# Fuzzy Search Dataset Actor (`dtrungtin/fuzzy-search-dataset-actor`) Actor Search any Apify dataset using typo-tolerant fuzzy matching. - **URL**: https://apify.com/dtrungtin/fuzzy-search-dataset-actor.md - **Developed by:** [Tin](https://apify.com/dtrungtin) (community) - **Categories:** Other, Developer tools - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.001 / actor start This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## Fuzzy Search Dataset Actor **Search any Apify dataset using typo-tolerant fuzzy matching.** Point this Actor at an existing dataset, provide a query, and get back ranked results — even when spellings are imperfect. Try it directly in [Apify Console](https://console.apify.com/). ### What does Fuzzy Search Dataset do? This Actor loads records from any Apify dataset and runs a fuzzy full-text search across one or more fields. It handles typos, partial matches, and word-order variations automatically. For example, searching `"iphon pro mx"` can still return results for `"iPhone 15 Pro Max"`. It's ideal for post-processing scraped data — after collecting a large dataset with another Actor, use this one to instantly build a search layer on top of it without any external infrastructure. ### Why use Fuzzy Search Dataset? - **No search engine needed** — works directly on any Apify dataset without Elasticsearch, Algolia, or similar tools - **Typo-tolerant** — handles misspellings, abbreviations, and partial queries out of the box - **Multi-field search** — search across `title`, `description`, `brand`, nested fields like `product.name`, or any combination - **Tunable relevance** — control strictness, field weights, and minimum match length to fit your data - **Automation-ready** — trigger via API, schedule it, or chain it after a scraping Actor in a workflow ### How to use Fuzzy Search Dataset 1. **Run a scraping Actor** to build the source dataset (or use any existing dataset you already have in Apify Console). 2. **Copy the dataset ID** from the dataset URL or the Storage section of Apify Console. 3. **Open this Actor** and paste the dataset ID into the **Dataset ID** field. 4. **Enter your search query** and choose which fields to search. 5. **Run the Actor** — results are written to its output dataset, ranked by relevance score. ### Input Configure the Actor in the **Input** tab or pass a JSON object via the API. | Field | Type | Required | Default | Description | |---|---|---|---|---| | `datasetId` | string | ✅ | — | ID of the Apify dataset to search | | `query` | string | ✅ | — | Text to search for | | `fields` | array of strings | | `["title"]` | Dataset fields to search (supports dot notation for nested fields) | | `limit` | integer | | `20` | Maximum number of results to return (1–1000) | | `threshold` | number | | `0.35` | Fuzzy strictness — `0.0` = exact only, `1.0` = match anything | | `ignoreLocation` | boolean | | `true` | Allow matches anywhere in the text, not just at the start | | `minMatchCharLength` | integer | | `2` | Minimum characters in a token before it counts as a match | | `includeScore` | boolean | | `true` | Attach a relevance score to each result (lower = better match) | | `includeMatches` | boolean | | `false` | Include matched text ranges — useful for keyword highlighting | | `extendedSearch` | boolean | | `false` | Enable advanced query syntax (`^starts-with`, `!exclude`, `=exact`) | | `weights` | object | | — | Per-field importance weights, e.g. `{"title": 0.7, "description": 0.3}` | **Example input:** ```json { "datasetId": "UoYaa1QjGdgdJrSHA", "query": "iphon pro max", "fields": ["title", "description"], "limit": 10, "threshold": 0.35, "weights": { "title": 0.8, "description": 0.2 } } ```` ### Output Results are pushed to the Actor's default dataset as a single object containing the query, total result count, and an array of ranked matches. **Example output:** ```json { "query": "iphon pro max", "totalResults": 3, "results": [ { "rank": 1, "score": 0.04, "item": { "title": "Apple iPhone 15 Pro Max", "description": "6.7-inch Super Retina XDR display, A17 Pro chip", "brand": "Apple", "price": 1199 } }, { "rank": 2, "score": 0.18, "item": { "title": "iPhone 14 Pro Max", "description": "48MP main camera, Dynamic Island", "brand": "Apple", "price": 999 } } ] } ``` You can download results in JSON, CSV, Excel, or HTML from the dataset tab in Apify Console or via the [Apify API](https://docs.apify.com/api/v2). ### Output data fields | Field | Format | Description | |---|---|---| | `query` | text | The search query that was used | | `totalResults` | number | Number of results returned | | `results[].rank` | number | 1-based position in results (1 = best match) | | `results[].score` | number | Relevance score (0.0 = perfect match, 1.0 = no match) | | `results[].item` | object | Full original record from the source dataset | ### Tips and advanced options **Tuning the threshold:** - `0.2` — strict, good for product codes and exact names - `0.35` — balanced (default), works well for product titles and descriptions - `0.6` — loose, useful for free-text fields or short queries **Multi-field search with weights:** To boost title matches above description matches, set `weights` to `{"title": 0.8, "description": 0.2}`. Weights must sum to 1.0 across all searched fields. **Advanced query syntax** (when `extendedSearch` is enabled): | Syntax | Meaning | Example | |---|---|---| | `=iphone` | Exact match | `=iPhone 15` | | `^apple` | Starts with | `^Apple` | | `!samsung` | Exclude | `!Samsung` | | `'pro` | Includes token | `'pro` | **Performance:** The Actor loads the entire dataset into memory. For datasets over 100k records, consider filtering the source dataset first or increasing the Actor's memory allocation. ### Pricing / Cost estimation This Actor processes data in-memory without using a browser or proxy, so compute costs are low. A typical run over 10,000 records completes in under 30 seconds. Apify provides a [free tier](https://apify.com/pricing) sufficient for many use cases. ### FAQ and support **Is this legal?** This Actor only reads from datasets you own or have access to on the Apify platform. It does not scrape any external websites. **Can I use this with datasets from other Actors?** Yes — as long as you have access to the dataset ID, this Actor can read it. **The results are empty or not what I expected.** Try raising the `threshold` value (e.g. to `0.5`), enabling `ignoreLocation`, or adding more fields to the `fields` array. **Found a bug or want a feature?** Open an issue in the Issues tab on this Actor's page. Custom solutions are also available — reach out via the Apify platform. ### Resources - [Apify SDK documentation](https://docs.apify.com/sdk/js) - [Apify Platform documentation](https://docs.apify.com/platform) - [Join the Apify developer community on Discord](https://discord.com/invite/jyEM2PRvMU) # Actor input Schema ## `datasetId` (type: `string`): The Apify dataset ID that contains the records you want to search. You can find this in the dataset URL or in Apify Console. Example: 'abc123DEF456'. ## `query` (type: `string`): The text users want to search for. Fuzzy search allows typos and partial matches. Example: searching for 'iphon pro' can still match 'iPhone 15 Pro Max'. ## `fields` (type: `array`): List of dataset fields to search in. You can search multiple fields at the same time. Examples: 'title', 'description', 'brand', 'category', or nested fields like 'product.name'. ## `limit` (type: `integer`): Maximum number of search results returned. Lower values improve performance. Recommended: 10 to 100. ## `threshold` (type: `number`): Controls how strict the fuzzy search is. Lower values require more accurate matches. Higher values allow more typos and loose matching. Recommended values: 0.2 = strict, 0.35 = balanced, 0.6 = very loose. ## `ignoreLocation` (type: `boolean`): When enabled, matches can appear anywhere inside the text. Example: searching 'pro' can match both 'iPhone Pro' and 'Professional Camera'. Recommended: enabled. ## `minMatchCharLength` (type: `integer`): Minimum number of characters required before fuzzy considers a match. Helps reduce noisy results for very short queries. Recommended: 2 or 3. ## `includeScore` (type: `boolean`): Adds a relevance score to each result. Lower scores mean better matches. Useful for debugging, sorting, or displaying search confidence. ## `includeMatches` (type: `boolean`): Returns detailed information about which text fragments matched the query. Useful for highlighting matched keywords in a frontend application. ## `extendedSearch` (type: `boolean`): Enables Fuzzy advanced query syntax. Examples: '^apple' = starts with apple, '!samsung' = exclude samsung, '=iphone' = exact match. Recommended for advanced users only. ## `weights` (type: `object`): Optional JSON object that controls which fields are more important during ranking. Higher weight means higher priority in search results. Example: {"title": 0.7, "description": 0.2, "brand": 0.1} ## Actor input object example ```json { "fields": [ "title" ], "limit": 20, "threshold": 0.35, "ignoreLocation": true, "minMatchCharLength": 2, "includeScore": true, "includeMatches": false, "extendedSearch": false } ``` # 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("dtrungtin/fuzzy-search-dataset-actor").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("dtrungtin/fuzzy-search-dataset-actor").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 dtrungtin/fuzzy-search-dataset-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dtrungtin/fuzzy-search-dataset-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fuzzy Search Dataset Actor", "description": "Search any Apify dataset using typo-tolerant fuzzy matching.", "version": "0.0", "x-build-id": "6Me8viIBc2t2TyVjk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dtrungtin~fuzzy-search-dataset-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dtrungtin-fuzzy-search-dataset-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dtrungtin~fuzzy-search-dataset-actor/runs": { "post": { "operationId": "runs-sync-dtrungtin-fuzzy-search-dataset-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dtrungtin~fuzzy-search-dataset-actor/run-sync": { "post": { "operationId": "run-sync-dtrungtin-fuzzy-search-dataset-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "datasetId", "query" ], "properties": { "datasetId": { "title": "Dataset ID", "type": "string", "description": "The Apify dataset ID that contains the records you want to search. You can find this in the dataset URL or in Apify Console. Example: 'abc123DEF456'." }, "query": { "title": "Search Query", "type": "string", "description": "The text users want to search for. Fuzzy search allows typos and partial matches. Example: searching for 'iphon pro' can still match 'iPhone 15 Pro Max'." }, "fields": { "title": "Search Fields", "type": "array", "description": "List of dataset fields to search in. You can search multiple fields at the same time. Examples: 'title', 'description', 'brand', 'category', or nested fields like 'product.name'.", "items": { "type": "string" }, "default": [ "title" ] }, "limit": { "title": "Maximum Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of search results returned. Lower values improve performance. Recommended: 10 to 100.", "default": 20 }, "threshold": { "title": "Fuzzy Match Strictness", "minimum": 0, "maximum": 1, "type": "number", "description": "Controls how strict the fuzzy search is. Lower values require more accurate matches. Higher values allow more typos and loose matching. Recommended values: 0.2 = strict, 0.35 = balanced, 0.6 = very loose.", "default": 0.35 }, "ignoreLocation": { "title": "Ignore Word Position", "type": "boolean", "description": "When enabled, matches can appear anywhere inside the text. Example: searching 'pro' can match both 'iPhone Pro' and 'Professional Camera'. Recommended: enabled.", "default": true }, "minMatchCharLength": { "title": "Minimum Match Length", "minimum": 1, "type": "integer", "description": "Minimum number of characters required before fuzzy considers a match. Helps reduce noisy results for very short queries. Recommended: 2 or 3.", "default": 2 }, "includeScore": { "title": "Include Relevance Score", "type": "boolean", "description": "Adds a relevance score to each result. Lower scores mean better matches. Useful for debugging, sorting, or displaying search confidence.", "default": true }, "includeMatches": { "title": "Include Match Details", "type": "boolean", "description": "Returns detailed information about which text fragments matched the query. Useful for highlighting matched keywords in a frontend application.", "default": false }, "extendedSearch": { "title": "Enable Advanced Search Syntax", "type": "boolean", "description": "Enables Fuzzy advanced query syntax. Examples: '^apple' = starts with apple, '!samsung' = exclude samsung, '=iphone' = exact match. Recommended for advanced users only.", "default": false }, "weights": { "title": "Field Importance Weights", "type": "object", "description": "Optional JSON object that controls which fields are more important during ranking. Higher weight means higher priority in search results. Example: {\"title\": 0.7, \"description\": 0.2, \"brand\": 0.1}" } } }, "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 } } } } } } } } } } ```