transcript_extractor.md

🚀 Transcript Extractor

Welcome to the Transcript Extractor module documentation! The TranscriptExtractor class is essential for extracting detailed information from EMS transcripts. This guide provides detailed information about its attributes, methods, and usage examples.

📚 Class: TranscriptExtractor

Description:

The TranscriptExtractor class is responsible for extracting detailed information from EMS transcripts using an AI model. It utilizes the ModelLoader and PromptManager classes to interact with the AI model and manage prompts effectively.

🏗️ Attributes:

• model_loader (ModelLoader): An instance of ModelLoader to interact with the AI model.
• prompt_manager (PromptManager): An instance of PromptManager to manage prompts.

🚀 Methods:

__init__(self, model_loader: ModelLoader, prompt_manager: PromptManager)

Description: Initializes the TranscriptExtractor with a ModelLoader and PromptManager instance.

Parameters:

• model_loader (ModelLoader): An instance of ModelLoader to interact with the AI model.
• prompt_manager (PromptManager): An instance of PromptManager to manage prompts.

Example:

from modules.model_loader import ModelLoader
from modules.prompt_manager import PromptManager
from modules.transcript_extractor import TranscriptExtractor

# Initialize ModelLoader and PromptManager
model_loader = ModelLoader(model_name="llama3.1")
prompt_manager = PromptManager(prompts={"extract": "Extract information from the following text: {text}"})

# Initialize TranscriptExtractor
extractor = TranscriptExtractor(model_loader=model_loader, prompt_manager=prompt_manager)

extract(self, transcript: str) -> str

Description: Extracts information from the transcript using specified prompts.

Parameters:

• transcript (str): The transcript to extract information from.

Returns:

• str: A string containing the extracted information.

Example:

transcript = "Patient John Doe, 45 years old, male, experiencing chest pain for the past 2 hours. History of hypertension and diabetes."
extracted_info = extractor.extract(transcript)
print("Extracted Information:")
print(extracted_info)

🌟 Usage Examples

Example 1: Initializing the TranscriptExtractor

from modules.model_loader import ModelLoader
from modules.prompt_manager import PromptManager
from modules.transcript_extractor import TranscriptExtractor

# Initialize ModelLoader with a specific model name
model_loader = ModelLoader(model_name="llama3.1")

# Initialize PromptManager with predefined prompts
prompts = {
    "extract": "Extract information from the following text: {text}"
}
prompt_manager = PromptManager(prompts=prompts, context_window_size=32000)

# Initialize TranscriptExtractor
extractor = TranscriptExtractor(model_loader=model_loader, prompt_manager=prompt_manager)

Example 2: Extracting Information from a Transcript

from modules.model_loader import ModelLoader
from modules.prompt_manager import PromptManager
from modules.transcript_extractor import TranscriptExtractor

# Initialize components
model_loader = ModelLoader(model_name="llama3.1")
prompt_manager = PromptManager(prompts={"extract": "Extract information from the following text: {text}"})
extractor = TranscriptExtractor(model_loader=model_loader, prompt_manager=prompt_manager)

# Extract information from a transcript
transcript = "Patient John Doe, 45 years old, male, experiencing chest pain for the past 2 hours. History of hypertension and diabetes."
extracted_info = extractor.extract(transcript)
print("Extracted Information:")
print(extracted_info)

🎉 Conclusion

The TranscriptExtractor class is an essential tool for extracting detailed information from EMS transcripts. By understanding its attributes and methods, you can effectively extract critical data and improve the overall accuracy of EMS documentation. For any questions or support, please contact ScottSucks.