Spaces:
Sleeping
Sleeping
File size: 6,538 Bytes
6252f54 | 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 | """
Embedding Pipeline — generates sentence embeddings for all Capability, SubCapability,
and Feature nodes and stores them in Neo4j vector indexes.
Uses sentence-transformers/all-MiniLM-L6-v2 (384-dim).
Runs on AMD ROCm (exposed as CUDA) or CPU.
Run after enrich_graph.py.
"""
import os
import sys
import logging
import numpy as np
import time
from dotenv import load_dotenv
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
load_dotenv()
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
FETCH_NODES_QUERY = """
MATCH (n)
WHERE (n:Capability OR n:SubCapability OR n:Feature)
AND n.id IS NOT NULL
AND n.embedding IS NULL
OPTIONAL MATCH (parent)<-[:PARENT_OF*1..3]-(domain:Domain)
WHERE parent = n OR (parent)-[:PARENT_OF*1..2]->(n)
RETURN
n.id AS node_id,
labels(n)[0] AS label,
n.name AS name,
n.description AS description,
[(sd:SubDomain)-[:PARENT_OF*0..2]->(n) | sd.name][0] AS subdomain_name,
[(d:Domain)-[:PARENT_OF*0..4]->(n) | d.name][0] AS domain_name
LIMIT $batch_size
"""
FETCH_NODES_WITH_EMBEDDING = """
MATCH (n)
WHERE (n:Capability OR n:SubCapability OR n:Feature)
AND n.id IS NOT NULL
RETURN
n.id AS node_id,
labels(n)[0] AS label,
n.name AS name,
n.description AS description,
[(sd:SubDomain)-[:PARENT_OF*0..2]->(n) | sd.name][0] AS subdomain_name,
[(d:Domain)-[:PARENT_OF*0..4]->(n) | d.name][0] AS domain_name
SKIP $skip
LIMIT $batch_size
"""
COUNT_UNEMBEDDED = """
MATCH (n)
WHERE (n:Capability OR n:SubCapability OR n:Feature)
AND n.embedding IS NULL
RETURN count(n) AS cnt
"""
COUNT_TOTAL_EMBEDDABLE = """
MATCH (n)
WHERE (n:Capability OR n:SubCapability OR n:Feature)
RETURN count(n) AS cnt
"""
def build_node_text(name: str, description: str | None, subdomain: str | None, domain: str | None) -> str:
"""Build enriched text for embedding — includes full path context."""
parts = []
if domain:
parts.append(f"Domain: {domain}")
if subdomain:
parts.append(f"SubDomain: {subdomain}")
parts.append(f"Capability: {name}")
if description:
parts.append(description[:300]) # truncate long descriptions
return " > ".join(parts[:3]) + (f". {description[:200]}" if description else "")
def embed_and_store(driver, database: str, embed_fn, embed_batch_size: int = 128, write_batch_size: int = 50):
"""Fetch all embeddable nodes, generate embeddings, store in Neo4j."""
from neo4j import GraphDatabase
with driver.session(database=database) as session:
total_result = session.run(COUNT_TOTAL_EMBEDDABLE).single()
total = total_result["cnt"] if total_result else 0
log.info(f"Total embeddable nodes: {total:,}")
skip = 0
total_embedded = 0
t0 = time.time()
while True:
rows = session.run(FETCH_NODES_WITH_EMBEDDING, batch_size=embed_batch_size, skip=skip).data()
if not rows:
break
texts = [
build_node_text(
r.get("name") or "",
r.get("description"),
r.get("subdomain_name"),
r.get("domain_name"),
)
for r in rows
]
node_ids = [r["node_id"] for r in rows]
embeddings = embed_fn(texts)
# Write embeddings in smaller batches
for i in range(0, len(rows), write_batch_size):
chunk_ids = node_ids[i:i+write_batch_size]
chunk_embs = embeddings[i:i+write_batch_size]
for node_id, emb in zip(chunk_ids, chunk_embs):
try:
session.run(
"MATCH (n {id: $node_id}) "
"CALL db.create.setVectorProperty(n, 'embedding', $embedding) YIELD node "
"RETURN node",
node_id=node_id,
embedding=emb.tolist(),
)
except Exception as e:
# Fallback: store as property directly
try:
session.run(
"MATCH (n {id: $node_id}) SET n.embedding = $embedding",
node_id=node_id,
embedding=emb.tolist(),
)
except Exception as e2:
log.warning(f"Could not store embedding for {node_id}: {e2}")
total_embedded += len(rows)
elapsed = time.time() - t0
rate = total_embedded / elapsed if elapsed > 0 else 0
log.info(f" Embedded {total_embedded:,}/{total:,} ({rate:.0f} nodes/s)")
skip += embed_batch_size
if len(rows) < embed_batch_size:
break
return total_embedded
def run_embedding_pipeline():
from neo4j import GraphDatabase
from dotenv import load_dotenv
load_dotenv()
uri = os.getenv("NEO4J_URI")
username = os.getenv("NEO4J_USERNAME")
password = os.getenv("NEO4J_PASSWORD")
database = os.getenv("NEO4J_DATABASE", "neo4j")
if not all([uri, username, password]):
log.error("Neo4j credentials not found")
sys.exit(1)
log.info(f"Loading embedding model: {EMBEDDING_MODEL}")
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(EMBEDDING_MODEL)
# Check for AMD ROCm
import torch
if torch.cuda.is_available():
device = "cuda"
log.info(f"Using AMD ROCm GPU: {torch.cuda.get_device_name(0)}")
if hasattr(torch.version, "hip") and torch.version.hip:
log.info(f"ROCm version: {torch.version.hip}")
model = model.to(device)
else:
device = "cpu"
log.info("Using CPU for embeddings")
def embed_fn(texts: list[str]) -> np.ndarray:
return model.encode(texts, batch_size=64, show_progress_bar=False, normalize_embeddings=True)
log.info(f"Connecting to Neo4j at {uri}...")
driver = GraphDatabase.driver(uri, auth=(username, password))
try:
total = embed_and_store(driver, database, embed_fn)
log.info(f"Embedding pipeline complete: {total:,} nodes embedded")
finally:
driver.close()
if __name__ == "__main__":
run_embedding_pipeline()
|