bubble/BUBBLE-IMPLEMENTATION-REPORT.md

BUBBLE Microservice: Implementation & Integration Report

1. Executive Summary

This document details the initial implementation of the BUBBLE microservice, a core component of the CHORUS ecosystem. BUBBLE functions as a specialized Decision Agent, providing on-demand analysis of decision provenance.

The work accomplished includes:

• Project Scaffolding: A complete Go module (gitea.deepblack.cloud/chorus/bubble) has been initialized and structured.
• Blueprint Extraction: All Python, JSON, SQL, and YAML snippets from the initial design conversation have been extracted and organized into a structured /src directory, serving as a clear blueprint for development.
• Core Data Structures: Go structs corresponding to the API and data models have been defined in models/models.go.
• Storage Layer: A flexible storage interface has been defined. A fully functional SQLite implementation has been created for persistence and testing, and a RocksDB implementation has been scaffolded for future high-performance use.
• API Layer: A runnable HTTP server using Go's standard library exposes the primary /decision/bundle endpoint.
• Core Logic: The central WalkBack algorithm has been implemented, featuring a breadth-first search (BFS) for graph traversal and a priority queue for scoring and ranking decision records.
• Testing: A minimal viable product has been successfully built and tested, confirming the API can serve requests by querying a seeded database.

BUBBLE is now a functional, testable microservice that fulfills its foundational role within the CHORUS architecture.

2. Integration with the CHORUS Ecosystem

BUBBLE does not operate in isolation; it is a critical, specialized tool designed to be called by other components, primarily SLURP.

2.1. Relationship with SLURP

The relationship is that of an orchestrator and a specialized tool:

1. SLURP is the Orchestrator: As the "Context Curator," SLURP is the entry point for all new information. When a BZZZ agent submits a Decision Record (DR), SLURP is responsible for the entire enrichment process.
2. BUBBLE is the Provenance Engine: SLURP's first step in enrichment is to understand the history behind the new DR. To do this, it makes a synchronous API call to BUBBLE's /decision/bundle endpoint.
3. Data Flow:
   • SLURP sends a start_id (the ID of the new DR) and a role to BUBBLE.
   • BUBBLE performs its high-speed, read-only "walk back" through the historical decision graph stored in its local RocksDB/SQLite database.
   • BUBBLE returns a "Decision Dossier" (the DecisionBundleResponse), which is a compact, scored, and ranked summary of the most relevant historical DRs.
   • SLURP takes this historical context from BUBBLE, combines it with conceptual context from other tools (like a RAG via n8n), and synthesizes the final, enriched context package.
   • SLURP then writes this complete package to the DHT using the UCXL protocol.

This architecture correctly places the responsibility of complex, stateful graph traversal on a dedicated service (BUBBLE), allowing SLURP to remain a more stateless orchestrator.

2.2. Interaction with BZZZ

BUBBLE has no direct interaction with BZZZ agents. BZZZ agents communicate with SLURP, which in turn uses BUBBLE as a backing service. This maintains a clear separation of concerns.

3. Role in Decision Record (DR) Management

BUBBLE is the primary tool for unlocking the value of the Decision Records that SLURP curates. While SLURP is the librarian that collects and catalogs the books, BUBBLE is the research assistant that reads them, understands their connections, and provides a concise summary on demand.

3.1. Answering "Why?"

By traversing the influences and supersedes edges in the provenance graph, BUBBLE provides the auditable trail required to answer fundamental questions about a project's history:

• "Why was this technical decision made?"
• "What alternatives were considered?"
• "What prior work influenced this new feature?"

3.2. Preventing Rework

The WalkBack algorithm, combined with future vector similarity scoring, is the mechanism that addresses the "we tried this already" problem. When SLURP receives a new proposal, it can ask BUBBLE to find semantically similar DRs in the past. If BUBBLE returns a previously rejected or superseded DR with a high score, SLURP can flag the new proposal as a potential repeat of past failures, saving significant development time.

3.3. Enforcing Constraints and Policies

While BUBBLE does not query the Policy Store directly, its output is a critical input for SLURP's policy enforcement. The Decision Dossier provides the historical context that SLURP needs to determine if a new decision violates established budgetary, technical, or security constraints.

4. Next Steps & Future Development

The current implementation provides a solid foundation. The following steps will build upon it to realize the full vision of the blueprint:

1. Full RocksDB Implementation: Complete the RocksDBStore methods to provide a high-performance alternative to SQLite.
2. Data Seeding/Syncing: Create a mechanism to sync or replicate the decision graph from the central DHT into BUBBLE's local stores.
3. Vector Search Integration: Integrate a vector search client (e.g., FAISS, Milvus) to implement the sim_score part of the WalkBack algorithm.
4. Caching Layer: Implement a caching mechanism (e.g., in-memory LRU or Redis) for frequently requested bundles to further improve performance.
5. LLM Integration for Summarization: While SLURP handles most LLM interaction, BUBBLE could potentially use a local model to generate the summary field within the DecisionBundleResponse for greater accuracy.