Weaviate's integration with [Google Gemini API](https://ai.google.dev/?utm_source=weaviate\&utm_medium=referral\&utm_campaign=partnerships\&utm_content=) and [Google Vertex AI](https://cloud.google.com/vertex-ai) APIs allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate vector index](#configure-the-vectorizer) to use a Google embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Google API key. 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_google_embedding.png) :::callout{intent="tip" title="Which Google service should I use?"} - **Google AI Studio (Gemini API)**: Simpler setup, ideal for prototyping and development. Get started quickly with just an API key. - **Vertex AI**: Enterprise-grade service with more features, better for production deployments with advanced requirements. ::: :::callout{intent="info" title="Gemini API availability"} Gemini API is not available in all regions. See [this page](https://ai.google.dev/gemini-api/docs/available-regions) for the latest information. ::: ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the Google vectorizer integration (`text2vec-google`) module. :::callout{intent="info" title="Module name change"} `text2vec-google` was called `text2vec-palm` in Weaviate versions prior to `v1.27`. ::: :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is enabled by default on Weaviate Cloud (WCD) instances. ::: :::accordion{title="For self-hosted users"} - 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. ::: ### API credentials You must provide valid API credentials to Weaviate for the appropriate integration. #### Google AI Studio (Gemini API) 1. Go to [Google AI Studio](https://aistudio.google.com/app/apikey/?utm_source=weaviate\&utm_medium=referral\&utm_campaign=partnerships\&utm_content=) 2. In the "API Keys" section create a new API key 3. Use the `X-Goog-Studio-Api-Key` header to provide your API key to Weaviate #### Vertex AI This is called an `access token` in Google Cloud. ##### Automatic token generation :::callout{intent="info" title="Not available on Weaviate Cloud instances"} This feature is not available on Weaviate cloud instances. ::: You can save your Google Vertex AI credentials and have Weaviate generate the necessary tokens for you. This enables use of IAM service accounts in private deployments that can hold Google credentials. To do so: - Set `USE_GOOGLE_AUTH` [environment variable](../database-configuration/overview.md#module-specific) to `true`. - Have the credentials available in one of the following locations. Once appropriate credentials are found, Weaviate uses them to generate an access token and authenticates itself against Vertex AI. Upon token expiry, Weaviate generates a replacement access token. In a containerized environment, you can mount the credentials file to the container. For example, you can mount the credentials file to the `/etc/weaviate/` directory and set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to `/etc/weaviate/google_credentials.json`. :::accordion{title="Search locations for Google Vertex AI credentials"} Once `USE_GOOGLE_AUTH` is set to `true`, Weaviate will look for credentials in the following places, preferring the first location found: 1. A JSON file whose path is specified by the `GOOGLE_APPLICATION_CREDENTIALS` environment variable. For workload identity federation, refer to [this link](https://cloud.google.com/iam/docs/how-to#using-workload-identity-federation) on how to generate the JSON configuration file for on-prem/non-Google cloud platforms. 2. A JSON file in a location known to the `gcloud` command-line tool. On Windows, this is `%APPDATA%/gcloud/application_default_credentials.json`. On other systems, `$HOME/.config/gcloud/application_default_credentials.json`. 3. On Google App Engine standard first generation runtimes (<= Go 1.9) it uses the appengine.AccessToken function. 4. On Google Compute Engine, Google App Engine standard second generation runtimes (>= Go 1.11), and Google App Engine flexible environment, it fetches credentials from the metadata server. ::: If you have the [Google Cloud CLI tool](https://cloud.google.com/cli) installed and set up, you can view your token by running the following command: ```shell gcloud auth print-access-token ``` ##### Token expiry for Vertex AI users :::callout{intent="warning" title="Important"} ::: By default, Google Cloud's OAuth 2.0 access tokens have a lifetime of 1 hour. You can create tokens that last up to 12 hours. To create longer lasting tokens, follow the instructions in the [Google Cloud IAM Guide](https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#rest_2). Since the OAuth token is only valid for a limited time, you **must** periodically replace the token with a new one. After you generate the new token, you have to re-instantiate your Weaviate client to use it. You can update the OAuth token manually, but manual updates may not be appropriate for your use case. You can also automate the OAth token update. Weaviate does not control the OAth token update procedure. However, here are some automation options: :::accordion{title="With Google Cloud CLI"} If you are using the Google Cloud CLI, write a script to periodically update the token and extract the results. Python code to extract the token looks like this: ```python client = re_instantiate_weaviate() ``` This is the `re_instantiate_weaviate` function: ```python import subprocess import weaviate def refresh_token() -> str: result = subprocess.run(["gcloud", "auth", "print-access-token"], capture_output=True, text=True) if result.returncode != 0: print(f"Error refreshing token: {result.stderr}") return None return result.stdout.strip() def re_instantiate_weaviate() -> weaviate.Client: token = refresh_token() client = weaviate.Client( url = "https://WEAVIATE_INSTANCE_URL", # Replace WEAVIATE_INSTANCE_URL with the URL additional_headers = { "X-Goog-Vertex-Api-Key": token, } ) return client # Run this every ~60 minutes client = re_instantiate_weaviate() ``` ::: :::accordion{title="With google-auth"} Another way is through Google's own authentication library `google-auth`. See the links to `google-auth` in [Python](https://google-auth.readthedocs.io/en/master/index.html) and [Node.js](https://cloud.google.com/nodejs/docs/reference/google-auth-library/latest) libraries. You can, then, periodically the `refresh` function ([see Python docs](https://google-auth.readthedocs.io/en/master/reference/google.oauth2.service_account.html#google.oauth2.service_account.Credentials.refresh)) to obtain a renewed token, and re-instantiate the Weaviate client. For example, you could periodically run: ```python client = re_instantiate_weaviate() ``` Where `re_instantiate_weaviate` is something like: ```python from google.auth.transport.requests import Request from google.oauth2.service_account import Credentials import weaviate import os def get_credentials() -> Credentials: credentials = Credentials.from_service_account_file( "path/to/your/service-account.json", scopes=[ "https://www.googleapis.com/auth/generative-language", "https://www.googleapis.com/auth/cloud-platform", ], ) request = Request() credentials.refresh(request) return credentials def re_instantiate_weaviate() -> weaviate.Client: from weaviate.classes.init import Auth weaviate_api_key = os.environ["WEAVIATE_API_KEY"] credentials = get_credentials() token = credentials.token client = weaviate.connect_to_weaviate_cloud( # e.g. if you use the Weaviate Cloud Service cluster_url="https://WEAVIATE_INSTANCE_URL", # Replace WEAVIATE_INSTANCE_URL with the URL auth_credentials=Auth.api_key(weaviate_api_key), # Replace with your Weaviate Cloud key headers={ "X-Goog-Vertex-Api-Key": token, }, ) return client # Run this every ~60 minutes client = re_instantiate_weaviate() ``` The service account key shown above can be generated by following [this guide](https://cloud.google.com/iam/docs/keys-create-delete). ::: #### Provide the API key Provide the API key to Weaviate at runtime, as shown in the examples below. Note the separate headers that are available for [Google AI Studio (Gemini API)](#google-ai-studio-gemini-api) and [Vertex AI](#vertex-ai) users. :::accordion{title="API key headers"} From `v1.27.7`, `v1.26.12` and `v1.25.27`, `X-Goog-Vertex-Api-Key` and `X-Goog-Studio-Api-Key` headers are supported for Vertex AI users and Gemini API respectively. We recommend these headers for highest compatibility. Consider `X-Google-Vertex-Api-Key`, `X-Google-Studio-Api-Key`, `X-Google-Api-Key` and `X-PaLM-Api-Key` deprecated. ::: :::code-group{sync="languages"} ```python title="Python" # Recommended: save sensitive data as environment variables vertex_key = os.getenv("VERTEX_API_KEY") studio_key = os.getenv("STUDIO_API_KEY") ``` ```typescript title="JavaScript/TypeScript" const vertexApiKey = process.env.VERTEX_API_KEY || ''; // Replace with your inference API key const studioApiKey = process.env.STUDIO_API_KEY || ''; // Replace with your inference API key ``` ```goraw title="Go" "X-Goog-Vertex-Key": os.Getenv("VERTEX_API_KEY"), "X-Goog-Studio-Key": os.Getenv("STUDIO_API_KEY"), ``` ::: ## Configure the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use a Google embedding model: :::callout{intent="info" title="Important: Different vectorizers for different services"} - **Google AI Studio (Gemini API)**: Use `text2vec_google_gemini()` - **Vertex AI**: Use `text2vec_google()` ::: You can [specify](#vectorizer-parameters) one of the [available models](#available-models) for Weaviate to use. The [default model](#available-models) is used if no model is specified. ### Google AI Studio (Gemini API) For Google AI Studio, use the Gemini-specific vectorizer. A Google Cloud project ID is not required. The Python and TypeScript clients set the Gemini API endpoint for you. :::code-group{sync="languages"} ```python title="Python" {5-10} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=Configure.Vectors.text2vec_google_gemini( name="title_vector", source_properties=["title"], # (Optional) To manually set the model ID model="gemini-embedding-2" ), # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-16} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecGoogleGemini({ name: 'title_vector', sourceProperties: ['title'], // (Optional) To manually set the model ID model: 'gemini-embedding-2' }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-21} // Define the collection basicGoogleStudioVectorizerDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-google": map[string]interface{}{ "properties": []string{"title"}, "apiEndpoint": "generativelanguage.googleapis.com", "modelId": "gemini-embedding-001", // (Optional) To manually set the model ID }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(basicGoogleStudioVectorizerDef).Do(ctx) if err != nil { panic(err) } ``` ::: ### Vertex AI For Vertex AI, use the `text2vec_google()` vectorizer. You must provide your Google Cloud `project_id`. :::code-group{sync="languages"} ```python title="Python" {5-11} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=Configure.Vectors.text2vec_google( name="title_vector", source_properties=["title"], project_id="", # Required for Vertex AI # (Optional) To manually set the model ID model="gemini-embedding-2" ), # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-17} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecGoogle({ name: 'title_vector', sourceProperties: ['title'], projectId: '', // (Optional) To manually set the model ID model: 'gemini-embedding-2' }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-20} // Define the collection basicGoogleVertexVectorizerDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-google": map[string]interface{}{ "projectId": "", "modelId": "gemini-embedding-001", // (Optional) To manually set the model ID }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(basicGoogleVertexVectorizerDef).Do(ctx) if err != nil { panic(err) } ``` ::: :::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 Google-specific options. **Google AI Studio (Gemini API) parameters:** - `modelId` (Optional): e.g. `gemini-embedding-001` **Vertex AI parameters:** - `projectId` (Required): Your Google Cloud project ID, e.g. `cloud-large-language-models` - `location` (Optional): The Google Cloud region to send requests to, e.g. `europe-west1`. - `apiEndpoint` (Optional): Regional endpoint, e.g. `us-central1-aiplatform.googleapis.com` - `modelId` (Optional): e.g. `gemini-embedding-001`, `text-embedding-005` Set `location` together with a matching `apiEndpoint` to keep data in a specific region. :::code-group{sync="languages"} ```python title="Python" {6-14,24-29} from weaviate.classes.config import Configure # For Vertex AI client.collections.create( "DemoCollection", vector_config=Configure.Vectors.text2vec_google_vertex( name="title_vector", source_properties=["title"], project_id="", # Required for Vertex AI # Further options # model="", # location="", # api_endpoint="", ), # Additional parameters not shown ) # clean up client.collections.delete("DemoCollection") # For Google AI Studio (Gemini API) client.collections.create( "DemoCollection", vector_config=Configure.Vectors.text2vec_google_gemini( name="title_vector", source_properties=["title"], # Further options model="gemini-embedding-2", ), # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-18} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecGoogle({ name: 'title_vector', sourceProperties: ['title'], projectId: '', // Required for Vertex AI // modelId: '', // apiEndpoint: '', // vectorizeClassName: false, }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-22} // Define the collection googleVectorizerFullDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-google": map[string]interface{}{ "properties": []string{"title"}, "projectId": "", // Required for Vertex AI "modelId": "textembedding-gecko@latest", // (Optional) To manually set the model ID "apiEndpoint": "", // (Optional) To manually set the API endpoint }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(googleVectorizerFullDef).Do(ctx) if err != nil { panic(err) } ``` ::: ## 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." } ]; ``` ```goraw title="Go" {9-44} var sourceObjects = []map[string]string{ {"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."}, } // Convert items into a slice of models.Object objects := []models.PropertySchema{} for i := range sourceObjects { objects = append(objects, map[string]interface{}{ // Populate the object with the data "title": sourceObjects[i]["title"], "description": sourceObjects[i]["description"], }) } // Batch write items batcher := client.Batch().ObjectsBatcher() for _, dataObj := range objects { batcher.WithObjects(&models.Object{ Class: "DemoCollection", Properties: dataObj, }) } // Flush batchRes, err := batcher.Do(ctx) // Error handling if err != nil { panic(err) } for _, res := range batchRes { if res.Result.Errors != nil { for _, err := range res.Result.Errors.Error { if err != nil { fmt.Printf("Error details: %v\n", *err) panic(err.Message) } } } } ``` ::: :::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 specified Google model. ![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_google_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) ``` ```goraw title="Go" {1-9} nearTextResponse, err := client.GraphQL().Get(). WithClassName("DemoCollection"). WithFields( graphql.Field{Name: "title"}, ). WithNearText(client.GraphQL().NearTextArgBuilder(). WithConcepts([]string{"A holiday film"})). WithLimit(2). Do(ctx) if err != nil { panic(err) } fmt.Printf("%v", nearTextResponse) ``` ::: ### 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) ``` ```goraw title="Go" {1-9} hybridResponse, err := client.GraphQL().Get(). WithClassName("DemoCollection"). WithFields( graphql.Field{Name: "title"}, ). WithHybrid(client.GraphQL().HybridArgumentBuilder(). WithQuery("A holiday film")). WithLimit(2). Do(ctx) if err != nil { panic(err) } fmt.Printf("%v", hybridResponse) ``` ::: ## References ### Available models **Google AI Studio (Gemini API):** - `gemini-embedding-2` - `gemini-embedding-001` (default) **Vertex AI:** `gemini-embedding-001`, `text-embedding-005`, and `text-multilingual-embedding-002` were added in `v1.31.5`, and backported to `v1.30.11`. - `gemini-embedding-2` - `gemini-embedding-001` (default) - `text-embedding-005` - `text-multilingual-embedding-002` :::accordion{title="Deprecated models"} The following models have been deprecated by Google and are no longer supported. They may not function as expected. - `text-embedding-004` - `embedding-001` - `textembedding-gecko@001` - `textembedding-gecko@002` - `textembedding-gecko@003` - `textembedding-gecko@latest` - `textembedding-gecko-multilingual@001` - `textembedding-gecko-multilingual@latest` - `text-embedding-preview-0409` - `text-multilingual-embedding-preview-0409` ::: ## Further resources ### Other integrations - [Google generative models + Weaviate](google-generative.md). ### 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. ### External resources - [Google Vertex AI](https://cloud.google.com/vertex-ai) - [Google Gemini API](https://ai.google.dev/?utm_source=weaviate\&utm_medium=referral\&utm_campaign=partnerships\&utm_content=) ## 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=