File size: 9,123 Bytes
56c8fdd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | from fastapi import FastAPI, Query, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import csv
import os
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
app = FastAPI(title="ErayNet Search API")
DATA_PATH = os.path.join(os.path.dirname(__file__), "..", "data", "cleaned", "abbreviations.csv")
class Entry(BaseModel):
id: int
raw_text: str
abbreviation: str
somali: str
italian: str
english: str
domain: str
pos: str
quality_score: float
review_status: str
notes: str
class SemanticEntry(BaseModel):
id: int
raw_text: str
abbreviation: str
somali: str
italian: str
english: str
domain: str
pos: str
quality_score: float
review_status: str
notes: str
score: float
class SemanticSearchResult(BaseModel):
entries: List[SemanticEntry]
total: int
query_type: str
class UnifiedSearchResult(BaseModel):
query: str
matched_by: str
entries: List[Entry]
total: int
class SearchResult(BaseModel):
entries: List[Entry]
total: int
query_type: str
def load_data():
entries = []
with open(DATA_PATH, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
entries.append(Entry(
id=int(row['id']),
raw_text=row['raw_text'],
abbreviation=row['abbreviation'],
somali=row['somali'],
italian=row['italian'],
english=row['english'],
domain=row['domain'],
pos=row['pos'],
quality_score=float(row['quality_score']),
review_status=row['review_status'],
notes=row['notes']
))
return entries
def build_search_index(entries):
documents = []
for e in entries:
doc = f"{e.abbreviation} {e.somali} {e.italian} {e.english} {e.raw_text}"
documents.append(doc)
vectorizer = TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 4))
tfidf_matrix = vectorizer.fit_transform(documents)
return vectorizer, tfidf_matrix
entries = load_data()
vectorizer, tfidf_matrix = build_search_index(entries)
@app.get("/search/exact", response_model=SearchResult)
def exact_match(
q: str = Query(..., description="Query string"),
domain: Optional[str] = Query(None, description="Filter by domain"),
pos: Optional[str] = Query(None, description="Filter by part of speech"),
review_status: Optional[str] = Query(None, description="Filter by review status")
):
q = q.lower().strip()
results = [
e for e in entries
if (q == e.abbreviation.lower() or q == e.somali.lower() or q == e.italian.lower() or q == e.english.lower())
and (domain is None or e.domain.lower() == domain.lower())
and (pos is None or e.pos.lower() == pos.lower())
and (review_status is None or e.review_status.lower() == review_status.lower())
]
return SearchResult(entries=results, total=len(results), query_type="exact")
@app.get("/search/partial", response_model=SearchResult)
def partial_match(
q: str = Query(..., description="Query string"),
domain: Optional[str] = Query(None, description="Filter by domain"),
pos: Optional[str] = Query(None, description="Filter by part of speech"),
review_status: Optional[str] = Query(None, description="Filter by review status")
):
q = q.lower().strip()
results = [
e for e in entries
if (q in e.abbreviation.lower() or q in e.somali.lower() or q in e.italian.lower() or q in e.english.lower())
and (domain is None or e.domain.lower() == domain.lower())
and (pos is None or e.pos.lower() == pos.lower())
and (review_status is None or e.review_status.lower() == review_status.lower())
]
return SearchResult(entries=results, total=len(results), query_type="partial")
@app.get("/search/semantic", response_model=SemanticSearchResult)
def semantic_search(
q: str = Query(..., description="Query string"),
top_k: int = Query(5, ge=1, le=20),
domain: Optional[str] = Query(None, description="Filter by domain"),
pos: Optional[str] = Query(None, description="Filter by part of speech"),
review_status: Optional[str] = Query(None, description="Filter by review status")
):
query_vec = vectorizer.transform([q])
similarities = cosine_similarity(query_vec, tfidf_matrix).flatten()
filtered_indices = []
for i, e in enumerate(entries):
if similarities[i] > 0:
if (domain is None or e.domain.lower() == domain.lower()) and \
(pos is None or e.pos.lower() == pos.lower()) and \
(review_status is None or e.review_status.lower() == review_status.lower()):
filtered_indices.append(i)
filtered_indices.sort(key=lambda i: similarities[i], reverse=True)
top_indices = filtered_indices[:top_k]
results = [
SemanticEntry(
id=entries[i].id,
raw_text=entries[i].raw_text,
abbreviation=entries[i].abbreviation,
somali=entries[i].somali,
italian=entries[i].italian,
english=entries[i].english,
domain=entries[i].domain,
pos=entries[i].pos,
quality_score=entries[i].quality_score,
review_status=entries[i].review_status,
notes=entries[i].notes,
score=round(float(similarities[i]), 2)
)
for i in top_indices
]
return SemanticSearchResult(entries=results, total=len(results), query_type="semantic")
@app.get("/search", response_model=UnifiedSearchResult)
def unified_search(
q: str = Query(..., description="Query string"),
domain: Optional[str] = Query(None, description="Filter by domain"),
pos: Optional[str] = Query(None, description="Filter by part of speech"),
review_status: Optional[str] = Query(None, description="Filter by review status")
):
q_lower = q.lower().strip()
def matches_filters(e):
return (domain is None or e.domain.lower() == domain.lower()) and \
(pos is None or e.pos.lower() == pos.lower()) and \
(review_status is None or e.review_status.lower() == review_status.lower())
exact_results = [
e for e in entries
if (q_lower == e.abbreviation.lower() or q_lower == e.somali.lower() or q_lower == e.italian.lower() or q_lower == e.english.lower())
and matches_filters(e)
]
if exact_results:
return UnifiedSearchResult(query=q, matched_by="exact", entries=exact_results, total=len(exact_results))
partial_results = [
e for e in entries
if (q_lower in e.abbreviation.lower() or q_lower in e.somali.lower() or q_lower in e.italian.lower() or q_lower in e.english.lower())
and matches_filters(e)
]
if partial_results:
return UnifiedSearchResult(query=q, matched_by="partial", entries=partial_results, total=len(partial_results))
query_vec = vectorizer.transform([q])
similarities = cosine_similarity(query_vec, tfidf_matrix).flatten()
filtered_indices = [
i for i in range(len(entries))
if similarities[i] > 0 and matches_filters(entries[i])
]
filtered_indices.sort(key=lambda i: similarities[i], reverse=True)
top_indices = filtered_indices[:5]
semantic_results = [entries[i] for i in top_indices]
return UnifiedSearchResult(query=q, matched_by="semantic", entries=semantic_results, total=len(semantic_results))
@app.get("/entries", response_model=List[Entry])
def list_entries(skip: int = 0, limit: int = 100):
return entries[skip:skip+limit]
@app.get("/entries/{entry_id}", response_model=Entry)
def get_entry(entry_id: int):
for e in entries:
if e.id == entry_id:
return e
raise HTTPException(status_code=404, detail="Entry not found")
@app.get("/domains")
def list_domains():
domains = sorted(set(e.domain for e in entries if e.domain))
return {"domains": domains}
@app.get("/pos-tags")
def list_pos_tags():
pos_tags = sorted(set(e.pos for e in entries if e.pos))
return {"pos_tags": pos_tags}
@app.get("/stats")
def get_stats():
total = len(entries)
domains = {}
pos_tags = {}
review_statuses = {}
for e in entries:
if e.domain:
domains[e.domain] = domains.get(e.domain, 0) + 1
if e.pos:
pos_tags[e.pos] = pos_tags.get(e.pos, 0) + 1
if e.review_status:
review_statuses[e.review_status] = review_statuses.get(e.review_status, 0) + 1
return {
"total_entries": total,
"domains": dict(sorted(domains.items(), key=lambda x: -x[1])),
"pos_tags": dict(sorted(pos_tags.items(), key=lambda x: -x[1])),
"review_statuses": dict(sorted(review_statuses.items(), key=lambda x: -x[1]))
}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |