and here's my code for postgres vector

and here's my code for postgres vector store:

from llama_index import VectorStoreIndex
from llama_index.node_parser import SimpleNodeParser
from llama_index.ingestion import IngestionPipeline, IngestionCache
from llama_index.ingestion.cache import RedisCache
from llama_index.vector_stores import PGVectorStore

pg_vector_store = PGVectorStore.from_params(
database=config["Database"]["DB_NAME"],
host=config["Database"]["DB_HOST"],
password=config["Database"]["DB_PASSWORD"],
port=config["Database"]["DB_PORT"],
user=config["Database"]["DB_USER"],
table_name= db_table_name,
embed_dim=384, # bge-small-v1.5 embedding dimension
hybrid_search=True,
text_search_config="english",
)

ingest_cache = IngestionCache(
cache=RedisCache.from_host_and_port(host="127.0.0.1", port=6379),
collection="my_test_cache",
)
pipeline = IngestionPipeline(
transformations=[
SimpleNodeParser(chunk_size=512, chunk_overlap=20),
embed_model,
],
vector_store = pg_vector_store,
cache = ingest_cache,
)

pipeline.run(documents)

# build index
vector_index = VectorStoreIndex.from_vector_store(vector_store = pg_vector_store, show_progress=True)