Add application file

app.py

@@ -0,0 +1,137 @@
+import streamlit as st
+import os
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import Chroma, Pinecone
+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, RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.document_loaders import UnstructuredPDFLoader, OnlinePDFLoader
+import pinecone
+
+# Set the path where you want to save the uploaded PDF file
+SAVE_DIR = "pdfs"
+
+
+st.header('Question Answering with your PDF file')
+st.write("Are you interested in chatting with your own documents, whether it is a text file, a PDF, or a website? LangChain makes it easy for you to do question answering with your documents.")
+def qa(file, query, chain_type, k,api_key_pinecode,index_name,environment_pinecode):
+    # load document
+    loader = PyPDFLoader(file)
+    #loader = UnstructuredPDFLoader(file)
+    #loader = OnlinePDFLoader("https://wolfpaulus.com/wp-content/uploads/2017/05/field-guide-to-data-science.pdf")
+    documents = loader.load()
+    #print("doccs",documents)
+    # split the documents into chunks
+    # text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    # texts = text_splitter.split_documents(documents)
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    # select which embeddings we want to use
+    embeddings = OpenAIEmbeddings()
+    # create the vectorestore to use as the index
+    # initialize pinecone
+    pinecone.init(
+        api_key=api_key_pinecode, #"8e9ae07c-dc96-466e-b1a1-519e2ccb4705",  # find at app.pinecone.io
+        environment=environment_pinecode #"northamerica-northeast1-gcp"  # next to api key in console
+    )
+
+    #index_name = "openaiindex"
+    index_name = index_name
+    #db = Chroma.from_documents(texts, embeddings)
+    #db = Pinecone.from_texts(texts, embeddings)
+    db = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)
+    # expose this index in 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
+
+
+
+
+
+
+
+
+
+with st.sidebar:
+    st.header('Configurations')
+    st.write("Enter OpenAI API key. This costs $. Set up billing at [OpenAI](https://platform.openai.com/account).")
+    apikey = st.text_input("Enter your OpenAI API Key here")
+    os.environ["OPENAI_API_KEY"] = apikey
+
+    st.write("Enter Pinecode API key.  [Pinecode](https://www.pinecone.io/).")
+
+    apikey2 = st.text_input("Enter your Pinecone Key here")
+
+    enviroment_pinecode = st.text_input("Enter your Pinecone your environment Key")
+
+    index_name = st.text_input("enter index-name")
+
+
+
+
+
+left_column, right_column = st.columns(2)
+    # You can use a column just like st.sidebar:
+
+
+
+
+
+
+
+
+with left_column:
+
+    # Add a file uploader to the app
+    uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+    # Check if a file has been uploaded
+    if uploaded_file is not None:
+        # Save the uploaded file to the specified directory
+        file_path = os.path.join(SAVE_DIR, uploaded_file.name)
+        # with open(file_path, "wb") as f:
+        #     f.write(uploaded_file.getbuffer())
+        st.success(f"File path {file_path}")
+    query = st.text_input("enter your question")
+    chain_type = st.selectbox(
+        'chain type',
+        ('stuff', 'map_reduce', "refine", "map_rerank"))
+    k = st.slider('Number of relevant chunks', 1, 5)
+
+    if st.button('Loading'):
+        # Or even better, call Streamlit functions inside a "with" block:
+        result=qa(file_path, query, chain_type, k, apikey2, index_name, enviroment_pinecode)
+
+
+
+
+        with right_column:
+
+            st.write("Output of your question")
+
+            #st.write(result)
+
+            #st.write(result['result'])
+            st.subheader("Result")
+            st.write(result['result'])
+
+            st.subheader("source_documents")
+            st.write(result['source_documents'][0])
+
+
+
+
+
+
+
+