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from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
import os
from constants import CHROMA_SETTINGS
persist_directory = "db"
def main():
for root, dirs, files in os.walk("docs"):
for file in files:
if file.endswith(".pdf"):
print(file)
loader = PDFMinerLoader(os.path.join(root, file))
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = text_splitter.split_documents(documents)
#create embeddings here
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
db.persist()
db=None
if __name__ == "__main__":
main()
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