#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
from pipecat.metrics.metrics import LLMTokenUsage
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai.llm import OpenAILLMService
[docs]
class NimLLMService(OpenAILLMService):
"""A service for interacting with NVIDIA's NIM (NVIDIA Inference Microservice) API.
This service extends OpenAILLMService to work with NVIDIA's NIM API while maintaining
compatibility with the OpenAI-style interface. It specifically handles the difference
in token usage reporting between NIM (incremental) and OpenAI (final summary).
Args:
api_key (str): The API key for accessing NVIDIA's NIM API
base_url (str, optional): The base URL for NIM API. Defaults to "https://integrate.api.nvidia.com/v1"
model (str, optional): The model identifier to use. Defaults to "nvidia/llama-3.1-nemotron-70b-instruct"
**kwargs: Additional keyword arguments passed to OpenAILLMService
"""
def __init__(
self,
*,
api_key: str,
base_url: str = "https://integrate.api.nvidia.com/v1",
model: str = "nvidia/llama-3.1-nemotron-70b-instruct",
**kwargs,
):
super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
# Counters for accumulating token usage metrics
self._prompt_tokens = 0
self._completion_tokens = 0
self._total_tokens = 0
self._has_reported_prompt_tokens = False
self._is_processing = False
async def _process_context(self, context: OpenAILLMContext):
"""Process a context through the LLM and accumulate token usage metrics.
This method overrides the parent class implementation to handle NVIDIA's
incremental token reporting style, accumulating the counts and reporting
them once at the end of processing.
Args:
context (OpenAILLMContext): The context to process, containing messages
and other information needed for the LLM interaction.
"""
# Reset all counters and flags at the start of processing
self._prompt_tokens = 0
self._completion_tokens = 0
self._total_tokens = 0
self._has_reported_prompt_tokens = False
self._is_processing = True
try:
await super()._process_context(context)
finally:
self._is_processing = False
# Report final accumulated token usage at the end of processing
if self._prompt_tokens > 0 or self._completion_tokens > 0:
self._total_tokens = self._prompt_tokens + self._completion_tokens
tokens = LLMTokenUsage(
prompt_tokens=self._prompt_tokens,
completion_tokens=self._completion_tokens,
total_tokens=self._total_tokens,
)
await super().start_llm_usage_metrics(tokens)
[docs]
async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
"""Accumulate token usage metrics during processing.
This method intercepts the incremental token updates from NVIDIA's API
and accumulates them instead of passing each update to the metrics system.
The final accumulated totals are reported at the end of processing.
Args:
tokens (LLMTokenUsage): The token usage metrics for the current chunk
of processing, containing prompt_tokens and completion_tokens counts.
"""
# Only accumulate metrics during active processing
if not self._is_processing:
return
# Record prompt tokens the first time we see them
if not self._has_reported_prompt_tokens and tokens.prompt_tokens > 0:
self._prompt_tokens = tokens.prompt_tokens
self._has_reported_prompt_tokens = True
# Update completion tokens count if it has increased
if tokens.completion_tokens > self._completion_tokens:
self._completion_tokens = tokens.completion_tokens