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This commit is contained in:
dhruvladia-sarvam
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from .llm import SarvamLLMService
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from .tts import *
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235
src/pipecat/services/sarvam/llm.py
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235
src/pipecat/services/sarvam/llm.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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"""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 typing import Any, 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
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from pydantic import BaseModel
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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.llm import OpenAILLMService
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from pipecat.services.sarvam._sdk import sdk_headers
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__all__ = ["SarvamLLMService"]
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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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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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"""
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SUPPORTED_MODELS = frozenset({"sarvam-30b", "sarvam-30b-16k", "sarvam-105b", "sarvam-105b-32k"})
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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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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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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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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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params = (params or SarvamLLMService.InputParams()).model_copy(deep=True)
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params.extra = self._build_extra_params(params)
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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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default_headers=default_headers,
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params=params,
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**kwargs,
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)
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def create_client(
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self,
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api_key=None,
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base_url=None,
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organization=None,
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project=None,
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default_headers=None,
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**kwargs,
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):
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"""Create OpenAI-compatible client for Sarvam API endpoint.
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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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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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base_url=base_url,
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organization=organization,
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project=project,
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default_headers=merged_headers,
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**kwargs,
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)
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def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
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"""Build parameters for Sarvam chat completion request.
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Starts from OpenAI-compatible defaults, then removes unsupported
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request fields and applies Sarvam-specific options.
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"""
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self._validate_tool_parameters(params_from_context)
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params = super().build_chat_completion_params(params_from_context)
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params.pop("stream_options", None)
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params.pop("max_completion_tokens", None)
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params.pop("service_tier", None)
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extra = self._settings.extra if isinstance(self._settings.extra, dict) else {}
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if "wiki_grounding" in extra and extra["wiki_grounding"] is not None:
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params["wiki_grounding"] = extra["wiki_grounding"]
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if "reasoning_effort" in extra and extra["reasoning_effort"] is not None:
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params["reasoning_effort"] = extra["reasoning_effort"]
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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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try:
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return await super().get_chat_completions(params_from_context)
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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 run_inference(
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self, context: LLMContext | OpenAILLMContext, max_tokens: Optional[int] = 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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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 _validate_tool_parameters(self, params_from_context: OpenAILLMInvocationParams):
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tools = params_from_context.get("tools", NOT_GIVEN)
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tool_choice = params_from_context.get("tool_choice", NOT_GIVEN)
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has_tools = (
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tools is not NOT_GIVEN
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and tools is not None
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and (not isinstance(tools, list) or len(tools) > 0)
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)
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has_tool_choice = tool_choice is not NOT_GIVEN and tool_choice is not None
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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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if has_tools and self._settings.model not in self.TOOL_CALLING_MODELS:
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allowed = ", ".join(sorted(self.TOOL_CALLING_MODELS))
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raise ValueError(
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f"Model '{self._settings.model}' does not support tools. "
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f"Supported models: {allowed}."
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)
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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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