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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Automatically detects and generates CSS selectors or XPath expressions for web page elements without requiring manual coding. The AI analyzes page structure to identify the correct data extraction points, eliminating the need for users to write custom selectors.
Scrapes data from websites that rely on JavaScript for dynamic content rendering, handling single-page applications and AJAX-loaded content. Executes JavaScript before extraction to capture fully-rendered page state.
Automatically adjusts extraction rules when website layouts or HTML structures change, reducing the need for manual rule updates. Uses AI to recognize equivalent elements across different page versions.
Provides free-tier access to core web scraping functionality with usage limitations, allowing users to test scraping jobs and extract data at limited scale without payment. Includes bandwidth and request caps.
Runs scraping jobs on a recurring schedule (daily, weekly, monthly) to continuously collect data from websites without manual intervention. Stores historical data for trend analysis.
Provides a visual interface for setting up web scraping jobs without writing code, using point-and-click selection and form-based configuration. Abstracts away technical complexity of HTTP requests and data parsing.
Converts scraped web data into structured formats (JSON, CSV, Excel) for use in databases, spreadsheets, and data analysis tools. Handles data cleaning and standardization.
Automatically checks and respects website robots.txt files and terms of service to ensure scraping activities are compliant with website policies. Provides warnings for potentially problematic scraping targets.
+2 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs MrScrapper at 46/100.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities