text: lines of code.Set environment variables and get packages:pip install openaipip install chromadbexport OPENAI_API_KEY="..."Run:from langchain.document_loaders import WebBaseLoaderfrom langchain.indexes import VectorstoreIndexCreator# Document loaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")# Index that wraps above stepsindex = VectorstoreIndexCreator().from_loaders([loader])# Question-answeringquestion = "What is Task Decomposition?"index.query(question)' Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It can be done using LLM with simple prompting, task-specific instructions, or human inputs. Tree of Thoughts (Yao et al. 2023) is an example of a task decomposition technique that explores multiple reasoning possibilities at each step and generates multiple thoughts per step, creating a tree structure.'Of course, some users do not want this level of abstraction.Below, we will discuss each stage in more detail.1. Loading, Splitting, Storageâ€‹1.1 Getting startedâ€‹Specify a Document loader.# Document loaderfrom langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()Split the Document into chunks for embedding and vector storage.# Splitfrom langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap = 0)all_splits = text_splitter.split_documents(data)Embed and store the splits in a vector database (Chroma).# Store from langchain.vectorstores import Chromafrom langchain.embeddings import OpenAIEmbeddingsvectorstore = Chroma.from_documents(documents=all_splits,embedding=OpenAIEmbeddings())Here are the three pieces together:1.2 Going