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Runtime error
| 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") | |