--- title: "GPT4All Embeddings with Weaviate" description: "This integration is deprecated and will be removed in a future release. We recommend using alternative model providers for new projects." url: https://docs.weaviate.io/weaviate/model-providers/gpt4all/embeddings --- # GPT4All Embeddings with Weaviate > [!WARNING] > > **Deprecated integration** > > This integration is deprecated and will be removed in a future release. We recommend using alternative model providers for new projects. > > For local AI model integrations, consider using [Ollama](https://docs.weaviate.io/weaviate/model-providers/ollama) or the [local HuggingFace](https://docs.weaviate.io/weaviate/model-providers/huggingface) model integrations. Weaviate's integration with GPT4All's models allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate vector index](https://docs.weaviate.io/weaviate/model-providers/gpt4all/embeddings#configure-the-vectorizer) to use an GPT4All embedding model, and Weaviate will generate embeddings for various operations using the specified model via the GPT4All inference container. This feature is called the *vectorizer*. At [import time](https://docs.weaviate.io/weaviate/model-providers/gpt4all/embeddings#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](https://docs.weaviate.io/weaviate/model-providers/gpt4all/embeddings#vector-near-text-search) and [hybrid](https://docs.weaviate.io/weaviate/model-providers/gpt4all/embeddings#hybrid-search) search operations, Weaviate converts text queries into embeddings. ![Embedding integration illustration](https://docs.weaviate.io/assets/images/integration_gpt4all_embedding-3058443a5ec7893b52f9ab1ae8ed8eba.png) This module is optimized for CPU using the [`ggml` library](https://github.com/ggerganov/ggml), allowing for fast inference even without a GPU. ## Requirements Currently, the GPT4All integration is only available for `amd64/x86_64` architecture devices, as the `gpt4all` library currently does not support ARM devices, such as Apple M-series. ### Weaviate configuration Your Weaviate instance must be configured with the GPT4All vectorizer integration (`text2vec-gpt4all`) module. **For Weaviate Cloud (WCD) users** This integration is not available for Weaviate Cloud (WCD) instances, as it requires a locally running GPT4All instance. **For self-hosted users** - Check the [cluster metadata](https://docs.weaviate.io/deploy/configuration/status#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](https://docs.weaviate.io/weaviate/configuration/modules) guide to enable the module in Weaviate. #### Configure the integration To use this integration, you must configure the container image of the GPT4All model, and the inference endpoint of the containerized model. The following example shows how to configure the GPT4All integration in Weaviate: #### Docker #### Docker Option 1: Use a pre-configured `docker-compose.yml` file Follow the instructions on the [Weaviate Docker installation configurator](https://docs.weaviate.io/deploy/installation-guides/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. ```yaml services: weaviate: # Other Weaviate configuration environment: ENABLE_MODULES: text2vec-gpt4all # Enable this module GPT4ALL_INFERENCE_API: http://text2vec-gpt4all:8080 # Set the inference API endpoint text2vec-gpt4all: # Set the name of the inference container image: cr.weaviate.io/semitechnologies/gpt4all-inference:all-MiniLM-L6-v2 ``` - `GPT4ALL_INFERENCE_API` environment variable sets the inference API endpoint - `text2vec-gpt4all` is the name of the inference container - `image` is the container image #### Kubernetes Configure the GPT4All integration in Weaviate by adding or updating the `text2vec-gpt4all` module in the `modules` section of the Weaviate Helm chart values file. For example, modify the `values.yaml` file as follows: ```yaml modules: text2vec-gpt4all: enabled: true tag: all-MiniLM-L6-v2 repo: semitechnologies/gpt4all-inference registry: cr.weaviate.io ``` See the [Weaviate Helm chart](https://github.com/weaviate/weaviate-helm/blob/master/weaviate/values.yaml) for an example of the `values.yaml` file including more configuration options. ### Credentials As this integration connects to a local GPT4All container, no additional credentials (e.g. API key) are required. Connect to Weaviate as usual, such as in the examples below. **Python** ```py import weaviate client = weaviate.connect_to_local() # Work with Weaviate client.close() ``` **JavaScript/TypeScript** ```ts import weaviate from '@weaviate/node' const client = await weaviate.connectToLocal() // Work with Weaviate client.close() ``` ## Configure the vectorizer [Configure a Weaviate index](https://docs.weaviate.io/weaviate/manage-collections/vector-config#specify-a-vectorizer) as follows to use a GPT4All embedding model: **Python** ```py from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_gpt4all( name="title_vector", source_properties=["title"], ) ], # Additional parameters not shown ) ``` **JavaScript/TypeScript** ```ts await client.collections.create({ name: 'DemoCollection', vectorizers: [ weaviate.configure.vectors.text2VecGPT4All({ name: 'title_vector', sourceProperties: ['title'], }), ], // Additional parameters not shown }); ``` Currently, the only available model is [`all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). **Vectorization behavior** Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects. Unless specified otherwise in the collection definition, the default behavior is to: - Only vectorize properties that use the `text` or `text[]` data type (unless [skipped](https://docs.weaviate.io/weaviate/manage-collections/vector-config#property-level-settings)) - Sort properties in alphabetical (a-z) order before concatenating values - If `vectorizePropertyName` is `true` (`false` by default) prepend the property name to each property value - Join the (prepended) property values with spaces - Prepend the collection name if `vectorizeClassName` is `true` (the default differs by integration — check this page's parameter table) Since `v1.27`, the string is sent to the model as is. Earlier versions lowercase it first, and you can restore that behavior with the [`LOWERCASE_VECTORIZATION_INPUT`](https://docs.weaviate.io/deploy/configuration/env-vars#module-specific) environment variable. ## Data import After configuring the vectorizer, [import data](https://docs.weaviate.io/weaviate/manage-objects/import) into Weaviate. Weaviate generates embeddings for text objects using the specified model. **Python** ```py source_objects = [ {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."}, {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."}, {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."}, {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."}, {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."} ] collection = client.collections.use("DemoCollection") with collection.batch.fixed_size(batch_size=200) as batch: for src_obj in source_objects: # The model provider integration will automatically vectorize the object batch.add_object( properties={ "title": src_obj["title"], "description": src_obj["description"], }, # vector=vector # Optionally provide a pre-obtained vector ) if batch.number_errors > 10: print("Batch import stopped due to excessive errors.") break failed_objects = collection.batch.failed_objects if failed_objects: print(f"Number of failed imports: {len(failed_objects)}") print(f"First failed object: {failed_objects[0]}") ``` **JavaScript/TypeScript** ```ts let srcObjects = [ { title: "The Shawshank Redemption", description: "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places." }, { title: "The Godfather", description: "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga." }, { title: "The Dark Knight", description: "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City." }, { title: "Jingle All the Way", description: "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve." }, { title: "A Christmas Carol", description: "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption." } ]; const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) let dataObjects = new Array(); for (let srcObject of srcObjects) { dataObjects.push({ title: srcObject.title, description: srcObject.description, }); } const response = await myCollection.data.insertMany(dataObjects); console.log(response); ``` > [!TIP] > > **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 specified GPT4All model. ![Embedding integration at search illustration](https://docs.weaviate.io/assets/images/integration_gpt4all_embedding_search-ffd26d15a0ed324f078e3a43fecea44e.png) ### Vector (near text) search When you perform a [vector search](https://docs.weaviate.io/weaviate/search/similarity#search-with-text), 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`. **Python** ```py collection = client.collections.use("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"]) ``` **JavaScript/TypeScript** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) let result; result = await myCollection.query.nearText( 'A holiday film', // The model provider integration will automatically vectorize the query { limit: 2, } ) console.log(JSON.stringify(result.objects, null, 2)); ``` ### Hybrid search > [!NOTE] > > **What is a hybrid search?** > > A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](https://docs.weaviate.io/weaviate/search/hybrid) to return the best matching objects from the database. When you perform a [hybrid search](https://docs.weaviate.io/weaviate/search/hybrid), 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`. **Python** ```py collection = client.collections.use("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"]) ``` **JavaScript/TypeScript** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) result = await myCollection.query.hybrid( 'A holiday film', // The model provider integration will automatically vectorize the query { limit: 2, } ) console.log(JSON.stringify(result.objects, null, 2)); ``` ## References ### Available models Currently, the only available model is [`all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). ## Further resources ### 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 collections](https://docs.weaviate.io/weaviate/manage-collections) and [How-to: Manage objects](https://docs.weaviate.io/weaviate/manage-objects) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](https://docs.weaviate.io/weaviate/search) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation. ### External resources - [GPT4All documentation](https://docs.gpt4all.io/)