| import json |
|
|
| from ._api import api_settings, embed |
| from ._graph import identifier |
| from ._state import cached_vectors, fingerprint |
| from ._vectors import validate_vectors |
| from .config import TARGETS |
|
|
|
|
| def embed_graph(graph, targets, cache, prop, batch_size, limit): |
| settings = api_settings() |
| source = fingerprint(settings) |
| counts = {} |
| for name in targets: |
| target = TARGETS[name] |
| if prop in {target.key, target.text, target.property}: |
| raise ValueError('API embeddings require a separate property, for example api_embedding.') |
| graph.check_source(target, prop, source) |
| dimension = settings['dimensions'] or None |
| existing = graph.run(f'MATCH (n:{target.label}) WHERE n.{identifier(prop)} IS NOT NULL RETURN DISTINCT size(n.{identifier(prop)}) AS dim') |
| if existing: |
| if len(existing) != 1 or (dimension and existing[0]['dim'] != dimension): |
| raise ValueError('Existing vector dimensions are inconsistent with configuration.') |
| dimension = existing[0]['dim'] |
| written = 0 |
| while not limit or written < limit: |
| size = min(batch_size, limit - written) if limit else batch_size |
| rows = graph.run(f'MATCH (n:{target.label}) WHERE n.{identifier(prop)} IS NULL AND n.{target.text} IS NOT NULL AND trim(n.{target.text}) <> "" RETURN n.{target.key} AS key,n.{target.text} AS text ORDER BY key LIMIT $size', size=size) |
| if not rows: |
| break |
| key = fingerprint({'source': source, 'target': name, 'rows': rows}) |
| vectors = cached_vectors(cache, key, lambda: embed(settings, [row['text'] for row in rows])) |
| vectors = validate_vectors(vectors, dimension) |
| dimension = len(vectors[0]) |
| graph.write(target, prop, source, [{'key': row['key'], 'vector': vector} for row, vector in zip(rows, vectors)]) |
| written += len(rows) |
| print(json.dumps({'target': name, 'vectors_added': written, 'dimension': dimension}), flush=True) |
| counts[name] = written |
| return counts |
|
|
