Create app.py

app.py

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+import os
+import gradio as gr
+from langchain_community.vectorstores import Chroma
+from langchain_community.chat_models import ChatOllama
+from langchain_community.embeddings import FastEmbedEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema.output_parser import StrOutputParser
+from langchain.prompts import PromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Initialize embeddings model and vector store
+embeddings_model = FastEmbedEmbeddings(model_name="BAAI/bge-small-en-v1.5")
+vector_store = None
+
+# Chat history (initialize with an empty list)
+chat_history = []
+
+# Store previous questions and their embeddings
+question_embeddings = []
+
+# Prompt templates for LLM
+prompt_with_context_template = """Analyze the following context and answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt_without_context_template = """Provide an answer to the question based on general knowledge.
+Question: {question}
+"""
+prompt_with_context = PromptTemplate.from_template(prompt_with_context_template)
+prompt_without_context = PromptTemplate.from_template(prompt_without_context_template)
+
+# Function to load, split PDFs, and store in vector store
+def process_documents(uploaded_files):
+    global vector_store
+    all_docs = []
+    for uploaded_file in uploaded_files:
+        # Load each PDF using PyPDFLoader
+        loader = PyPDFLoader(uploaded_file)
+        pages = loader.load_and_split()
+
+        # Split documents into chunks
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        docs = text_splitter.split_documents(pages)
+        all_docs.extend(docs)
+
+    # Create or update the vector store
+    if vector_store is None:
+        vector_store = Chroma.from_documents(all_docs, embeddings_model)
+    else:
+        vector_store.add_documents(all_docs)
+    
+    return f"Uploaded {len(uploaded_files)} files and indexed {len(all_docs)} chunks."
+
+# Function to handle question answering with RAG and maintain chat history
+def answer_question(question):
+    global vector_store, chat_history, question_embeddings
+    
+    # Set up retriever and LLM
+    retriever = vector_store.as_retriever() if vector_store else None
+    llm = ChatOllama(model="llama3:latest", verbose=True)
+
+    if retriever:
+        # Define the RAG chain with document context
+        chain = (
+            {"context": retriever, "question": RunnablePassthrough()}
+            | prompt_with_context
+            | llm
+            | StrOutputParser()
+        )
+        # Process user question through RAG chain with context
+        answer = chain.invoke(question).capitalize()
+    else:
+        # Define the RAG chain without document context
+        chain = (
+            {"question": RunnablePassthrough()}
+            | prompt_without_context
+            | llm
+            | StrOutputParser()
+        )
+        # Process user question through RAG chain without context
+        answer = chain.invoke(question).capitalize()
+
+    # Append the question and answer to the chat history
+    chat_history.append((f"Q: {question}", f"A: {answer}"))
+    
+    # Encode the current question and store its embedding
+    current_question_embedding = embeddings_model.embed_query(question)
+    question_embeddings.append(current_question_embedding)
+    
+    # Find related questions
+    related_question = "No related questions found."
+    if question_embeddings:
+        # Compute similarity between current question and previous questions
+        similarities = cosine_similarity([current_question_embedding], question_embeddings)
+        related_idx = np.argmax(similarities)
+        if similarities[0][related_idx] > 0.5:
+            related_question = chat_history[related_idx][0]
+    
+    # Format the chat history for display
+    chat_display = "\n\n".join([f"{q}\n{a}" for q, a in chat_history])
+    
+    return answer, chat_display, related_question
+
+# Function to clear the vector store
+def clear_documents():
+    global vector_store
+    if vector_store is not None:
+        vector_store.delete_collection()
+        vector_store = None
+    return "Document collection cleared.", chat_history, ""
+
+# Gradio interface
+with gr.Blocks() as demo:
+    # Main layout with two columns
+    with gr.Row():
+        # Left column for file upload and question input
+        with gr.Column(scale=1):
+            file_uploader = gr.File(label="Upload PDFs", file_types=[".pdf"], file_count="multiple", type="filepath")
+            upload_button = gr.Button("Upload and Process")
+            clear_button = gr.Button("Clear Document Collection")
+            status_display = gr.Textbox(label="Status", lines=2)
+
+            question_input = gr.Textbox(label="Ask a question about the documents")
+            ask_button = gr.Button("Ask")
+        
+        # Center column for answer and chat history
+        with gr.Column(scale=2):
+            answer_display = gr.Textbox(label="Answer", lines=4)
+            chat_history_display = gr.Textbox(label="Chat History", lines=10, interactive=False)
+            related_question_display = gr.Textbox(label="Related Question", lines=4, interactive=False)
+
+    # Link buttons to functions
+    upload_button.click(process_documents, inputs=[file_uploader], outputs=[status_display])
+    ask_button.click(answer_question, inputs=[question_input], outputs=[answer_display, chat_history_display, related_question_display])
+    clear_button.click(clear_documents, outputs=[status_display, chat_history_display, related_question_display])
+
+# Launch the app
+demo.launch(inline=False)