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Locally Hosted Transformers Text Embeddings + Weaviate

New Documentation

The model provider integration pages are new and still undergoing improvements. We appreciate any feedback on this forum thread.

Weaviate's integration with the Hugging Face Transformers library allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate vector index to use the Transformers integration, and configure the Weaviate instance with a model image, and Weaviate will generate embeddings for various operations using the specified model in the Transformers inference container. This feature is called the vectorizer.

At import time, Weaviate generates text object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings.

Embedding integration illustration

Requirements

Weaviate configuration

Your Weaviate instance must be configured with the Hugging Face Transformers vectorizer integration (text2vec-transformers) module.

For Weaviate Cloud (WCD) users

This integration is not available for Weaviate Cloud (WCD) serverless instances, as it requires spinning up a container with the Hugging Face model.

Enable the integration module

Configure the integration

To use this integration, you must configure the container image of the Hugging Face Transformers model, and the inference endpoint of the containerized model.

The following example shows how to configure the Hugging Face Transformers integration in Weaviate:

Docker Option 1: Use a pre-configured docker-compose.yml file

Follow the instructions on the Weaviate Docker installation configurator to download a pre-configured docker-compose.yml file with a selected model


Docker Option 2: Add the configuration manually

Alternatively, add the configuration to the docker-compose.yml file manually as in the example below.

version: '3.4'
services:
weaviate:
# Other Weaviate configuration
environment:
TRANSFORMERS_INFERENCE_API: http://t2v-transformers:8080 # Set the inference API endpoint
t2v-transformers: # Set the name of the inference container
image: cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1
environment:
ENABLE_CUDA: 0 # Set to 1 to enable
  • TRANSFORMERS_INFERENCE_API environment variable sets the inference API endpoint
  • t2v-transformers is the name of the inference container
  • image is the container image
  • ENABLE_CUDA environment variable enables GPU usage

Set image from a list of available models to specify a particular model to be used.

Credentials

As this integration runs a local container with the Transformers model, no additional credentials (e.g. API key) are required.

Configure the vectorizer

Configure a Weaviate index to use the Transformer inference container by setting the vectorizer as follows:

from weaviate.classes.config import Configure

client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_transformers(
name="title_vector",
source_properties=["title"]
)
],
# Additional parameters not shown
)
Model selection via container image used

Model selection in this integration is done by selecting the appropriate container image in the integration.

Data import

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

collection = client.collections.get("DemoCollection")

with collection.batch.dynamic() as batch:
for src_obj in source_objects:
weaviate_obj = {
"title": src_obj["title"],
"description": src_obj["description"],
}

# The model provider integration will automatically vectorize the object
batch.add_object(
properties=weaviate_obj,
# vector=vector # Optionally provide a pre-obtained vector
)
Re-use existing vectors

If you already have a compatible model vector available, you can provide it directly to Weaviate. This can be useful if you have already generated embeddings using the same model and want to use them in Weaviate, such as when migrating data from another system.

Searches

Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the Transformers inference container.

Embedding integration at search illustration

When you perform a vector search, Weaviate converts the text query into an embedding using the specified model and returns the most similar objects from the database.

The query below returns the n most similar objects from the database, set by limit.

collection = client.collections.get("DemoCollection")

response = collection.query.near_text(
query="A holiday film", # The model provider integration will automatically vectorize the query
limit=2
)

for obj in response.objects:
print(obj.properties["title"])
What is a hybrid search?

A hybrid search performs a vector search and a keyword (BM25) search, before combining the results to return the best matching objects from the database.

When you perform a hybrid search, Weaviate converts the text query into an embedding using the specified model and returns the best scoring objects from the database.

The query below returns the n best scoring objects from the database, set by limit.

collection = client.collections.get("DemoCollection")

response = collection.query.hybrid(
query="A holiday film", # The model provider integration will automatically vectorize the query
limit=2
)

for obj in response.objects:
print(obj.properties["title"])

References

Vectorizer parameters

The following examples show how to configure Transformers-specific options.

Inference URL parameters

Optionally, if your stack includes multiple inference containers, specify the inference container(s) to use with a collection.

If no parameters are specified, the default inference URL from the Weaviate configuration is used.

Specify inferenceUrl for a single inference container.

Specify passageInferenceUrl and queryInferenceUrl if using a DPR model.

Additional parameters

  • poolingStrategy – the pooling strategy to use when the input exceeds the model's context window.
from weaviate.classes.config import Configure

client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_transformers(
name="title_vector",
source_properties=["title"],
# Further options
pooling_strategy="masked_mean",
inference_url="<custom_transformers_url>", # For when using multiple inference containers
passage_inference_url="<custom_transformers_url>", # For when using DPR models
query_inference_url="<custom_transformers_url>", # For when using DPR models
)
],
# Additional parameters not shown
)

Available models

Lists of pre-built Docker images for this integration are available in the tabs below. If you do not have a GPU available, we recommend using an ONNX-enabled image for CPU inference.

You can also build your own Docker image

info

These models benefit from GPU acceleration. Enable CUDA acceleration where available through your Docker or Kubernetes configuration.

See the full list
Model NameImage Name
distilbert-base-uncased (Info)cr.weaviate.io/semitechnologies/transformers-inference:distilbert-base-uncased
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
sentence-transformers/multi-qa-MiniLM-L6-cos-v1 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1
sentence-transformers/multi-qa-mpnet-base-cos-v1 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-mpnet-base-cos-v1
sentence-transformers/all-mpnet-base-v2 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-mpnet-base-v2
sentence-transformers/all-MiniLM-L12-v2 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-MiniLM-L12-v2
sentence-transformers/paraphrase-multilingual-mpnet-base-v2 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-paraphrase-multilingual-mpnet-base-v2
sentence-transformers/all-MiniLM-L6-v2 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-MiniLM-L6-v2
sentence-transformers/multi-qa-distilbert-cos-v1 (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-distilbert-cos-v1
sentence-transformers/gtr-t5-base (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-gtr-t5-base
sentence-transformers/gtr-t5-large (Info)cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-gtr-t5-large
google/flan-t5-base (Info)cr.weaviate.io/semitechnologies/transformers-inference:google-flan-t5-base
google/flan-t5-large (Info)cr.weaviate.io/semitechnologies/transformers-inference:google-flan-t5-large
BAAI/bge-small-en-v1.5 (Info)cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-small-en-v1.5
BAAI/bge-base-en-v1.5 (Info)cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-base-en-v1.5

Advanced configuration

Run a separate inference container

As an alternative, you can run the inference container independently from Weaviate. To do so, follow these steps:

  • Enable text2vec-transformers and omit t2v-transformers container parameters in your Weaviate configuration
  • Run the inference container separately, e.g. using Docker, and
  • Use TRANSFORMERS_INFERENCE_API or inferenceUrl to set the URL of the inference container.

For example, run the container with Docker:

docker run -itp "8000:8080" semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1

Then, set TRANSFORMERS_INFERENCE_API="http://localhost:8000". If Weaviate is part of the same Docker network, as a part of the same docker-compose.yml file, you can use the Docker networking/DNS, such as TRANSFORMERS_INFERENCE_API=http://t2v-transformers:8080.

Further resources

Chunking

This integration automatically chunks text if it exceeds the model's maximum token length before it is passed to the model. It will then return the pooled vectors.

See HuggingFaceVectorizer.vectorizer() for the exact implementation.

Code examples

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

  • The how-to: manage data guides show how to perform data operations (i.e. create, update, delete).
  • The how-to: search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

Model licenses

Each of the compatible models has its own license. For detailed information, review the license for the model you are using in the Hugging Face Model Hub.

It is your responsibility to evaluate whether the terms of its license(s), if any, are appropriate for your intended use.

Custom models

To run the integration with a custom model, refer to the custom image guide.

External resources

If you have any questions or feedback, let us know in the user forum.