Update utils.py

utils.py

@@ -1,17 +1,109 @@
 import streamlit as st
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from pypdf import PdfReader
+from langchain.schema import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from pinecone import Pinecone as PineconeClient
+from langchain.chains.question_answering import load_qa_chain
+from datetime import datetime
+from langchain_community.vectorstores import Pinecone
 import os
+import time
+
+
+def get_pdf_text(pdf_doc):
+    text = ""
+    pdf_reader = PdfReader(pdf_doc)
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def create_docs(user_pdf_list, unique_id):
+    docs=[]
+    for filename in user_pdf_list:
+        chunks = get_pdf_text(filename)
+
+        docs.append(Document(
+            page_content = chunks,
+            metadata = {"name":filename.name, "type=": filename.type, "size": filename.size, "unique_id": unique_id, 'time': datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+        ))
 
-def save_pdf_to_directory(uploaded_file, directory):
-    if uploaded_file is not None:
-        # Define directory to save file
-        
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        
-        # Save uploaded PDF file to directory
-        with open(os.path.join(directory, uploaded_file.name), "wb") as pdf_file:
-            pdf_file.write(uploaded_file.getbuffer())
         
-        st.success(f"File '{uploaded_file.name}' saved successfully!")
+    return docs
+
+# transform documents
+def split_docs(documents, chunk_size=400, chunk_overlap=20):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    docs = text_splitter.split_documents(documents)
+    
+    return docs
+
+def get_embeddings():
+    embedding = OpenAIEmbeddings()
+    return embedding
+
+
+def push_to_pinecone(docs, embedding):
+
+    pc = PineconeClient(api_key=os.environ.get("PINECONE_API_KEY"))
+    index_name=os.environ.get("PINECONE_INDEX_NAME")
+    index = pc.Index(index_name)
+
+    index.delete(delete_all=True, namespace='rag_bot')    
+    
+    vector = []
+    for i, doc in enumerate(docs):
+        entry = { "id": str(i),
+                "values": embedding.embed_query(doc.page_content),
+                "metadata":doc.metadata}
+        vector.append(entry)
+
+    
+    index = Pinecone.from_documents(docs, embedding, index_name = index_name, namespace='rag_bot')
+
+    st.sidebar.write("This 30 seconds delay is added Manually... \n(because I'm using some free resources)")
+    time.sleep(30)
+
+    return index
+
+
+
+#Function to pull index data from Pinecone
+def pull_from_pinecone(embeddings):
+
+    pinecone_apikey = os.environ.get("PINECONE_API_KEY")
+    pinecone_index_name =os.environ.get("PINECONE_INDEX_NAME")
+
+    PineconeClient(
+    api_key=pinecone_apikey
+    )
+
+    #PineconeStore is an alias name of Pinecone class, please look at the imports section at the top :)
+    index = Pinecone.from_existing_index(pinecone_index_name, embeddings, namespace='rag_bot')
+
+    return index
+
+
+
+
+def get_similar_doc(query, embedding,k=2):
+
+    pc = PineconeClient(api_key=os.environ.get("PINECONE_API_KEY"))
+    index_name=os.environ.get("PINECONE_INDEX_NAME")
+    index = pc.Index(index_name)
+
+    index = pull_from_pinecone(embeddings=embedding)
+    similar_doc = index.similarity_search_with_score(query, int(k))
+    
+    return [doc for doc, similarity_score in similar_doc]
+    
+
 
+def get_answer(query, embedding, k=2):
+    llm=ChatOpenAI(temperature=0.5)
+    chain = load_qa_chain(llm, chain_type="stuff")
 
+    relevent_doc = get_similar_doc(query, embedding,k=2)
+    response = chain.run(input_documents = relevent_doc, question=query)
+    return response