Vector dimension error: expected dim: 768, got 1024 AND Error in new_retrieved_facts: Expecting value: line 1 column 1 (char 0)

[Nevermetyou65]

Hi, I am trying to use huggingface embedding model with mem0.
Below is config I use.

from mem0 import Memory

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "mem0_ollama",
            "host": "localhost",
            "port": 6333,
            "embedding_model_dims" : 768,
        },
    },
    "llm": {
        "provider": "ollama",
        "config": {
            "model": "qwen3:4b",
            "temperature": 0,
            "ollama_base_url": "my url",
        },
    },
    "embedder": {
        "provider": "huggingface",
        "config": {
            "model": "Qwen/Qwen3-Embedding-0.6B",
            "embedding_dims" : 768,
        },
    },
}

# Initialize Memory with the configuration
m = Memory.from_config(config)
m.add("I'm visiting Paris", user_id="john")

Even though, I specify embedding dims to be 768, I still got error

UnexpectedResponse: Unexpected Response: 400 (Bad Request)
Raw response content:
b'{"status":{"error":"Wrong input: Vector dimension error: expected dim: 768, got 1024"},"time":0.000841958}'

Could someone help me?

I am using mem0 from pypi, version 0.1.116

I also tried this with TEI

from mem0 import Memory

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "mem0_ollama",
            "host": "localhost",
            "port": 6333,
        },
    },
    "llm": {
        "provider": "ollama",
        "config": {
            "model": "qwen3:8b",
            "temperature": 0,
            "ollama_base_url": my url,
        },
    },
    "embedder": {
        "provider": "huggingface",
        "config": {"huggingface_base_url": my url},
    },
}

# Initialize Memory with the configuration
m = Memory.from_config(config)
m.add("I'm visiting Paris", user_id="john")

I got this error

Error in new_retrieved_facts: Expecting value: line 1 column 1 (char 0)
{'results': []}

[Nevermetyou65]

@deshraj Could you help me with this?