Build a simple RAG Based Chatbot with LangChain
In this blog post, Ill show you how to build a special type of chatbot called a RAG (Retrieval-Augmented Generation) chatbot. This chatbot is designed to handle very specific topics or documents, generating detailed and accurate responses to complex queries that standard chatbots might struggle with.
Why RAG Chatbot?
A typical chatbot often relies on predefined responses or simple rule-based systems. This means it might not be very good at handling unusual or detailed questions. The RAG technique combines information retrieval with text generation, allowing the chatbot to pull in relevant information and create responses on the fly. This enables the chatbot to deliver more nuanced and contextually relevant answers.
Example Scenario: Document-Based Query Handling
To illustrate how a RAG chatbot works, let’s create a chatbot that answers questions based on a specific document, such as a research paper or a technical manual. This scenario showcases the chatbots ability to provide detailed insights and answers based on the content of a given document.
What Makes It Special?
Tools and Techniques
When building our RAG (Retrieval-Augmented Generation) chatbot, several key tools and techniques will be utilized to ensure it effectively handles document-based queries. Here’s an overview of the tools and techniques involved:
1. Information Retrieval
The chatbot will search for and retrieve relevant information from the provided document. This involves:
Extracting text from various document formats (e.g., PDFs, DOCX).Vector Search: Using Pinecone to store and efficiently retrieve vector embeddings representing the document content.
Contextual Search:
Ensuring that the retrieved information is relevant to the users query by leveraging similarity search techniques.
2. Text Generation
After retrieving the relevant data, the chatbot will use text generation techniques to create coherent and contextually appropriate responses. This involves:
Using Hugging Face models to convert text into vector embeddings for both document content and user queries.
Response Generation:
Generating responses based on the context provided by the retrieved information. This ensures that the answers are not just direct copies of the document but are tailored to the users specific question.
3. Customization
To make the chatbot more effective and relevant, we will customize it to understand and respond to queries related to the specific document. This involves:
Adjusting the language model to handle the nuances and specific topics of the document.
Specialized Responses:
Ensuring the chatbot can provide detailed and accurate responses based on the unique aspects of the document. This might include configuring the model to understand domain-specific jargon or context.Understanding LLM and RAG
If youre involved in the tech world, especially in AI, you’ve probably come across the term LLM. With the rise of generative AI, LLM has become a buzzword among developers and AI enthusiasts. But what exactly is an LLM?
What is an LLM?
Large Language Models (LLMs) are advanced tools in Natural Language Processing (NLP) designed to understand and generate human-like text. These models are trained on extensive datasets to handle a wide range of language tasks, from answering questions to creating text based on context.
Key Features of LLMs:
LLMs can process and generate text in a nuanced way, capturing the subtleties of human language.
They use transformer models, a type of neural network that learns context and meaning from sequences of text. This technology enables LLMs to understand not just individual words but also their relationships within sentences and paragraphs.
Example:
ChatGPT, developed by OpenAI, is a well-known LLM that uses the GPT-3.5 and GPT-4 models. These models generate coherent and contextually relevant responses based on user input.
Mixtral8x7b is another powerful model known for its high performance, comparable to or even surpassing models like Llama 70B and GPT-3.5. It is available for free, making it a great choice for various applications.
Challenges with Generic LLMs
Despite their capabilities, LLMs have limitations, particularly when dealing with specialized topics. Here are two main challenges:
Lack of Knowledge:
Sometimes, an LLM may not have information on very niche or specialized topics because it wasnt trained on that specific data. For example, it might not provide accurate answers on specialized legal rules or specific medical conditions.
Hallucination:
LLMs can sometimes generate incorrect or misleading information, a phenomenon known as hallucination. This occurs when the model, lacking detailed knowledge in a particular area, produces responses that sound plausible but are inaccurate.
Introducing RAG: How to Enhance LLMs with Retrieval-Augmented Generation
Top Current Large Language Models (LLMs)
Here’s a look at some of the most prominent large language models (LLMs) today. These models are at the forefront of natural language processing and have shaped the development of future models.
BERT
Claude
Cohere
Ernie
Falcon 40B
Gemini
Gemma
GPT-3
GPT-3.5
GPT-4
GPT-4o
Lamda
Llama
Mistral
Orca
Palm
What is RAG?
RAG, which stands for Retrieval-Augmented Generation, is a technique that enhances the knowledge of Large Language Models (LLMs) by integrating additional data sources. This helps LLMs provide more accurate and relevant answers to specific queries.
RAG consists of two main components:
This involves gathering data from various sources and organizing it in a way that makes it easy for the system to access. Think of it as creating a well-organized library where each book (or piece of data) is catalogued for quick retrieval.
RAG works through two primary processes:
In simpler terms, RAG boosts the capabilities of LLMs by pulling in additional information when needed. This helps the chatbot give better answers by supplementing its general knowledge with specific, targeted data.
Architecture of a RAG-Based Chatbot
The architecture of a RAG-based chatbot typically looks like this:
The user asks a question.
The system searches the indexed data for relevant information:
The retrieved information is provided to the LLM.
The LLM uses both the retrieved data and its own training to generate a response.
The chatbot delivers a more accurate and relevant answer.
Useful Tools for RAG
LangChain is a powerful tool for implementing RAG. Here’s what you need to know about it:
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What is LangChain?
LangChain is an open-source framework designed for building applications that use language models. It’s available in Python and JavaScript, making it accessible for developers working in different programming environments.
Why Use LangChain?
LangChain simplifies the process of integrating language models into applications. It provides components that help with tasks such as text summarization, tagging, and more. For this blog, we’ll focus on how LangChain can be used to create and manage a RAG-based chatbot.
Hugging Face: A Key Resource for Machine Learning Models
What is Hugging Face?
Hugging Face is a popular open-source platform that focuses on data science and machine learning. It’s a community-driven hub where users can share and access a wide range of machine learning models. Here’s why Hugging Face is a valuable resource:
Hugging Face offers a diverse collection of pre-trained models. These models cover various fields, including:
Many models available on Hugging Face come with built-in inference capabilities. This means you can easily integrate these models into your applications to perform tasks such as text generation, image classification, and more.
Why Use Hugging Face?
You can quickly find and use pre-trained models without needing to train them from scratch. This saves time and resources, especially for complex tasks.
Hugging Face is supported by a vibrant community of developers and researchers. This means you can benefit from shared knowledge, tutorials, and ongoing updates to models.
The platform provides tools and libraries, such as the transformers library, which makes it straightforward to incorporate these models into your projects.
How Hugging Face Helps
Whether you’re working on a chatbot, a recommendation system, or any other AI-driven application, Hugging Face provides the resources you need. You can leverage their models to enhance your applications with advanced capabilities, without having to build and train models from scratch.
By using Hugging Face, you can focus on building and refining your applications while relying on high-quality, pre-trained models to handle complex tasks.
Pinecone: Understanding Vector Databases
What is a Vector Database?
A vector database stores data as vectors, which are arrays of numbers. For example, a vector might look like this: [0.1, 3.21, -1.3, 9.2, …]. This method allows for efficient similarity searches by grouping similar data together. Vector databases are particularly useful for tasks where you need to quickly find and retrieve data based on similarity, such as in machine learning applications.
About Pinecone
Pinecone is a cloud-based vector database optimized for machine learning tasks. It excels in storing and retrieving dense vector embeddings, which makes it especially useful for improving the performance of Large Language Models (LLMs) and other AI systems. Key features of Pinecone include:
Pinecone provides fast access to data, which is ideal for applications like chatbots where quick responses are essential.
It offers a free tier that allows you to store up to 100,000 vectors, making it accessible for both small and large-scale projects.
Ease of Use:
Compared to other open-source vector databases like Chroma, Weaviate, and Milvus, Pinecone is known for its simplicity and user-friendly interface.
Creating a Document-Based Chatbot with RAG, Pinecone, and Hugging Face
In this guide, we will create a document-based chatbot using the Retrieval-Augmented Generation (RAG) approach. We will leverage Hugging Face models for generating embeddings and responses, and Pinecone as a vector database to store and retrieve information. Here’s a step-by-step guide to building a RAG-based chatbot.
1. Setup Overview
Before diving into the implementation, ensure you have:
1. Hugging Face Account: For accessing pre-trained models.
2. Pinecone Account: For vector storage and retrieval.
3. Python Environment: With necessary libraries installed.
2. Setting Up Your Environment
2.1 Create Accounts
- Sign up here
- Create an access token under your profile settings.
Pinecone:
- Sign up here
- Create a new project and obtain an API key.
2.2 Prepare Your Project Directory
1. Create a Project Directory:
- Name it Chatbot.
2. Setup Environment Variables:
- Inside the Chatbot directory, create a file named .env:
# .env file
PINECONE_API_KEY=your_pinecone_api_key
HUGGINGFACE_API_KEY=your_huggingface_api_key
3. Create Python Files:
- Create main.py with an empty Chatbot class:
# main.py
class Chatbot:
pass
4. Install Dependencies:
- Create a requirements.txt file:
langchain==0.1.1
pinecone-client==2.2.4
python-dotenv==1.0.0
streamlit==1.29.0
Install dependencies:
pip install -r requirements.txt
3. Implementing the Chatbot
3.1 Adding Embedding and Retrieval
1. Update main.py:
- Import necessary libraries and initialize Pinecone and Hugging Face:
import pinecone
from dotenv import load_dotenv
import os
from langchain import HuggingFace
load_dotenv()
class Chatbot:
def __init__(self):
# Initialize Pinecone
self.pinecone_api_key = os.getenv('PINECONE_API_KEY')
pinecone.init(api_key=self.pinecone_api_key)
self.index_name = "chatbot-index"
self.index = pinecone.Index(self.index_name)
# Initialize Hugging Face
self.hf_api_key = os.getenv('HUGGINGFACE_API_KEY')
self.hf_model = HuggingFace(model_name="gpt-3.5-turbo", api_key=self.hf_api_key)
def retrieve_data(self, query):
# Retrieve relevant data from Pinecone
response = self.index.query(vector=query, top_k=5)
return response['matches']
def generate_response(self, context, query):
# Generate response using Hugging Face model
prompt = f"Context: {context}\n\nQuery: {query}\n\nResponse:"
response = self.hf_model.generate(prompt)
return response
```
3.2 Embedding Creation from PDF Document
1. Add a Function to Process PDF:
- Extract text from PDF and create embeddings:
from PyPDF2 import PdfReader
import numpy as np
def process_pdf(pdf_url):
# Read PDF and extract text
reader = PdfReader(pdf_url)
text = " ".join(page.extract_text() for page in reader.pages)
# Generate embeddings for the extracted text
embeddings = self.hf_model.embed(text)
return embeddings
2. Add Text to Pinecone:
- Add a function to index the document text:
def index_document(self, text, vector):
self.index.upsert([(text, vector)])
4. Building the User Interface
4.1 Create a Streamlit App
1. Create streamlit_app.py:
- Set up the Streamlit app to interact with the chatbot:
import streamlit as st
from main import Chatbot
# Initialize the chatbot
chatbot = Chatbot()
st.title("Document-Based Chatbot")
# Input PDF URL
pdf_url = st.text_input("Enter PDF URL:")
if pdf_url:
embeddings = chatbot.process_pdf(pdf_url)
chatbot.index_document("PDF Document", embeddings)
# User input
query = st.text_input("Ask your question:")
if query:
context_matches = chatbot.retrieve_data(query)
context = " ".join([match['text'] for match in context_matches])
response = chatbot.generate_response(context, query)
st.write("Response:", response)
2. Run the Streamlit App:
- Launch the app with:
```bash
streamlit run streamlit_app.py
5. Final Steps
5.1 Testing and Validation
5.2 Optimizations
6. Conclusion
You have successfully built a document-based chatbot using the RAG approach with Pinecone and Hugging Face. This chatbot can handle document-based queries effectively by combining retrieval and generation methods.
7. Additional Resources
?? Passionate Explorer in AI, ML & Data Science ?? | Turning Data into Insights ?? | AI Enthusiast | Open to Exciting Opportunities ??
1 个月Very informative Bushra Akram
Electrical Engr|Power Sector Expert|AI & ML Engr
1 个月Useful tips
Amazon PPC and Keyword Research Expert, SEO Manager , Digital Marketing,AI Prompt Engineer,Data Scientist
1 个月Very informative
Country Head @ Vast Technologies | IT Infrastructure, Security
1 个月Bushra, your insights on building a RAG chatbot are invaluable! It's inspiring to see an AI & Machine Learning Engineer share such practical knowledge. Keep up the fantastic work!
Data Science | Machine Learning | AI | DL-CNN-CV | NLP
1 个月Very very informative M-blog for building RAG base applications, Thanks Bushra Akram for sharing with us.