Build a chat bot with Langchain HuggingFace & Flan T5 Model LLM

  1. Understanding the Components: To begin our journey, let's familiarize ourselves with the key components of our chatbot:


  1. Langchain HuggingFace: Langchain is a language technology company that specializes in developing state-of-the-art natural language processing (NLP) models. Their HuggingFace library is widely used in the NLP community and provides access to a vast array of pre-trained models.

Flan T5 Model LLM: Flan T5 Model LLM is a language model built upon the popular T5 architecture developed by Google. It excels at various NLP tasks, including text summarization, translation, and conversational AI.

Setting Up the Environment: Before diving into the code, it's important to set up the necessary environment. Install the required dependencies, including Langchain HuggingFace and Flan T5 Model LLM. Additionally, ensure you have Python installed on your system.

In today's digital era, chatbots have become increasingly prevalent in various domains, from customer support to virtual assistants. Their ability to simulate human-like conversations has revolutionized the way businesses interact with their customers. In this blog post, we will explore how to build a conversational chatbot using the powerful combination of Langchain HuggingFace and Flan T5 Model LLM.

Preparing the Data: To train our chatbot, we need a dataset of conversational dialogues. This dataset should consist of input and output pairs, where the input is the user's message, and the output is the chatbot's response. You can curate your dataset or use publicly available datasets, depending on your specific use case.

Building the Chatbot Model: Utilizing the power of Langchain HuggingFace and Flan T5 Model LLM, we can construct a chatbot model with a few lines of code. Begin by loading the Flan T5 Model LLM from the HuggingFace model repository. Then, fine-tune the model using your conversational dataset.

Training the Chatbot: Once the model is set up, it's time to train our chatbot. This process involves feeding the input-output pairs from the dataset to the model and optimizing its parameters using techniques like gradient descent. Training may take some time, depending on the size of your dataset and the complexity of the conversations.

Evaluating and Fine-tuning: After training, it's crucial to evaluate the performance of the chatbot. You can assess the quality of its responses by interacting with it manually or employing automated evaluation metrics like perplexity or BLEU score. Based on the evaluation, you may need to fine-tune the model further to enhance its conversational abilities.

Deploying the Chatbot: With a trained and fine-tuned chatbot model, it's time to bring it to life! Deploying the chatbot can involve various approaches, depending on your requirements. You may choose to integrate it into a web application, messaging platforms like Slack or Telegram, or even a voice-enabled device.


!pip install -q beautifulsoup4
!pip install -q langchain
!pip install -q huggingface_hub

02. Usage

!pip install -q sentence_transformers
!pip install -q faiss-cpu
!pip install pickle

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain import HuggingFaceHub
import os
from langchain import PromptTemplate
import pickle


#Loading your Text Data

loader = TextLoader("data.txt")
document = loader.load()

#Formating the data

text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap=0, separators=[" ", ",", "\n"])

docs = text_splitter.split_documents(document)

# get model embedings
embedding = HuggingFaceEmbeddings()

db = FAISS.from_documents(docs, embedding)

#Setting up the Large Language Model

model_kwargs={"temperature":0.1, "max_length":256}

chain = load_qa_chain(llm, chain_type="stuff")

query = "importance of SEO configuration and optimization"

docs = db.similarity_search(query)
chain.run(input_documents=docs, question=query)

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  • Category : LLM
  • Time Read:10 Min
  • Source: QuestionDataBot
  • Author: Partener Link
  • Date: June 18, 2023, 7:52 p.m.
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