This code implements a basic Retrieval-Augmented Generation (RAG) system for processing and querying PDF documents. The system encodes the document content into a vector store, which can then be queried to retrieve relevant information.
Key Components
- PDF processing and text extraction
- Text chunking for manageable processing
- Vector store creation using FAISS and OpenAI embeddings
- Retriever setup for querying the processed documents
- Evaluation of the RAG system
Method Details
Document Preprocessing
- The PDF is loaded using PyPDFLoader.
- The text is split into chunks using RecursiveCharacterTextSplitter with specified chunk size and overlap.
Text Cleaning
A custom function replace_t_with_space is applied to clean the text chunks. This likely addresses specific formatting issues in the PDF.
Vector Store Creation
- OpenAI embeddings are used to create vector representations of the text chunks.
- A FAISS vector store is created from these embeddings for efficient similarity search.
Retriever Setup
- A retriever is configured to fetch the top 2 most relevant chunks for a given query.
Encoding Function
The encode_pdf function encapsulates the entire process of loading, chunking, cleaning, and encoding the PDF into a vector store.
Key Features
- Modular Design: The encoding process is encapsulated in a single function for easy reuse.
- Configurable Chunking: Allows adjustment of chunk size and overlap.
- Efficient Retrieval: Uses FAISS for fast similarity search.
- Evaluation: Includes a function to evaluate the RAG system’s performance.
Usage Example
The code includes a test query: “What is the main cause of climate change?”. This demonstrates how to use the retriever to fetch relevant context from the processed document.
Evaluation
The system includes an evaluate_rag function to assess the performance of the retriever, though the specific metrics used are not detailed in the provided code.
Benefits of this Approach
- Scalability: Can handle large documents by processing them in chunks.
- Flexibility: Easy to adjust parameters like chunk size and number of retrieved results.
- Efficiency: Utilizes FAISS for fast similarity search in high-dimensional spaces.
- Integration with Advanced NLP: Uses OpenAI embeddings for state-of-the-art text representation.
This simple RAG system provides a solid foundation for building more complex information retrieval and question-answering systems. By encoding document content into a searchable vector store, it enables efficient retrieval of relevant information in response to queries. This approach is particularly useful for applications requiring quick access to specific information within large documents or document collections.
Import libraries and environment variables
import os
import sys
from dotenv import load_dotenv
# Load environment variables from a .env file
load_dotenv()
# Set the OpenAI API key environment variable (comment out if not using OpenAI)
if not os.getenv('OPENAI_API_KEY'):
os.environ["OPENAI_API_KEY"] = input("Please enter your OpenAI API key: ")
else:
os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')
sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) # Add the parent directory to the path since we work with notebooks
from helper_functions import *
from evaluation.evalute_rag import *
Read Docs
path = "../data/Understanding_Climate_Change.pdf"
Encode document
def encode_pdf(path, chunk_size=1000, chunk_overlap=200):
"""
Encodes a PDF book into a vector store using OpenAI embeddings.
Args:
path: The path to the PDF file.
chunk_size: The desired size of each text chunk.
chunk_overlap: The amount of overlap between consecutive chunks.
Returns:
A FAISS vector store containing the encoded book content.
"""
# Load PDF documents
loader = PyPDFLoader(path)
documents = loader.load()
# Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap, length_function=len
)
texts = text_splitter.split_documents(documents)
cleaned_texts = replace_t_with_space(texts)
# Create embeddings (Tested with OpenAI and Amazon Bedrock)
embeddings = get_langchain_embedding_provider(EmbeddingProvider.OPENAI)
#embeddings = get_langchain_embedding_provider(EmbeddingProvider.AMAZON_BEDROCK)
# Create vector store
vectorstore = FAISS.from_documents(cleaned_texts, embeddings)
return vectorstore
chunks_vector_store = encode_pdf(path, chunk_size=1000, chunk_overlap=200)
Create retriever
chunks_query_retriever = chunks_vector_store.as_retriever(search_kwargs={"k": 2})
Test retriever
test_query = "What is the main cause of climate change?"
context = retrieve_context_per_question(test_query, chunks_query_retriever)
show_context(context)
c:\Users\N7\PycharmProjects\llm_tasks\RAG_TECHNIQUES\.venv\Lib\site-packages\langchain_core\_api\deprecation.py:139: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead. warn_deprecated(
Context 1: driven by human activities, particularly the emission of greenhou se gases. Chapter 2: Causes of Climate Change Greenhouse Gases The primary cause of recent climate change is the increase in greenhouse gases in the atmosphere. Greenhouse gases, such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), trap heat from the sun, creating a "greenhouse effect." This effect is essential for life on Earth, as it keeps the planet warm enough to support life. However, human activities have intensified this natural process, leading to a warmer climate. Fossil Fuels Burning fossil fuels for energy releases large amounts of CO2. This includes coal, oil, and natural gas used for electricity, heating, and transportation. The industrial revolution marked the beginning of a significant increase in fossil fuel consumption, which continues to rise today. Coal Context 2: Most of these climate changes are attributed to very small variations in Earth's orbit that change the amount of solar energy our planet receives. During the Holocene epoch, which began at the end of the last ice age, human societies f lourished, but the industrial era has seen unprecedented changes. Modern Observations Modern scientific observations indicate a rapid increase in global temperatures, sea levels, and extreme weather events. The Intergovernmental Panel on Climate Change (IPCC) has documented these changes extensively. Ice core samples, tree rings, and ocean sediments provide a historical record that scientists use to understand past climate conditions and predict future trends. The evidence overwhelmingly shows that recent changes are primarily driven by human activities, particularly the emission of greenhou se gases. Chapter 2: Causes of Climate Change Greenhouse Gases
Evaluate results
#Note - this currently works with OPENAI only
evaluate_rag(chunks_query_retriever)
Dr. MD Harun Ar Rashid, FCPS, MD, PhD, is a highly respected medical specialist celebrated for his exceptional clinical expertise and unwavering commitment to patient care. With advanced qualifications including FCPS, MD, and PhD, he integrates cutting-edge research with a compassionate approach to medicine, ensuring that every patient receives personalized and effective treatment. His extensive training and hands-on experience enable him to diagnose complex conditions accurately and develop innovative treatment strategies tailored to individual needs. In addition to his clinical practice, Dr. Harun Ar Rashid is dedicated to medical education and community outreach, often participating in initiatives that promote health awareness and advance medical knowledge. His career is a testament to the high standards represented by his credentials, and he continues to contribute significantly to his field, driving improvements in both patient outcomes and healthcare practices.
