How to Build a Custom Chatbot Using LangChain

Build Custom Chatbot is a powerful way to automate tasks, improve user engagement, and provide real-time assistance. Using LangChain, you can build an intelligent chatbot that processes documents and answers user queries. Below are step-by-step instructions to develop your chatbot.

Step 1: Set Up the Environment

Install the required Python libraries that use to build custom chatbot:

pip install langchain PyPDF2 sentence-transformers faiss-cpu

Step 2: Configure Hugging Face API Key

To use Hugging Face models, set up your API key. Replace your_huggingface_api_key with your actual key:

import os
os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'your_huggingface_api_key'

Step 3: Load and Process PDF Data

Load the document you want the chatbot to process:

from langchain.document_loaders import PyPDFLoader
pdf_loader = PyPDFLoader('path_to_your_pdf_file.pdf')
pages = pdf_loader.load()

Step 4: Split Text for Efficient Processing

LLMs has a specific length of text processing at a time, so we cannot give all pdf information at once, therefore we need to divide the text into smaller chunks for better analysis:

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
documents = text_splitter.split_documents(pages)

Step 5: Generate Embeddings for Text

Use a pre-trained Hugging Face model to generate embeddings for efficient information retrieval:

from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

Step 6: Create a Vector Store

Store the embeddings in a vector database for quick retrieval:

from langchain.vectorstores import FAISS

vector_store = FAISS.from_documents(documents, embeddings)

Step 7: Set Up the Language Model

Use a Hugging Face model as the language model for the chatbot:

from langchain.llms import HuggingFaceHub

llm = HuggingFaceHub(
repo_id="google/flan-t5-large", model_kwargs={"temperature": 0, "max_length": 512})

Step 8: Create the Retrieval-Based QA Chain

Integrate the language model with the vector store to answer queries:

from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_store.as_retriever())

Step 9: Define a Function to Ask Questions

Build a function that sends queries to the chatbot:

def ask_question(chain, question):
return chain.run(question)

Example usage

question = "What is the main topic of the document?"
nswer = ask_question(qa_chain, question)
print("Question:", question)
print("Answer:", answer)

Conclusion: Build Custom Chatbot

Congratulations! You’ve successfully built a custom chatbot using LangChain. This chatbot can process PDFs, retrieve relevant information, and provide intelligent responses. Customize it further to suit your specific needs.