File size: 4,155 Bytes
53d8e52 226a55c efb1b7a 53d8e52 bca9228 53d8e52 32c2394 53d8e52 e5702bf 11c9bc2 53d8e52 32c2394 c316c4f 226a55c c316c4f bca9228 c316c4f 32c2394 c316c4f 11c9bc2 c316c4f 226a55c c316c4f 226a55c c316c4f 32c2394 53d8e52 bca9228 32c2394 53d8e52 32c2394 53d8e52 32c2394 c316c4f 32c2394 bca9228 32c2394 bca9228 648f1a1 bca9228 648f1a1 bca9228 c316c4f bca9228 648f1a1 bca9228 648f1a1 bca9228 648f1a1 53d8e52 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
import os
import streamlit as st
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders import PyPDFLoader
# Initialize session state variables
if "messages" not in st.session_state:
st.session_state.messages = []
if "chain" not in st.session_state:
st.session_state.chain = None
def create_sidebar():
with st.sidebar:
st.title("PDF Chat")
st.markdown("### Quick Demo of RAG")
api_key = st.text_input("OpenAI API Key:", type="password")
st.markdown("""
### Tools Used
- OpenAI
- LangChain
- FAISS
### Steps
1. Add API key
2. Upload PDF
3. Chat!
""")
return api_key
def process_pdfs(papers, api_key):
"""Process PDFs and return whether processing was successful"""
if not papers:
return False
with st.spinner("Processing PDFs..."):
try:
embeddings = OpenAIEmbeddings(openai_api_key=api_key)
all_texts = []
for paper in papers:
file_path = os.path.join('./uploads', paper.name)
os.makedirs('./uploads', exist_ok=True)
with open(file_path, "wb") as f:
f.write(paper.getbuffer())
loader = PyPDFLoader(file_path)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
texts = text_splitter.split_documents(documents)
all_texts.extend(texts)
os.remove(file_path)
vectorstore = FAISS.from_documents(all_texts, embeddings)
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True,
output_key="answer"
)
st.session_state.chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_key=api_key),
retriever=vectorstore.as_retriever(),
memory=memory,
return_source_documents=False,
chain_type="stuff"
)
st.success(f"Processed {len(papers)} PDF(s) successfully!")
return True
except Exception as e:
st.error(f"Error processing PDFs: {str(e)}")
return False
def main():
st.set_page_config(page_title="PDF Chat")
api_key = create_sidebar()
if not api_key:
st.warning("Please enter your OpenAI API key")
return
st.title("Chat with PDF")
papers = st.file_uploader("Upload PDFs", type=["pdf"], accept_multiple_files=True)
if papers:
if st.button("Process PDFs"):
process_pdfs(papers, api_key)
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Ask about your PDFs"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
if st.session_state.chain is None:
response = "Please upload and process a PDF first."
else:
with st.spinner("Thinking..."):
result = st.session_state.chain({"question": prompt})
response = result["answer"]
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
if __name__ == "__main__":
main() |