Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Said Lfagrouche_RAG_Based_on_ syllabi_app
+"""
+
+import getpass
+import gradio as gr
+import os
+import pprint
+import sys
+
+from google.colab import drive
+from gradio.themes.base import Base
+from icecream import ic
+from pymongo import MongoClient
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from weaviate.embedded import EmbeddedOptions
+
+# langchain imports
+from langchain.callbacks.tracers import ConsoleCallbackHandler
+from langchain.document_loaders import PyPDFLoader, TextLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import PromptTemplate, ChatPromptTemplate
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import MongoDBAtlasVectorSearch, Weaviate
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI
+
+# langchain_community imports
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFacePipeline
+
+# Get secret keys.
+os.environ["OPENAI_API_KEY"] = getpass.getpass()
+os.environ["MONGO_URI"] = getpass.getpass()
+
+# Retrieve environment variables.
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+MONGO_URI = os.getenv('MONGO_URI')
+
+# For Google Colab.
+# Mount (connect) our Google Drive to our Colab environment.
+# This will establish a connection to our Google Drive, making it accessible from our Colab notebook.
+drive.mount("/content/drive/")
+
+# For Google Colab.
+! ls "/content/drive/MyDrive/RAG Project"
+
+# For Google Colab.
+# Append our directory path to the Python system path.
+directory_path = "/content/drive/MyDrive/RAG Project"
+
+sys.path.append(directory_path)
+
+# Print the updated system path to the console.
+print("sys.path =", sys.path)
+
+# Get all the filenames under our directory path.
+my_pdfs = os.listdir(directory_path)
+my_pdfs
+
+# Connect to MongoDB Atlas cluster using the connection string.
+cluster = MongoClient(MONGO_URI)
+
+# Define the MongoDB database and collection name.
+DB_NAME = "pdfs"
+COLLECTION_NAME = "pdfs_collection"
+
+# Connect to the specific collection in the database.
+MONGODB_COLLECTION = cluster[DB_NAME][COLLECTION_NAME]
+
+vector_search_index = "vector_index"
+
+# Load the PDF.
+loaders = []
+for my_pdf in my_pdfs:
+  my_pdf_path = os.path.join(directory_path, my_pdf)
+  loaders.append(PyPDFLoader(my_pdf_path))
+
+print("len(loaders) =", len(loaders))
+
+loaders
+
+# Load the PDF.
+# data = [loader.load() for loader in loaders]
+
+data = []
+for loader in loaders:
+  data.append(loader.load())
+
+print("len(data) =", len(data), "\n")
+
+# First PDF file.
+data[0]
+
+# Initialize the text splitter.
+# Uses a text splitter to split the data into smaller documents.
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
+text_splitter
+
+# docs = [text_splitter.split_documents(doc) for doc in data]
+
+docs = []
+for doc in data:
+  chunk = text_splitter.split_documents(doc)
+  docs.append(chunk)
+
+# Debugging purposes.
+# Print the number of total documents to be stored in the vector database.
+total = 0
+for i in range(len(docs)):
+  if i == len(docs) - 1:
+    print(len(docs[i]), end="")
+  else:
+    print(len(docs[i]), "+ " ,end="")
+  total += len(docs[i])
+print(" =", total, " total documents\n")
+
+# Print the first document.
+print(docs[0], "\n\n\n")
+
+# Print the total number of PDF files.
+# docs is a list of lists where each list stores all the documents for one PDF file.
+print(len(docs))
+
+docs
+
+# Merge the documents to be embededed and store them in the vector database.
+merged_documents = []
+
+for doc in docs:
+  merged_documents.extend(doc)
+
+# Print the merged list of all the documents.
+print("len(merged_documents) =", len(merged_documents))
+print(merged_documents)
+
+# Hugging Face model for embeddings.
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+model_kwargs = {'device': 'cpu'}
+embeddings = HuggingFaceEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+)
+
+import weaviate
+from weaviate.embedded import EmbeddedOptions
+
+client = weaviate.Client(
+    embedded_options=EmbeddedOptions()
+)
+
+vector_search = Weaviate.from_documents(
+    client = client,
+    documents = merged_documents,
+    embedding = OpenAIEmbeddings(),
+    by_text = False
+)
+# At this point, 'docs' are split and indexed in Weaviate, enabling text search capabilities.
+
+# Semantic Search.
+# query = "When is the spring recess at The City College of New York for Spring 2024?"
+query = "What are the professor names for this semester"
+results = vector_search.similarity_search(query=query, k=10) # 10 most similar documents.
+
+print("\n")
+pprint.pprint(results)
+# ic(results) # Debugging purposes.
+
+# Semantic Search with Score.
+# query = "When is the spring recess at The City College of New York for Spring 2024?"
+query = "Where is operating system exam taken?"
+results = vector_search.similarity_search_with_score(
+   query = query, k = 10 # 10 most similar documents.
+)
+
+pprint.pprint(results)
+# ic(results) # Debugging purposes.
+
+# Filter on metadata.
+# Semantic search with filtering.
+query = "Where is Data tools and algorithm exam taken?"
+
+results = vector_search.similarity_search_with_score(
+   query = query,
+   k = 10, # 10 most similar documents.
+   pre_filter = { "page": { "$eq": 1 } } # Filtering on the page number.
+)
+
+pprint.pprint(results)
+# ic(results) # Debugging purposes.
+
+# Instantiate Weaviate Vector Search as a retriever
+retriever = vector_search.as_retriever(
+   search_type = "similarity", # similarity, mmr, similarity_score_threshold. https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever
+   search_kwargs = {"k": 5, "score_threshold": 0.89}
+)
+
+# Define a prompt template.
+# Define a LangChain prompt template to instruct the LLM to use our documents as the context.
+# LangChain passes these documents to the {context} input variable and the user's query to the {question} variable.
+template = """
+Use the following pieces of context to answer the question at the end.
+If you do not know the answer, just say that you do not know, do not try to make up an answer.
+
+{context}
+
+Question: {question}
+"""
+
+custom_rag_prompt = PromptTemplate.from_template(template)
+
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.2) # Increasing the temperature, the model becomes more creative and takes longer for inference.
+
+# Input : docs (list of documents)
+# Output: A single string that concatenates the page_content of each document in the list, separated by two newline characters.
+def format_docs(docs):
+   return "\n\n".join(doc.page_content for doc in docs)
+
+# Regular chain format is defined as: chain = context_setup | prompt_template | model | output_parser
+
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}  # Setup the context and question for the chain
+    | custom_rag_prompt  # Apply a custom prompt template to format the input for the LLM
+    | llm  # Process the formatted input through a language model (LLM)
+    | StrOutputParser()  # Parse the LLM's output into a structured format
+)
+
+# Prompt the chain.
+query = "What is student favourite class"
+answer = rag_chain.invoke(query)
+
+print("\nQuestion: " + query)
+print("Answer: " + answer)
+
+# Return the source documents
+documents = retriever.get_relevant_documents(query)
+
+print("\nSource documents:")
+pprint.pprint(documents)
+
+# Input : query.
+# Output: answer.
+def get_response(query):
+  return rag_chain.invoke(query)
+
+# Gradio application.
+with gr.Blocks(theme=Base(), title="RAG QA App Using Spring 2024 Syllabuses PDFs, Weaviate As The Vector Database, and Gradio") as demo:
+    gr.Markdown(
+        """
+        # RAG Question Answering App Using PDF Files, MongoDB As The Vector Database, and Gradio
+        """)
+    textbox = gr.Textbox(label="Question:")
+    with gr.Row():
+        button = gr.Button("Submit", variant="primary")
+    with gr.Column():
+        output1 = gr.Textbox(lines=1, max_lines=10, label="Answer:")
+
+
+# Call get_response function upon clicking the Submit button.
+    button.click(get_response, textbox, outputs=[output1])
+
+demo.launch(share=True)
+
+