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Use Settings pattern and access self._settings directly for Sarvam-specific params

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Remove settings.extra bridge, _update_settings override, deprecated model init param,
and _extract_sarvam_extra_from_settings in favor of direct self._settings access.
This commit is contained in:
Mark Backman
2026-03-19 09:00:14 -04:00
parent edf4ba45a5
commit 99b478b897
1 changed files with 14 additions and 57 deletions
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71
src/pipecat/services/sarvam/llm.py
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@@ -23,7 +23,7 @@ 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, _warn_deprecated_param, is_given
from pipecat.services.settings import _NotGiven, is_given
_T = TypeVar("_T")
@@ -60,15 +60,14 @@ class SarvamLLMService(OpenAILLMService):
)
_TOOL_CALLING_MODELS = _SUPPORTED_MODELS
Settings = SarvamLLMSettings
_settings: SarvamLLMSettings
_settings: Settings
def __init__(
self,
*,
api_key: str,
base_url: str = "https://api.sarvam.ai/v1",
model: Optional[str] = None,
settings: Optional[SarvamLLMSettings] = None,
settings: Optional[Settings] = None,
default_headers: Optional[Mapping[str, str]] = None,
**kwargs,
):
@@ -77,37 +76,17 @@ class SarvamLLMService(OpenAILLMService):
Args:
api_key: Sarvam API key used for both OpenAI auth and Sarvam subscription header.
base_url: Sarvam OpenAI-compatible base URL.
model: Sarvam model identifier. Supported values: ``sarvam-30b``,
``sarvam-30b-16k``, ``sarvam-105b``, ``sarvam-105b-32k``.
.. deprecated:: 0.0.105
Use ``settings=SarvamLLMSettings(model=...)`` instead.
settings: Runtime-updatable settings. When provided alongside deprecated
parameters, ``settings`` values take precedence.
settings: Runtime-updatable settings.
default_headers: Additional HTTP headers to include in requests.
**kwargs: Additional keyword arguments passed to ``OpenAILLMService``.
"""
# 1. Initialize default_settings with hardcoded defaults
default_settings = SarvamLLMSettings(model="sarvam-30b")
# Initialize default_settings with hardcoded defaults
default_settings = self.Settings(model="sarvam-30b")
# 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
# 3. Apply settings delta (canonical API, always wins)
# Apply settings delta (canonical API, always wins)
if settings is not None:
default_settings.apply_update(settings)
# 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))
self._validate_model(default_settings.model)
super().__init__(
@@ -160,26 +139,16 @@ 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"]
if "reasoning_effort" in extra and extra["reasoning_effort"] is not None:
params["reasoning_effort"] = extra["reasoning_effort"]
if is_given(self._settings.wiki_grounding) and self._settings.wiki_grounding is not None:
params["wiki_grounding"] = self._settings.wiki_grounding
if (
is_given(self._settings.reasoning_effort)
and self._settings.reasoning_effort is not None
):
params["reasoning_effort"] = self._settings.reasoning_effort
return params
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)
delta.extra.update(sarvam_extra)
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.
@@ -223,18 +192,6 @@ class SarvamLLMService(OpenAILLMService):
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]:
updates: dict[str, Any] = {}
wiki_grounding = getattr(settings_obj, "wiki_grounding", _NOT_GIVEN)
if is_given(wiki_grounding):
updates["wiki_grounding"] = wiki_grounding
reasoning_effort = getattr(settings_obj, "reasoning_effort", _NOT_GIVEN)
if is_given(reasoning_effort):
updates["reasoning_effort"] = reasoning_effort
return updates
def _validate_tool_parameters(self, params_from_context: OpenAILLMInvocationParams):
tools = params_from_context.get("tools", NOT_GIVEN)
tool_choice = params_from_context.get("tool_choice", NOT_GIVEN)