# Text Embeddings Weaviate's integration with the Hugging Face Transformers library allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate vector index](#configure-the-vectorizer) to use the Transformers integration, and [configure the Weaviate instance](#weaviate-configuration) 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](#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](#vector-near-text-search) and [hybrid](#hybrid-search) search operations, Weaviate converts text queries into embeddings. ![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_transformers_embedding.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the Hugging Face Transformers vectorizer integration (`text2vec-transformers`) module. :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is not available for Weaviate Cloud (WCD) instances, as it requires spinning up a container with the Hugging Face model. ::: #### Enable the integration module - Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) guide to enable the module in Weaviate. #### Configure the integration To use this integration, 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: ::::tabs{sync="deployments"} :::tab{title="Docker"} #### Docker Option 1: Use a pre-configured `docker-compose.yml` file Follow the instructions on the [Weaviate Docker installation configurator](../installation/installation-guides-docker-installation.md#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-transformers # Enable this module TRANSFORMERS_INFERENCE_API: http://text2vec-transformers:8080 # Set the inference API endpoint text2vec-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 - `text2vec-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](#available-models) to specify a particular model to be used. ::: :::tab{title="Kubernetes"} Configure the Hugging Face Transformers integration in Weaviate by adding or updating the `text2vec-transformers` module in the `modules` section of the Weaviate Helm chart values file. For example, modify the `values.yaml` file as follows: ```yaml modules: text2vec-transformers: enabled: true tag: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2 repo: semitechnologies/transformers-inference registry: cr.weaviate.io envconfig: enable_cuda: true ``` If you are using a [DPR](https://huggingface.co/docs/transformers/en/model_doc/dpr) model, also configure the parameters listed under `passageQueryServices`. 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. Set `tag` from a [list of available models](#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. Connect to Weaviate as usual, such as in the examples below. :::code-group{sync="languages"} ```python title="Python" ``` ```typescript title="JavaScript/TypeScript" ``` ::: ## Configure the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use the Transformer inference container: :::code-group{sync="languages"} ```python title="Python" {5-10} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_transformers( name="title_vector", source_properties=["title"] ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-15} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecTransformers({ name: 'title_vector', sourceProperties: ['title'], }, ), ], // Additional parameters not shown }); ``` ::: :::callout{intent="note" title="Choose a container image to select a model"} To choose a model, select the [container image](#configure-the-integration) that hosts it. ::: :::accordion{title="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](../how-to-manage-collections/vector-config.md#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 class name (unless `vectorizeClassName` is `false`) - Convert the produced string to lowercase ::: ### 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](https://huggingface.co/docs/transformers/en/model_doc/dpr) model. #### Additional parameters - `poolingStrategy`: the pooling strategy to use when the input exceeds the model's context window. - Default: `masked_mean`. Allowed values: `masked_mean` or `cls`. ([Read more on this topic.](https://arxiv.org/abs/1908.10084)) :::code-group{sync="languages"} ```python title="Python" {5-15} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_transformers( name="title_vector", source_properties=["title"], # Further options pooling_strategy="masked_mean", inference_url="", # For when using multiple inference containers # passage_inference_url="", # For when using DPR models # query_inference_url="", # For when using DPR models ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecTransformers({ name: 'title_vector', sourceProperties: ['title'], // Further options // poolingStrategy: 'masked_mean', // inferenceUrl: '', // For when using multiple inference containers // passageInferenceUrl: ``, // For when using DPR models // queryInferenceUrl: ``, // For when using DPR models }, ), ], // highlight-end // Additional parameters not shown }); ``` ::: ## Data import After configuring the vectorizer, [import data](../how-to-manage-objects/import.md) into Weaviate. Weaviate generates embeddings for text objects using the specified model. :::code-group{sync="languages"} ```python title="Python" {13-20} 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]}") ``` ```typescript title="JavaScript/TypeScript" 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." } ]; ``` ::: :::callout{intent="tip" title="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](/assets/docs/weaviate/model-providers/_includes/integration_transformers_embedding_search.png) ### Vector (near text) search When you perform a [vector search](../how-to-query-search/similarity.md#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`. :::code-group{sync="languages"} ```python title="Python" {3-6} 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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ::: ### Hybrid search :::callout{intent="info" title="What is a hybrid search?"} A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](../how-to-query-search/hybrid.md) to return the best matching objects from the database. ::: When you perform a [hybrid search](../how-to-query-search/hybrid.md), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} 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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ::: ## References ### 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](transformers-embeddings-custom-image.md) :::::tabs{sync="deployments"} ::::tab{title="Single container models"} :::callout{intent="info"} These models benefit from GPU acceleration. Enable CUDA acceleration where available through your [Docker or Kubernetes configuration](#weaviate-configuration). ::: :::accordion{title="See the full list"} | Model Name | Image Name | | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | | `distilbert-base-uncased` ([Info](https://huggingface.co/distilbert-base-uncased)) | `cr.weaviate.io/semitechnologies/transformers-inference:distilbert-base-uncased` | | `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` ([Info](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2` | | `sentence-transformers/multi-qa-MiniLM-L6-cos-v1` ([Info](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1` | | `sentence-transformers/multi-qa-mpnet-base-cos-v1` ([Info](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-mpnet-base-cos-v1` | | `sentence-transformers/all-mpnet-base-v2` ([Info](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-mpnet-base-v2` | | `sentence-transformers/all-MiniLM-L12-v2` ([Info](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-MiniLM-L12-v2` | | `sentence-transformers/paraphrase-multilingual-mpnet-base-v2` ([Info](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-paraphrase-multilingual-mpnet-base-v2` | | `sentence-transformers/all-MiniLM-L6-v2` ([Info](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-MiniLM-L6-v2` | | `sentence-transformers/multi-qa-distilbert-cos-v1` ([Info](https://huggingface.co/sentence-transformers/multi-qa-distilbert-cos-v1)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-multi-qa-distilbert-cos-v1` | | `sentence-transformers/gtr-t5-base` ([Info](https://huggingface.co/sentence-transformers/gtr-t5-base)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-gtr-t5-base` | | `sentence-transformers/gtr-t5-large` ([Info](https://huggingface.co/sentence-transformers/gtr-t5-large)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-gtr-t5-large` | | `google/flan-t5-base` ([Info](https://huggingface.co/google/flan-t5-base)) | `cr.weaviate.io/semitechnologies/transformers-inference:google-flan-t5-base` | | `google/flan-t5-large` ([Info](https://huggingface.co/google/flan-t5-large)) | `cr.weaviate.io/semitechnologies/transformers-inference:google-flan-t5-large` | | `BAAI/bge-small-en-v1.5` ([Info](https://huggingface.co/BAAI/bge-small-en-v1.5)) | `cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-small-en-v1.5` | | `BAAI/bge-base-en-v1.5` ([Info](https://huggingface.co/BAAI/bge-base-en-v1.5)) | `cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-base-en-v1.5` | ::: :::: ::::tab{title="DPR models"} :::callout{intent="info"} [DPR](https://huggingface.co/docs/transformers/en/model_doc/dpr) models use two inference containers, one for the passage encoder and one for the query encoder. These models benefit from GPU acceleration. Enable CUDA acceleration where available through your [Docker or Kubernetes configuration](#weaviate-configuration). ::: :::accordion{title="See the full list"} | Model Name | Image Name | | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | | `facebook/dpr-ctx_encoder-single-nq-base` ([Info](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base)) | `cr.weaviate.io/semitechnologies/transformers-inference:facebook-dpr-ctx_encoder-single-nq-base` | | `facebook/dpr-question_encoder-single-nq-base` ([Info](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base)) | `cr.weaviate.io/semitechnologies/transformers-inference:facebook-dpr-question_encoder-single-nq-base` | | `vblagoje/dpr-ctx_encoder-single-lfqa-wiki` ([Info](https://huggingface.co/vblagoje/dpr-ctx_encoder-single-lfqa-wiki)) | `cr.weaviate.io/semitechnologies/transformers-inference:vblagoje-dpr-ctx_encoder-single-lfqa-wiki` | | `vblagoje/dpr-question_encoder-single-lfqa-wiki` ([Info](https://huggingface.co/vblagoje/dpr-question_encoder-single-lfqa-wiki)) | `cr.weaviate.io/semitechnologies/transformers-inference:vblagoje-dpr-question_encoder-single-lfqa-wiki` | | Bar-Ilan University NLP Lab Models | | | `biu-nlp/abstract-sim-sentence` ([Info](https://huggingface.co/biu-nlp/abstract-sim-sentence)) | `cr.weaviate.io/semitechnologies/transformers-inference:biu-nlp-abstract-sim-sentence` | | `biu-nlp/abstract-sim-query` ([Info](https://huggingface.co/biu-nlp/abstract-sim-query)) | `cr.weaviate.io/semitechnologies/transformers-inference:biu-nlp-abstract-sim-query` | ::: :::: ::::tab{title="Snowflake"} :::callout{intent="info"} Snowflake's [Arctic](https://huggingface.co/collections/Snowflake/arctic-embed-661fd57d50fab5fc314e4c18) embedding models are also available. These models benefit from GPU acceleration. Enable CUDA acceleration where available through your [Docker or Kubernetes configuration](#weaviate-configuration). ::: :::accordion{title="See the full list"} | Model Name | Image Name | | ---------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | | `Snowflake/snowflake-arctic-embed-xs` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-xs` | | `Snowflake/snowflake-arctic-embed-s` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-s` | | `Snowflake/snowflake-arctic-embed-m` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-m)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-m` | | `Snowflake/snowflake-arctic-embed-l` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-l` | ::: :::: ::::tab{title="ONNX (CPU)"} :::callout{intent="info"} ONNX-enabled images use [ONNX Runtime](https://onnxruntime.ai/docs/) for faster inference on CPUs. They are quantized for ARM64 and AMD64 (AVX2) hardware. They are indicated by the `-onnx` suffix in the image name. ::: :::accordion{title="See the full list"} | Model Name | Image Name | | ---------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | `sentence-transformers/all-MiniLM-L6-v2` ([Info](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)) | `cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-MiniLM-L6-v2-onnx` | | `BAAI/bge-small-en-v1.5` ([Info](https://huggingface.co/BAAI/bge-small-en-v1.5)) | `cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-small-en-v1.5-onnx` | | `BAAI/bge-base-en-v1.5` ([Info](https://huggingface.co/BAAI/bge-base-en-v1.5)) | `cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-base-en-v1.5-onnx` | | `BAAI/bge-m3` ([Info](https://huggingface.co/BAAI/bge-m3)) | `cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-m3-onnx` | | `Snowflake/snowflake-arctic-embed-xs` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-xs-onnx` | | `Snowflake/snowflake-arctic-embed-s` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-s-onnx` | | `Snowflake/snowflake-arctic-embed-m` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-m)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-m-onnx` | | `Snowflake/snowflake-arctic-embed-l` ([Info](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)) | `cr.weaviate.io/semitechnologies/transformers-inference:snowflake-snowflake-arctic-embed-l-onnx` | ::: :::: ::::: We add new model support over time. For the latest list of available models, see the Docker Hub tags for the [transformers-inference](https://hub.docker.com/r/semitechnologies/transformers-inference/tags) container. ## 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 `text2vec-transformers` container parameters in your [Weaviate configuration](#weaviate-configuration) - Run the inference container separately, e.g. using Docker, and - Use `TRANSFORMERS_INFERENCE_API` or [`inferenceUrl`](#configure-the-vectorizer) to set the URL of the inference container. For example, run the container with Docker: ```shell 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://text2vec-transformers:8080`. ## Further resources ### Other integrations - [Transformers multi-modal embedding models + Weaviate](transformers-embeddings-multimodal.md). - [Transformers reranker models + Weaviate](transformers-reranker.md). ### 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()](https://github.com/weaviate/text2vec-transformers-models/blob/main/vectorizer.py) 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 collections](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](../how-to-query-search/index.md) 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](https://huggingface.co/models). 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](transformers-embeddings-custom-image.md). ### External resources - Hugging Face [Model Hub](https://huggingface.co/models) ## Questions and feedback Have a question or feedback? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=