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wrapper fixes

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This commit is contained in:
dhruvladia-sarvam
2026-03-17 02:47:47 +05:30
committed by Mark Backman
parent 9bbc28bc9a
commit edf4ba45a5
3 changed files with 33 additions and 31 deletions
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31
examples/foundational/55zzq-update-settings-sarvam-llm.py
View File

@@ -23,7 +23,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.openai.base_llm import OpenAILLMSettings
from pipecat.services.sarvam.llm import SarvamLLMService
from pipecat.services.sarvam.stt import SarvamSTTService
from pipecat.services.sarvam.tts import SarvamTTSService
@@ -60,34 +59,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info("Starting bot")
stt = SarvamSTTService(
model="saaras:v3",
settings=SarvamSTTService.Settings(model="saaras:v3"),
api_key=_require_env("SARVAM_API_KEY"),
)
tts = SarvamTTSService(
model="bulbul:v3",
settings=SarvamTTSService.Settings(model="bulbul:v3"),
api_key=_require_env("SARVAM_API_KEY"),
)
llm = SarvamLLMService(
api_key=_require_env("SARVAM_API_KEY"),
model="sarvam-30b",
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."
),
)
messages: list[Any] = [
{
"role": "system",
"content": (
"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(messages)
context = LLMContext()
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -117,12 +106,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
messages.append({"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()])
await asyncio.sleep(10)
logger.info("Updating Sarvam LLM settings: temperature=0.1")
await task.queue_frame(LLMUpdateSettingsFrame(delta=OpenAILLMSettings(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):

1
src/pipecat/services/sarvam/__init__.py
View File

@@ -4,5 +4,4 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
from .llm import SarvamLLMService
from .tts import *

32
src/pipecat/services/sarvam/llm.py
View File

@@ -25,7 +25,6 @@ from pipecat.services.sarvam._sdk import sdk_headers
from pipecat.services.settings import NOT_GIVEN as _NOT_GIVEN
from pipecat.services.settings import _NotGiven, _warn_deprecated_param, is_given
__all__ = ["SarvamLLMService", "SarvamLLMSettings"]
_T = TypeVar("_T")
@@ -56,10 +55,10 @@ class SarvamLLMService(OpenAILLMService):
- raw Sarvam server error passthrough
"""
SUPPORTED_MODELS = frozenset({"sarvam-30b", "sarvam-30b-16k", "sarvam-105b", "sarvam-105b-32k"})
TOOL_CALLING_MODELS = frozenset(
_SUPPORTED_MODELS = frozenset(
{"sarvam-30b", "sarvam-30b-16k", "sarvam-105b", "sarvam-105b-32k"}
)
_TOOL_CALLING_MODELS = _SUPPORTED_MODELS
Settings = SarvamLLMSettings
_settings: SarvamLLMSettings
@@ -94,6 +93,8 @@ class SarvamLLMService(OpenAILLMService):
# 2. Apply direct init arg overrides (deprecated)
if model is not None:
# Keep deprecated init arg for backward compatibility while steering callers
# to settings=SarvamLLMService.Settings(model=...).
_warn_deprecated_param("model", SarvamLLMSettings, "model")
default_settings.model = model
@@ -101,8 +102,9 @@ class SarvamLLMService(OpenAILLMService):
if settings is not None:
default_settings.apply_update(settings)
# BaseOpenAILLMService stores settings as OpenAILLMSettings, so keep
# Sarvam-specific runtime knobs in ``extra``.
# BaseOpenAILLMService currently stores settings as OpenAILLMSettings.
# Preserve Sarvam-only runtime knobs in ``extra`` so they survive
# initialization and future update frames.
default_settings.extra = dict(default_settings.extra)
default_settings.extra.update(self._extract_sarvam_extra_from_settings(default_settings))
@@ -158,6 +160,7 @@ class SarvamLLMService(OpenAILLMService):
params.pop("max_completion_tokens", None)
params.pop("service_tier", None)
# Sarvam-only fields are bridged through settings.extra (see __init__ and _update_settings).
extra = self._settings.extra if isinstance(self._settings.extra, dict) else {}
if "wiki_grounding" in extra and extra["wiki_grounding"] is not None:
params["wiki_grounding"] = extra["wiki_grounding"]
@@ -168,6 +171,8 @@ class SarvamLLMService(OpenAILLMService):
async def _update_settings(self, delta: OpenAILLMSettings) -> dict[str, Any]:
"""Apply settings updates, preserving Sarvam-specific runtime knobs."""
# LLMUpdateSettingsFrame commonly carries OpenAILLMSettings deltas.
# Lift Sarvam-only fields into delta.extra before delegating to base.
sarvam_extra = self._extract_sarvam_extra_from_settings(delta)
if sarvam_extra:
delta.extra = dict(delta.extra)
@@ -176,7 +181,13 @@ class SarvamLLMService(OpenAILLMService):
return await super()._update_settings(delta)
async def _call_with_raw_sarvam_errors(self, awaitable: Awaitable[_T]) -> _T:
"""Await an OpenAI call while preserving Sarvam raw error payloads."""
"""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):
@@ -208,8 +219,8 @@ class SarvamLLMService(OpenAILLMService):
)
def _validate_model(self, model: str):
if model not in self.SUPPORTED_MODELS:
allowed = ", ".join(sorted(self.SUPPORTED_MODELS))
if model not in self._SUPPORTED_MODELS:
allowed = ", ".join(sorted(self._SUPPORTED_MODELS))
raise ValueError(f"Unsupported Sarvam LLM model '{model}'. Allowed values: {allowed}.")
def _extract_sarvam_extra_from_settings(self, settings_obj: Any) -> dict[str, Any]:
@@ -238,8 +249,9 @@ class SarvamLLMService(OpenAILLMService):
if has_tool_choice and not has_tools:
raise ValueError("Sarvam requires non-empty `tools` when `tool_choice` is provided.")
if has_tools and self._settings.model not in self.TOOL_CALLING_MODELS:
allowed = ", ".join(sorted(self.TOOL_CALLING_MODELS))
# Validate early to provide deterministic errors before network calls.
if has_tools and self._settings.model not in self._TOOL_CALLING_MODELS:
allowed = ", ".join(sorted(self._TOOL_CALLING_MODELS))
raise ValueError(
f"Model '{self._settings.model}' does not support tools. "
f"Supported models: {allowed}."