使用云AI提供商创建嵌入

import neo4j


URI = '<URI for Neo4j database>'
AUTH = ('<Username>', '<Password>')
DB_NAME = '<Database name>'  # examples: 'recommendations-50', 'neo4j'

openAI_token = '<OpenAI API token>'


def main():
    driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)  (1)
    driver.verify_connectivity()

    batch_size = 100
    batch_n = 1
    movies_batch = []
    with driver.session(database=DB_NAME) as session:
        # Fetch `Movie` nodes
        result = session.run('MATCH (m:Movie) RETURN m.plot AS plot, m.title AS title')
        for record in result:
            title = record.get('title')
            plot = record.get('plot')

            if title is not None and plot is not None:
                movies_batch.append({
                    'title': title,
                    'plot': plot,
                    'to_encode': f'Title: {title}\nPlot: {plot}'  (2)
                })

            # Import a batch; flush buffer
            if len(movies_batch) == batch_size:  (3)
                import_batch(driver, movies_batch, batch_n)
                movies_batch = []
                batch_n += 1

        # Flush last batch
        import_batch(driver, movies_with_embeddings, batch_n)

    # Import complete, show counters
    records, _, _ = driver.execute_query('''
    MATCH (m:Movie WHERE m.embedding IS NOT NULL)
    RETURN count(*) AS countMoviesWithEmbeddings, size(m.embedding) AS embeddingSize
    ''', database_=DB_NAME)
    print(f"""
Embeddings generated and attached to nodes.
Movie nodes with embeddings: {records[0].get('countMoviesWithEmbeddings')}.
Embedding size: {records[0].get('embeddingSize')}.
    """)


def import_batch(driver, nodes, batch_n):
    # Generate and store embeddings for Movie nodes
    driver.execute_query('''
    CALL genai.vector.encodeBatch($listToEncode, 'OpenAI', { token: $token }) YIELD index, vector  (4)
    MATCH (m:Movie {title: $movies[index].title, plot: $movies[index].plot})  (5)
    CALL db.create.setNodeVectorProperty(m, 'embedding', vector)  (6)
    ''', movies=nodes, listToEncode=[movie['to_encode'] for movie in nodes], token=openAI_token,
    database_=DB_NAME)
    print(f'Processed batch {batch_n}')


if __name__ == '__main__':
    main()

'''
Movie nodes with embeddings: 9083.
Embedding size: 1536.
'''