This is a Streamlit application that allows users to upload a PDF document and ask questions about its content using AI-powered natural language processing (NLP) tools. The app uses Langchain, OpenAI's GPT-4 model, and FAISS (Facebook AI Similarity Search) for document retrieval and question answering.
The following libraries are required for this project:
faiss-cpu>= 1.10.0langchain-community>= 0.3.18langchain-huggingface>= 0.1.2langchain-openai>= 0.3.7langchain>= 0.3.19openai>= 1.64.0pypdf>= 5.3.0sentence-transformers>= 3.4.1streamlit>= 1.42.2tiktoken>= 0.9.0scripts.secret(for storing your OpenAI API key)scripts.document_loader(for loading and splitting PDF documents)
You can install the necessary dependencies using pip:
pip install streamlit langchain faiss openaiThe application runs as follows:
- User can upload a PDF document.
- Application processes the document by splitting it into chunks.
- Creates a vector database for efficient document retrieval using OpenAI embeddings.
- Use the GPT-4 model to answer questions related to the document.
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Document Upload The user can upload a PDF document via the Streamlit interface. The file is saved temporarily on the server, and the document is then processed into text chunks for easier retrieval.
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Text Splitting and Vector Database Creation Once the document is uploaded:
- The document is loaded and split into smaller chunks to optimize retrieval performance.
- Embeddings (numerical representations of the document's contents) are generated using the OpenAIEmbeddings model.
- A FAISS vector store is created from these embeddings, allowing efficient similarity search.
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Question-Answering System The system utilizes a retrieval-based approach (RAG - Retrieval-Augmented Generation) to answer user queries. When a user asks a question, the system retrieves the most relevant chunks from the document using FAISS and processes them with GPT-4 to generate a meaningful answer.
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User Interface The application presents:
- An input field to upload the PDF file.
- A text box to ask questions about the document.
- Responses generated by the AI based on the content of the document.
The required libraries are imported to handle document processing, vector database creation, and AI-powered responses:
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain.chains import create_retrieval_chain
from langchain_openai import ChatOpenAI
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from scripts.secret import OPENAI_KEY
from scripts.document_loader import load_document
import streamlit as stThe Streamlit app is initialized, with the title displayed and a file uploader for PDFs:
st.title("AI-Powered Document Q&A")
uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")When a file is uploaded, the following steps are performed:
- The document is saved temporarily.
- The document is split into chunks using the load_document function.
temp_file = "./temp.pdf"
with open(temp_file, "wb") as file:
file.write(uploaded_file.getvalue())
chunks = load_document(temp_file)Embeddings are generated using OpenAI's model (text-embedding-ada-002), and a FAISS vector store is created to hold these embeddings:
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_KEY, model="text-embedding-ada-002")
vector_db = FAISS.from_documents(chunks, embeddings)The question-answering chain is created by combining the retriever (FAISS) and the document processing chain (StuffDocumentsChain). The model used for answering questions is GPT-4:
retriever = vector_db.as_retriever()
llm = ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=OPENAI_KEY)
system_prompt = "You are a helpful assistant. Use the given context to answer the question."
prompt = ChatPromptTemplate.from_messages([("system", system_prompt), ("human", "{input}")])
question_answer_chain = create_stuff_documents_chain(llm, prompt)
chain = create_retrieval_chain(retriever, question_answer_chain)The user is prompted to input a question, and the system responds with an answer based on the document's content:
question = st.text_input("Ask a question about the document:")
if question:
response = chain.invoke({"input": question})['answer']
st.write(response)To run the app, execute the following command in your terminal:
streamlit run app.pyThis will start the Streamlit server and open the app in your browser.
This project is licensed under the MIT License.