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🦹🏻‍♀️ SpeechGraph Module

The SpeechGraph module defines a class for creating and executing a graph that scrapes the web, provides an answer to a given prompt, and generates an audio file.

Classes​

SpeechGraph​

SpeechGraph is a scraping pipeline that scrapes the web, provides an answer to a given prompt, and generates an audio file.

Attributes​

  • prompt (str): The prompt for the graph.
  • source (str): The source of the graph.
  • config (dict): Configuration parameters for the graph.
  • schema (str): The schema for the graph output.
  • llm_model: An instance of a language model client, configured for generating answers.
  • embedder_model: An instance of an embedding model client, configured for generating embeddings.
  • verbose (bool): A flag indicating whether to show print statements during execution.
  • headless (bool): A flag indicating whether to run the graph in headless mode.
  • model_token (int): The token limit for the language model.

Methods​

  • __init__(self, prompt: str, source: str, config: dict, schema: Optional[str] = None)

    • Initializes the SpeechGraph with a prompt, source (URL or local directory), configuration, and schema.
    • Args:
      • prompt (str): The prompt for the graph.
      • source (str): The source of the graph (URL or local directory).
      • config (dict): Configuration parameters for the graph.
      • schema (Optional[str]): The schema for the graph output.

  • _create_graph(self) -> BaseGraph

    • Creates the graph of nodes representing the workflow for web scraping and audio generation.
    • Returns: An instance of BaseGraph.

  • run(self) -> str

    • Executes the scraping process, generates the answer to the prompt, and saves the audio file.
    • Returns: The answer to the prompt.

Example Usage​

Here is an example of how to use the SpeechGraph class:

from speech_graph import SpeechGraph

# Define the prompt, source (URL or local directory), and configuration
prompt = "List me all the attractions in Chioggia and generate an audio summary."
source = "https://en.wikipedia.org/wiki/Chioggia"
config = {
    "llm": {"model": "gpt-3.5-turbo"},
    "tts_model": {"engine": "Google", "language": "en"}
}

# Create the speech graph
speech_graph = SpeechGraph(prompt, source, config)

# Run the speech graph
result = speech_graph.run()

print(result)