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fixes post PR 4081

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dhruvladia-sarvam
2026-03-23 23:45:27 +05:30
parent 8a4f6b486e
commit 3428a4c6ad
1 changed files with 34 additions and 94 deletions
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128
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")
@@ -58,17 +58,15 @@ class SarvamLLMService(OpenAILLMService):
_SUPPORTED_MODELS = frozenset(
{"sarvam-30b", "sarvam-30b-16k", "sarvam-105b", "sarvam-105b-32k"}
)
_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,
):
@@ -77,39 +75,33 @@ 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 defaults with concrete values for Sarvam-specific fields.
default_settings = self.Settings(
model="sarvam-30b",
system_instruction=None,
frequency_penalty=NOT_GIVEN,
presence_penalty=NOT_GIVEN,
seed=NOT_GIVEN,
temperature=NOT_GIVEN,
top_p=NOT_GIVEN,
top_k=None,
max_tokens=NOT_GIVEN,
max_completion_tokens=NOT_GIVEN,
filter_incomplete_user_turns=False,
user_turn_completion_config=None,
extra={},
wiki_grounding=None,
reasoning_effort=None,
)
# 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__(
api_key=api_key,
base_url=base_url,
@@ -117,6 +109,9 @@ class SarvamLLMService(OpenAILLMService):
default_headers=default_headers,
**kwargs,
)
# Keep Sarvam-specific settings object so runtime updates include
# ``wiki_grounding`` and ``reasoning_effort`` without extra bridging.
self._settings = default_settings
def create_client(
self,
@@ -160,26 +155,16 @@ 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.
@@ -193,7 +178,7 @@ class SarvamLLMService(OpenAILLMService):
except (APITimeoutError, asyncio.TimeoutError, httpx.TimeoutException):
raise
except Exception as e:
raise RuntimeError(self._format_raw_server_error(e)) from e
raise RuntimeError(str(e)) from e
async def get_chat_completions(
self, params_from_context: OpenAILLMInvocationParams
@@ -218,23 +203,6 @@ class SarvamLLMService(OpenAILLMService):
)
)
def _validate_model(self, model: str):
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]:
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)
@@ -249,34 +217,6 @@ class SarvamLLMService(OpenAILLMService):
if has_tool_choice and not has_tools:
raise ValueError("Sarvam requires non-empty `tools` when `tool_choice` is provided.")
# 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}."
)
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")