Feat add litellm extension

.env.example

@@ -51,6 +51,25 @@ COSY_TTS_KEY=
 # ElevenLabs TTS key
 ELEVENLABS_TTS_KEY=
 
+# Extension: litellm
+# Parameter Configuration Methods
+# Method 1
+#    Using Environment Variables, refer to https://docs.litellm.ai/docs/providers
+#    For example:
+#        OpenAI
+#            OPENAI_API_KEY=<your-api-key>
+#            OPENAI_API_BASE=<openai-api-base>
+#        AWS Bedrock
+#            AWS_ACCESS_KEY_ID=<your-aws-access-key-id>
+#            AWS_SECRET_ACCESS_KEY=<your-aws-secret-access-key>
+#            AWS_REGION_NAME=<aws-region-name>
+# Method 2
+#    Passing Parameters, specify LITELLM_API_KEY and LITELLM_PROVIDER
+#    provider: refer to the provider_list in https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py
+LITELLM_API_KEY=
+LITELLM_MODEL=gpt-4o-mini
+LITELLM_PROVIDER=
+
 # Extension: openai_chatgpt
 # OpenAI API key
 OPENAI_API_KEY=

agents/addon/extension/elevenlabs_tts_python/__init__.py

@@ -1,5 +1,6 @@
 from . import elevenlabs_tts_addon
+from .extension import EXTENSION_NAME
 from .log import logger
 
 
-logger.info("elevenlabs_tts_python extension loaded")
+logger.info(f"{EXTENSION_NAME} extension loaded")

agents/addon/extension/elevenlabs_tts_python/elevenlabs_tts.py

@@ -42,9 +42,7 @@ def default_elevenlabs_tts_config() -> ElevenlabsTTSConfig:
 class ElevenlabsTTS:
     def __init__(self, config: ElevenlabsTTSConfig) -> None:
         self.config = config
-        self.client = ElevenLabs(
-            api_key=config.api_key, timeout=config.request_timeout_seconds
-        )
+        self.client = ElevenLabs(api_key=config.api_key, timeout=config.request_timeout_seconds)
 
     def text_to_speech_stream(self, text: str) -> Iterator[bytes]:
         audio_stream = self.client.generate(

agents/addon/extension/elevenlabs_tts_python/elevenlabs_tts_addon.py

@@ -11,10 +11,11 @@
     register_addon_as_extension,
     RteEnv,
 )
+from .extension import EXTENSION_NAME
 from .log import logger
 
 
-@register_addon_as_extension("elevenlabs_tts_python")
+@register_addon_as_extension(EXTENSION_NAME)
 class ElevenlabsTTSExtensionAddon(Addon):
     def on_create_instance(self, rte: RteEnv, addon_name: str, context) -> None:
         logger.info("on_create_instance")

agents/addon/extension/elevenlabs_tts_python/elevenlabs_tts_extension.py

@@ -11,15 +11,12 @@
 import time
 
 from rte import (
-    Addon,
     Extension,
-    register_addon_as_extension,
     RteEnv,
     Cmd,
     CmdResult,
     StatusCode,
     Data,
-    MetadataInfo,
 )
 from .elevenlabs_tts import default_elevenlabs_tts_config, ElevenlabsTTS
 from .pcm import PcmConfig, Pcm
@@ -62,68 +59,44 @@ def on_start(self, rte: RteEnv) -> None:
         try:
             elevenlabs_tts_config.api_key = rte.get_property_string(PROPERTY_API_KEY)
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_string {PROPERTY_API_KEY} error: {e}"
-            )
+            logger.warning(f"on_start get_property_string {PROPERTY_API_KEY} error: {e}")
             return
 
         try:
             model_id = rte.get_property_string(PROPERTY_MODEL_ID)
             if len(model_id) > 0:
                 elevenlabs_tts_config.model_id = model_id
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_string {PROPERTY_MODEL_ID} error: {e}"
-            )
+            logger.warning(f"on_start get_property_string {PROPERTY_MODEL_ID} error: {e}")
 
         try:
-            optimize_streaming_latency = rte.get_property_int(
-                PROPERTY_OPTIMIZE_STREAMING_LATENCY
-            )
+            optimize_streaming_latency = rte.get_property_int(PROPERTY_OPTIMIZE_STREAMING_LATENCY)
             if optimize_streaming_latency > 0:
-                elevenlabs_tts_config.optimize_streaming_latency = (
-                    optimize_streaming_latency
-                )
+                elevenlabs_tts_config.optimize_streaming_latency = optimize_streaming_latency
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_int {PROPERTY_OPTIMIZE_STREAMING_LATENCY} error: {e}"
-            )
+            logger.warning(f"on_start get_property_int {PROPERTY_OPTIMIZE_STREAMING_LATENCY} error: {e}")
 
         try:
-            request_timeout_seconds = rte.get_property_int(
-                PROPERTY_REQUEST_TIMEOUT_SECONDS
-            )
+            request_timeout_seconds = rte.get_property_int(PROPERTY_REQUEST_TIMEOUT_SECONDS)
             if request_timeout_seconds > 0:
                 elevenlabs_tts_config.request_timeout_seconds = request_timeout_seconds
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_int {PROPERTY_REQUEST_TIMEOUT_SECONDS} error: {e}"
-            )
+            logger.warning(f"on_start get_property_int {PROPERTY_REQUEST_TIMEOUT_SECONDS} error: {e}")
 
         try:
-            elevenlabs_tts_config.similarity_boost = rte.get_property_float(
-                PROPERTY_SIMILARITY_BOOST
-            )
+            elevenlabs_tts_config.similarity_boost = rte.get_property_float(PROPERTY_SIMILARITY_BOOST)
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_float {PROPERTY_SIMILARITY_BOOST} error: {e}"
-            )
+            logger.warning(f"on_start get_property_float {PROPERTY_SIMILARITY_BOOST} error: {e}")
 
         try:
-            elevenlabs_tts_config.speaker_boost = rte.get_property_bool(
-                PROPERTY_SPEAKER_BOOST
-            )
+            elevenlabs_tts_config.speaker_boost = rte.get_property_bool(PROPERTY_SPEAKER_BOOST)
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_bool {PROPERTY_SPEAKER_BOOST} error: {e}"
-            )
+            logger.warning(f"on_start get_property_bool {PROPERTY_SPEAKER_BOOST} error: {e}")
 
         try:
             elevenlabs_tts_config.stability = rte.get_property_float(PROPERTY_STABILITY)
         except Exception as e:
-            logger.warning(
-                f"on_start get_property_float {PROPERTY_STABILITY} error: {e}"
-            )
+            logger.warning(f"on_start get_property_float {PROPERTY_STABILITY} error: {e}")
 
         try:
             elevenlabs_tts_config.style = rte.get_property_float(PROPERTY_STYLE)
@@ -133,9 +106,7 @@ def on_start(self, rte: RteEnv) -> None:
         # create elevenlabsTTS instance
         self.elevenlabs_tts = ElevenlabsTTS(elevenlabs_tts_config)
 
-        logger.info(
-            f"ElevenlabsTTS succeed with model_id: {self.elevenlabs_tts.config.model_id}, VoiceId: {self.elevenlabs_tts.config.voice_id}"
-        )
+        logger.info(f"ElevenlabsTTS succeed with model_id: {self.elevenlabs_tts.config.model_id}, VoiceId: {self.elevenlabs_tts.config.voice_id}")
 
         # create pcm instance
         self.pcm = Pcm(PcmConfig())
@@ -186,9 +157,7 @@ def on_data(self, rte: RteEnv, data: Data) -> None:
         try:
             text = data.get_property_string(DATA_IN_TEXT_DATA_PROPERTY_TEXT)
         except Exception as e:
-            logger.warning(
-                f"on_data get_property_string {DATA_IN_TEXT_DATA_PROPERTY_TEXT} error: {e}"
-            )
+            logger.warning(f"on_data get_property_string {DATA_IN_TEXT_DATA_PROPERTY_TEXT} error: {e}")
             return
 
         if len(text) == 0:
@@ -207,9 +176,7 @@ def process_text_queue(self, rte: RteEnv):
             logger.debug(f"process_text_queue, text: [{msg.text}]")
 
             if msg.received_ts < self.outdate_ts:
-                logger.info(
-                    f"textChan interrupt and flushing for input text: [{msg.text}], received_ts: {msg.received_ts}, outdate_ts: {self.outdate_ts}"
-                )
+                logger.info(f"textChan interrupt and flushing for input text: [{msg.text}], received_ts: {msg.received_ts}, outdate_ts: {self.outdate_ts}")
                 continue
 
             start_time = time.time()
@@ -224,9 +191,7 @@ def process_text_queue(self, rte: RteEnv):
 
             for chunk in self.pcm.read_pcm_stream(audio_stream, self.pcm_frame_size):
                 if msg.received_ts < self.outdate_ts:
-                    logger.info(
-                        f"textChan interrupt and flushing for input text: [{msg.text}], received_ts: {msg.received_ts}, outdate_ts: {self.outdate_ts}"
-                    )
+                    logger.info(f"textChan interrupt and flushing for input text: [{msg.text}], received_ts: {msg.received_ts}, outdate_ts: {self.outdate_ts}")
                     break
 
                 if not chunk:
@@ -238,9 +203,7 @@ def process_text_queue(self, rte: RteEnv):
                 pcm_frame_read += n
 
                 if pcm_frame_read != self.pcm.get_pcm_frame_size():
-                    logger.debug(
-                        f"the number of bytes read is [{pcm_frame_read}] inconsistent with pcm frame size",
-                    )
+                    logger.debug(f"the number of bytes read is [{pcm_frame_read}] inconsistent with pcm frame size")
                     continue
 
                 self.pcm.send(rte, buf)
@@ -250,28 +213,16 @@ def process_text_queue(self, rte: RteEnv):
 
                 if first_frame_latency == 0:
                     first_frame_latency = int((time.time() - start_time) * 1000)
-                    logger.info(
-                        f"first frame available for text: [{msg.text}], received_ts: {msg.received_ts}, first_frame_latency: {first_frame_latency}ms",
-                    )
+                    logger.info(f"first frame available for text: [{msg.text}], received_ts: {msg.received_ts}, first_frame_latency: {first_frame_latency}ms")
 
                 logger.debug(f"sending pcm data, text: [{msg.text}]")
 
             if pcm_frame_read > 0:
                 self.pcm.send(rte, buf)
                 sent_frames += 1
-                logger.info(
-                    f"sending pcm remain data, text: [{msg.text}], pcm_frame_read: {pcm_frame_read}"
-                )
+                logger.info(f"sending pcm remain data, text: [{msg.text}], pcm_frame_read: {pcm_frame_read}")
 
             finish_latency = int((time.time() - start_time) * 1000)
-            logger.info(
-                f"send pcm data finished, text: [{msg.text}], received_ts: {msg.received_ts}, read_bytes: {read_bytes}, sent_frames: {sent_frames}, \
+            logger.info(f"send pcm data finished, text: [{msg.text}], received_ts: {msg.received_ts}, read_bytes: {read_bytes}, sent_frames: {sent_frames},
                 first_frame_latency: {first_frame_latency}ms, finish_latency: {finish_latency}ms"
             )
-
-
-@register_addon_as_extension("elevenlabs_tts_python")
-class ElevenlabsTTSExtensionAddon(Addon):
-    def on_create_instance(self, rte: RteEnv, addon_name: str, context) -> None:
-        logger.info("on_create_instance")
-        rte.on_create_instance_done(ElevenlabsTTSExtension(addon_name), context)

agents/addon/extension/elevenlabs_tts_python/extension.py

@@ -0,0 +1 @@
+EXTENSION_NAME = "elevenlabs_tts_python"

agents/addon/extension/elevenlabs_tts_python/log.py

@@ -1,11 +1,10 @@
 import logging
+from .extension import EXTENSION_NAME
 
-logger = logging.getLogger("elevenlabs_tts_python")
+logger = logging.getLogger(EXTENSION_NAME)
 logger.setLevel(logging.INFO)
 
-formatter = logging.Formatter(
-    "%(asctime)s - %(name)s - %(levelname)s - %(process)d - [%(filename)s:%(lineno)d] - %(message)s"
-)
+formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(process)d - [%(filename)s:%(lineno)d] - %(message)s")
 
 console_handler = logging.StreamHandler()
 console_handler.setFormatter(formatter)

agents/addon/extension/elevenlabs_tts_python/pcm.py

@@ -22,9 +22,7 @@ def get_pcm_frame(self, buf: memoryview) -> PcmFrame:
         frame.set_number_of_channels(self.config.num_channels)
         frame.set_timestamp(self.config.timestamp)
         frame.set_data_fmt(PcmFrameDataFmt.INTERLEAVE)
-        frame.set_samples_per_channel(
-            self.config.samples_per_channel // self.config.channel
-        )
+        frame.set_samples_per_channel(self.config.samples_per_channel // self.config.channel)
 
         frame.alloc_buf(self.get_pcm_frame_size())
         frame_buf = frame.lock_buf()
@@ -35,24 +33,19 @@ def get_pcm_frame(self, buf: memoryview) -> PcmFrame:
         return frame
 
     def get_pcm_frame_size(self) -> int:
-        return (
-            self.config.samples_per_channel
-            * self.config.channel
-            * self.config.bytes_per_sample
-        )
+        return (self.config.samples_per_channel * self.config.channel * self.config.bytes_per_sample)
 
     def new_buf(self) -> bytearray:
         return bytearray(self.get_pcm_frame_size())
 
-    def read_pcm_stream(
-        self, stream: Iterator[bytes], chunk_size: int
-    ) -> Iterator[bytes]:
+    def read_pcm_stream(self, stream: Iterator[bytes], chunk_size: int) -> Iterator[bytes]:
         chunk = b""
         for data in stream:
             chunk += data
             while len(chunk) >= chunk_size:
                 yield chunk[:chunk_size]
                 chunk = chunk[chunk_size:]
+
         if chunk:
             yield chunk
 

agents/addon/extension/litellm_python/__init__.py

@@ -0,0 +1,6 @@
+from . import litellm_addon
+from .extension import EXTENSION_NAME
+from .log import logger
+
+
+logger.info(f"{EXTENSION_NAME} extension loaded")

agents/addon/extension/litellm_python/extension.py

@@ -0,0 +1 @@
+EXTENSION_NAME = "litellm_python"

agents/addon/extension/litellm_python/litellm.py

@@ -0,0 +1,79 @@
+import litellm
+import random
+from typing import Dict, List, Optional
+
+
+class LiteLLMConfig:
+    def __init__(self,
+                 api_key: str,
+                 base_url: str,
+                 frequency_penalty: float,
+                 max_tokens: int,
+                 model: str,
+                 presence_penalty: float,
+                 prompt: str,
+                 provider: str,
+                 temperature: float,
+                 top_p: float,
+                 seed: Optional[int] = None,):
+        self.api_key = api_key
+        self.base_url = base_url
+        self.frequency_penalty = frequency_penalty
+        self.max_tokens = max_tokens
+        self.model = model
+        self.presence_penalty = presence_penalty
+        self.prompt = prompt
+        self.provider = provider
+        self.seed = seed if seed is not None else random.randint(0, 10000)
+        self.temperature = temperature
+        self.top_p = top_p
+
+    @classmethod
+    def default_config(cls):
+        return cls(
+            api_key="",
+            base_url="",
+            max_tokens=512,
+            model="gpt-4o-mini",
+            frequency_penalty=0.9,
+            presence_penalty=0.9,
+            prompt="You are a voice assistant who talks in a conversational way and can chat with me like my friends. I will speak to you in English or Chinese, and you will answer in the corrected and improved version of my text with the language I use. Don’t talk like a robot, instead I would like you to talk like a real human with emotions. I will use your answer for text-to-speech, so don’t return me any meaningless characters. I want you to be helpful, when I’m asking you for advice, give me precise, practical and useful advice instead of being vague. When giving me a list of options, express the options in a narrative way instead of bullet points.",
+            provider="",
+            seed=random.randint(0, 10000),
+            temperature=0.1,
+            top_p=1.0
+        )
+
+
+class LiteLLM:
+    def __init__(self, config: LiteLLMConfig):
+        self.config = config
+
+    def get_chat_completions_stream(self, messages: List[Dict[str, str]]):
+        kwargs = {
+            "api_key": self.config.api_key,
+            "base_url": self.config.base_url,
+            "custom_llm_provider": self.config.provider,
+            "frequency_penalty": self.config.frequency_penalty,
+            "max_tokens": self.config.max_tokens,
+            "messages": [
+                {
+                    "role": "system",
+                    "content": self.config.prompt,
+                },
+                *messages,
+            ],
+            "model": self.config.model,
+            "presence_penalty": self.config.presence_penalty,
+            "seed": self.config.seed,
+            "stream": True,
+            "temperature": self.config.temperature,
+            "top_p": self.config.top_p,
+        }
+
+        try:
+            response = litellm.completion(**kwargs)
+
+            return response
+        except Exception as e:
+            raise Exception(f"get_chat_completions_stream failed, err: {e}")