fix llm wrapper redundancy and restore run_inference parity
This commit is contained in:
dhruvladia-sarvam
@@ -8,20 +8,40 @@
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import asyncio
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import json
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from typing import Any, Literal, Mapping, Optional
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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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import httpx
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from loguru import logger
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from openai import NOT_GIVEN, APITimeoutError
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from pydantic import BaseModel
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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 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, _warn_deprecated_param, is_given
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__all__ = ["SarvamLLMService"]
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__all__ = ["SarvamLLMService", "SarvamLLMSettings"]
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_T = TypeVar("_T")
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@dataclass
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class SarvamLLMSettings(OpenAILLMSettings):
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"""Settings for SarvamLLMService.
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Parameters:
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wiki_grounding: Sarvam wiki grounding toggle.
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reasoning_effort: Reasoning effort level (low, medium, high).
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"""
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wiki_grounding: bool | None | _NotGiven = field(default_factory=lambda: _NOT_GIVEN)
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reasoning_effort: Literal["low", "medium", "high"] | None | _NotGiven = field(
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default_factory=lambda: _NOT_GIVEN
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)
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class SarvamLLMService(OpenAILLMService):
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@@ -40,59 +60,59 @@ class SarvamLLMService(OpenAILLMService):
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TOOL_CALLING_MODELS = frozenset(
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{"sarvam-30b", "sarvam-30b-16k", "sarvam-105b", "sarvam-105b-32k"}
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)
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class InputParams(OpenAILLMService.InputParams):
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"""Configuration parameters for Sarvam LLM service.
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Parameters:
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frequency_penalty: Penalty for frequent tokens (-2.0 to 2.0).
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presence_penalty: Penalty for new tokens (-2.0 to 2.0).
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seed: Random seed for deterministic outputs.
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temperature: Sampling temperature (0.0 to 2.0).
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top_k: Top-k sampling parameter (currently ignored by OpenAI client).
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top_p: Top-p (nucleus) sampling parameter (0.0 to 1.0).
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max_tokens: Maximum tokens in response.
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max_completion_tokens: Maximum completion tokens (not sent to Sarvam API).
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service_tier: Service tier (not sent to Sarvam API).
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extra: Additional model-specific parameters.
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wiki_grounding: Sarvam wiki grounding toggle.
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reasoning_effort: Reasoning effort level (low, medium, high).
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"""
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wiki_grounding: Optional[bool] = None
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reasoning_effort: Optional[Literal["low", "medium", "high"]] = None
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Settings = SarvamLLMSettings
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_settings: SarvamLLMSettings
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def __init__(
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self,
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*,
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api_key: str,
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model: str = "sarvam-30b",
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base_url: str = "https://api.sarvam.ai/v1",
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model: Optional[str] = None,
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settings: Optional[SarvamLLMSettings] = None,
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default_headers: Optional[Mapping[str, str]] = None,
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params: Optional[InputParams] = None,
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**kwargs,
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):
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"""Initialize Sarvam LLM service.
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Args:
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api_key: Sarvam API key used for both OpenAI auth and Sarvam subscription header.
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model: Sarvam model identifier. Supported values: ``sarvam-30b``, ``sarvam-105b``.
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base_url: Sarvam OpenAI-compatible base URL.
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model: Sarvam model identifier. Supported values: ``sarvam-30b``,
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``sarvam-30b-16k``, ``sarvam-105b``, ``sarvam-105b-32k``.
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.. deprecated:: 0.0.105
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Use ``settings=SarvamLLMSettings(model=...)`` instead.
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settings: Runtime-updatable settings. When provided alongside deprecated
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parameters, ``settings`` values take precedence.
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default_headers: Additional HTTP headers to include in requests.
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params: Input parameters for model configuration.
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**kwargs: Additional keyword arguments passed to ``OpenAILLMService``.
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"""
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self._validate_model(model)
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# 1. Initialize default_settings with hardcoded defaults
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default_settings = SarvamLLMSettings(model="sarvam-30b")
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params = (params or SarvamLLMService.InputParams()).model_copy(deep=True)
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params.extra = self._build_extra_params(params)
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# 2. Apply direct init arg overrides (deprecated)
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if model is not None:
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_warn_deprecated_param("model", SarvamLLMSettings, "model")
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default_settings.model = model
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# 3. Apply settings delta (canonical API, always wins)
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if settings is not None:
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default_settings.apply_update(settings)
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# BaseOpenAILLMService stores settings as OpenAILLMSettings, so keep
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# Sarvam-specific runtime knobs in ``extra``.
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default_settings.extra = dict(default_settings.extra)
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default_settings.extra.update(self._extract_sarvam_extra_from_settings(default_settings))
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self._validate_model(default_settings.model)
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super().__init__(
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api_key=api_key,
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base_url=base_url,
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model=model,
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settings=default_settings,
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default_headers=default_headers,
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params=params,
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**kwargs,
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)
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@@ -110,13 +130,11 @@ class SarvamLLMService(OpenAILLMService):
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Ensures Sarvam auth and SDK identification headers are always attached.
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"""
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merged_headers = dict(default_headers or {})
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# sdk_headers() carries Pipecat User-Agent and should override caller-provided value.
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merged_headers.update(sdk_headers())
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if api_key:
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merged_headers["api-subscription-key"] = api_key
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# Keep SDK User-Agent stable even when caller-provided headers include User-Agent.
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merged_headers["User-Agent"] = sdk_headers()["User-Agent"]
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logger.debug(f"Creating Sarvam client with API {base_url}")
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return super().create_client(
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api_key=api_key,
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@@ -148,38 +166,63 @@ class SarvamLLMService(OpenAILLMService):
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return params
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async def get_chat_completions(self, params_from_context: OpenAILLMInvocationParams):
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"""Get streaming chat completions with Sarvam raw error passthrough."""
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async def _update_settings(self, delta: OpenAILLMSettings) -> dict[str, Any]:
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"""Apply settings updates, preserving Sarvam-specific runtime knobs."""
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sarvam_extra = self._extract_sarvam_extra_from_settings(delta)
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if sarvam_extra:
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delta.extra = dict(delta.extra)
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delta.extra.update(sarvam_extra)
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return await super()._update_settings(delta)
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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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try:
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return await super().get_chat_completions(params_from_context)
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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, context: LLMContext | OpenAILLMContext, max_tokens: Optional[int] = None
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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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try:
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return await super().run_inference(context, max_tokens=max_tokens)
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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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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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raise ValueError(f"Unsupported Sarvam LLM model '{model}'. Allowed values: {allowed}.")
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def _build_extra_params(self, params: BaseModel) -> dict[str, Any]:
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extra = dict(getattr(params, "extra", {}) or {})
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if getattr(params, "wiki_grounding", None) is not None:
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extra["wiki_grounding"] = params.wiki_grounding
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if getattr(params, "reasoning_effort", None) is not None:
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extra["reasoning_effort"] = params.reasoning_effort
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return extra
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def _extract_sarvam_extra_from_settings(self, settings_obj: Any) -> dict[str, Any]:
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updates: dict[str, Any] = {}
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wiki_grounding = getattr(settings_obj, "wiki_grounding", _NOT_GIVEN)
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if is_given(wiki_grounding):
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updates["wiki_grounding"] = wiki_grounding
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reasoning_effort = getattr(settings_obj, "reasoning_effort", _NOT_GIVEN)
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if is_given(reasoning_effort):
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updates["reasoning_effort"] = reasoning_effort
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return updates
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def _validate_tool_parameters(self, params_from_context: OpenAILLMInvocationParams):
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tools = params_from_context.get("tools", NOT_GIVEN)
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