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# Import necessary libraries
import requests
import numpy as np
import fasttext
import torch
from typing import List
from rank_bm25 import BM25L
from normalizer import Normalizer
from fastapi import HTTPException
from sentence_transformers import SentenceTransformer, util
# Initialization
normalizer = Normalizer()
model_path = "Abdul-Ib/gte-small-2024"
semantic_model = SentenceTransformer(model_path, cache_folder="./assets")
categorizer = fasttext.load_model("./assets/categorization_pipeline.ftz")
category_map = np.load("./assets/category_map.npy", allow_pickle=True).item()
def make_request(url: str) -> dict:
"""
Make a GET request to the given URL and return the JSON response.
Args:
- url (str): The URL to make the request to.
Returns:
- dict: The JSON response.
Raises:
- HTTPException: If the request fails with a non-200 status code.
"""
try:
response = requests.get(url)
if response.status_code == 200:
return response.json()
else:
raise HTTPException(
status_code=response.status_code,
detail=f"Request failed with status code: {response.status_code}",
)
except Exception as e:
raise HTTPException(
status_code=404,
detail=f"An error occurred during the request: {e}",
)
def full_text_search(query: str, keyword_search: BM25L) -> np.ndarray:
"""
Perform full-text search using the given query and BM25L model.
Args:
- query (str): The query to search for.
- keyword_search (BM25L): The BM25L model for keyword search.
Returns:
- np.ndarray: The scores of the search results.
"""
try:
tokenized_query = query.split(" ")
ft_scores = keyword_search.get_scores(tokenized_query)
return ft_scores
except Exception as e:
# Handle exceptions such as AttributeError and ValueError
raise HTTPException(
status_code=500,
detail=f"An error occurred during full-text search: {e}",
)
def semantic_search(query: str, doc_embeddings: torch.Tensor) -> torch.Tensor:
"""
Perform semantic search using the given query and document embeddings.
Args:
- query (str): The query to search for.
- doc_embeddings (np.ndarray): The document embeddings for semantic search.
Returns:
- np.ndarray: The cosine similarity scores of the search results.
"""
try:
query_embedding = semantic_model.encode(
query, convert_to_tensor=True
)
cos_sim = util.cos_sim(query_embedding, doc_embeddings)[0]
return cos_sim
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred during semantic search: {e}",
)
def hybrid_search(
keyword_scores: np.ndarray, semantic_scores: torch.Tensor, alpha: float = 0.7
) -> np.ndarray:
"""
Perform hybrid search combining keyword and semantic scores.
Args:
- keyword_scores (np.ndarray): The keyword search scores.
- semantic_scores (np.ndarray): The semantic search scores.
- alpha (float): The weight for the keyword scores.
Returns:
- np.ndarray: The hybrid scores.
"""
try:
keyword_scores = 2 / np.pi * np.arctan(keyword_scores) - 0.5
keyword_scores[keyword_scores < 0] = 0
hybrid_scores = alpha * keyword_scores + (1 - alpha) * semantic_scores.numpy()
return hybrid_scores
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred during hybrid search: {e}",
)
def rerank_results(request_json: List[dict], hybrid_scores: np.ndarray) -> List[dict]:
"""
Rerank search results based on hybrid scores.
Args:
- request_json (List[dict]): The list of search results.
- hybrid_scores (np.ndarray): The hybrid scores.
Returns:
- List[dict]: The reranked search results.
"""
try:
for index, product in enumerate(request_json):
product["score"] = hybrid_scores[index]
return sorted(request_json, key=lambda k: k["score"], reverse=True)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred during reranking: {e}",
)
def calculate_interrelations(
request_json: List[dict],
doc_embeddings: np.ndarray,
interrelation_threshold: float = 0.9,
) -> None:
"""
Calculate interrelations between products based on cosine similarity of their embeddings.
Args:
- request_json (List[dict]): The list of products.
- doc_embeddings (np.ndarray): The document embeddings for products.
- interrelation_threshold (float): How similar two products are.
Returns:
- None
"""
try:
for product in request_json:
product["interrelations"] = []
for index, embedding_1 in enumerate(doc_embeddings):
for j, embedding_2 in enumerate(doc_embeddings):
if index != j:
cos_score = util.cos_sim(embedding_1, embedding_2)
if cos_score > interrelation_threshold:
request_json[index]["interrelations"].append(
request_json[j]["key"]
)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred during interrelation calculation: {e}",
)
def check_validity(query: str, keyword_search: BM25L) -> np.ndarray:
"""
Check the validity of the input query against keyword match search.
This function attempts to find valid search results for the input query by following these steps:
1. Perform a keyword match search on the original query.
2. If any matches are found in step 1, return the search scores.
3. Generate a modified query by keeping only one character from the original query and perform a keyword match search.
4. If any matches are found in step 3, return the search scores.
5. Check the spelling of the original query. If the spelling correction is successful,
perform a keyword match search with the corrected query.
6. If any matches are found in step 5, return the search scores.
7. If none of the attempts yield non-zero scores, return the scores of the original query.
Args:
- query (str): The input query to check its validity.
- keyword_search (BM25L): The BM25L model for keyword search.
Returns:
- np.ndarray: The scores of the search results.
"""
try:
# Step 1: Perform keyword match search on the original query
keyword_scores = full_text_search(query, keyword_search)
# Step 2: If any matches found in step 1, return the search scores
if max(keyword_scores) != 0.0:
return keyword_scores
# Step 3: Generate a modified query by keeping only one character and perform a keyword match search
one_char_query = normalizer.keep_one_char(query)
one_char_scores = full_text_search(one_char_query, keyword_search)
# Step 4: If any matches found in step 3, return the search scores
if max(one_char_scores) != 0.0:
return one_char_scores
# Step 5: Check spelling of the original query and perform a keyword match search with the corrected query
spelled_query = normalizer.check_spelling(query)
# Step 6: If any matches found in step 5, return the search scores
if spelled_query is not None:
spelled_scores = full_text_search(spelled_query, keyword_search)
if max(spelled_scores) != 0.0:
return spelled_scores
# Step 7: If none of the attempts yield non-zero scores, return the scores of the original query
return keyword_scores
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred during query validity check: {e}",
)
def is_cheapest(queries: list, request_json: list) -> None:
"""
Check which product is the cheapest within the same category as
each input query.
Args:
queries (list): List of input queries
request_json (list): List of products
"""
try:
for query in queries:
query_categories = [
category_map[category]
for category in categorizer.predict(query, k=3, threshold=0.5)[0]
]
min_idx = 0
min_price = float('inf') # Initialize min_price as positive infinity
for idx, product in enumerate(request_json):
if (
product["Inferred Category"] in query_categories
and product["price"] <= min_price
):
min_idx = idx
min_price = product["price"] # Update min_price if a cheaper product is found
for product in request_json:
product["cheapest"] = False # Reset "cheapest" field for all products
request_json[min_idx]["cheapest"] = True # Mark the cheapest product for the current query
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"An error occurred during cheapest product identification: {e}",
)
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