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jhpiedrahitao
2025-06-18 10:53:06 -05:00
parent 03a067d3e6
commit fae2d272d5
2 changed files with 48 additions and 36 deletions
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66
src/pipecat/services/sambanova/llm.py
View File

@@ -10,6 +10,7 @@ from typing import Any, Dict, List, Optional
from loguru import logger
from openai import AsyncStream
from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
from pipecat.frames.frames import (
LLMTextFrame,
)
@@ -35,37 +36,42 @@ class SambaNovaLLMService(OpenAILLMService): # type: ignore
self,
*,
api_key: str,
model: str = 'Llama-4-Maverick-17B-128E-Instruct',
base_url: str = 'https://api.sambanova.ai/v1',
model: str = "Llama-4-Maverick-17B-128E-Instruct",
base_url: str = "https://api.sambanova.ai/v1",
**kwargs: Dict[Any, Any],
) -> None:
super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
def create_client(
self, api_key: Optional[str] = None, base_url: Optional[str] = None, **kwargs: Dict[Any, Any]
self,
api_key: Optional[str] = None,
base_url: Optional[str] = None,
**kwargs: Dict[Any, Any],
) -> Any:
"""Create OpenAI-compatible client for SambaNova API endpoint."""
logger.debug(f'Creating SambaNova client with API {base_url}')
logger.debug(f"Creating SambaNova client with API {base_url}")
return super().create_client(api_key, base_url, **kwargs)
async def get_chat_completions(self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]) -> Any:
async def get_chat_completions(
self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
) -> Any:
"""Get chat completions from SambaNova API endpoint."""
params = {
'model': self.model_name,
'stream': True,
'messages': messages,
'tools': context.tools,
'tool_choice': context.tool_choice,
'stream_options': {'include_usage': True},
'temperature': self._settings['temperature'],
'top_p': self._settings['top_p'],
'max_tokens': self._settings['max_tokens'],
'max_completion_tokens': self._settings['max_completion_tokens'],
"model": self.model_name,
"stream": True,
"messages": messages,
"tools": context.tools,
"tool_choice": context.tool_choice,
"stream_options": {"include_usage": True},
"temperature": self._settings["temperature"],
"top_p": self._settings["top_p"],
"max_tokens": self._settings["max_tokens"],
"max_completion_tokens": self._settings["max_completion_tokens"],
}
params.update(self._settings['extra'])
params.update(self._settings["extra"])
chunks = await self._client.chat.completions.create(**params)
return chunks
@@ -78,13 +84,15 @@ class SambaNovaLLMService(OpenAILLMService): # type: ignore
arguments_list = []
tool_id_list = []
func_idx = 0
function_name = ''
arguments = ''
tool_call_id = ''
function_name = ""
arguments = ""
tool_call_id = ""
await self.start_ttfb_metrics()
chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions(context)
chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions(
context
)
async for chunk in chunk_stream:
if chunk.usage:
@@ -120,9 +128,9 @@ class SambaNovaLLMService(OpenAILLMService): # type: ignore
functions_list.append(function_name)
arguments_list.append(arguments)
tool_id_list.append(tool_call_id)
function_name = ''
arguments = ''
tool_call_id = ''
function_name = ""
arguments = ""
tool_call_id = ""
func_idx += 1
if tool_call.function and tool_call.function.name:
function_name += tool_call.function.name
@@ -135,8 +143,10 @@ class SambaNovaLLMService(OpenAILLMService): # type: ignore
# When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
# we need to get LLMTextFrame for the transcript
elif hasattr(chunk.choices[0].delta, 'audio') and chunk.choices[0].delta.audio.get('transcript'):
await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio['transcript']))
elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
"transcript"
):
await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"]))
# if we got a function name and arguments, check to see if it's a function with
# a registered handler. If so, run the registered callback, save the result to
@@ -150,7 +160,9 @@ class SambaNovaLLMService(OpenAILLMService): # type: ignore
function_calls = []
for function_name, arguments, tool_id in zip(functions_list, arguments_list, tool_id_list):
for function_name, arguments, tool_id in zip(
functions_list, arguments_list, tool_id_list
):
# This allows compatibility until SambaNova API introduces indexing in tool calls.
if len(arguments) < 1:
continue
@@ -165,4 +177,4 @@ class SambaNovaLLMService(OpenAILLMService): # type: ignore
)
)
await self.run_function_calls(function_calls)
await self.run_function_calls(function_calls)

18
src/pipecat/services/sambanova/stt.py
View File

@@ -27,9 +27,9 @@ class SambaNovaSTTService(BaseWhisperSTTService): # type: ignore
def __init__(
self,
*,
model: str = 'Whisper-Large-v3',
model: str = "Whisper-Large-v3",
api_key: Optional[str] = None,
base_url: str = 'https://api.sambanova.ai/v1',
base_url: str = "https://api.sambanova.ai/v1",
language: Optional[Language] = Language.EN,
prompt: Optional[str] = None,
temperature: Optional[float] = None,
@@ -50,16 +50,16 @@ class SambaNovaSTTService(BaseWhisperSTTService): # type: ignore
# Build kwargs dict with only set parameters
kwargs = {
'file': ('audio.wav', audio, 'audio/wav'),
'model': self.model_name,
'response_format': 'json',
'language': self._language,
"file": ("audio.wav", audio, "audio/wav"),
"model": self.model_name,
"response_format": "json",
"language": self._language,
}
if self._prompt is not None:
kwargs['prompt'] = self._prompt
kwargs["prompt"] = self._prompt
if self._temperature is not None:
kwargs['temperature'] = self._temperature
kwargs["temperature"] = self._temperature
return await self._client.audio.transcriptions.create(**kwargs)
return await self._client.audio.transcriptions.create(**kwargs)