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from src.helper import load_pdf_file, text_split, download_hugging_face_embeddings
from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from dotenv import load_dotenv
import os
load_dotenv()
PINECONE_API_KEY=os.environ.get('PINECONE_API_KEY')
os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
extracted_data=load_pdf_file(data='Data/')
text_chunks=text_split(extracted_data)
embeddings = download_hugging_face_embeddings()
pc = Pinecone(api_key=PINECONE_API_KEY)
index_name = "medchatbot"
pc.create_index(
name=index_name,
dimension=384,
metric="cosine",
spec=ServerlessSpec(
cloud="aws",
region="us-east-1"
)
)
# Embed each chunk and upsert the embeddings into your Pinecone index.
docsearch = PineconeVectorStore.from_documents(
documents=text_chunks,
index_name=index_name,
embedding=embeddings,
)
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