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.
This commit is contained in:
Mark Backman
178
examples/foundational/14y-function-calling-sarvam.py
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178
examples/foundational/14y-function-calling-sarvam.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.sarvam.llm import SarvamLLMService
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from pipecat.services.sarvam.stt import SarvamSTTService
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from pipecat.services.sarvam.tts import SarvamTTSService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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async def fetch_weather_from_api(params: FunctionCallParams):
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await params.result_callback({"conditions": "nice", "temperature": "75"})
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async def fetch_restaurant_recommendation(params: FunctionCallParams):
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await params.result_callback({"name": "The Golden Dragon"})
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = SarvamSTTService(
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api_key=os.getenv("SARVAM_API_KEY"),
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settings=SarvamSTTService.Settings(
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model="saarika:v2.5",
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),
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)
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tts = SarvamTTSService(
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api_key=os.getenv("SARVAM_API_KEY"),
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settings=SarvamTTSService.Settings(
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model="bulbul:v2",
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voice="manisha",
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),
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)
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llm = SarvamLLMService(
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api_key=os.getenv("SARVAM_API_KEY"),
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settings=SarvamLLMService.Settings(
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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.",
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),
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)
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# You can also register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
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@llm.event_handler("on_function_calls_started")
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async def on_function_calls_started(service, function_calls):
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await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the user's location.",
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},
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},
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required=["location", "format"],
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)
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restaurant_function = FunctionSchema(
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name="get_restaurant_recommendation",
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description="Get a restaurant recommendation",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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},
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required=["location"],
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)
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tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
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context = LLMContext(tools=tools)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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user_aggregator,
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llm,
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tts,
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transport.output(),
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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@@ -186,6 +186,7 @@ TESTS_14 = [
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("14v-function-calling-openai.py", EVAL_WEATHER),
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("14w-function-calling-mistral.py", EVAL_WEATHER),
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("14x-function-calling-openpipe.py", EVAL_WEATHER),
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("14y-function-calling-sarvam.py", EVAL_WEATHER),
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("14-function-calling-openai-responses.py", EVAL_WEATHER),
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("14-function-calling-openai-responses.py", EVAL_WEATHER_AND_RESTAURANT),
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# Video
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@@ -6,27 +6,19 @@
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"""Sarvam LLM service implementation using OpenAI-compatible interface."""
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import asyncio
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import json
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from dataclasses import dataclass, field
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from typing import Any, Awaitable, Literal, Mapping, Optional, TypeVar
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from typing import Literal, Mapping, Optional
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import httpx
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from loguru import logger
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from openai import NOT_GIVEN, APITimeoutError, AsyncStream
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from openai.types.chat import ChatCompletionChunk
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from openai import NOT_GIVEN
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from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.openai.base_llm import OpenAILLMSettings
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.sarvam._sdk import sdk_headers
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from pipecat.services.settings import NOT_GIVEN as _NOT_GIVEN
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from pipecat.services.settings import _NotGiven, is_given
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_T = TypeVar("_T")
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@dataclass
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class SarvamLLMSettings(OpenAILLMSettings):
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@@ -44,15 +36,10 @@ class SarvamLLMSettings(OpenAILLMSettings):
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class SarvamLLMService(OpenAILLMService):
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"""Sarvam LLM service using Sarvam's OpenAI-compatible chat completions API.
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"""A service for interacting with Sarvam's API using the OpenAI-compatible interface.
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This service extends ``OpenAILLMService`` while adding Sarvam-specific behavior:
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- model allow-list validation
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- request shaping for Sarvam-compatible parameters
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- Sarvam auth header wiring (``api-subscription-key``)
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- SDK User-Agent propagation on every API call
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- raw Sarvam server error passthrough
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This service extends OpenAILLMService to connect to Sarvam's API endpoint while
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maintaining full compatibility with OpenAI's interface and functionality.
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"""
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_SUPPORTED_MODELS = frozenset(
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@@ -153,44 +140,6 @@ class SarvamLLMService(OpenAILLMService):
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return params
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async def _call_with_raw_sarvam_errors(self, awaitable: Awaitable[_T]) -> _T:
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"""Await an OpenAI call while preserving Sarvam raw error payloads.
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BaseOpenAILLMService handles pipeline-frame exceptions via push_error(),
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but direct helper methods like ``get_chat_completions`` and
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``run_inference`` are often consumed directly. We normalize those errors
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here so applications consistently receive server-provided messages.
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"""
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try:
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return await awaitable
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except (APITimeoutError, asyncio.TimeoutError, httpx.TimeoutException):
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raise
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except Exception as e:
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raise RuntimeError(self._format_raw_server_error(e)) from e
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async def get_chat_completions(
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self, params_from_context: OpenAILLMInvocationParams
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) -> AsyncStream[ChatCompletionChunk]:
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"""Get streaming chat completions with Sarvam raw error passthrough."""
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return await self._call_with_raw_sarvam_errors(
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super().get_chat_completions(params_from_context)
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)
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async def run_inference(
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self,
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context: LLMContext | OpenAILLMContext,
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max_tokens: Optional[int] = None,
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system_instruction: Optional[str] = None,
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) -> Optional[str]:
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"""Run one-shot inference and preserve Sarvam raw server errors."""
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return await self._call_with_raw_sarvam_errors(
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super().run_inference(
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context,
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max_tokens=max_tokens,
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system_instruction=system_instruction,
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)
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)
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def _validate_model(self, model: str):
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if model not in self._SUPPORTED_MODELS:
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allowed = ", ".join(sorted(self._SUPPORTED_MODELS))
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@@ -209,35 +158,3 @@ class SarvamLLMService(OpenAILLMService):
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if has_tool_choice and not has_tools:
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raise ValueError("Sarvam requires non-empty `tools` when `tool_choice` is provided.")
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def _format_raw_server_error(self, error: Exception) -> str:
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raw_message = self._extract_raw_server_message(error)
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return f"Sarvam server error: {raw_message}"
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def _extract_raw_server_message(self, error: Exception) -> str:
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body = getattr(error, "body", None)
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if body is not None:
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return self._payload_to_message(body)
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response = getattr(error, "response", None)
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if response is not None:
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try:
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return self._payload_to_message(response.json())
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except Exception:
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text = getattr(response, "text", None)
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if text:
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return str(text)
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return str(error)
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def _payload_to_message(self, payload: Any) -> str:
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if isinstance(payload, dict):
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error_obj = payload.get("error")
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if isinstance(error_obj, dict) and isinstance(error_obj.get("message"), str):
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return error_obj["message"]
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if isinstance(payload.get("message"), str):
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return payload["message"]
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return json.dumps(payload, ensure_ascii=False)
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if isinstance(payload, list):
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return json.dumps(payload, ensure_ascii=False)
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return str(payload)
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