generated from coulomb/repo-seed
242 lines
6.0 KiB
Markdown
242 lines
6.0 KiB
Markdown
# Kontextual Engine Product Requirements Document V0.1
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## kontextual-engine
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---
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## 1. Product Overview
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### 1.1 Product Name
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**kontextual-engine**
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---
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### 1.2 Product Definition
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kontextual-engine is an **AI-first, headless knowledge and content engine** that manages, transforms, and operates structured information across heterogeneous data sources.
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It provides a **persistent, service-oriented runtime for knowledge systems**, enabling automated and agent-driven workflows over structured and semi-structured data.
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---
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## 2. Product Intent
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### 2.1 Problem Statement
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Modern knowledge systems face several limitations:
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* Content is fragmented across formats, tools, and storage systems
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* Automation and orchestration of knowledge workflows are ad-hoc
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* AI interaction lacks stable, persistent context
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* Systems either focus on tooling (too low-level) or platforms (too rigid)
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This results in inefficient knowledge reuse, poor traceability, and limited scalability.
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---
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### 2.2 Intended Outcomes
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kontextual-engine enables:
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* Persistent, structured **knowledge environments** across domains
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* Unified handling of **multi-format data and files**
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* AI-driven **interaction, transformation, and orchestration** of knowledge
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* Efficient **retrieval, composition, and reuse of information**
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* Stable APIs for integrating knowledge into applications and systems
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---
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### 2.3 Success Criteria
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The product is successful when:
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* Knowledge can be **persisted, queried, and transformed across formats**
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* AI agents can operate on knowledge with **context continuity and efficiency**
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* Workflows can be **automated and orchestrated reliably**
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* Systems can integrate with the engine via **clear, stable interfaces**
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* markitect-tool and other primitives can be used seamlessly within the engine
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---
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## 3. Scope Definition
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### 3.1 In Scope
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* Persistent storage and management of structured knowledge
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* Multi-format data handling (markdown, documents, files, datasets)
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* Knowledge ingestion, normalization, and indexing
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* Workflow orchestration for transformation, generation, and analysis
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* API and service interfaces for knowledge access and operations
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* AI/LLM-driven interaction and automation
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* Integration with lower-layer tooling (e.g. markitect-tool)
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---
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### 3.2 Out of Scope
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* Low-level markdown parsing or transformation primitives
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* CLI-first tooling or standalone document manipulation
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* Domain-specific knowledge models or project-level content
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* Visual UI applications (headless system only)
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* Direct ownership of LLM provider integrations (delegated to libraries like `llm-connect`)
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---
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### 3.3 Boundary Clarification
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kontextual-engine provides a **runtime system**, not primitives or projects:
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* Tooling primitives → `markitect-tool`
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* Project/application usage → `infospace-bench`
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---
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## 4. Functional Expectations
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### 4.1 Core Capabilities
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The product must support:
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* **Knowledge Persistence**
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Store and manage structured knowledge across collections and domains
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* **Ingestion & Normalization**
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Convert heterogeneous data into structured representations
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* **Transformation & Composition**
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Apply workflows to generate, modify, and combine knowledge artifacts
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* **Query & Retrieval**
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Provide efficient access to knowledge via APIs
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* **Workflow Orchestration**
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Coordinate multi-step operations and dependencies
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* **AI Interaction Layer**
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Enable LLM-driven interaction, reasoning, and automation
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---
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### 4.2 Interaction Modes
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* API-first (service endpoints)
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* Agent-driven execution
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* Programmatic integration
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---
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## 5. Non-Functional Expectations
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### 5.1 Performance
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* Scalable handling of large and heterogeneous data sets
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* Efficient retrieval and transformation operations
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---
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### 5.2 Reliability
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* Consistent and deterministic system behavior where applicable
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* Robust handling of failures in workflows and external dependencies
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---
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### 5.3 Extensibility
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* Modular architecture supporting plugins and adapters
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* Ability to integrate new data sources and workflows
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---
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### 5.4 Usability
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* Clear API surface for integration
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* Predictable behavior across operations
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* Minimal friction for common workflows
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---
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## 6. Assumptions and Dependencies
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### 6.1 Assumptions
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* Knowledge systems benefit from persistent, structured representation
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* AI agents are primary consumers and operators of knowledge workflows
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* Multiple data formats must be supported
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---
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### 6.2 Dependencies
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* markitect-tool for markdown-native operations
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* llm-connect (or equivalent) for LLM integration
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* Underlying storage systems (filesystem, databases, object storage)
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---
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## 7. Constraints
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* Must remain **format-agnostic at the system level**
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* Must maintain **clear separation from tooling and project layers**
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* Must avoid vendor lock-in and provider-specific coupling
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* Must support both deterministic and AI-driven operations
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---
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## 8. Risks
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* Scope creep toward full application/platform ownership
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* Over-complex orchestration reducing usability
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* Tight coupling to specific data formats or tools
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* AI-driven behavior reducing predictability
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---
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## 9. Related Systems
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* **markitect-tool** – syntax layer (markdown primitives)
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* **infospace-bench** – application layer (knowledge projects)
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* **llm-connect** – LLM abstraction layer
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---
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## 10. Ecosystem Context
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This product is part of a layered knowledge system:
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```text id="m2k9s4"
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markitect-tool → makes markdown structured and manipulable
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kontextual-engine → makes knowledge persistent and operable
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infospace-bench → makes knowledge concrete and meaningful
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```
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Layers:
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* **Syntax layer** → markitect-tool
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* **System layer** → kontextual-engine
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* **Application layer** → infospace-bench
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---
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## 11. PRD Type
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**Hybrid / Boundary PRD**
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This PRD defines system-level intent and constraints while allowing architectural flexibility and iterative development.
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---
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# 🧠 Final insight (important)
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If markitect-tool was about:
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> **“making knowledge manipulable”**
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Then kontextual-engine is about:
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> **“making knowledge operational”**
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That distinction will keep this repo from turning into an unbounded platform.
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