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1d32142 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 | """Vector storage and retrieval for donor/volunteer embeddings.
Uses the existing my_embeddings table in Supabase with pgvector extension.
"""
import json
from typing import List, Optional, Dict, Any, Union
from dataclasses import dataclass
import numpy as np
def _parse_json_field(value: Union[str, dict, None]) -> dict:
"""Safely parse a JSON field that might already be a dict (psycopg3 auto-parses)."""
if value is None:
return {}
if isinstance(value, dict):
return value
if isinstance(value, str):
try:
return json.loads(value)
except json.JSONDecodeError:
return {}
return {}
@dataclass
class SimilarityResult:
"""Result from similarity search.
Attributes:
id: The source_id of the matched form.
form_data: The original form data as a dictionary.
score: Similarity score (higher is more similar).
form_type: Type of form ("donor" or "volunteer").
distance: Raw L2 distance from query.
"""
id: str
form_data: Dict[str, Any]
score: float
form_type: str
distance: float = 0.0
class DonorVectorStore:
"""Vector storage and retrieval for donor/volunteer embeddings.
Uses the existing my_embeddings table schema:
- source_id: form ID
- chunk_index: always 0 (single embedding per form)
- text_content: JSON serialized form data
- metadata: {"form_type": "donor"|"volunteer", ...}
- embedding: VECTOR(1024)
Attributes:
pool: AsyncConnectionPool for database connections.
"""
def __init__(self, pool):
"""Initialize vector store.
Args:
pool: AsyncConnectionPool from psycopg_pool
"""
self.pool = pool
async def store_embedding(
self,
form_id: str,
form_type: str,
embedding: np.ndarray,
form_data: Dict[str, Any]
) -> int:
"""Store form embedding in my_embeddings table.
Args:
form_id: Unique identifier for the form.
form_type: Type of form ("donor" or "volunteer").
embedding: The 1024-dimensional embedding vector.
form_data: Original form data to store.
Returns:
The database ID of the inserted record.
"""
embedding_list = embedding.tolist()
form_json = json.dumps(form_data, default=str)
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute(
"""
INSERT INTO my_embeddings
(source_id, chunk_index, text_content, metadata, embedding)
VALUES (%s, %s, %s, %s, %s::vector)
RETURNING id
""",
(
form_id,
0, # Single embedding per form
form_json,
json.dumps({"form_type": form_type}),
embedding_list
)
)
result = await cur.fetchone()
return result[0]
async def update_embedding(
self,
form_id: str,
embedding: np.ndarray,
form_data: Optional[Dict[str, Any]] = None
) -> bool:
"""Update an existing embedding.
Args:
form_id: The form ID to update.
embedding: New embedding vector.
form_data: Optional updated form data.
Returns:
True if update succeeded, False if record not found.
"""
embedding_list = embedding.tolist()
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
if form_data:
form_json = json.dumps(form_data, default=str)
await cur.execute(
"""
UPDATE my_embeddings
SET embedding = %s::vector, text_content = %s
WHERE source_id = %s
""",
(embedding_list, form_json, form_id)
)
else:
await cur.execute(
"""
UPDATE my_embeddings
SET embedding = %s::vector
WHERE source_id = %s
""",
(embedding_list, form_id)
)
return cur.rowcount > 0
async def delete_embedding(self, form_id: str) -> bool:
"""Delete an embedding by form ID.
Args:
form_id: The form ID to delete.
Returns:
True if deletion succeeded, False if record not found.
"""
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute(
"DELETE FROM my_embeddings WHERE source_id = %s",
(form_id,)
)
return cur.rowcount > 0
async def get_embedding(self, form_id: str) -> Optional[SimilarityResult]:
"""Get a specific embedding by form ID.
Args:
form_id: The form ID to retrieve.
Returns:
SimilarityResult if found, None otherwise.
"""
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute(
"""
SELECT source_id, text_content, metadata
FROM my_embeddings
WHERE source_id = %s
""",
(form_id,)
)
row = await cur.fetchone()
if not row:
return None
form_data = _parse_json_field(row[1])
metadata = _parse_json_field(row[2])
return SimilarityResult(
id=row[0],
form_data=form_data,
form_type=metadata.get("form_type", "unknown"),
score=1.0,
distance=0.0,
)
async def find_similar(
self,
query_embedding: np.ndarray,
form_type: Optional[str] = None,
limit: int = 10,
country_filter: Optional[str] = None,
exclude_ids: Optional[List[str]] = None
) -> List[SimilarityResult]:
"""Find similar donors/volunteers using vector similarity.
Uses L2 distance (Euclidean) with IVFFlat index for efficient search.
Args:
query_embedding: The query embedding vector.
form_type: Optional filter for "donor" or "volunteer".
limit: Maximum number of results to return.
country_filter: Optional filter for country code.
exclude_ids: Optional list of form IDs to exclude.
Returns:
List of SimilarityResult ordered by similarity (highest first).
"""
embedding_list = query_embedding.tolist()
# Build query with optional filters
query = """
SELECT
source_id,
text_content,
metadata,
embedding <-> %s::vector AS distance
FROM my_embeddings
WHERE 1=1
"""
params: List[Any] = [embedding_list]
if form_type:
query += " AND metadata->>'form_type' = %s"
params.append(form_type)
if country_filter:
query += " AND text_content ILIKE %s"
params.append(f'%"country": "{country_filter}"%')
if exclude_ids:
placeholders = ", ".join(["%s"] * len(exclude_ids))
query += f" AND source_id NOT IN ({placeholders})"
params.extend(exclude_ids)
query += " ORDER BY distance ASC LIMIT %s"
params.append(limit)
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute(query, params)
rows = await cur.fetchall()
results = []
for row in rows:
form_data = _parse_json_field(row[1])
metadata = _parse_json_field(row[2])
distance = float(row[3])
results.append(SimilarityResult(
id=row[0],
form_data=form_data,
form_type=metadata.get("form_type", "unknown"),
score=1.0 / (1.0 + distance), # Convert distance to similarity
distance=distance
))
return results
async def find_by_causes(
self,
target_causes: List[str],
query_embedding: np.ndarray,
limit: int = 20
) -> List[SimilarityResult]:
"""Hybrid search: filter by causes, rank by embedding similarity.
Combines keyword filtering with vector similarity for better
recommendations when specific causes are targeted.
Args:
target_causes: List of cause categories to match.
query_embedding: The query embedding for ranking.
limit: Maximum number of results to return.
Returns:
List of SimilarityResult matching causes, ranked by similarity.
"""
embedding_list = query_embedding.tolist()
# Build ILIKE clauses for cause filtering
cause_conditions = " OR ".join([
"text_content ILIKE %s" for _ in target_causes
])
cause_params = [f"%{cause}%" for cause in target_causes]
query = f"""
SELECT
source_id,
text_content,
metadata,
embedding <-> %s::vector AS distance
FROM my_embeddings
WHERE ({cause_conditions})
ORDER BY distance ASC
LIMIT %s
"""
params = [embedding_list] + cause_params + [limit]
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute(query, params)
rows = await cur.fetchall()
results = []
for row in rows:
form_data = _parse_json_field(row[1])
metadata = _parse_json_field(row[2])
distance = float(row[3])
results.append(SimilarityResult(
id=row[0],
form_data=form_data,
form_type=metadata.get("form_type", "unknown"),
score=1.0 / (1.0 + distance),
distance=distance
))
return results
async def count_by_type(self) -> Dict[str, int]:
"""Get count of embeddings by form type.
Returns:
Dictionary with counts: {"donor": N, "volunteer": M, "total": N+M}
"""
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute("""
SELECT
metadata->>'form_type' as form_type,
COUNT(*) as count
FROM my_embeddings
GROUP BY metadata->>'form_type'
""")
rows = await cur.fetchall()
counts = {"donor": 0, "volunteer": 0, "total": 0}
for row in rows:
form_type = row[0] or "unknown"
count = row[1]
if form_type in counts:
counts[form_type] = count
counts["total"] += count
return counts
async def find_by_form_type(
self, form_type: str, limit: int = 500
) -> List[SimilarityResult]:
"""Get all entries of a specific form type.
Args:
form_type: Type of form ("donor", "volunteer", or "client").
limit: Maximum number of results to return.
Returns:
List of SimilarityResult for the specified form type.
"""
query = """
SELECT
source_id,
text_content,
metadata
FROM my_embeddings
WHERE metadata->>'form_type' = %s
LIMIT %s
"""
async with self.pool.connection() as conn:
async with conn.cursor() as cur:
await cur.execute(query, (form_type, limit))
rows = await cur.fetchall()
results = []
for row in rows:
form_data = _parse_json_field(row[1])
metadata = _parse_json_field(row[2])
results.append(
SimilarityResult(
id=row[0],
form_data=form_data,
form_type=metadata.get("form_type", form_type),
score=1.0,
distance=0.0,
)
)
return results
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