Question Answering app with Python and Pinecone

qa.py

import pandas as pd
import itertools
import pinecone
from sentence_transformers import SentenceTransformer

api_key = "YOUR API KEY FROM PINECONE CONSOLE"
pinecone.init(api_key=api_key, environment='us-west1-gcp')

index_name = "question-answering"

index = pinecone.Index(index_name=index_name)

DATA_FILE = f"comcast.csv"

pd.set_option("display.max_colwidth", 500)

df = pd.read_csv(
    f"{DATA_FILE}",  usecols=["Ticket", "CustomerComplaint"], index_col=False
)
df = df.sample(frac=1).reset_index(drop=True)
df.drop_duplicates(inplace=True)
df['Ticket'] = df['Ticket'].apply(str)

model = SentenceTransformer("average_word_embeddings_glove.6B.300d")

df["question_vector"] = df.CustomerComplaint.apply(lambda x: model.encode(str(x)).tolist())

def chunks(iterable, batch_size=100):
    it = iter(iterable)
    chunk = tuple(itertools.islice(it, batch_size))
    while chunk:
        yield chunk
        chunk = tuple(itertools.islice(it, batch_size))

for batch in chunks(zip(df.Ticket, df.question_vector)):
    index.upsert(vectors=batch)

query_questions = [
    "Wifi Not working",
]

query_vectors = [model.encode(str(question)).tolist() for question in query_questions]

query_results = index.query(queries=query_vectors, top_k=5)

for question, res in zip(query_questions, query_results.results):
    print("\n\n\n Original question : " + str(question))
    print("\n Most similar questions based on pinecone vector search: \n")

    ids = [match.id for match in res.matches]
    scores = [match.score for match in res.matches]
    df_result = pd.DataFrame(
        {
            "Ticket#": ids,
            "Question": [
                df[df.Ticket == _id].CustomerComplaint.values[0] for _id in ids
            ],
            "Score": scores,
        }
    )
    print(df_result)