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import json
import os
import numpy as np
import openai
import pandas as pd
import requests
from scipy.spatial.distance import cosine
def cosine_similarity(vec1, vec2):
try:
return 1 - cosine(vec1, vec2)
except:
print(vec1.shape, vec2.shape)
def get_embedding_from_api(word, model="vicuna-7b-v1.1"):
if "ada" in model:
resp = openai.Embedding.create(
model=model,
input=word,
)
embedding = np.array(resp["data"][0]["embedding"])
return embedding
url = "http://localhost:8000/v1/embeddings"
headers = {"Content-Type": "application/json"}
data = json.dumps({"model": model, "input": word})
response = requests.post(url, headers=headers, data=data)
if response.status_code == 200:
embedding = np.array(response.json()["data"][0]["embedding"])
return embedding
else:
print(f"Error: {response.status_code} - {response.text}")
return None
def create_embedding_data_frame(data_path, model, max_tokens=500):
df = pd.read_csv(data_path, index_col=0)
df = df[["Time", "ProductId", "UserId", "Score", "Summary", "Text"]]
df = df.dropna()
df["combined"] = (
"Title: " + df.Summary.str.strip() + "; Content: " + df.Text.str.strip()
)
top_n = 1000
df = df.sort_values("Time").tail(top_n * 2)
df.drop("Time", axis=1, inplace=True)
df["n_tokens"] = df.combined.apply(lambda x: len(x))
df = df[df.n_tokens <= max_tokens].tail(top_n)
df["embedding"] = df.combined.apply(lambda x: get_embedding_from_api(x, model))
return df
def search_reviews(df, product_description, n=3, pprint=False, model="vicuna-7b-v1.1"):
product_embedding = get_embedding_from_api(product_description, model=model)
df["similarity"] = df.embedding.apply(
lambda x: cosine_similarity(x, product_embedding)
)
results = (
df.sort_values("similarity", ascending=False)
.head(n)
.combined.str.replace("Title: ", "")
.str.replace("; Content:", ": ")
)
if pprint:
for r in results:
print(r[:200])
print()
return results
def print_model_search(input_path, model):
print(f"Model: {model}")
df = create_embedding_data_frame(input_path, model)
print("search: delicious beans")
results = search_reviews(df, "delicious beans", n=5, model=model)
print(results)
print("search: whole wheat pasta")
results = search_reviews(df, "whole wheat pasta", n=5, model=model)
print(results)
print("search: bad delivery")
results = search_reviews(df, "bad delivery", n=5, model=model)
print(results)
input_datapath = "amazon_fine_food_review.csv"
if not os.path.exists(input_datapath):
raise Exception(
f"Please download data from: https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews"
)
print_model_search(input_datapath, "vicuna-7b-v1.1")
print_model_search(input_datapath, "text-similarity-ada-001")
print_model_search(input_datapath, "text-embedding-ada-002")
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