Hi I’m attempting to include memory in my agents using vertexai embedding models but keep running into validation error for the embedder parameter. I’ve try using a dictionary as well through google ai but run into a value error. Any tips to solve this?
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I have same error with this. I don’t know why but may be the pydantic version issue that pydantic version 2 need dictionary for the input data. I also try with dictionary format but error occurs.
embedding_config={
"provider": "vertexai",
"config": {
"api_key": "my api key",
"model_name": "text-multilingual-embedding-002",
"region": "us-central1",
"project_id": "myprojectid",
}
}
#for the crew
return Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
knowledge_sources=[self.date_source],
memory=True,
embedder=embedding_config,
)
[2025-01-31 08:49:25][ERROR]: Failed to upsert documents: Expected Embedings to be non-empty list or numpy array, got in upsert.
[2025-01-31 08:49:25][WARNING]: Failed to init knowledge: Expected Embedings to be non-empty list or numpy array, got in upsert.
ERROR:root:Error during short_term search: Expected Embedings to be non-empty list or numpy array, got in query.
ERROR:root:Error during entities search: Expected Embedings to be non-empty list or numpy array, got in query
I’m not sure but the document for the vertex ai can be outdated.