AIassistant.py

ASTRA_DB_SECURE_BUNDLE_PATH = "*"
ASTRA_DB_APPLICATION_TOKEN = "*"
ASTRA_DB_CLIENT_ID = "*"
ASTRA_DB_CLIENT_SECRET = "*"
ASTRA_DB_KEYSPACE = "search"
OPENAI_API_KEY = "*"

from langchain.vectorstores.cassandra import Cassandra
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings

from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
from datasets import load_dataset

cloud_config= {
'secure_connect_bundle': ASTRA_DB_SECURE_BUNDLE_PATH
}
auth_provider = PlainTextAuthProvider(ASTRA_DB_CLIENT_ID, ASTRA_DB_CLIENT_SECRET)
cluster = Cluster(cloud=cloud_config, auth_provider=auth_provider)
astraSession = cluster.connect()

llm =OpenAI(openai_api_key=OPENAI_API_KEY)
myEmbedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

myCassandraVStore = Cassandra(
    embedding=myEmbedding,
    session=astraSession,
    keyspace=ASTRA_DB_KEYSPACE,
    table_name="minni_ass_demo",
)

print("Loading data from huggingface")
myDataset = load_dataset("Biddls/Onion_News", split="train")
headlines = myDataset["text"][:50]

print("/nGenerating embeddings and storing AstraDB")
myCassandraVStore.add_texts(headlines)

print("Inserted %i headlines.\n" % len(headlines))

vectorIndex = VectorStoreIndexWrapper(vectorstore=myCassandraVStore)

first_question = True
while True:
    if first_question:
        query_text = input("\nEnter your question (or type 'qzuit to exit): ")
        first_question = False
    else:
        query_text = input("\nWhat's your next question (or type 'quit' to exit): ") 
    if query_text.lower() == 'quit':
        break

    print("QUESTION: \"%s\"" % query_text)   
    answer = vectorIndex.query(query_text, llm=llm).strip()
    print("ANSWER: \"%s\"\n" % answer)  

    print("DOCUMENTS BY RELEVANCE:")
    for doc, score in myCassandraVStore.simmilarity_search_with_score(query_text, k=4):
        print(" %0.4f \"%s ...\"" % (score, doc.page_content[:60]))