Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
rom langchain.document_loaders import DirectoryLoader
|
2 |
+
from langchain.text_splitter import CharacterTextSplitter
|
3 |
+
import os
|
4 |
+
import pinecone
|
5 |
+
from langchain.vectorstores import Pinecone
|
6 |
+
from langchain.embeddings.openai import OpenAIEmbeddings
|
7 |
+
from langchain.chains import RetrievalQA
|
8 |
+
from langchain.chat_models import ChatOpenAI
|
9 |
+
import streamlit as st
|
10 |
+
from dotenv import load_dotenv
|
11 |
+
|
12 |
+
load_dotenv()
|
13 |
+
|
14 |
+
|
15 |
+
PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
|
16 |
+
PINECONE_ENV = os.getenv('PINECONE_ENV')
|
17 |
+
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
|
18 |
+
|
19 |
+
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
|
20 |
+
|
21 |
+
|
22 |
+
def doc_preprocessing():
|
23 |
+
loader = DirectoryLoader(
|
24 |
+
'data/',
|
25 |
+
glob='**/*.pdf', # only the PDFs
|
26 |
+
show_progress=True
|
27 |
+
)
|
28 |
+
docs = loader.load()
|
29 |
+
text_splitter = CharacterTextSplitter(
|
30 |
+
chunk_size=1000,
|
31 |
+
chunk_overlap=0
|
32 |
+
)
|
33 |
+
docs_split = text_splitter.split_documents(docs)
|
34 |
+
return docs_split
|
35 |
+
|
36 |
+
@st.cache_resource
|
37 |
+
def embedding_db():
|
38 |
+
# we use the openAI embedding model
|
39 |
+
embeddings = OpenAIEmbeddings()
|
40 |
+
pinecone.init(
|
41 |
+
api_key=PINECONE_API_KEY,
|
42 |
+
environment=PINECONE_ENV
|
43 |
+
)
|
44 |
+
docs_split = doc_preprocessing()
|
45 |
+
doc_db = Pinecone.from_documents(
|
46 |
+
docs_split,
|
47 |
+
embeddings,
|
48 |
+
index_name='langchain-demo-indexes'
|
49 |
+
)
|
50 |
+
return doc_db
|
51 |
+
|
52 |
+
llm = ChatOpenAI()
|
53 |
+
doc_db = embedding_db()
|
54 |
+
|
55 |
+
def retrieval_answer(query):
|
56 |
+
qa = RetrievalQA.from_chain_type(
|
57 |
+
llm=llm,
|
58 |
+
chain_type='stuff',
|
59 |
+
retriever=doc_db.as_retriever(),
|
60 |
+
)
|
61 |
+
query = query
|
62 |
+
result = qa.run(query)
|
63 |
+
return result
|
64 |
+
|
65 |
+
def main():
|
66 |
+
st.title("Question and Answering App powered by LLM and Pinecone")
|
67 |
+
|
68 |
+
text_input = st.text_input("Ask your query...")
|
69 |
+
if st.button("Ask Query"):
|
70 |
+
if len(text_input)>0:
|
71 |
+
st.info("Your Query: " + text_input)
|
72 |
+
answer = retrieval_answer(text_input)
|
73 |
+
st.success(answer)
|
74 |
+
|
75 |
+
if __name__ == "__main__":
|
76 |
+
main()
|