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125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
"""
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SpeechGraph Module
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"""
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from scrapegraphai.utils.save_audio_from_bytes import save_audio_from_bytes
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from ..models import OpenAITextToSpeech
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from .base_graph import BaseGraph
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from ..nodes import (
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FetchNode,
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ParseNode,
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RAGNode,
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GenerateAnswerNode,
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TextToSpeechNode,
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)
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from .abstract_graph import AbstractGraph
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class SpeechGraph(AbstractGraph):
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"""
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SpeechyGraph is a scraping pipeline that scrapes the web, provide an answer to a given prompt, and generate an audio file.
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Attributes:
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prompt (str): The prompt for the graph.
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source (str): The source of the graph.
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config (dict): Configuration parameters for the graph.
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llm_model: An instance of a language model client, configured for generating answers.
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embedder_model: An instance of an embedding model client, configured for generating embeddings.
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verbose (bool): A flag indicating whether to show print statements during execution.
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headless (bool): A flag indicating whether to run the graph in headless mode.
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model_token (int): The token limit for the language model.
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Args:
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prompt (str): The prompt for the graph.
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source (str): The source of the graph.
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config (dict): Configuration parameters for the graph.
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Example:
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>>> speech_graph = SpeechGraph(
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... "List me all the attractions in Chioggia and generate an audio summary.",
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... "https://en.wikipedia.org/wiki/Chioggia",
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... {"llm": {"model": "gpt-3.5-turbo"}}
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"""
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def __init__(self, prompt: str, source: str, config: dict):
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super().__init__(prompt, config, source)
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self.input_key = "url" if source.startswith("http") else "local_dir"
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def _create_graph(self) -> BaseGraph:
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"""
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Creates the graph of nodes representing the workflow for web scraping and audio generation.
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Returns:
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BaseGraph: A graph instance representing the web scraping and audio generation workflow.
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"""
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fetch_node = FetchNode(
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input="url | local_dir",
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output=["doc"]
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)
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parse_node = ParseNode(
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input="doc",
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output=["parsed_doc"],
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node_config={
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"chunk_size": self.model_token
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}
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)
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rag_node = RAGNode(
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input="user_prompt & (parsed_doc | doc)",
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output=["relevant_chunks"],
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node_config={
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"llm_model": self.llm_model,
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"embedder_model": self.embedder_model }
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)
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generate_answer_node = GenerateAnswerNode(
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input="user_prompt & (relevant_chunks | parsed_doc | doc)",
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output=["answer"],
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node_config={
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"llm_model": self.llm_model
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}
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)
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text_to_speech_node = TextToSpeechNode(
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input="answer",
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output=["audio"],
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node_config={
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"tts_model": OpenAITextToSpeech(self.config["tts_model"])
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}
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)
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return BaseGraph(
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nodes=[
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fetch_node,
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parse_node,
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rag_node,
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generate_answer_node,
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text_to_speech_node
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],
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edges=[
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(fetch_node, parse_node),
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(parse_node, rag_node),
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(rag_node, generate_answer_node),
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(generate_answer_node, text_to_speech_node)
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],
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entry_point=fetch_node
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)
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def run(self) -> str:
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"""
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Executes the scraping process and returns the answer to the prompt.
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Returns:
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str: The answer to the prompt.
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"""
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inputs = {"user_prompt": self.prompt, self.input_key: self.source}
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self.final_state, self.execution_info = self.graph.execute(inputs)
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audio = self.final_state.get("audio", None)
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if not audio:
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raise ValueError("No audio generated from the text.")
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save_audio_from_bytes(audio, self.config.get(
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"output_path", "output.mp3"))
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print(f"Audio saved to {self.config.get('output_path', 'output.mp3')}")
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return self.final_state.get("answer", "No answer found.") |