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Article embedding generator — T1.6 (Người B)
Calls embedding service provided by Người A (T6.2).
Writes embeddings back to Neo4j Article nodes.
Interface contract with Người A (T6.2)
---------------------------------------
API format:
POST {EMBED_SERVICE_URL}/embed
Body: {"texts": ["text1", "text2", ...]}
Response: {"embeddings": [[float, ...], ...]} # 1024-dim each
Interface contract with cross_reference / application layer (Người C)
----------------------------------------------------------------------
After T1.6 completes:
- Article.embedding property exists (1024-dim float array)
- Neo4j vector index "article_embeddings" is created and populated
- Người C queries this index via:
CALL db.index.vector.queryNodes("article_embeddings", 20, $query_vector)
"""
from __future__ import annotations
import logging
import os
import time
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from neo4j import Driver
logger = logging.getLogger(__name__)
# Batch sizes from spec
EMBED_BATCH_SIZE = 512 # articles per embedding API call
NEO4J_BATCH_SIZE = 1_000 # articles per Neo4j write transaction
EMBED_DIM = 1024 # harrier-0.6b (updated) output dimension
VECTOR_INDEX_NAME = "article_embeddings" # used by Người C's queries
# Retry configuration for embedding service
EMBED_MAX_RETRIES = 3
EMBED_RETRY_DELAY = 2.0 # seconds
class ArticleEmbedder:
"""
Generates embeddings for all Article nodes and stores them in Neo4j.
Usage
-----
embedder = ArticleEmbedder(
driver=neo4j_driver,
embed_service_url=os.getenv("EMBED_SERVICE_URL"),
)
stats = embedder.embed_all()
# {"total": N, "embedded": N, "errors": N}
"""
def __init__(
self,
driver: "Driver",
embed_service_url: Optional[str] = None,
*,
embed_batch_size: int = EMBED_BATCH_SIZE,
neo4j_batch_size: int = NEO4J_BATCH_SIZE,
) -> None:
self._driver = driver
self._url = embed_service_url or os.getenv("EMBED_SERVICE_URL", "http://localhost:8001")
self._embed_batch = embed_batch_size
self._neo4j_batch = neo4j_batch_size
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def embed_all(self, *, overwrite: bool = False) -> dict[str, int]:
"""
Embed all Article nodes that don't yet have an embedding.
Parameters
----------
overwrite : bool
If True, re-embed all articles (even those with existing embeddings).
Default False for idempotent re-runs.
Returns
-------
dict with keys: total, embedded, errors
"""
stats = {"total": 0, "embedded": 0, "errors": 0}
where_clause = "WHERE a.embedding IS NULL" if not overwrite else ""
query = f"""
MATCH (a:Article) {where_clause}
OPTIONAL MATCH (d:Document)-[:HAS_ARTICLE]->(a)
OPTIONAL MATCH (d2:Document)-[:HAS_CHAPTER]->(ch:Chapter)-[:HAS_ARTICLE]->(a)
WITH a, coalesce(d.title, d2.title, "Văn bản") AS doc_title, coalesce(ch.title, "") AS ch_title
RETURN a.uid AS uid,
doc_title + " - " + ch_title + " - " + coalesce(a.title, "") + " - " + coalesce(a.clean_text, "") AS rich_text
"""
with self._driver.session() as session:
records = session.run(query).data()
stats["total"] = len(records)
for i in range(0, len(records), self._embed_batch):
batch = records[i : i + self._embed_batch]
uids = [r["uid"] for r in batch]
texts = [r["rich_text"] for r in batch]
try:
embeddings = self._call_embed_service(texts)
update_query = """
UNWIND $batch AS row
MATCH (a:Article {uid: row.uid})
SET a.embedding = row.embedding
"""
batch_data = [{"uid": uid, "embedding": emb} for uid, emb in zip(uids, embeddings)]
with self._driver.session() as session:
session.run(update_query, batch=batch_data)
stats["embedded"] += len(batch)
except Exception as exc:
logger.error("Failed to embed batch: %s", exc)
stats["errors"] += len(batch)
try:
self._ensure_vector_index()
except Exception as exc:
logger.error("Failed to ensure vector index: %s", exc)
return stats
def embed_article(self, uid: str, text: str) -> Optional[list[float]]:
"""
Embed a single article and write to Neo4j. Returns the embedding vector.
Useful for incremental updates or testing.
"""
try:
emb = self._call_embed_service([text])[0]
query = "MATCH (a:Article {uid: $uid}) SET a.embedding = $embedding"
with self._driver.session() as session:
session.run(query, uid=uid, embedding=emb)
return emb
except Exception as exc:
logger.error("Failed to embed article %s: %s", uid, exc)
return None
def verify_embeddings(self) -> dict[str, int]:
"""
Check that all Article nodes have a 1024-dim embedding.
Returns: {"total_articles": N, "with_embedding": N, "missing": N, "wrong_dim": N}
"""
query = f"""
MATCH (a:Article)
RETURN
count(a) AS total_articles,
count(a.embedding) AS with_embedding,
count(CASE WHEN size(a.embedding) <> {EMBED_DIM} THEN 1 END) AS wrong_dim
"""
with self._driver.session() as session:
res = session.run(query).single()
return dict(res) if res else {}
# ------------------------------------------------------------------
# Private helpers
# ------------------------------------------------------------------
def _call_embed_service(self, texts: list[str]) -> list[list[float]]:
"""
Call Người A's embedding API with retry logic.
"""
import requests
last_error = None
for attempt in range(EMBED_MAX_RETRIES):
try:
resp = requests.post(
self._url + "/embed",
json={"texts": texts},
timeout=60,
)
resp.raise_for_status()
data = resp.json()
embeddings = data.get("embeddings", [])
if len(embeddings) != len(texts):
raise RuntimeError(f"Expected {len(texts)} embeddings, got {len(embeddings)}")
if embeddings and len(embeddings[0]) != EMBED_DIM:
raise RuntimeError(f"Expected {EMBED_DIM} dims, got {len(embeddings[0])}")
return embeddings
except Exception as e:
last_error = e
if attempt < EMBED_MAX_RETRIES - 1:
delay = EMBED_RETRY_DELAY * (2 ** attempt)
logger.warning(
f"Embedding service failed (attempt {attempt + 1}/{EMBED_MAX_RETRIES}), "
f"retrying in {delay}s: {e}"
)
time.sleep(delay)
raise last_error # type: ignore[misc]
def _ensure_vector_index(self) -> None:
"""
Create the Neo4j vector index if it doesn't exist.
Safe to call multiple times (IF NOT EXISTS).
Cypher:
CREATE VECTOR INDEX article_embeddings IF NOT EXISTS
FOR (a:Article) ON (a.embedding)
OPTIONS {indexConfig: {
`vector.dimensions`: 1024,
`vector.similarity_function`: 'cosine'
}}
"""
query = f"""
CREATE VECTOR INDEX {VECTOR_INDEX_NAME} IF NOT EXISTS
FOR (a:Article) ON (a.embedding)
OPTIONS {{indexConfig: {{
`vector.dimensions`: {EMBED_DIM},
`vector.similarity_function`: 'cosine'
}}}}
"""
with self._driver.session() as session:
session.run(query)
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