Weaviate is a vector database. [Vector databases](https://weaviate.io/blog/what-is-a-vector-database) store data objects and vectors that represent those objects. The vector embedding is also called an "embedding." When you import data, you can pass pre-existing vectors or generate new ones. "Bring your own vectors" means you supply the vector embeddings when you upload your data. The embeddings you provide can be custom vectors or vectors that are pre-generated using a [model provider](../model-provider-integrations/index.md). This guide discusses importing data that has pre-existing vectors. ## Guide steps To use this guide to import data that has pre-existing vectors, follow these steps: 1. [Review the setup steps.](#setup) 2. [Connect to a Weaviate instance.](#connect-to-weaviate) 3. [Import data that specifies its own vectors.](#import-data-and-vectors) 4. [Perform a vector search.](#query) ## Setup This section introduces setup requirements and the example data that this guide uses. ### Example Data The example data is based on a set of ten questions from the "Jeopardy!" television program. The [import data file](https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json) is a JSON formatted document that contains the vectors and the underlying data. :::accordion{title="The sample data"} The JSON file is based on this data. The vector embeddings are generated with the OpenAI API [`text-embedding-ada-002` model](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings). Your vectors can come from any source, including your own vectorizer model or another model provider such as Cohere or Hugging Face. | Category | Question | Answer | Vector | | :------- | :---------------------------------------------------------------------------------------------------------------- | :---------------------- | :-------------------------------------------------- | | SCIENCE | This organ removes excess glucose from the blood & stores it as glycogen | Liver | \[ -0.006632288, -0.0042016874, ..., -0.020163147 ] | | ANIMALS | It's the only living mammal in the order Proboseidea | Elephant | \[ -0.0166891, -0.00092290324, ..., -0.032253385 ] | | ANIMALS | The gavial looks very much like a crocodile except for this bodily feature | the nose or snout | \[ -0.015592773, 0.019883318, ..., 0.0033349802 ] | | ANIMALS | Weighing around a ton, the eland is the largest species of this animal in Africa | Antelope | \[ 0.014535263, -0.016103541, ..., -0.025882969 ] | | ANIMALS | Heaviest of all poisonous snakes is this North American rattlesnake | the diamondback rattler | \[ -0.0030859283, 0.015239313, ..., -0.021798335 ] | | SCIENCE | 2000 news: the Gunnison sage grouse isn't just another northern sage grouse, but a new one of this classification | species | \[ -0.0090561025, 0.011155112, ..., -0.023036297 ] | | SCIENCE | A metal that is "ductile" can be pulled into this while cold & under pressure | wire | \[ -0.02735741, 0.01199829, ..., 0.010396339 ] | | SCIENCE | In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance | DNA | \[ -0.014227471, 0.020493254, ..., -0.0027445166 ] | | SCIENCE | Changes in the tropospheric layer of this are what gives us weather | the atmosphere | \[ 0.009625228, 0.027518686, ..., -0.0068922946 ] | | SCIENCE | In 70-degree air, a plane traveling at about 1,130 feet per second breaks it | Sound barrier | \[ -0.0013459147, 0.0018580769, ..., -0.033439033 ] | ::: ### Weaviate Instance Weaviate is open source. You can [run Weaviate](../installation/index.md) locally, in the cloud, or as a service. If you want to follow this guide without setting up an instance of your own, consider using a free cluster in [Weaviate Cloud](../cloud/quickstart.md). ### Client library [Client libraries](../client-libraries/index.md) simplify working with Weaviate. Clients are available for multiple programming languages. This guide provides examples in Python, TypeScript, Java, C#, and cURL. To install a client library, use the installer for the client language: ::::tabs{sync="languages"} :::tab{title="Python"} The Python client requires Weaviate `v1.23.7` and later. ```bash pip install -U weaviate-client ``` ::: :::tab{title="JavaScript/TypeScript"} ```bash npm install weaviate-client ``` ::: :::tab{title="Java"} ```xml io.weaviate client6 6.2.0 ``` ::: :::tab{title="C#"} ```bash dotnet add package Weaviate.Client --version 1.2.0 ``` ::: :::: ## Connect to Weaviate This guide uses a Weaviate Cloud instance to host the collection. To [connect to a Weaviate Cloud instance](../manage-clusters/connect.md), you need the following information: - The Weaviate URL - The Weaviate API key If you are using a free cluster, the URL and API keys are listed in the [details panel](../manage-clusters/connect.md) for your instance. To connect to Weaviate, run the code for your language to create a client object. Re-use the `client` object in the later steps to connect to your instance and run the sample code. To connect, use the `REST Endpoint` and the `Admin` API key stored as environment variables: :::::tabs{sync="languages"} :::tab{title="Python"} ```python import weaviate from weaviate.classes.init import Auth # Best practice: store your credentials in environment variables weaviate_url = os.environ["WEAVIATE_URL"] weaviate_api_key = os.environ["WEAVIATE_API_KEY"] # Connect to Weaviate Cloud client = weaviate.connect_to_weaviate_cloud( cluster_url=weaviate_url, auth_credentials=Auth.api_key(weaviate_api_key), ) print(client.is_ready()) # Should print: `True` client.close() # Free up resources ``` ::: :::tab{title="JavaScript/TypeScript"} ```typescript // Set these environment variables // WEAVIATE_URL your WCD instance URL // WEAVIATE_API_KEY your WCD instance API key const weaviateURL = process.env.WEAVIATE_URL as string const weaviateKey = process.env.WEAVIATE_API_KEY as string const client: WeaviateClient = await weaviate.connectToWeaviateCloud(weaviateURL, { authCredentials: new weaviate.ApiKey(weaviateKey), } ) ``` ::: ::::tab{title="Go"} ```python // Set these environment variables // WEAVIATE_HOSTNAME Your Weaviate instance hostname // WEAVIATE_API_KEY Your Weaviate instance API key package main import ( "context" "fmt" "os" "github.com/weaviate/weaviate-go-client/v5/weaviate" "github.com/weaviate/weaviate-go-client/v5/weaviate/auth" ) // Create the client func CreateClient() { cfg := weaviate.Config{ Host: os.Getenv("WEAVIATE_HOSTNAME"), Scheme: "https", AuthConfig: auth.ApiKey{Value: os.Getenv("WEAVIATE_API_KEY")}, Headers: nil, } client, err := weaviate.NewClient(cfg) if err != nil { fmt.Println(err) } // Check the connection live, err := client.Misc().LiveChecker().Do(context.Background()) if err != nil { panic(err) } fmt.Printf("%v", live) } func main() { CreateClient() } ``` :::callout{intent="warning"} This client uses the `hostname` parameter (without the `https` scheme) instead of a complete `URL`. ::: :::: :::tab{title="Java"} ```java // Best practice: store your credentials in environment variables String weaviateUrl = System.getenv("WEAVIATE_URL"); String weaviateApiKey = System.getenv("WEAVIATE_API_KEY"); WeaviateClient client = WeaviateClient.connectToWeaviateCloud(weaviateUrl, // Replace with your Weaviate Cloud URL weaviateApiKey // Replace with your Weaviate Cloud key ); System.out.println(client.isReady()); // Should print: `True` client.close(); // Free up resources ``` ::: :::tab{title="C#"} ```csharp // Best practice: store your credentials in environment variables string weaviateUrl = Environment.GetEnvironmentVariable("WEAVIATE_URL"); string weaviateApiKey = Environment.GetEnvironmentVariable("WEAVIATE_API_KEY"); WeaviateClient client = await Connect.Cloud( weaviateUrl, // Replace with your Weaviate Cloud URL weaviateApiKey // Replace with your Weaviate Cloud key ); var isReady = await client.IsReady(); Console.WriteLine(isReady); ``` ::: :::tab{title="cURL"} ```bash # Set these environment variables # WEAVIATE_URL your Weaviate instance URL # WEAVIATE_API_KEY your Weaviate instance API key curl https://${WEAVIATE_URL}/v1/meta -H "Authorization: Bearer ${WEAVIATE_API_KEY}" | jq ``` ::: ::::: ## Collection definition Weaviate stores [data](../concepts/data.md) in collections. Each data object in a collection has a set of properties and a vector embedding. Before you import data, you should create a [collection definition](../reference-configuration/collections.md) to define the data properties for the collection. The data property definitions are called a schema. Vectorizers are defined at the collection level. Additional details, such as the specific model or behavior, are sometimes defined at the property level. This guide assumes you already have vector embeddings for your data. Since the vector embeddings are included with the import file, the sample configuration code sets the vectorizer to `none` on the collection level . For more on configurations, see [vectorizer settings](../how-to-manage-collections/vector-config.md#specify-vectorizer-settings). ### Define the collection :::code-group{sync="languages"} ```python title="Python" import weaviate.classes as wvc # Create the collection. Weaviate's autoschema feature will infer properties when importing. questions = client.collections.create( "Question", vector_config=wvc.config.Configure.Vectors.self_provided(), ) ``` ```typescript title="JavaScript/TypeScript" const newCollection = await client.collections.create({ name: 'JeopardyQuestion', vectorizers: vectors.selfProvided(), }); console.log('We have a new collection!', newCollection['name']); ``` ```java title="Java" // Create the collection. client.collections.create(collectionName, col -> col .properties(Property.text("answer"), Property.text("question"), Property.text("category")) .vectorConfig(VectorConfig.selfProvided())); ``` ```csharp title="C#" // Create the collection. await client.Collections.Create( new CollectionCreateParams { Name = collectionName, Properties = [ Property.Text("answer"), Property.Text("question"), Property.Text("category"), ], // Configure the "default" vector to be SelfProvided (BYOV) VectorConfig = Configure.Vector("default", v => v.SelfProvided()), } ); ``` ```bash title="Curl" # Requiring `bash` above enables process substitution support, used when redirecting the output of jq to the while loop. echo "Downloading the data file..." curl -O -L "https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny+vectors.json" --no-progress-meter # Replace with your Weaviate endpoint and Weaviate Admin API key WEAVIATE_URL=https://WEAVIATE_INSTANCE_URL # Replace WEAVIATE_INSTANCE_URL with your instance URL WEAVIATE_API_KEY=YOUR_WEAVIATE_API_KEY SCHEMA_API_URL="$WEAVIATE_URL/v1/schema" BATCH_API_URL="$WEAVIATE_URL/v1/batch/objects" GRAPHQL_URL="$WEAVIATE_URL/v1/graphql" BATCH_SIZE=100 # Uncomment to delete all Question objects if you see a "Name 'Question' already used" error # curl -X DELETE $SCHEMA_API_URL/Question -H "Authorization: Bearer $WEAVIATE_API_KEY" echo "Creating the schema. Weaviate's autoschema feature will infer class properties when importing..." echo '{ "class": "Question", "vectorizer": "text2vec-openai" }' | curl --no-progress-meter \ -X POST \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $WEAVIATE_API_KEY" \ -d @- \ -o /dev/null \ $SCHEMA_API_URL ``` ::: ### Optional: Set a compatible vectorizer If Weaviate has an [integration](../model-provider-integrations/index.md) for the vectorizer that you use to generate your custom vectors, consider adding your vectorizer to the collection definition. If you specify a vectorizer, Weaviate can generate new vectors when it needs them. In this example, the vectors are generated by the OpenAI `ada-002` model. The Weaviate `text2vec-openai` integration can access the `ada-002` model, so you would specify `text2vec-openai` in the collection definition. At import time, Weaviate uses the vectors you provide even if the collection specifies a vectorizer. ## Import data and vectors For large datasets, batch import is more efficient than importing individual objects. This batch import code imports the question objects and their vectors. :::code-group{sync="languages"} ```python title="Python" import requests fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json" # This file includes pre-generated vectors url = f"https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/{fname}" resp = requests.get(url) data = json.loads(resp.text) # Load data question_objs = list() for i, d in enumerate(data): question_objs.append(wvc.data.DataObject( properties={ "answer": d["Answer"], "question": d["Question"], "category": d["Category"], }, vector=d["vector"] )) questions = client.collections.use("Question") questions.data.insert_many(question_objs) ``` ```typescript title="JavaScript/TypeScript" type JeopardyItem = { Answer: string; Question: string; Category: string; vector: number[], } async function getJsonData(): Promise { const file = await fetch('https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json'); return await file.json(); } async function importQuestionsWithVectors() { // Get the questions directly from the URL const data = await getJsonData(); const jeopardyCollection = client.collections.use('JeopardyQuestion'); let itemsToInsert = new Array() for (const item of data) { // Construct the object to add to insert with insertMany() itemsToInsert.push({ properties: { answer: item.Answer, question: item.Question, category: item.Category, }, vectors: item.vector, }) } const res = await jeopardyCollection.data.insertMany(itemsToInsert) console.log(`Finished importing ${res.allResponses.length} objects.`); } await importQuestionsWithVectors(); ``` ```java title="Java" {25-31} String fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json"; String url = "https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/" + fname; HttpClient httpClient = HttpClient.newHttpClient(); HttpRequest request = HttpRequest.newBuilder().uri(URI.create(url)).build(); HttpResponse responseHttp = httpClient.send(request, HttpResponse.BodyHandlers.ofString()); String responseBody = responseHttp.body(); ObjectMapper objectMapper = new ObjectMapper(); List data = objectMapper.readValue(responseBody, new TypeReference<>() {}); // Get a handle to the collection CollectionHandle> questions = client.collections.use(collectionName); // Declare the list with the specific generic signature required by insertMany List>> questionObjs = new ArrayList<>(); for (JeopardyQuestionWithVector d : data) { Map properties = Map.of("answer", d.answer, "question", d.question, "category", d.category); // Use the explicit Builder to construct the object with a custom vector questionObjs.add(WeaviateObject.of(v -> v.properties(properties) .uuid(UUID.randomUUID().toString()) .vectors(Vectors.of(d.vector)))); } // Pass the correctly typed list directly to insertMany var insertManyResponse = questions.data.insertMany(questionObjs); if (!insertManyResponse.errors().isEmpty()) { System.err.printf("Number of failed imports: %d\n", insertManyResponse.errors().size()); System.err.printf("First failed object error: %s\n", insertManyResponse.errors().get(0)); } ``` ```csharp title="C#" {17-27} var fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json"; var url = $"https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/{fname}"; using var httpClient = new HttpClient(); var responseBody = await httpClient.GetStringAsync(url); var data = JsonSerializer.Deserialize>(responseBody); // Get a handle to the collection var questions = client.Collections.Use(collectionName); // Using Insert in a loop allows explicit vector assignment per object. // Using Task.WhenAll creates a parallel batch-like effect. var insertTasks = data.Select(d => { return questions.Data.Insert( // Pass properties as an Anonymous Type properties: new { answer = d.Answer, question = d.Question, category = d.Category, }, // Explicitly pass the vector vectors: d.Vector ); }); await Task.WhenAll(insertTasks); ``` ::: :::callout{intent="tip" title="Vectors are not object properties"} If a vector is listed in the properties section, Weaviate processes it as a regular property rather than as a vector embedding. Be careful not to mislabel vectors as properties when you specify the elements of your batch import. ::: ## Query In order to query your stored vectors, Weaviate needs a vector embedding of the query. - If the collection definition specifies a vectorizer, Weaviate uses that vectorizer to generate a vector embedding. - If the collection doesn't specify a vectorizer, provide a query vector when you search. When you create a query vector, use the same vectorizer that you use to create the data embeddings. This [`nearVector`](../how-to-query-search/similarity.md#search-with-a-vector) query supplies a query vector. The query vector is an embedding of the query string, "biology". :::code-group{sync="languages"} ```python title="Python" query_vector = 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import time time.sleep(2) # Sleep so we don't query before async indexing finishes response = questions.query.near_vector( near_vector=query_vector, limit=2, return_metadata=wvc.query.MetadataQuery(certainty=True) ) print(response) ``` ```typescript title="JavaScript/TypeScript" const jeopardyCollection = client.collections.use('JeopardyQuestion'); const weaviateVector = 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const result = await jeopardyCollection.query.nearVector(weaviateVector, { limit: 2, returnProperties: ['question', 'answer', 'category'], }) console.log(result.objects) ``` ```java title="Java" float[] queryVector = data.get(0).vector; // Use a vector from the dataset for a reliable query // Added a small sleep to ensure indexing is complete Thread.sleep(2000); var response = questions.query.nearVector(queryVector, q -> q.limit(2).returnMetadata(Metadata.CERTAINTY)); System.out.println(response); ``` ```csharp title="C#" var queryVector = data[0].Vector; // Use a vector from the dataset for a reliable query // Added a small delay to ensure indexing is complete await Task.Delay(2000); var response = await questions.Query.NearVector( queryVector, limit: 2, returnMetadata: MetadataOptions.Certainty ); Console.WriteLine(JsonSerializer.Serialize(response.Objects)); ``` ```bash title="Curl" echo '{ "query": "{ Get { Question( nearVector: { vector: 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} limit: 2 ) { question answer category _additional { certainty distance } } } }" }' | curl --no-progress-meter \ -X POST \ -H 'Content-Type: application/json' \ -H "Authorization: Bearer $WEAVIATE_API_KEY" \ -d @- \ $GRAPHQL_URL \ | jq ``` ::: :::accordion{title="Query response"} The response is like this: ```json { "data": { "Get": { "Question": [ { "answer": "DNA", "category": "SCIENCE", "question": "In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance" }, { "answer": "Liver", "category": "SCIENCE", "question": "This organ removes excess glucose from the blood & stores it as glycogen" } ] } } } ``` ::: ## Related pages - [Batch import](../how-to-manage-objects/import.md) - [Manage collections](../how-to-manage-collections/index.md) - [Vector similarity search](../how-to-query-search/similarity.md) ## 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=