Libraries tagged by read model

# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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The files crawlers and AI agents read — sitemap.xml, robots.txt, llms.txt and llms-full.txt — generated from your own models and managed from a Filament 5 panel. Pluggable page sources, reciprocal hreflang clusters with x-default, manual sitemap URLs that never shadow a real page, an automatic sitemap index past 50 000 URLs, per-language llms.txt, an editor page for robots.txt and llms.txt, and scheduled generation.

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A stripped down active-record'ish query layer for fetching data from your database.

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Read Model JSON-RPC 2.0 implementation

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Read Model Elasticsearch implementation

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Doctrine implementation for the Read Model Domain

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Read Model Domain implementation

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A DynamoDB ReadModel implementation for Broadway.

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Make Eloquent models read-only after they're locked. Prevents updates and deletes based on a `locked` attribute.

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A Read-only model trait for assisting dealign with read only models.

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B2B tools for the official Sylius MCP server: signed-in buyers use Claude or ChatGPT to list and reorder their orders, quick order by SKU, request quotes and read their trade credit statement (OAuth 2.1 with PKCE, checkout handoff to the shop)

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Config-driven read-only API for Laravel. Expose your data via simple GET requests—define routes, tables or Eloquent models, columns, and relations in one config file. Optional API key auth.

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Immutable, brand-scoped activity ledger for Statamic: the append-only source of truth for what happened, without read models or dashboards.

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Laravel integration for the Askr application server: session, cache, locks, queue and broadcasting drivers plus automatic page caching (pages are tagged with the models they read) — Redis-free, with an optional durable/replicated SQL Anywhere backend.

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BPMN 2.0 in plain PHP: a typed model of processes, read from and written to XML with their diagram, built in code, validated, and walked path by path

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