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import os | |
from langchain.chains import RetrievalQA | |
from langchain.llms import OpenAI | |
from langchain.document_loaders import TextLoader | |
from langchain.document_loaders import PyPDFLoader | |
from langchain.indexes import VectorstoreIndexCreator | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.vectorstores import Chroma | |
import tempfile | |
import altair | |
import streamlit as st | |
from streamlit import file_uploader | |
def qa(file, query, chain_type, k): | |
#load doc | |
loader = PyPDFLoader(file) | |
documents = loader.load() | |
#split doc in chunks | |
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
texts = text_splitter.split_documents(documents) | |
#select embeddings we want to use | |
embeddings = OpenAIEmbeddings() | |
#create vectorstore to use as the index | |
db = Chroma.from_documents(texts,embeddings) | |
#expose this index to a retriever interface | |
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k}) | |
#create a chain to answer questions | |
qa = RetrievalQA.from_chain_type( | |
llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True) | |
result = qa({"query": query}) | |
print(result['result']) | |
return result | |
def qa_result(file, query, chain_type, k): | |
if file is not None: | |
with tempfile.NamedTemporaryFile(delete=False) as temp_file: | |
temp_file.write(file.read()) | |
result = qa(temp_file.name, query, chain_type, k) | |
st.markdown(f"**Result:** {result['result']}") | |
st.write("Relevant source text:") | |
for doc in result["source_documents"]: | |
st.write('--------------------------------------------------------------') | |
st.write(doc.page_content) | |
def main(): | |
st.markdown(""" | |
## π€ Question Answering with your PDF file | |
1. Upload a PDF file. | |
2. Enter your OpenAI API key. | |
3. Type a question and click "Run". | |
""") | |
file = st.file_uploader("Upload a PDF file", type=["pdf"]) | |
openaikey = st.text_input("Enter your OpenAI API key:") | |
query = st.text_input("Enter your question:") | |
chain_type = st.radio('Chain type', ['stuff', 'map_reduce', "refine", "map_rerank"]) | |
k = st.slider("Number of relevant chunks", 1, 5, 2) | |
run_button = st.button("Run") | |
if run_button: | |
os.environ["OPENAI_API_KEY"] = openaikey | |
qa_result(file, query, chain_type, k) | |
if __name__ == '__main__': | |
main() |