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Chad's big patient intake PR (#40)

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* at least it runs, kind of

* wip

* wip with user response aggregator

* frame and pipeline docstrings

* Getting started on docstrings

* finish docstrings for aggregators

* patient intake is working!

* cleanup

* cleanup

---------

Co-authored-by: Moishe Lettvin <moishel@gmail.com>
This commit is contained in:
chadbailey59
2024-03-07 17:41:32 -06:00
committed by GitHub
parent 2bcb4966d3
commit 3c5f4800d4
12 changed files with 481 additions and 35 deletions
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34
src/dailyai/services/ai_services.py
View File

@@ -12,9 +12,11 @@ from dailyai.pipeline.frames import (
LLMMessagesQueueFrame,
LLMResponseEndFrame,
LLMResponseStartFrame,
LLMFunctionCallFrame,
Frame,
TextFrame,
TranscriptionQueueFrame,
UserStoppedSpeakingFrame
)
from abc import abstractmethod
@@ -65,6 +67,11 @@ class AIService(FrameProcessor):
class LLMService(AIService):
def __init__(self, messages=None, tools=None):
super().__init__()
self._tools = tools
self._messages = messages
@abstractmethod
async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
yield ""
@@ -73,12 +80,27 @@ class LLMService(AIService):
async def run_llm(self, messages) -> str:
pass
async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
async def process_frame(self, frame: Frame, tool_choice: str = None) -> AsyncGenerator[Frame, None]:
if isinstance(frame, LLMMessagesQueueFrame):
yield LLMResponseStartFrame()
async for text_chunk in self.run_llm_async(frame.messages):
yield TextFrame(text_chunk)
yield LLMResponseEndFrame()
function_name = ""
arguments = ""
if isinstance(frame, LLMMessagesQueueFrame):
yield LLMResponseStartFrame()
async for text_chunk in self.run_llm_async(frame.messages, tool_choice):
if isinstance(text_chunk, str):
yield TextFrame(text_chunk)
elif text_chunk.function:
if text_chunk.function.name:
# function_name += text_chunk.function.name
yield LLMFunctionCallFrame(function_name=text_chunk.function.name, arguments=None)
if text_chunk.function.arguments:
# arguments += text_chunk.function.arguments
yield LLMFunctionCallFrame(function_name=None, arguments=text_chunk.function.arguments)
if (function_name and arguments):
function_name = ""
arguments = ""
yield LLMResponseEndFrame()
else:
yield frame
@@ -129,7 +151,7 @@ class TTSService(AIService):
# Convenience function to send the audio for a sentence to the given queue
async def say(self, sentence, queue: asyncio.Queue):
await self.run_to_queue(queue, [TextFrame(sentence)])
await self.run_to_queue(queue, [LLMResponseStartFrame(), TextFrame(sentence), LLMResponseEndFrame()])
class ImageGenService(AIService):

21
src/dailyai/services/azure_ai_services.py
View File

@@ -2,6 +2,7 @@ import aiohttp
import asyncio
import io
import json
import time
from openai import AsyncAzureOpenAI
import os
@@ -48,8 +49,8 @@ class AzureTTSService(TTSService):
class AzureLLMService(LLMService):
def __init__(self, *, api_key, endpoint, api_version="2023-12-01-preview", model):
super().__init__()
def __init__(self, *, api_key, endpoint, api_version="2023-12-01-preview", model, tools=None, messages=None):
super().__init__(tools=tools, messages=messages)
self._model: str = model
self._client = AsyncAzureOpenAI(
@@ -58,16 +59,22 @@ class AzureLLMService(LLMService):
api_version=api_version,
)
async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
async def run_llm_async(self, messages, tool_choice=None) -> AsyncGenerator[str, None]:
messages_for_log = json.dumps(messages)
self.logger.debug(f"Generating chat via azure: {messages_for_log}")
chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages)
if self._tools:
tools = self._tools
else:
tools = None
start_time = time.time()
chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages, tools=tools, tool_choice=tool_choice)
self.logger.info(f"=== Azure OpenAI LLM TTFB: {time.time() - start_time}")
async for chunk in chunks:
if len(chunk.choices) == 0:
continue
if chunk.choices[0].delta.content:
if chunk.choices[0].delta.tool_calls:
yield chunk.choices[0].delta.tool_calls[0]
elif chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
async def run_llm(self, messages) -> str | None:

1
src/dailyai/services/base_transport_service.py
View File

@@ -220,7 +220,6 @@ class BaseTransportService():
self.interrupt()
pipeline_task = asyncio.create_task(pipeline.run_pipeline())
started = False
continue
if not started:
await self.send_queue.put(StartFrame())

5
src/dailyai/services/daily_transport_service.py
View File

@@ -249,8 +249,9 @@ class DailyTransportService(BaseTransportService, EventHandler):
participantId = message["participantId"]
elif "session_id" in message:
participantId = message["session_id"]
frame = TranscriptionQueueFrame(message["text"], participantId, message["timestamp"])
asyncio.run_coroutine_threadsafe(self.receive_queue.put(frame), self._loop)
if self._my_participant_id and participantId != self._my_participant_id:
frame = TranscriptionQueueFrame(message["text"], participantId, message["timestamp"])
asyncio.run_coroutine_threadsafe(self.receive_queue.put(frame), self._loop)
def on_transcription_stopped(self, stopped_by, stopped_by_error):
pass

24
src/dailyai/services/open_ai_services.py
View File

@@ -1,6 +1,7 @@
import aiohttp
from PIL import Image
import io
import time
from openai import AsyncOpenAI
import json
@@ -10,8 +11,8 @@ from dailyai.services.ai_services import LLMService, ImageGenService
class OpenAILLMService(LLMService):
def __init__(self, *, api_key, model="gpt-4"):
super().__init__()
def __init__(self, *, api_key, model="gpt-4", tools=None, messages=None):
super().__init__(tools=tools, messages=messages)
self._model = model
self._client = AsyncOpenAI(api_key=api_key)
@@ -19,19 +20,26 @@ class OpenAILLMService(LLMService):
return await self._client.chat.completions.create(
stream=stream,
messages=messages,
model=self._model
model=self._model,
tools=self._tools
)
async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
async def run_llm_async(self, messages, tool_choice=None) -> AsyncGenerator[str, None]:
messages_for_log = json.dumps(messages)
self.logger.debug(f"Generating chat via openai: {messages_for_log}")
chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages)
if self._tools:
tools = self._tools
else:
tools = None
start_time = time.time()
chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages, tools=tools, tool_choice=tool_choice)
self.logger.info(f"=== OpenAI LLM TTFB: {time.time() - start_time}")
async for chunk in chunks:
if len(chunk.choices) == 0:
continue
if chunk.choices[0].delta.content:
if chunk.choices[0].delta.tool_calls:
yield chunk.choices[0].delta.tool_calls[0]
elif chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
async def run_llm(self, messages) -> str | None: