Information & Knowledge Engine: News, Topics, and Personalization
Turning Data Streams into Owner-Relevant Insight
Introduction
The Information & Knowledge Engine transforms raw information sources into concise, relevant insight for the owner. Its goal is not infinite search or autonomous discovery, but controlled retrieval, filtering, and summarization aligned with explicitly defined interests. This engine answers a practical question: what information is worth interrupting the owner for?
Design Principles
The engine follows strict principles:
– owner-centric relevance
– explicit topic boundaries
– deterministic filtering
– minimal data retention
– no behavioral profiling
Information is pulled on demand or on schedule, processed locally, and presented in a compact form.
Information Sources
Typical sources include:
– RSS feeds (news, science, technology)
– curated websites
– documentation and reference material
– optional search APIs
All sources are explicitly configured by the owner. There is no automatic source discovery.
Topic Model
Topics define what the assistant cares about. Each topic is a static definition including:
– keywords and phrases
– trusted sources
– update frequency
– summarization depth
Examples: “Raspberry Pi”, “Edge AI”, “Space exploration”, “Medical research”. Topics do not evolve automatically.
Retrieval Pipeline
The retrieval process is linear and auditable:
Source Fetch → Content Extraction → Topic Matching → Ranking → Summarization → Delivery
Each step produces intermediate results that can be logged or inspected during debugging.
Filtering and Ranking
Filtering removes irrelevant or low-quality content early. Ranking prioritizes items based on:
– topic relevance
– source trust level
– recency
– owner-defined importance
No engagement-based or popularity-based ranking is used.
Summarization Strategy
Summarization is concise and purpose-driven. The engine produces:
– headline
– short abstract
– optional bullet highlights
Summaries are generated locally when possible or via external APIs if explicitly allowed. Raw articles are not stored long-term.
Personalization Without Profiling
Personalization is declarative, not inferred. The owner defines interests, preferred depth, and delivery times. The system does not learn interests implicitly from reading behavior. This avoids hidden profiling and maintains predictability.
Delivery Modes
Information can be delivered via:
– spoken briefings
– on-demand queries
– scheduled digests
– silent notifications
Delivery mode is bound to scenarios and identity context. Owner presence overrides all other delivery rules.
Freshness and Caching
To balance freshness and efficiency, content is cached briefly. Cache lifetimes are topic-specific and conservative. Expired data is discarded automatically. There is no historical content archive unless explicitly enabled by the owner.
Failure Handling
If a source is unavailable or parsing fails, the engine degrades gracefully. Partial results are acceptable; blocking the system is not. Errors are logged without triggering retries that could cause excessive network activity.
Security and Privacy
External requests are minimized and transparent. No tracking parameters are added. The engine never transmits identity or conversational context to external sources. Network access is strictly scoped.
Integration with Dialogue and Scenarios
The Knowledge Engine exposes structured results to the Dialogue Engine, which formats responses according to scenario constraints. The Knowledge Engine never speaks directly and never triggers actions on its own.
What Comes Next
With information flow under control, the next article introduces the Automation & Services Engine: email handling, notifications, calls, and emergency workflows—integrated safely into a personal Edge AI system.
