215 lines
7.1 KiB
Python
215 lines
7.1 KiB
Python
import asyncio
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import re
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from tblib import Frame
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from dailyai.pipeline.frame_processor import FrameProcessor
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from dailyai.pipeline.frames import (
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ControlFrame,
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EndPipeFrame,
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EndFrame,
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LLMMessagesQueueFrame,
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LLMResponseEndFrame,
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Frame,
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TextFrame,
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TranscriptionQueueFrame,
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)
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from dailyai.pipeline.pipeline import Pipeline
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from dailyai.services.ai_services import AIService
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from typing import AsyncGenerator, Coroutine, List, Text
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class LLMContextAggregator(AIService):
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def __init__(
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self,
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messages: list[dict],
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role: str,
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bot_participant_id=None,
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complete_sentences=True,
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pass_through=True,
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):
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super().__init__()
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self.messages = messages
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self.bot_participant_id = bot_participant_id
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self.role = role
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self.sentence = ""
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self.complete_sentences = complete_sentences
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self.pass_through = pass_through
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async def process_frame(
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self, frame: Frame
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) -> AsyncGenerator[Frame, None]:
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# We don't do anything with non-text frames, pass it along to next in the pipeline.
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if not isinstance(frame, TextFrame):
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yield frame
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return
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# Ignore transcription frames from the bot
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if isinstance(frame, TranscriptionQueueFrame):
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if frame.participantId == self.bot_participant_id:
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return
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# The common case for "pass through" is receiving frames from the LLM that we'll
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# use to update the "assistant" LLM messages, but also passing the text frames
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# along to a TTS service to be spoken to the user.
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if self.pass_through:
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yield frame
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# TODO: split up transcription by participant
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if self.complete_sentences:
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# type: ignore -- the linter thinks this isn't a TextQueueFrame, even
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# though we check it above
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self.sentence += frame.text
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if self.sentence.endswith((".", "?", "!")):
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self.messages.append({"role": self.role, "content": self.sentence})
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self.sentence = ""
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yield LLMMessagesQueueFrame(self.messages)
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else:
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# type: ignore -- the linter thinks this isn't a TextQueueFrame, even
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# though we check it above
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self.messages.append({"role": self.role, "content": frame.text})
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yield LLMMessagesQueueFrame(self.messages)
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async def finalize(self) -> AsyncGenerator[Frame, None]:
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# Send any dangling words that weren't finished with punctuation.
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if self.complete_sentences and self.sentence:
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self.messages.append({"role": self.role, "content": self.sentence})
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yield LLMMessagesQueueFrame(self.messages)
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class LLMUserContextAggregator(LLMContextAggregator):
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def __init__(
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self, messages: list[dict], bot_participant_id=None, complete_sentences=True
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):
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super().__init__(
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messages, "user", bot_participant_id, complete_sentences, pass_through=False
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)
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class LLMAssistantContextAggregator(LLMContextAggregator):
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def __init__(
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self, messages: list[dict], bot_participant_id=None, complete_sentences=True
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):
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super().__init__(
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messages,
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"assistant",
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bot_participant_id,
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complete_sentences,
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pass_through=True,
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)
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class SentenceAggregator(FrameProcessor):
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def __init__(self):
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self.aggregation = ""
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async def process_frame(
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self, frame: Frame
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) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, TextFrame):
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m = re.search("(.*[?.!])(.*)", frame.text)
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if m:
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yield TextFrame(self.aggregation + m.group(1))
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self.aggregation = m.group(2)
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else:
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self.aggregation += frame.text
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elif isinstance(frame, EndFrame):
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if self.aggregation:
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yield TextFrame(self.aggregation)
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yield frame
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else:
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yield frame
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class LLMFullResponseAggregator(FrameProcessor):
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def __init__(self):
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self.aggregation = ""
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async def process_frame(
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self, frame: Frame
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) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, TextFrame):
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self.aggregation += frame.text
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elif isinstance(frame, LLMResponseEndFrame):
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yield TextFrame(self.aggregation)
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self.aggregation = ""
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else:
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yield frame
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class StatelessTextTransformer(FrameProcessor):
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def __init__(self, transform_fn):
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self.transform_fn = transform_fn
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, TextFrame):
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result = self.transform_fn(frame.text)
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if isinstance(result, Coroutine):
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result = await result
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yield TextFrame(result)
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else:
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yield frame
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class ParallelPipeline(FrameProcessor):
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def __init__(self, pipeline_definitions: List[List[FrameProcessor]]):
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self.sources = [asyncio.Queue() for _ in pipeline_definitions]
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self.sink: asyncio.Queue[Frame] = asyncio.Queue()
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self.pipelines: list[Pipeline] = [
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Pipeline(
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pipeline_definition,
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source,
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self.sink,
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)
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for source, pipeline_definition in zip(self.sources, pipeline_definitions)
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]
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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for source in self.sources:
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await source.put(frame)
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await source.put(EndPipeFrame())
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await asyncio.gather(*[pipeline.run_pipeline() for pipeline in self.pipelines])
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seen_ids = set()
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while not self.sink.empty():
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frame = await self.sink.get()
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# de-dup frames. Because the convention is to yield a frame that isn't processed,
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# each pipeline will likely yield the same frame, so we will end up with _n_ copies
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# of unprocessed frames where _n_ is the number of parallel pipes that don't
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# process that frame.
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if id(frame) in seen_ids:
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continue
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seen_ids.add(id(frame))
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# Skip passing along EndParallelPipeQueueFrame, because we use them for our own flow control.
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if not isinstance(frame, EndPipeFrame):
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yield frame
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class GatedAggregator(FrameProcessor):
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def __init__(self, gate_open_fn, gate_close_fn, start_open):
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self.gate_open_fn = gate_open_fn
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self.gate_close_fn = gate_close_fn
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self.gate_open = start_open
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self.accumulator: List[Frame] = []
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if self.gate_open:
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if self.gate_close_fn(frame):
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self.gate_open = False
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else:
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if self.gate_open_fn(frame):
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self.gate_open = True
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if self.gate_open:
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yield frame
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if self.accumulator:
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for frame in self.accumulator:
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yield frame
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self.accumulator = []
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else:
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self.accumulator.append(frame)
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