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# 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))
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