamLLMService(OpenAILLMService): return params - async def _call_with_raw_sarvam_errors(self, awaitable: Awaitable[_T]) -> _T: - """Await an OpenAI call while preserving Sarvam raw error payloads. - - BaseOpenAILLMService handles pipeline-frame exceptions via push_error(), - but direct helper methods like ``get_chat_completions`` and - ``run_inference`` are often consumed directly. We normalize those errors - here so applications consistently receive server-provided messages. - """ - try: - return await awaitable - except (APITimeoutError, asyncio.TimeoutError, httpx.TimeoutException): - raise - except Exception as e: - raise RuntimeError(self._format_raw_server_error(e)) from e - - async def get_chat_completions( - self, params_from_context: OpenAILLMInvocationParams - ) -> AsyncStream[ChatCompletionChunk]: - """Get streaming chat completions with Sarvam raw error passthrough.""" - return await self._call_with_raw_sarvam_errors( - super().get_chat_completions(params_from_context) - ) - - async def run_inference( - self, - context: LLMContext | OpenAILLMContext, - max_tokens: Optional[int] = None, - system_instruction: Optional[str] = None, - ) -> Optional[str]: - """Run one-shot inference and preserve Sarvam raw server errors.""" - return await self._call_with_raw_sarvam_errors( - super().run_inference( - context, - max_tokens=max_tokens, - system_instruction=system_instruction, - ) - ) - def _validate_model(self, model: str): if model not in self._SUPPORTED_MODELS: allowed = ", ".join(sorted(self._SUPPORTED_MODELS)) @@ -209,35 +158,3 @@ class SarvamLLMService(OpenAILLMService): if has_tool_choice and not has_tools: raise ValueError("Sarvam requires non-empty `tools` when `tool_choice` is provided.") - - def _format_raw_server_error(self, error: Exception) -> str: - raw_message = self._extract_raw_server_message(error) - return f"Sarvam server error: {raw_message}" - - def _extract_raw_server_message(self, error: Exception) -> str: - body = getattr(error, "body", None) - if body is not None: - return self._payload_to_message(body) - - response = getattr(error, "response", None) - if response is not None: - try: - return self._payload_to_message(response.json()) - except Exception: - text = getattr(response, "text", None) - if text: - return str(text) - - return str(error) - - def _payload_to_message(self, payload: Any) -> str: - if isinstance(payload, dict): - error_obj = payload.get("error") - if isinstance(error_obj, dict) and isinstance(error_obj.get("message"), str): - return error_obj["message"] - if isinstance(payload.get("message"), str): - return payload["message"] - return json.dumps(payload, ensure_ascii=False) - if isinstance(payload, list): - return json.dumps(payload, ensure_ascii=False) - return str(payload)