Key Architectural Terms

Self-contained

A self-contained component operates independently and has everything it needs to perform its function without external dependencies. In UNPARTY's architecture, this means:

• Each component has a clearly defined single responsibility

• Components don't share resources or state with other components

• Components can be tested, modified, or replaced without affecting the rest of the system

• All necessary logic for the component's function is encapsulated within its boundaries

Stateless

Stateless design means components don't retain information between operations. In the context of UNPARTY:

• Components process each request independently of any previous requests

• No information is stored between operations

• Each inference call is processed as a standalone event

• The same input will always produce the same output

• No session data or history is maintained within components

Context

Context refers to the additional information and parameters that enhance the meaning and relevance of an inference call. In UNPARTY's implementation:

• Raw user input is enriched with supplementary information

• Context includes metadata, parameters, and configuration that help the LLM understand the request

• Context can be technical (like formatting requirements), semantic (like topic categorization), or operational (like processing instructions)

• Context is additive and accumulative as it passes through components

Wrapper

A wrapper is an architectural pattern that encapsulates and enhances the functionality of a process without changing its core operation. UNPARTY as a wrapper:

• Acts as an intermediate layer between user input and LLM processing

• Enhances inference calls without modifying their fundamental purpose

• Provides a consistent interface for context enrichment

• Maintains separation between the context-adding logic and the core LLM functionality

• Creates a pipeline for sequential context enhancement

To ensure that we define embeddings and clusters in a way that aligns with your code architecture, we need to extract their meaning and role within your system based on the provided structure. Here’s how we can define them based on the provided EmbeddingConfiguration, ClusteringCoordinator, and related components:

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Embeddings

Definition:

An embedding is a vector representation of text, calculated using EmbeddingConfiguration parameters such as vector dimensions. It translates textual data into a numeric form that machine learning models can process. In your system, embeddings are:

• Dynamically generated using EmbeddingConfiguration.

• Context-specific, adapting to user preferences, device capabilities, and content metrics.

• Represented as an array of Double values.

Role in Architecture:

1. Representation:

   • Embeddings serve as a mathematical representation of user-provided text (Entry.content).

   • Generated dynamically using generateEmbedding(for:) methods.

2. Storage:

   • Embeddings are stored in Entry objects:

     struct Entry {
         let content: String
         var embeddings: [Double]
     }

3. Pipeline Contribution:

   • Used in the clustering process to group entries based on similarity.

   • Contribute to metrics like context density or confidence levels.

4. Configuration:

   • Managed by EmbeddingConfiguration:

     • Vector dimensions are dynamically calculated based on device capabilities and other factors.

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Clusters

Definition:

A cluster is a group of entries with similar embeddings, determined by clustering algorithms like K-Means. It provides semantic grouping for user data and serves as the foundation for tasks such as content analysis, categorization, or personalized recommendations.

Role in Architecture:

1. Representation:

   • Clusters are defined by the Cluster struct:

     struct Cluster {
         let id: UUID
         let entries: [Entry]
         let threshold: Float
         let category: String?
         let metadata: [String: CodableValue]
     }

2. Pipeline Contribution:

   • Clusters are generated by the ClusteringCoordinator, which processes embeddings using a clustering algorithm (e.g., K-Means).

   • They are stored and updated in the StorageProvider:

     protocol StorageProvider {
         func fetchClusters() async throws -> [Cluster]
         func updateCluster(_ cluster: Cluster) async throws
     }

3. Metadata:

   • Each cluster contains metadata, such as a category or custom properties, for additional context or classification.

4. Threshold:

   • Clustering uses a similarity threshold (e.g., cosine similarity) to decide whether an entry belongs to an existing cluster or forms a new one.

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Embedding vs. Cluster

Aspect	Embedding	Cluster
Definition	Numeric representation of text.	Group of entries with similar embeddings.
Data Type	[Double] (vector)	Cluster struct
Generation	Dynamically created using EmbeddingConfiguration.	Created by ClusteringCoordinator.
Role	Basis for clustering and semantic search.	Semantic grouping for higher-level analysis.
Storage	Stored in Entry as embeddings.	Stored as Cluster objects in StorageProvider.
Usage	Input for clustering algorithms.	Output of clustering algorithms.

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Relationship in Your Architecture

1. Pipeline Flow:

   • Text (Entry.content) → Generate Embedding → Clustering → Create/Update Clusters.

2. Clustering Process:

   • Embeddings are grouped into clusters based on similarity thresholds.

   • Each cluster represents a category or semantic group.

3. Storage and Access:

   • Embeddings are tied to individual Entry objects.

   • Clusters are higher-level groupings, stored and retrieved independently.

4. Dynamic Adaptation:

   • Embedding dimensions and clustering thresholds adapt to user preferences, content metrics, and device capabilities, ensuring scalability and performance.

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Example Definitions in Your Code

Embeddings

/// A vector representation of text, enabling machine learning-based operations.
struct Embedding {
    let values: [Double]
    let confidence: Double // Reflects the reliability of the embedding
}

Clusters

/// A group of similar entries determined by clustering algorithms.
struct Cluster: Codable {
    let id: UUID
    let entries: [Entry]          // The entries that form the cluster
    let threshold: Float          // Similarity threshold
    let category: String?         // Optional category for grouping
    let metadata: [String: Any]   // Additional context
}

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Summary

In your architecture:

• Embeddings are the atomic, numerically encoded form of user text.

• Clusters are higher-order structures derived from embeddings to group semantically related content.

• Both are dynamically adaptable, ensuring the pipeline remains robust and scalable across varying contexts.