From 1c1fa0db65c7dec3495ef5b2a6542487a7ebe305 Mon Sep 17 00:00:00 2001
From: Mark Backman <mark@daily.co>
Date: Tue, 3 Dec 2024 23:12:38 -0500
Subject: [PATCH] Add a NIM LLM service

---
 src/pipecat/services/nim.py | 105 ++++++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 src/pipecat/services/nim.py

diff --git a/src/pipecat/services/nim.py b/src/pipecat/services/nim.py
new file mode 100644
index 000000000..0ce0171c9
--- /dev/null
+++ b/src/pipecat/services/nim.py
@@ -0,0 +1,105 @@
+#
+# Copyright (c) 2024, 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 import OpenAILLMService
+
+
+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
+
+    Example:
+        ```python
+        service = NimLLMService(
+            api_key="your-api-key",
+            model="nvidia/llama-3.1-nemotron-70b-instruct"
+        )
+        ```
+    """
+
+    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)
+
+    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