Pricing

Pay per usage

Vector Embeddings Generator

Turn any text into semantic embedding vectors — perfect for search, similarity matching, clustering, and recommendations. Just feed your texts as JSON or a URL and get 768-dimensional vectors back. Powered by nomic-embed-text-v1.5 with 8K token context. No GPU needed.

Pricing

Pay per usage

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Developer

Matej Hamas

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Monthly active users

2 days ago

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Vector Embeddings Generator API

Below, you can find a list of relevant HTTP API endpoints for calling the Vector Embeddings Generator Actor. For this, you’ll need an Apify account. Replace <YOUR_API_TOKEN> in the URLs with your Apify API token, which you can find under Integrations in Apify Console. For details, see the API reference.

Run Actor

POST
https://api.apify.com/v2/acts/mhamas~vector-embeddings-generator/runs?token=<YOUR_API_TOKEN>

Note: By adding the method=POST query parameter, this API endpoint can be called using a GET request and thus used in third-party webhooks. Please refer to our Run Actor API documentation.

Run Actor synchronously and get dataset items

POST
https://api.apify.com/v2/acts/mhamas~vector-embeddings-generator/run-sync-get-dataset-items?token=<YOUR_API_TOKEN>

Note: This endpoint supports both POST and GET request methods. However, only the POST method allows you to pass input data. For more information, please refer to our Run Actor synchronously and get dataset items API documentation.

Get Actor

GET
https://api.apify.com/v2/acts/mhamas~vector-embeddings-generator?token=<YOUR_API_TOKEN>

For more information, please refer to our Get Actor API documentation.

Actors can be used to scrape web pages, extract data, or automate browser tasks. Use the Vector Embeddings Generator API programmatically via the Apify API.

You can choose from:

Vector Embeddings Generator API in Python

Vector Embeddings Generator API in JavaScript

Vector Embeddings Generator API through CLI

Vector Embeddings Generator OpenAPI definition

You can start Vector Embeddings Generator with the Apify API by sending an HTTP POST request to the Run Actorendpoint. An Actor’s input and its content type can be passed as a payload of the POST request, and additional options can be specified using URL query parameters. The Vector Embeddings Generator is identified within the API by its ID, which is the creator’s username and the name of the Actor.

When the Vector Embeddings Generator run finishes you can list the data from its default dataset(storage) via the API or you can preview the data directly on Apify Console.

Rag Vector Store Writer

labrat011/rag-vector-store-writer

Apify Actor that writes embedding vectors to Pinecone or Qdrant vector databases. Chains directly with RAG Embedding Generator output or accepts raw vectors with metadata. Handles batching, retries, collection creation, metadata mapping, and ID generation. Bring your own vector DB API key.

mick_

Rag Embedding Generator

labrat011/rag-embedding-generator

Generate vector embeddings from text or chunked datasets using OpenAI or Cohere. Chains with RAG Content Chunker for end-to-end RAG pipelines. Outputs raw vectors ready for any vector database.

mick_

Similarity Graph From Embeddings

mhamas/similarity-graph-from-embeddings

Builds a similarity graph from vector embeddings. Fetches vectors from URLs, computes pairwise cosine similarities using optimized linear algebra, and connects each point to its K nearest neighbors - revealing hidden clusters and relationships in your high-dimensional data.

Matej Hamas

Vector Loader — Document Embedding & Vector DB Ingestion

apricot_blackberry/vector-loader

Documents → embeddings → vector DB. Chunking, embedding generation, and ingestion for Pinecone, Weaviate, or Chroma.

Creator Fusion

Product Matching Vectorizer

tri_angle/product-matching-vectorizer

Builds a FAISS vector database from products in an Apify dataset using an ONNX embedding model. The resulting index is saved to a Key-Value Store for fast similarity search. After uploading your dataset to the vector database, use our E-commerce Product Matching Tool to find matching products.

Tri⟁angle

RAG Pipeline

labrat011/rag-pipeline

One-click RAG pipeline: chunks text, generates embeddings, and stores vectors in Pinecone or Qdrant. Provide your content and API keys -- the orchestrator handles the rest.

mick_

UUID Generator

apizy/uuid-generator

Generate UUID v1, v3, v4, v5 instantly. Perfect for test data, unique IDs, database seeding, and development workflows. Choose random v4 (most common), time-based v1, or deterministic v3/v5. Customize hyphen format. Export results via Dataset or API. Fast, no-code tool with scheduling and monitoring

Apizy

Rag Content Chunker

labrat011/rag-content-chunker

Turn raw text, Markdown, or Apify datasets into token-perfect RAG chunks with deterministic IDs, source metadata, and a billing-ready summary—ready for embeddings or vector DBs without extra glue code.

mick_

Docs to Markdown + AI Embeddings → Vector DB Crawler

badruddeen/docs-to-markdown-ai-embeddings---vector-db-crawler

Turn any documentation site into clean Markdown, intelligently chunked content with embeddings (Azure/OpenAI), and directly upsert into MongoDB Atlas, Pinecone, Weaviate, Qdrant, or Milvus — ready for RAG, AI assistants, and semantic search in minutes.