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from sentence_transformers import SentenceTransformer
from dotenv import load_dotenv, find_dotenv
from dbcone import getDatabase
from dbcone import getDatabaseIndex
import os
import uuid
import pandas as pd
import numpy as np
from pathlib import Path
from summary import generate_combined_summary_and_key_points
sentence_model = None
inputDir = None
outputDir = None
topic = None
db_index_name = None
db_namespace_name = None
def initialize_model():
global sentence_model
sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
def get_model():
if sentence_model is None:
initialize_model()
return sentence_model
def get_sentence_embedding(sentence):
model = get_model()
return model.encode(sentence)
def getOutputDir(outputDirectory):
outputDir = Path(outputDirectory)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
return outputDir
def read_files(inputDirectory, outputDirectory, topic=None):
inputDir = Path(inputDirectory)
embeded_lst = []
if ( (not os.path.exists(inputDir)) or (not os.path.isdir(inputDir)) ):
return embeded_lst
files = os.listdir(inputDir)
if topic is None:
topic = os.path.basename(inputDir)
if len(files) <= 0:
return embeded_lst
outputDir = getOutputDir(outputDirectory)
for file in files:
if file.endswith(".txt"):
file_path = os.path.join(inputDir, file)
if os.path.isfile(file_path):
with open(file_path, 'r') as f:
text = f.read()
embedding = get_sentence_embedding(text)
f.close()
if not os.path.isfile(os.path.join(outputDir, file)):
os.rename(file_path, os.path.join(outputDir, file))
else:
os.remove(file_path)
(topic_gen, summary, keypoints) = generate_combined_summary_and_key_points(text)
if (topic_gen is not None):
topic += " - " + topic_gen
embeded_lst.append(
{
"id" : str(uuid.uuid4().hex),
"metadata": {
'text':text,
"topic": topic,
"summary": summary,
"keypoints":keypoints
},
"values": embedding.tolist()
}
)
return embeded_lst
def save_to_database(embeded_lst, index_name = 'test_videos' ,namespace="sample-namespace"):
if len(embeded_lst) > 0 :
db_index = getDatabaseIndex(index_name)
db_index.upsert(
vectors=embeded_lst,
namespace=namespace
)
def embed_text_files(inputDir, outputDir, topic):
return read_files(inputDirectory=inputDir, outputDirectory=outputDir, topic=topic)
def configureApp(given_topic):
global inputDir, outputDir, topic, db_index_name, db_namespace_name
currPath = Path.cwd()
inputDir = os.path.join( currPath, 'output')
outputDir = os.path.join(currPath, 'processed')
topic = given_topic
db_index_name = 'samplevideos'
db_namespace_name="video-namespace"
load_dotenv(find_dotenv('Keys1.env'))
initialize_model()
getDatabase()
return True
def fetch_from_database(search_text, topics =[] ,top_k = 5, index_name = 'test-videos' ,namespace="sample-namespace"):
db_index = getDatabaseIndex(index_name)
results = db_index.query(namespace=namespace,
vector=np.array(get_sentence_embedding(search_text)).tolist(),
top_k=top_k,
include_values=True,
include_metadata=True,
filter={
"topic": {"$in": topics},
}
)
return results
def captureData():
global inputDir, outputDir, topic, db_index_name, db_namespace_name
embeded_lst = embed_text_files(inputDir, outputDir, topic)
save_to_database(embeded_lst, index_name =db_index_name, namespace=db_namespace_name)
def queryRepository(search_text, topic):
global db_index_name, db_namespace_name
result = fetch_from_database(search_text, topics=[topic], index_name = db_index_name, namespace=db_namespace_name)
print(f'Results: {result}')
def mainApp(topic):
configureApp(topic)
captureData()
if __name__ == "__main__":
mainApp()
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