# Dutch Real Estate Vector Search API (Funda, VBO, Huispedia,..) (`ramin332/vector-api`) Actor Semantic vector search over Dutch property listings (Funda, VBO, Huispedia and Pararius). Drop-in Qdrant-compatible API: point qdrant-client at it, send a query vector, get scoped, ranked matches. Build AI property search, RAG, and recommendations. Get an API key at portal.househugger.nl. - **URL**: https://apify.com/ramin332/vector-api.md - **Developed by:** [Ramin Ariana](https://apify.com/ramin332) (community) - **Categories:** Real estate, AI, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Dutch Real Estate Vector Search API — Funda, Vastgoed Nederland & Huispedia listings, AI semantic search Semantic **vector search over Dutch property listings** (Funda, Vastgoed Nederland, Huispedia, and more), served as a drop-in **Qdrant-compatible API**. Point the official `qdrant-client` at this endpoint, send a query vector, and get back the most similar `woningen` — ranked by meaning, not keywords. Built for developers shipping **AI property search, RAG, and recommendation** features over the Netherlands housing market. > Powered by [Househugger](https://portal.househugger.nl). Same infrastructure that runs > in production — this is a hosted, production endpoint you can call today. ### What you get - **Semantic / vector search** over Dutch real estate listings — embeddings-based similarity, so "licht appartement met tuin bij het water" matches on *intent*, not exact words. - **Qdrant-native** — use the real `qdrant-client` (Python/JS/Rust) unchanged. No new SDK to learn. `search`, `query`, `scroll`, retrieve-by-id all supported. - **Scoped & safe** — every request is filtered to exactly the data slice your key is entitled to. You can narrow further; you can never see another tenant's data. - **Fast, read-optimized** — a thin proxy over a managed Qdrant Cloud collection. ### Use cases - AI-powered property search & discovery for a portal or app - Retrieval-Augmented Generation (RAG) over housing data for an LLM assistant - "Similar homes" / recommendation engines - Lead enrichment and market analysis over Funda / Vastgoed Nederland listings ### How to call it This runs in **Standby mode** — a always-on HTTP server, not a batch job. Authenticate with your API key and use `qdrant-client` as usual: ```python from qdrant_client import QdrantClient client = QdrantClient( url="https://vectorapi.househugger.nl", api_key="", # sent as the api-key header ) hits = client.query_points( collection_name="vectors", query=my_query_vector, # bring your own embedding limit=10, ).points ```` You bring the query vector (embed with any model you like); the API returns scoped, ranked matches. Writes and admin calls are rejected — this endpoint is **read-only** by design. ### Get access You need a Househugger API key. It's the real gate — calls without a valid key return `401`. 👉 **[Sign up & get your API key at portal.househugger.nl](https://portal.househugger.nl)** Pick a plan, self-serve a key, and start querying in minutes. ### Notes - **Does:** forward semantic reads (search / query / scroll / retrieve-by-id) with your entitlement enforced on every filter and prefetch. - **Does not:** embed text for you, ingest, upsert, or expose data outside your slice. *** *Keywords: Dutch real estate API, Funda data, Vastgoed Nederland, Huispedia, Netherlands property search, woning zoeken, vector database, Qdrant, semantic search, embeddings, RAG, AI real estate, huizen API.* # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ramin332/vector-api").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("ramin332/vector-api").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call ramin332/vector-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ramin332/vector-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dutch Real Estate Vector Search API (Funda, VBO, Huispedia,..)", "description": "Semantic vector search over Dutch property listings (Funda, VBO, Huispedia and Pararius). Drop-in Qdrant-compatible API: point qdrant-client at it, send a query vector, get scoped, ranked matches. Build AI property search, RAG, and recommendations. Get an API key at portal.househugger.nl.", "version": "0.1", "x-build-id": "BHw7P6WyYyrThD46R" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ramin332~vector-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ramin332-vector-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ramin332~vector-api/runs": { "post": { "operationId": "runs-sync-ramin332-vector-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ramin332~vector-api/run-sync": { "post": { "operationId": "run-sync-ramin332-vector-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": {} }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```