# Authorization header is correct, but the token seems invalid

**URL:** <https://forum.weaviate.io/t/authorization-header-is-correct-but-the-token-seems-invalid/3223>\
**Category:** Support\
**Created:** [July 30, 2024, 5:18pm UTC](https://forum.weaviate.io/t/authorization-header-is-correct-but-the-token-seems-invalid/3223 "2024-07-30T17:18:19Z")\
**Posts on this page:** 1\
**Showing post:** 2

<div class="post-metadata">

**Author:** ![DudaNogueira](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/dudanogueira/32/7846_2.png) [@DudaNogueira](https://forum.weaviate.io/u/DudaNogueira)\
**Post date:** [July 31, 2024, 8:48pm UTC](https://forum.weaviate.io/t/authorization-header-is-correct-but-the-token-seems-invalid/3223/2 "2024-07-31T20:48:06Z")

</div>

hi @Anna_Caroline_Symond !!

Welcome to our community 🤗

This message comes directly from hugging face Api. It indicates that your Hugging Face api token is wrong, not Weaviate’s.

Also, there are some other issues with this code, like not passing the vectors your encoded yourself. This will trigger Weaviate to vectorize your object using the Hugging Face.

You also created a named vector that uses `title` property as source, but was using `text` as the property receiving the data.

I also changed the code to use batch insert, as they can perform better for large amounts of data ingestion.

Here is my take on this:

```python
import weaviate
import os
import weaviate.classes as wvc
from sentence_transformers import SentenceTransformer

client = weaviate.connect_to_local(
    headers={
        "X-HuggingFace-Api-Key": os.getenv("HUGGINGFACE_APIKEY")
        #"X-HuggingFace-Api-Key": "WRONG"
    }
)
print(client.is_ready())
client.collections.delete("Test")
try:
    collection = client.collections.create(
        name="Test",
        vectorizer_config=[
            wvc.config.Configure.NamedVectors.text2vec_huggingface(
                name="title_vector",
                source_properties=["title"],
                model="sentence-transformers/all-MiniLM-L6-v2",
            )
        ],
    )
    model = SentenceTransformer("all-MiniLM-L6-v2")
    lines = ["Something about dogs", "Something about wolfs"]

    vectors = model.encode(lines)

    objs = []
    for line, vector in zip(lines, vectors):
        with collection.batch.dynamic() as batch:
            batch.add_object(
                properties={"title": line},
                vector={
                    "title_vector": vector
                }
            )
        if collection.batch.failed_objects:
            print("FAILED OBJECTS", collection.batch.failed_objects)
finally:
    client.close()

```

Now you can check if your objects were indeed ingested:

```python
client.connect()
objects = collection.query.fetch_objects(include_vector=True).objects
print(objects[0].vector)
print(objects[0].properties)
client.close()

```

It should output something similar to:

> {‘title\_vector’: [-0.05317388474941254, 0.02120402827858925, 0.06296592950820923, …, 0.09222045540809631]}  
> {‘title’: ‘Something about dogs’}

Now, we can query this using Hugging face Api, or vectorizing the query ourselves:

```python
client.connect()
collection = client.collections.get("Test")
objects = collection.query.near_text(
    "pet animals", 
    target_vector="title_vector", 
    return_metadata=wvc.query.MetadataQuery(distance=True),
    include_vector=True
).objects
for object in objects:
    print("#" * 10)
    print(object.metadata.distance)
    print(object.properties)
client.close()

```

Outputs:

> ##########  
> 0.3560590147972107  
> {‘title’: ‘Something about dogs’}  
> ##########  
> 0.5862653255462646  
> {‘title’: ‘Something about wolfs’}

If you don’t want to use the hugging face api, you can vectorize your query. Instead of `near_text`, you use `near_vector`:

```python
client.connect()
collection = client.collections.get("Test")

query_vector = vectors = model.encode(["pet animals"])

objects = collection.query.near_vector(
    near_vector=query_vector[0], 
    target_vector="title_vector",
    return_metadata=wvc.query.MetadataQuery(distance=True),
    include_vector=True
).objects
for object in objects:
    print("#" * 10)
    print(object.metadata.distance)
    print(object.properties)
client.close()

```

Let me know if this helps 🙂

---

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