Memory issue when using the Gemini API

CrewAI Community Support
1

I’m having a problem applying the memory in my agent system, although my agent manages to return the response correctly in its output, after that I receive an error:

Failed to add to long term memory: Failed to convert text into a Pydantic model due to the following error: litellm.AuthenticationError: geminiException - {
“error”: {
“code”: 400,
“message”: “API key not valid. Please pass a valid API key.”,
“status”: “INVALID_ARGUMENT”,
“details”: [
{
“@type”: “type.googleapis.com/google.rpc.ErrorInfo”,
“reason”: “API_KEY_INVALID”,
“domain”: “googleapis.com”,
“metadata”: {
“service”: “generativelanguage.googleapis.com”
}
},
{
“@type”: “type.googleapis.com/google.rpc.LocalizedMessage”,
“locale”: “en-US”,
“message”: “API key not valid. Please pass a valid API key.”
}
]
}
}

When I switch to the Gemini key for the OpenAi one, this problem does not occur.

To use the memory I had to configure an embedder model, I chose an ollama model with 1536 embedding dimension, because without this it would give another error.

my crew setup:

crew = Crew(
agents=[agent],
tasks=[task],
verbose=True,
memory=True,
embedder={
“provider”: “ollama”,
“config”: {
“model”: “rjmalagon/gte-qwen2-1.5b-instruct-embed-f16:latest”
}
},
knowledge_sources=[json_source],
)

2

I have the same thing with embedders and memory. The key is provided, but for some reason is not passed. Have you figured it out, @Marcos_Maio ?

# embedder
embedder_config = {
    "provider": "google",
    "config": {
        "api_key": os.getenv("GOOGLE_API_KEY"),
        "model": "models/text-embedding-004"
    }
}

and then using it in crew:
dynamic_crew = Crew(
                agents=crew_agents,
                tasks=crew_tasks,
                verbose=True,
                memory = True,
                embedder = embedder_config,
                long_term_memory = LongTermMemory(
                    storage=LTMSQLiteStorage(
                        db_path="./memorydb/dynamic_crew_%s/long_term_memory_storage.db" % user_id
                    )
                ),
                short_term_memory = ShortTermMemory(
                    storage = RAGStorage(
                            embedder_config=embedder_config,
                            type="short_term",
                            path="./memorydb/dynamic_crew_%s/" % user_id
                        )
                    ),
                entity_memory = EntityMemory(
                    storage=RAGStorage(
                        embedder_config=embedder_config,
                        type="short_term",
                        path="./memorydb/dynamic_crew_%s/" % user_id
                    )
                ),
            )