gradio_embedding.py

from langchain_community.document_loaders.text import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from setup import *

# Use a relative path:
file = "Amazon_sagemaker_Faq.txt"  # Assuming you have a data folder in your project

loader = TextLoader(file_path=file)
pages = []
for page in loader.load():
    pages.append(page)

docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50,
    add_start_index=True,
    separators=["\n", "\n\n"]
)

all_splits = text_splitter.split_documents(docs)
print(f"Split blog post into {len(all_splits)} sub-documents.")

# Instead of Windows absolute path for persistence:
# persist_directory = "D:\\Education\\AI\\AI-Agents\\Agentic-RAG"

# Use a relative path:
persist_directory = "./chroma_db"  # This will create a chroma_db folder in your app's directory

vector_store = Chroma.from_documents(
    documents=all_splits,
    collection_name='sagemaker-chroma',
    persist_directory=persist_directory,
    embedding=embeddings
)