Update app.py

app.py

@@ -9,4 +9,171 @@ from langchain.document_loaders import TextLoader
 # This library will handle the splitting part of the data
 from langchain.text_splitter import CharacterTextSplitter
 # This library will handle embedding of data
-from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
+from pinecone import Pinecone, ServerlessSpec
+from langchain.llms import HuggingFaceHub
+from langchain import PromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFaceHub
+from langchain.vectorstores import Pinecone
+
+
+template = """
+You are a MLOPs engineer. The user will ask you a question about Machine Learning Operations.
+Use the following piece of context to answer the question.
+If you don't know the answer, just say don't know/
+Keep the answer brief
+
+Context: {context}
+Question: {question}
+Answer:
+
+"""
+
+def setup_retrieval_qa_system(doc_directory, question, chunk_size=500, chunk_overlap=100):
+    load_dotenv()
+
+    hugging_face = os.getenv("Hugging_face_key")
+    if not hugging_face:
+        raise ValueError("HuggingFace API key is missing. Please set it in the .env file.")
+    os.environ['HUGGINGFACEHUB_API_TOKEN'] = hugging_face
+
+    pc = os.getenv("PCToken")
+    PINECONE_API_KEY = os.getenv("PCToken")
+    
+    if not pc:
+        raise ValueError("pc API key is missing. Please set it in the .env file.")
+    os.environ['PCToken'] = pc
+
+    # We are initializing the cloud platform over here
+    cloud = os.environ.get("PINECONE_CLOUD") or "aws"
+    # We are going to give a region for aws
+    region = os.environ.get("PINECONE_REGION") or "us-east-1"
+    # Initialize the client
+    serv = ServerlessSpec(cloud = cloud, region = region)
+
+    index_name = "Bhagya-27thoct"
+
+    # We are check if the name of our index is not existing in pinecone directory
+    if index_name not in pc.list_indexes().names():
+      # if not then we will create a index for us
+      pc.create_index(
+          name = index_name,
+          dimension = 768,
+          metric = "cosine",
+          spec = serv
+      )
+      # Waiting till the machine has not created the index
+      while not pc.describe_index(index_name).status['ready']:
+        time.sleep(1)
+    
+    # Check to see if the index is ready
+    print("Index before inserting")
+    print(pc.Index(index_name).describe_index_stats())
+
+    all_docs = []
+    with st.spinner('Loading and processing documents...'):
+        for file_name in os.listdir(doc_directory):
+            file_path = os.path.join(doc_directory, file_name)
+            loader = PyPDFLoader(file_path)
+            docs = loader.load()
+            all_docs.extend(docs)
+
+        text_splitter = CharacterTextSplitter(chunk_size = chunk_size, chunk_overlap = chunk_overlap)
+        #text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+        #splitted_chunks = text_splitter.split_documents(all_docs)
+        splitted_chunks = text_splitter.split_documents(all_docs)
+
+        #embedding_model = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+        embedding_model = HuggingFaceInstructEmbeddings(model_name="mistralai/Mixtral-8x7B-Instruct-v0.1")
+        vector_db = FAISS.from_documents(splitted_chunks, embedding_model)
+        retriever = vector_db.as_retriever()
+        
+        # IF the index is not there in the index list
+        if index_name not in pc.list_indexes():
+            docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name = index_name)
+        else:
+            docsearch = PineconeVectorStore.from_existing_index(index_name, embeddings, pinecone_index = pc.Index(index_name))
+            
+    llm = HuggingFaceHub(
+        repo_id = model_id,
+        model_kwargs = {"temperature" : 0.8, "top_k" : 50},
+        huggingfacehub_api_token = hugging_face
+    )
+    #llm = ChatGroq(model="llama3-8b-8192")
+    prompt = PromptTemplate(
+        template = template,
+        input_variables = ["context", "question"]
+    )
+    rag_chain = (
+        {"context" : docsearch.as_retriever(), "question" : RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    llm = HuggingFaceHub(
+        repo_id=model_id,
+        model_kwargs={"temperature": 0.8, "top_k": 50},
+        huggingfacehub_api_token=hugging_face
+    )
+
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=docsearch.as_retriever(),
+    )
+    #retrieval_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+    with st.spinner('Finding the best answer...'):
+        result = qa_chain.run(query)
+    
+    # with st.spinner('Finding the best answer...'):
+    #     result = retrieval_chain.invoke(question)
+    
+    return result['result']
+
+def main():
+    st.title("📝 Document-Based Question Answering System with Groq")
+
+    st.sidebar.header("Configuration")
+
+    # File uploader for PDFs
+    uploaded_files = st.sidebar.file_uploader("Upload PDF documents", type="pdf", accept_multiple_files=True)
+    
+    # Get the document directory from the user
+    doc_directory = st.text_input("Or enter the document directory path directly:", "")
+
+    # Set chunk size and overlap
+    chunk_size = st.sidebar.slider("Set chunk size", 100, 1000, 500)
+    chunk_overlap = st.sidebar.slider("Set chunk overlap", 0, 200, 100)
+
+    # Input for the question
+    question = st.text_input("Enter your question:")
+
+    # Button to trigger the QA system
+    if st.button("Get Answer"):
+        if uploaded_files:
+            doc_directory = "/tmp/streamlit_uploaded_docs"
+            os.makedirs(doc_directory, exist_ok=True)
+            for file in uploaded_files:
+                with open(os.path.join(doc_directory, file.name), "wb") as f:
+                    f.write(file.getbuffer())
+        elif not doc_directory:
+            st.warning("Please upload PDF files or provide a document directory.")
+            return
+
+        if question:
+            try:
+                result = setup_retrieval_qa_system(doc_directory, question, chunk_size, chunk_overlap)
+                st.success("Answer found!")
+                st.write(f"**Answer:** {result}")
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+        else:
+            st.warning("Please provide a question.")
+
+if __name__ == "__main__":
+    main()