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e37b348 342c06d e37b348 870c233 e37b348 870c233 e37b348 870c233 e37b348 870c233 cb655fd 870c233 e37b348 | 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 | import os
import json
import streamlit as st
from tempfile import NamedTemporaryFile
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFDirectoryLoader,PyPDFLoader
from langchain_community.docstore.document import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI,OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.runnables import RunnableParallel,RunnablePassthrough
key1 = os.environ.get("OPENAI_API_KEY")
# load and split the documents using pypdf and text splitters
def load_and_split_document(uploaded_file):
"""Loads and splits the document into pages."""
bytes_data = uploaded_file.read()
with NamedTemporaryFile(delete=False) as tmp:
tmp.write(bytes_data)
data = PyPDFLoader(tmp.name).load_and_split()
os.remove(tmp.name)
return data
#create embeddings and store them in a database
def embeddings(key):
return OpenAIEmbeddings(openai_api_key = key)
def create_database(data,embeddings):
db = FAISS.from_documents(data,embeddings)
db.save_local("faiss_database")
# create the prompt,model and chain using langchain
prompt = """
Answer the question based only on the following context:
{context}
Question: {question}
"""
def get_model(key,model_name = "gpt-3.5-turbo"):
chat_model = ChatOpenAI(openai_api_key = key,model_name=model_name)
return chat_model
def response(database,model,question):
prompt_val = ChatPromptTemplate.from_template(prompt)
retreiver = database.as_retriever()
parser = StrOutputParser()
chain = (
{'context': retreiver,'question':RunnablePassthrough()}
| prompt_val
| model
| parser
)
ans = chain.invoke(question)
return ans
def main():
st.set_page_config("Chat Q&A")
st.header("Chat with your pdf documents")
uploaded_file = st.file_uploader("Upload your document", type=["pdf"])
if uploaded_file:
st.header("update vector store")
if st.button("update vectors"):
with st.spinner("processing..."):
data = load_and_split_document(uploaded_file)
embed = embeddings(key1)
create_database(data,embed)
st.success("done")
question = st.text_input("Ask question about your documents")
if question:
if st.button("OpenAI result"):
with st.spinner("processing..."):
embed = embeddings(key1)
database = FAISS.load_local("faiss_database",embed,allow_dangerous_deserialization=True)
model = get_model(key1)
st.write(response(database,model,question))
st.success("done")
if __name__=="__main__":
main()
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