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from langchain_community.vectorstores import Chroma
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
import streamlit as st, os
from utils import st_def, utilities
openai_api_key = st_def.st_sidebar()
def load_docs(directory):
loader = DirectoryLoader(directory)
documents = loader.load()
return documents
with st.spinner('Loading files...'):
documents = load_docs('data/pets_txt/')
file_names = [os.path.basename(doc.metadata['source']) for doc in documents]
st.write('\n\n'.join(file_names))
def split_docs(documents,chunk_size=1000,chunk_overlap=20):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
docs = text_splitter.split_documents(documents)
return docs
docs = split_docs(documents)
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = Chroma.from_documents(documents=docs,embedding=embedding_function,)
if "messages2" not in st.session_state:
st.session_state.messages2 = [] #111
st.session_state.messages2.append({"role": "system", 'content': "hi"})
st.session_state.messages2.append({"role": "assistant", "content": "How May I Help You Today💬?"})
for message in st.session_state.messages2[1:]:
with st.chat_message(message["role"]): st.markdown(message["content"]) #222
if prompt := st.chat_input("💬Ask me anything about the documents above!🍦"):
with st.chat_message("user"): st.markdown(prompt)
st.session_state.messages2.append({"role": "user", "content": prompt})
matching_docs = db.similarity_search(prompt)
with st.chat_message("assistant"):
st.markdown(matching_docs[0].page_content)
st.session_state.messages2.append({"role": "assistant", "content": matching_docs[0].page_content})
# query = "What are the emotional benefits of owning a pet?"
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