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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+t_kjv.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,92 @@
+import streamlit as st
+from utils import *
+
+
+# clear the chat history from streamlit session state
+def clear_history():
+    if 'history' in st.session_state:
+        del st.session_state['history']
+
+
+if __name__ == "__main__":
+    import os
+
+    # loading the OpenAI api key from .env
+    from dotenv import load_dotenv, find_dotenv
+
+    load_dotenv(find_dotenv(), override=True)
+
+    # st.image('img.png')
+
+    st.subheader('Load a Document and Ask a Question')
+    with st.sidebar:
+        # text_input for the OpenAI API key (alternative to python-dotenv and .env)
+        api_key = st.text_input('OpenAI API Key:', type='password')
+        if api_key:
+            os.environ['OPENAI_API_KEY'] = api_key
+
+        # file uploader widget
+        uploaded_file = st.file_uploader('Upload a file:', type=['pdf', 'docx', 'txt'])
+
+        # chunk size number widget
+        chunk_size = st.number_input('Chunk size:', min_value=100, max_value=2048, value=512, on_change=clear_history)
+
+        # k number input widget
+        k = st.number_input('k', min_value=1, max_value=20, value=3, on_change=clear_history)
+
+        # add data button widget
+        add_data = st.button('Add Data', on_click=clear_history)
+
+        if add_data:
+            if api_key:
+                if uploaded_file and add_data:  # if the user browsed a file
+                    with st.spinner('Reading, chunking and embedding file ...'):
+                        # writing the file from RAM to the current directory on disk
+                        bytes_data = uploaded_file.read()
+                        file_name = os.path.join('./', uploaded_file.name)
+                        with open(file_name, 'wb') as f:
+                            f.write(bytes_data)
+
+                        data = load_document(file_name)
+                        chunks = chunk_data(data, chunk_size=chunk_size)
+                        st.write(f'Chunk size: {chunk_size}, Chunks: {len(chunks)}')
+
+                        tokens, embedding_cost = calculate_embedding_cost(chunks)
+                        st.write(f'Embedding cost: ${embedding_cost:.4f}')
+
+                        # creating the embeddings and returning the Chroma vector store
+                        vector_store = create_embeddings(chunks)
+
+                        # saving the vector store in the streamlit session state (to be persistent between reruns)
+                        st.session_state.vs = vector_store
+                        st.success('File uploaded, chunked and embedded successfully.')
+            else:
+                st.error("Please provide your OpenAI API key above.....")
+
+    # user's question text input widget
+    q = st.text_input('Ask a question about the content of your file:')
+    if q:  # if the user entered a question and hit enter
+        if 'vs' in st.session_state:  # if there's the vector store (user uploaded, split and embedded a file)
+            vector_store = st.session_state.vs
+            st.write(f'k: {k}')
+            answer = ask_and_get_answer(vector_store, q, k)
+
+            # text area widget for the LLM answer
+            st.text_area('LLM Answer: ', value=answer)
+
+            st.divider()
+
+            # if there's no chat history in the session state, create it
+            if 'history' not in st.session_state:
+                st.session_state.history = ''
+
+            # the current question and answer
+            value = f'Q: {q} \nA: {answer}'
+
+            st.session_state.history = f'{value} \n {"-" * 100} \n {st.session_state.history}'
+            h = st.session_state.history
+
+            # text area widget for the chat history
+            st.text_area(label='Chat History', value=h, key='history', height=400)
+
+# run the app: streamlit run ./chat_with_documents.py

requirements.txt

@@ -0,0 +1,7 @@
+openai
+langchain
+chromadb
+docx2txt
+pypdf
+streamlit
+tiktoken

t_kjv.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7ce786526557549671d397cdccceb0704cd5c181cb912bcd33693601326ec16
+size 3765976

utils.py

@@ -0,0 +1,67 @@
+import os
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+
+
+# loading PDF, DOCX and TXT files as LangChain Documents
+def load_document(file):
+    import os
+    name, extension = os.path.splitext(file)
+
+    if extension == '.pdf':
+        from langchain.document_loaders import PyPDFLoader
+        print(f'Loading {file}')
+        loader = PyPDFLoader(file)
+    elif extension == '.docx':
+        from langchain.document_loaders import Docx2txtLoader
+        print(f'Loading {file}')
+        loader = Docx2txtLoader(file)
+    elif extension == '.txt':
+        from langchain.document_loaders import TextLoader
+        loader = TextLoader(file)
+    else:
+        print('Document format is not supported!')
+        return None
+
+    data = loader.load()
+    return data
+
+
+# splitting data in chunks
+def chunk_data(data, chunk_size=256, chunk_overlap=20):
+    from langchain.text_splitter import RecursiveCharacterTextSplitter
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    chunks = text_splitter.split_documents(data)
+    return chunks
+
+
+# create embeddings using OpenAIEmbeddings() and save them in a Chroma vector store
+def create_embeddings(chunks):
+    embeddings = OpenAIEmbeddings()
+    vector_store = Chroma.from_documents(chunks, embeddings)
+
+    # if you want to use a specific directory for chromadb
+    # vector_store = Chroma.from_documents(chunks, embeddings, persist_directory='./mychroma_db')
+    return vector_store
+
+
+def ask_and_get_answer(vector_store, q, k=3):
+    from langchain.chains import RetrievalQA
+    from langchain.chat_models import ChatOpenAI
+
+    llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=1)
+    retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': k})
+    chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+
+    answer = chain.run(q)
+    return answer
+
+
+# calculate embedding cost using tiktoken
+def calculate_embedding_cost(texts):
+    import tiktoken
+    enc = tiktoken.encoding_for_model('text-embedding-ada-002')
+    total_tokens = sum([len(enc.encode(page.page_content)) for page in texts])
+    # print(f'Total Tokens: {total_tokens}')
+    # print(f'Embedding Cost in USD: {total_tokens / 1000 * 0.0004:.6f}')
+    return total_tokens, total_tokens / 1000 * 0.0004