In the rapidly evolving landscape of artificial intelligence, Retrieval Augmented Generation (RAG) systems have emerged as a powerful paradigm, revolutionizing how we interact with and extract value from large volumes of information. By seamlessly blending the strengths of information retrieval and language generation, RAG systems offer a sophisticated approach to creating AI-powered applications that are both knowledgeable and contextually aware.
Retrieval plus Generation
At the heart of RAG systems lies a brilliant synergy between two components: retrieval and generation. The retrieval mechanism acts as an intelligent librarian, swiftly going through vast repositories of information to pinpoint the most relevant pieces of data. This process is not merely a simple keyword search; it is an exploration of semantic relationships within the data. Once the relevant information is retrieved, the generation component comes into play. Leveraging advanced language models, this component crafts coherent, contextually relevant responses that seamlessly integrate the retrieved information with the user's query. This mixture of retrieval and generation allows RAG systems to provide answers that are not only accurate but also richly informed by a broad knowledge base.
Embeddings: The Language of Vector Spaces
Central to the efficiency of RAG systems is the concept of embeddings. These mathematical representations transform text into dense vectors, capturing the semantic essence of words, sentences, or entire documents. In the multidimensional space of embeddings, similar concepts cluster together, allowing for nuanced comparisons and relationships to be discovered. The power of embeddings lies in their ability to capture complex linguistic relationships in a format that computers can process efficiently. This transformation enables RAG systems to understand context, detect similarities, and make inferences that go beyond simple keyword matching, bringing us closer to true natural language understanding (NLU).
Efficient Storage and Retrieval
As the volume of information grows, the importance of efficient storage and retrieval mechanisms becomes important. Vector databases, specialized for handling embedding-based data, play a crucial role in RAG systems. These databases are optimized for high-dimensional vector operations, allowing for rapid similarity searches across millions or even billions of data points. The efficiency of these storage and retrieval systems directly impacts the responsiveness and scalability of RAG applications. By leveraging advanced indexing techniques and optimized search algorithms, RAG systems can provide immediate responses, even when dealing with massive datasets. This capability is essential for creating real-time, interactive AI applications that can keep pace with user demands.
Prompt Engineering
While the retrieval and generation components form the core of RAG systems, the art of prompt engineering acts as the conductor, orchestrating how these components interact to produce meaningful outputs. Carefully crafted prompts serve as instructions, context providers, and guardrails for the language model, ensuring that the generated responses are not only relevant but also aligned with the intended tone, style, and purpose of the application. Effective prompt engineering requires a deep understanding of both the capabilities and limitations of language models. It involves anticipating potential pitfalls, providing necessary context, and structuring queries in ways that elicit the most useful and accurate responses. As RAG systems evolve, the role of prompt engineering continues to grow in importance, becoming a crucial skill for AI developers and content creators alike.
Gradio
The final piece of the puzzle in making RAG systems accessible and user-friendly is the interface through which users interact with the AI. Gradio, an open-source library, has emerged as a game-changer in this arena, dramatically simplifying the process of creating polished, interactive interfaces for AI models. With Gradio, developers can rapidly prototype and deploy user interfaces for their RAG systems with just a few lines of code. This ease of use democratizes AI application development, allowing researchers, developers, and even non-technical users to create functional, attractive interfaces for their AI models. By lowering the barrier to entry for creating AI-powered applications, Gradio plays a crucial role in accelerating the adoption and practical application of RAG systems across various domains.
An alternative to Gradio is Streamlit which has similar functionality.
We will implement a RAG chatbot based on an epub file containing Paul Graham's many articles. The chatbot is then tested using a Gradio interface.
Implementation details
Gradio is used to build the front-end of the app.
I am using langchain to build the app, an alternative is llama-index.
I am also using Groq's llama3 instance as the LLM of choice.
I am generating embeddings using a local instance of Ollama which uses llama2 by default.
Then I store these embeddings using the chromadb vectorstore which is saved on the computer.
the libraries used are:
langchain: a framework for developing applications powered by language models.
pydantic: Data validation and settings management using Python type annotations.
chromadb: Open-source embedding database for AI applications.
gradio: Quickly create customizable UI components for machine learning models.
unstructured: Library for preprocessing and extracting content from raw documents. In our case, it's an important module for processing epub files.
langchain-community: Community-contributed components for LangChain.
pypandoc: Python wrapper for Pandoc, a universal document converter. Also important for processing epub files.
sentence-transformers: Framework for state-of-the-art sentence and text embeddings.
openai: Official Python client library for the OpenAI API.
langchain-groq: Integration of Groq's AI models with LangChain.
%pip install langchain
%pip install pydantic
%pip install chromadb
%pip install gradio
%pip install unstructured
%pip install langchain-community
%pip install pypandoc
%pip install sentence-transformers
%pip install openai
%pip install langchain-groq
import pypandoc
pypandoc.download_pandoc()
Loading the EPUB file containing Paul Graham's articles.
Then we split the document into smaller pieces which helps the LLM able to parse the document.
from langchain_community.document_loaders import UnstructuredEPubLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
loader = UnstructuredEPubLoader("./graham.epub")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
Using Ollama Embeddings to create vector representations of text
from langchain_community.embeddings import OllamaEmbeddings
# OllamaEmbeddings use Llama2 by default
embedding_function = OllamaEmbeddings()
Next we use Chromadb to create and persist a vector store of embeddings
then storing the embeddings on disk for future use
# Embed and store the texts
from langchain.vectorstores import Chroma
db = Chroma.from_documents(documents, embedding_function)
# Embed and store the texts
# persist_directory stores the embeddings on disk
persist_directory = 'db'
db.persist()
db = None
# Now we can load the persisted database from disk, and use it as normal.
db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function)
below we load environment variables
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()
# Get the API key from the environment variable
groq_api_key = os.environ.get("GROQ_API_KEY")
Initializing ChatGroq as the LLM of choice (or Ollama)
from langchain_community.llms import Ollama
from langchain_groq import ChatGroq
# instead of ChatGroq, we could use local models like llama3 using ollama
# llm = Ollama(model="llama3")
llm = ChatGroq(
temperature=0,
model="llama3-70b-8192",
api_key=groq_api_key
)
Next thing is to define a prompt template for the chatbot
then creating a RetrievalQA chain combining the LLM, vector store, and prompt template.
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import gradio as gr
# Wrap the prompt and Gateway Route into a chain
template = """[INST] <>
You are a chatbot based on articles written by Paul Graham. Answer Accordingly.
If you don't know the answer, say so, do not make up your answers.
<>
{context}
{question} [/INST]
"""
prompt = PromptTemplate(input_variables=['context', 'question'],
template=template)
retrieval_qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(),
chain_type_kwargs={"prompt": prompt}
)
def qa_function(question):
result = retrieval_qa_chain({"query": question})
return result["result"]
Creating a user-friendly interface for the chatbot
Launching the interface with a public sharing link
# Create the Gradio interface
iface = gr.Interface(
fn=qa_function,
inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
outputs="text",
title="Paul Graham Q&A System",
description="Ask questions about Paul Graham's writings",
)
# Launch the interface
iface.launch(share=True)
The gradio app can be rendered in the jupyter notebook as seen below with a sample question.
