Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-m3")
vector_store = PGVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=db_password,
    port=url.port,
    user=db_user,
    table_name=table_name,
    embed_dim=1024,  # HF embedding dimension
)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

vector_store_connection_string = f"postgresql://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}"
url = make_url(vector_store_connection_string)

vector_store = PGVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=db_password,
    port=url.port,
    user=db_user,
    table_name=table_name,
    embed_dim=1024,  # HF embedding dimension
)

# Create the storage context and index
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, show_progress=True
)

print("Indexing complete. Data stored in PGVector.")

DataError: (psycopg2.errors.DataException) different vector dimensions 1536 and 1024 [SQL: SELECT public.data_building_permits_data.id, public.data_building_permits_data.node_id

"llm = OpenAI(model="gpt-4-turbo", temperature=0)
Settings.llm = llm

embed_model = OpenAIEmbedding(model="text-embedding-3-small")
Settings.embed_model = embed_model
#logging.info(" LLM MODEL OPENAI" + llm.model)
logging.info("Initialized OpenAI")
query_engine = loaded_index.as_query_engine(streaming=False) #, text_qa_template=text_qa_template, llm=llm)
logging.info("Initialized QueryEngine")"

The second more simpler implementation of my bot : 
# Initialize your index
pinecone_index = pc.Index(index_name)

vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
loaded_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
logging.info("Index loaded from Pinecone")

query_engine = loaded_index.as_query_engine()