Hi, I can’t find a solution to use memory within VertexAI authentication
I’ve tried multiple things like changing the names of the values on the vertex_embedder_config dictionary, removing the api_key row and I’m still stuck on the same error:
ERROR: Error during short_term save: Expected Embedings to be non-empty list or numpy array, got in add.
vertex_embedder_config = {
"provider": "vertexai",
"config": {
"project_id": vertex_ai_project,
"region": vertex_ai_location,
"api_key": service_account_key_string
\# textembedding-gecko, text-multilingual-embedding-002, models/text-embedding-004
"model_name": "textembedding-gecko"
}
}
crew_email_rag_storage = ShortTermMemory(
storage = RAGStorage(
type = 'short_term',
embedder_config=vertex_embedder_config,
path=crew_email_memory_path
)
)
crew_email_rag_storage_entity = EntityMemory(
storage=RAGStorage(
type = 'short_term',
embedder_config=vertex_embedder_config,
\# type="short_term", # Ou "long_term" se aplicável
path=crew_email_memory_path
)
)
crew_email_long_term_memory = LongTermMemory(
storage=LTMSQLiteStorage(db_path=crew_email_memory_path)
)
self.crew_email = Crew(
agents=\[self.agent….\],
tasks=\[self.task….\],
memory = True,
short_term_memory= crew_email_rag_storage,
entity_memory= crew_email_rag_storage_entity,
long_term_memory=crew_email_long_term_memory,
cache=True,
verbose=False
)