Get Started with Chroma DB and Retrieval Models using LangChain

A walk-through tutorial on how to use your data and ask questions using LangChain

In this tutorial, we will provide a walk-through example of how to use your data and ask questions using LangChain. The steps are the following:

DeepLearning.AI

1. Load the Document
2. Create chunks using a text splitter
3. Create embeddings from the chunks
4. Store the embeddings in a vector database (Chroma DB in our case)
5. Use a retrieval model to get similar documents to your question
6. Feed the ChatGPT model with the content of similar documents to get a tailored answer
7. Explain who to keep the memory of your conversation to have a chat with your data

Let’s jump into the coding part!

Load the required libraries

from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from…