utilities/transcripts.py

# Imports for Transcript Loader
import os
import webvtt
import re
from datetime import datetime
from llama_index import Document


# Imports for Document Embedder
import gc
import re

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from pinecone.grpc import PineconeGRPC
from pinecone import ServerlessSpec

from llama_index.vector_stores import PineconeVectorStore
from llama_index.node_parser import SemanticSplitterNodeParser
from llama_index.ingestion import IngestionPipeline





class VTTTranscriptLoader:
    """
    vtt file ingestion and cleaning. This was done because vtt files
    are not recognized by llamaindex. The output should mirror that of
    any document loader from llamaindex or langchain.
    """

    def __init__(self, file_path):
        self.fp = file_path
        self.data = None

    def open_vtt(self, file_path, plaintext=True):
        """Read VTT file."""
        if plaintext:
            with open(file_path, "r") as f:
                data = f.readlines()
        else:
            data = webvtt.read(file_path)
        return data

    def extract_speaker_name(self, text):
        """Extracts the speaker name from a VTT caption."""
        match = re.search(r"<v (.*?)>", text)
        if match:
            return match.group(1)
        else:
            return None

    def extract_speaker_words(self, captions):
        """Extracts the speaker text from a VTT caption."""
        return [caption.text for caption in captions]

    def merge_speaker_words(self, words, speakers, split=True):
        """Joins speaker names with their words."""
        # Extract speaker names
        speaker_list = [self.extract_speaker_name(line) for line in speakers if self.extract_speaker_name(line)]
        # Extract words
        words_list = self.extract_speaker_words(words)
        # Combine speaker names and words
        combined_list = list(zip(speaker_list, words_list))
        # Return the combined list as a single string if split is False
        if not split:
            combined_list = '\n'.join([f"{name}: '{text}'" for name, text in combined_list])
        return combined_list, speaker_list

    def get_metadata(self, speaker_list, file_path):
        """Generates metadata for the transcript."""
        # Meeting length
        time_format = "%H:%M:%S.%f"
        sess = self.open_vtt(file_path, plaintext=False)

        dt1 = datetime.strptime(sess[0].start, time_format)
        dt2 = datetime.strptime(sess[-1].end, time_format)

        minutes = (dt2 - dt1).seconds / 60
        # Meeting date
        match = re.search(r"\d{4}[-_]\d{2}[-_]\d{2}", file_path)
        if match:
            date_str = match.group().replace('_', '-')
            date_obj = datetime.strptime(date_str, "%Y-%m-%d").date()
        else:
            date_obj = None

        # Pull dictionary here
        output = {
            'title': file_path,
            'duration': minutes,
            'meeting_date': date_obj.strftime("%Y-%m-%d"),
            'speakers': list(set(speaker_list)),
        }

        return output

    def manual_document(self, output, metadata):
        """Create document manually"""
        document = Document(text=output)
        document.metadata = metadata
        return document

    def process_file(self, file_path):
        """Processes a single VTT file and returns the combined speaker names and words."""
        # Get words as webvtt captions
        words = self.open_vtt(file_path, plaintext=False)
        # Get speaker lines as plaintext
        speaker = self.open_vtt(file_path, plaintext=True)
        # Combine speaker names and words
        output, speaker_list = self.merge_speaker_words(words, speaker, split=False)
        # Get session data as dictionary
        metadata = self.get_metadata(speaker_list, file_path)

        return self.manual_document(output, metadata)

    def load(self):
        """Processes all VTT files in the directory or the single file and returns a list of results."""
        results = []
        if os.path.isdir(self.fp):
            for root, _, files in os.walk(self.fp):
                for file in files:
                    if file.endswith('.vtt'):
                        file_path = os.path.join(root, file)
                        transcript = self.process_file(file_path)
                        results.append(transcript)
        else:
            transcript = self.process_file(self.fp)
            results.append(transcript)
        return results


class DocumentEmbedder:
    """
    Takes a document and embeds it directly into a pinecone data store.
    Process retrieves, cleans, embeds, and sends the documents to vector
    store.

    Currently supports hugginface embeddings only. Gotta keep things cheap.
    """

    def __init__(self, api_keys, files, embedding, index_name):
        # api keys
        self.pinecone_api_key = api_keys['pinecone']
        self.openai_api_key = api_keys['openai']
        self.huggingface_api_key = api_keys['huggingface']
        # pinecone
        self.embedding = embedding
        self.vector_db = index_name
        # basic items
        self.files = files
        self.interactive = interactive

    
    def clean_text(self, content: str) -> str:
        """
        Remove unwanted characters and patterns in text input.
        :param content: Text input.
        :return: Cleaned version of original text input.
        """

        # Fix hyphenated words broken by newline
        content = re.sub(r'(\w+)-\n(\w+)', r'\1\2', content)

        # Remove specific unwanted patterns and characters
        unwanted_patterns = [
            "\\n", "  —", "——————————", "—————————", "—————",
            r'\\u[\dA-Fa-f]{4}', r'\uf075', r'\uf0b7'
        ]
        for pattern in unwanted_patterns:
            content = re.sub(pattern, "", content)

        # Fix improperly spaced hyphenated words and normalize whitespace
        content = re.sub(r'(\w)\s*-\s*(\w)', r'\1-\2', content)
        content = re.sub(r'\s+', ' ', content)

        return content

    
    def create_embedder(self):
        """Get the right embedding model"""
        
        embedding = HuggingFaceEmbedding(model_name=self.embedding)
        return embedding


    def pinecone_pipeline(self, embedding):
        """Initialize pinecone connection and vectorstore"""

        # connect
        pc = PineconeGRPC(api_key=self.pinecone_api_key)

        # Create your index if index does not exist
        indexes = [i.name for i in pc.list_indexes()]
        index_exists = any([self.vector_db in i for i in indexes])

        if index_exists:
            print("Index already exists")
        else:
            print("Creating index")
            pc.create_index(
                self.vector_db,
                dimension=768,
                metric="cosine",
                spec=ServerlessSpec(cloud="aws", region="us-east-1"),
        )

        # Initialize your index
        pinecone_index = pc.Index(self.vector_db)

        # Initialize VectorStore
        vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

        # create pipeline (abstracts away the need to adaptively process and batch)
        pipeline = IngestionPipeline(
            transformations=[
                # creating appropriate chunks and cutoffs (this needs to be worked on).
                SemanticSplitterNodeParser(
                    buffer_size=10, # 1 = each sentence is a node
                    breakpoint_percentile_threshold=95,
                    embed_model=embedding,
                    ),
                embedding,
                ],
                vector_store=vector_store
            )

        return pipeline

    
    def embed(self):
        """stringing process above to embed and upsert directly to pinecone"""

        # read_file
        print("reading files")
        results = self.files

        # Call clean function
        print("cleaning files")
        for d in range(len(results)):
            results[d].text = self.clean_text(results[d].text)

        # set up embedder
        print("retrieving embedder")
        embedder = self.create_embedder()

        # set up pinecone pipeline
        print("initializing pinecone db")
        pipeline = self.pinecone_pipeline(embedder)

        # run pinecone in batches (of 1) for memory preservation.
        print("reading into pinecone db")
        batchsize = 1
        for i in range(0, len(results), batchsize):
            gc.collect()
            batch = pipeline.run(documents=results[i:i+batchsize])
            print("completed batch %s" % ((i+batchsize)/batchsize))