pip install langchain_openai langchain_core python-dotenv bs4 pypdf cassio beautifulsoup4 langchain-groq wikipedia arxiv langchainhub

import os, bs4from langchain_groq import ChatGroqfrom langchain_community.document_loaders import WebBaseLoaderfrom langchain_community.embeddings import OllamaEmbeddingsfrom langchain_community.embeddings import OpenAIEmbeddingsfrom langchain.chains import create_retrieval_chainfrom langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.indexes.vectorstore import VectorStoreIndexWrapperfrom langchain_core.prompts import ChatPromptTemplatefrom langchain.vectorstores.cassandra import Cassandraimport cassio

groq_api_key=""
os.environ["OPENAI_API_KEY"]=""
## connection of the ASTRA DBASTRA_DB_APPLICATION_TOKEN="" # enter the "AstraCS:..." string found in in your Token JSON file"ASTRA_DB_ID=""cassio.init(token=ASTRA_DB_APPLICATION_TOKEN,database_id=ASTRA_DB_ID)

loader=WebBaseLoader(web_paths=("<https://lilianweng.github.io/posts/2023-06-23-agent/>",), bs_kwargs=dict(parse_only=bs4.SoupStrainer( class_=("post-title","post-content","post-header")
 )))
text_documents=loader.load()

embeddings=OpenAIEmbeddings()astra_vector_store=Cassandra(embedding=embeddings,table_name="qa_mini_demo",session=None,keyspace=None
)
llm=ChatGroq(groq_api_key=groq_api_key, model_name="mixtral-8x7b-32768")
prompt = ChatPromptTemplate.from_template("""Answer the following question based only on the provided context.Think step by step before providing a detailed answer.I will tip you $1000 if the user finds the answer helpful.<context>{context}</context>
Question: {input}""")
retriever=astra_vector_store.as_retriever()document_chain=create_stuff_documents_chain(llm,prompt)retrieval_chain=create_retrieval_chain(retriever,document_chain)
response=retrieval_chain.invoke({"input":"Chain of thought (CoT; Wei et al. 2022) has become a standard prompting technique"})response