Spartee

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# create the vector embedding for the query
query_embedding = model.encode("That is a happy person")

Spartee

def cosine_similarity(a, b):
    return np.dot(a, b)/(norm(a)*norm(b))

Spartee

Query: That is a happy person 

That is a very happy person -> similarity score = 0.94291496
That is a happy dog -> similarity score = 0.69457746
Today is a sunny day -> similarity score = 0.25687605

Spartee

from redis import Redis
from redis.commands.search.field import VectorField, TagField

# Function to create a flat (brute-force) search index with Redis/RediSearch
# Could also be a HNSW index
def create_flat_index(redis_conn: Redis, number_of_vectors: int, distance_metric: str='COSINE'):
		
	image_field = VectorField("img_vector",
	                          "FLAT", {"TYPE": "FLOAT32",
	                                   "DIM": 512,

Spartee

import os
import openai
from typing import List, Tuple

openai.api_key = os.environ["OPENAI_API_KEY"]


from langchain.document_loaders import ArxivLoader
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings

Spartee

import numpy as np

from numpy.linalg import norm
from sentence_transformers import SentenceTransformer

# Define the model we want to use (it'll download itself)
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

sentences = [
  "That is a very happy person",