6cf660e622
- Allow filtering on Tenant Types & searching for parts of Tenant names - Implement health checks - Start Prometheus monitoring (needs to be finalized) - Refine audio_processor and srt_processor to reduce duplicate code and support for larger files - Introduce repopack to reason in LLMs about the code
90 lines
3.6 KiB
Python
90 lines
3.6 KiB
Python
# transcription_processor.py
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from common.utils.model_utils import create_language_template
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from .processor import Processor
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnablePassthrough
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class TranscriptionProcessor(Processor):
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def __init__(self, tenant, model_variables, document_version):
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super().__init__(tenant, model_variables, document_version)
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self.chunk_size = model_variables['processing_chunk_size']
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self.chunk_overlap = model_variables['processing_chunk_overlap']
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def process(self):
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self._log("Starting Transcription processing")
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try:
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transcription = self._get_transcription()
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chunks = self._chunk_transcription(transcription)
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markdown_chunks = self._process_chunks(chunks)
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full_markdown = self._combine_markdown_chunks(markdown_chunks)
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self._save_markdown(full_markdown)
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self._log("Finished processing Transcription")
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return full_markdown, self._extract_title_from_markdown(full_markdown)
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except Exception as e:
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self._log(f"Error processing Transcription: {str(e)}", level='error')
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raise
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def _get_transcription(self):
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# This method should be implemented by child classes
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raise NotImplementedError
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def _chunk_transcription(self, transcription):
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=self.chunk_size,
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chunk_overlap=self.chunk_overlap,
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length_function=len,
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separators=["\n\n", "\n", " ", ""]
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)
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return text_splitter.split_text(transcription)
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def _process_chunks(self, chunks):
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self._log("Generating markdown from transcription")
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llm = self.model_variables['llm']
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template = self.model_variables['transcript_template']
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language_template = create_language_template(template, self.document_version.language)
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transcript_prompt = ChatPromptTemplate.from_template(language_template)
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setup = RunnablePassthrough()
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output_parser = StrOutputParser()
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chain = setup | transcript_prompt | llm | output_parser
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markdown_chunks = []
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previous_part = ""
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for i, chunk in enumerate(chunks):
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self._log(f"Processing chunk {i + 1} of {len(chunks)}")
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self._log(f"Previous part: {previous_part}")
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input_transcript = {
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'transcript': chunk,
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'previous_part': previous_part
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}
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markdown = chain.invoke(input_transcript)
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markdown = self._clean_markdown(markdown)
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markdown_chunks.append(markdown)
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# Extract the last part for the next iteration
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lines = markdown.split('\n')
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last_header = None
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for line in reversed(lines):
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if line.startswith('#'):
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last_header = line
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break
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if last_header:
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header_index = lines.index(last_header)
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previous_part = '\n'.join(lines[header_index:])
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else:
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previous_part = lines[-1] if lines else ""
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return markdown_chunks
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def _combine_markdown_chunks(self, markdown_chunks):
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return "\n\n".join(markdown_chunks)
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def _extract_title_from_markdown(self, markdown):
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lines = markdown.split('\n')
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for line in lines:
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if line.startswith('# '):
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return line[2:].strip()
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return "Untitled Transcription" |