process_incoming.py

import pandas as pd 
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np 
import joblib 
import requests


def create_embedding(text_list):
    # https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings
    r = requests.post("http://localhost:11434/api/embed", json={
        "model": "bge-m3",
        "input": text_list
    })

    embedding = r.json()["embeddings"] 
    return embedding


df = joblib.load('embeddings.joblib')


incoming_query = input("Ask a Question: ")
question_embedding = create_embedding([incoming_query])[0] 

# Find similarities of question_embedding with other embeddings
# print(np.vstack(df['embedding'].values))
# print(np.vstack(df['embedding']).shape)
similarities = cosine_similarity(np.vstack(df['embedding']), [question_embedding]).flatten()
# print(similarities)
top_results = 30
max_indx = similarities.argsort()[::-1][0:top_results]
# print(max_indx)
new_df = df.loc[max_indx] 
# print(new_df[["title", "number", "text"]])

for index, item in new_df.iterrows():
    print(index, item["title"], item["number"], item["text"], item["start"], item["end"])