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import streamlit as st
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
@st.cache_resource()
def load_embedding_model(model):
"""
sentence-transformers/all-mpnet-base-v2
sentence-transformers/all-MiniLM-L6-v2
"""
model = HuggingFaceEmbeddings(model_name=model)
return model
def load_vector_store():
"""
Loads a simple vector store
I didn't use @st.cache because I want to
load vector store on every page load
"""
model = load_embedding_model("sentence-transformers/all-MiniLM-L6-v2")
vector_store = Chroma(
collection_name="main_store",
embedding_function=model,
)
return vector_store
def process_pdf(pdf, vector_store):
"""
Loads a pdf and splits it into chunks
"""
loader = PyPDFLoader(pdf)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vector_store.add_documents(splits)
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