RAG BOT code

ragchatbot/app.py

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+import streamlit as st
+import PyPDF2
+import os
+import logging
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+import requests
+import json
+from datetime import datetime
+import pickle
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Parseable configuration
+PARSEABLE_URL = "https://demo.parseable.com/api/v1/logstream"
+PARSEABLE_STREAM = "ragbot"
+
+# OpenAI API key (replace with your actual key)
+os.environ["OPENAI_API_KEY"] = "<open_ai_key>"
+
+# File to store chat history
+CHAT_HISTORY_FILE = "chat_history.pkl"
+
+def send_log_to_parseable(log_data):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": "Basic YWRtaW46YWRtaW4=" # Basic auth with username and password
+    }
+    try:
+        response = requests.post(f"{PARSEABLE_URL}/{PARSEABLE_STREAM}", headers=headers, json=log_data)
+        response.raise_for_status()
+        logger.info("Log sent to Parseable successfully")
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Failed to send log to Parseable: {e}")
+
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def create_vector_store(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = text_splitter.split_text(text)
+
+    embeddings = OpenAIEmbeddings()
+    vector_store = FAISS.from_texts(chunks, embeddings)
+
+    return vector_store
+
+def create_conversation_chain(vector_store):
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vector_store.as_retriever(),
+        return_source_documents=True
+    )
+    return conversation_chain
+
+def load_chat_history():
+    if os.path.exists(CHAT_HISTORY_FILE):
+        with open(CHAT_HISTORY_FILE, 'rb') as f:
+            return pickle.load(f)
+    return []
+
+def save_chat_history(chat_history):
+    with open(CHAT_HISTORY_FILE, 'wb') as f:
+        pickle.dump(chat_history, f)
+
+def main():
+    st.title("RAG Conversation Bot with PDF Upload")
+
+    st.write("Note: Chat history is stored locally on your device.")
+
+    uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+    if uploaded_file is not None:
+        text = extract_text_from_pdf(uploaded_file)
+        vector_store = create_vector_store(text)
+        conversation_chain = create_conversation_chain(vector_store)
+
+        st.success("PDF uploaded and processed successfully!")
+
+        chat_history = load_chat_history()
+
+        for message in chat_history:
+            st.write(f"{'User' if message['is_user'] else 'Bot'}: {message['text']}")
+
+        user_question = st.text_input("Ask a question about the PDF:")
+
+        if user_question:
+            start_time = datetime.now()
+            result = conversation_chain({"question": user_question, "chat_history": [(msg['text'], '') for msg in chat_history if not msg['is_user']]})
+            end_time = datetime.now()
+
+            answer = result["answer"]
+            st.write("Bot:", answer)
+
+            chat_history.append({"is_user": True, "text": user_question})
+            chat_history.append({"is_user": False, "text": answer})
+            save_chat_history(chat_history)
+
+            # Prepare log data
+            log_data = {
+                "timestamp": datetime.now().isoformat(),
+                "pdf_name": uploaded_file.name,
+                "question": user_question,
+                "answer": answer,
+                "processing_time_ms": (end_time - start_time).total_seconds() * 1000
+            }
+
+            # Send log to Parseable
+            send_log_to_parseable(log_data)
+
+if __name__ == "__main__":
+    main()

ragchatbot/requirements.txt

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+streamlit
+PyPDF2
+langchain
+openai
+faiss-cpu
+requests