Pricing

from $0.40 / 1,000 results

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.

Pricing

from $0.40 / 1,000 results

Rating

0.0

(0)

Developer

Mick

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

Categories

Agents

Automation

You can access the Rag Vector Store Writer programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {}
9
10# Run the Actor and wait for it to finish
11run = client.actor("labrat011/rag-vector-store-writer").call(run_input=run_input)
12
13# Fetch and print Actor results from the run's dataset (if there are any)
14print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
15for item in client.dataset(run["defaultDatasetId"]).iterate_items():
16    print(item)
17
18# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Rag Vector Store Writer API in Python

The Apify API client for Python is the official library that allows you to use Rag Vector Store Writer API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Rag Vector Store Writer API in JavaScript

Rag Vector Store Writer API through CLI

Rag Vector Store Writer OpenAPI definition

Rag Vector Store Writer API

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

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

Qdrant Integration

apify/qdrant-integration

Transfer data from Apify Actors to a Qdrant vector database.

Apify

4.5

Website Content Vector Retriever

hamza.alwan/website-content-vector-retriever

Hamza Alwan

LLM Data Pipeline Pro

sanztheo/llm-data-pipeline-pro

Transform websites into LLM training data. Scrape, validate, deduplicate, chunk for RAG, and export to OpenAI/Anthropic/Mistral formats. Built-in PII detection and GDPR compliance. Vector DB export to Pinecone & Qdrant.

Theo Sanz

Vector Db Migrator

fiery_dream/vector-db-migrator

Migrate embeddings between Pinecone, Weaviate, Chroma, and Qdrant with metadata preservation and validation. No free migration tools exist in the market.

Cody Churchwell

Docs To Rag

gabrielaxy/docs-to-rag

Transform documentation websites into RAG-ready chunks with semantic understanding, quality scoring, and direct vector database integration.

Gabriel Antony Xaviour

OpenAI Vector Store Integration

jiri.spilka/openai-vector-store-integration

This integration uploads data from Apify Actors to the OpenAI Vector Store linked to OpenAI Assistant.

Jiří Spilka

216

4.8

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

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.