# Context Pipeline Modules

In a context computation pipeline, each module has a specific role in processing data to compute the final context. 
Together, these modules form a pipeline that transforms raw input into a rich, refined context suitable for various applications, ranging from business intelligence to real-time decision-making systems. Each module is designed to be focused on a single aspect of the processing, adhering to the principles of modularity and separation of concerns. This design allows for easier maintenance, scalability, and reuse of the pipeline’s components.

## Core Modules

### Reader
The **Reader** module is the entry point of the pipeline, responsible for ingesting raw data. It reads input from various sources such as files, databases, streams, or external APIs. The reader parses the incoming data into a format that can be processed by subsequent stages of the pipeline.

### Builder
After the Reader module has ingested the data, the **Builder** module takes over to construct an initial context. This involves assembling the raw data into a more structured and coherent form, often translating it into objects or data structures that are easier to manipulate and analyze. The Builder sets up the foundation for further enrichment and refinement by providing the basic structure and content.

### Enricher
The **Enricher** (which you referred to as 'enriched') module enhances the initial context with additional data and insights. It might augment the context with metadata, associate it with related information from other sources, or infer additional attributes. This module’s goal is to add value to the basic context created by the Builder, making it more comprehensive and useful for downstream processing.

### Refiner
The **Refiner** module’s job is to fine-tune the context. This may involve filtering out irrelevant data, normalizing values, deduplicating records, or performing other quality-improvement actions. The Refiner ensures that the context is accurate, relevant, and ready for final consumption. Its role is crucial for maintaining the integrity and quality of the computed context.

### Writer
Finally, the **Writer** module is responsible for outputting the computed context. It takes the refined context and writes it to the destination system or storage. This could involve persisting the data to a database, sending it over a network, or writing it to a file system. The Writer must ensure that the data is stored in the correct format and is accessible for its intended use, whether for immediate consumption or long-term storage.

## Additional Modules

### Validator
Before, during, or after the builder phase, a **Validator** module can be crucial to ensure that the data conforms to certain standards or schemas. It checks the data for errors, inconsistencies, or deviations from the expected format, and can either correct issues or flag them for review.

### Transformer
A **Transformer** module may be employed to convert data from one format to another, to restructure it for different uses, or to map data fields from the source structure to the destination structure. This is particularly useful when interfacing with multiple systems that use different data formats.

### Aggregator
An **Aggregator** module would combine data from multiple sources, which is often necessary when constructing a comprehensive context. It may summarize data, compute aggregates like sums or averages, or concatenate information from various records into a single context.

### Annotator
An **Annotator** module could add metadata or descriptive tags to the context to facilitate easier retrieval, understanding, or categorization of the data. This module might use predefined rules or machine learning models to generate annotations.

### Filter
Similar to the refiner but more focused on exclusion, a **Filter** module would remove unwanted or unnecessary data from the context. This could be based on specific criteria, such as date ranges, relevance scores, or user preferences.

### Normalizer
A **Normalizer** module might be necessary to standardize data formats, units of measure, or other value representations. For example, it could ensure that all dates are in ISO format or that all monetary values are in a standard currency.

### Deduplicator
The **Deduplicator** (or deduplication module) would be responsible for identifying and removing duplicate records from the context to avoid redundant processing and to ensure the uniqueness of context data.

### Sequencer
A **Sequencer** module could order events or data points within the context according to a specified sequence, such as chronological order or priority, which is important for time-sensitive processing.

### Cacher
To improve performance, a **Cacher** module can temporarily store frequently accessed data. This enables quicker retrieval and reduces the load on the data source.

### Security Enforcer
A **Security Enforcer** module would manage access controls and ensure data protection standards are met. This could include encryption, tokenization, or redaction of sensitive information.

### Monitor
A **Monitor** module would keep track of the health and performance of the context processing pipeline. It would log errors, alert system administrators to issues, and provide analytics on the pipeline's operation.

### Adapter
An **Adapter** module serves as a bridge between different systems or components that may not directly interoperate. It helps integrate external systems or legacy systems with the pipeline.

Incorporating these additional modules into a context processing pipeline can help address various operational, data quality, performance, and security concerns, thereby creating a robust and flexible system capable of handling complex data processing tasks.