BE-013 — Implement AI Assistant API

[dDevAhmed]

BE-013 — Implement AI Assistant API

📚 Overview

The AI Assistant API provides the backend infrastructure that powers intelligent interactions across the TruthBounty ecosystem.

Rather than operating as a decision-making component, the AI Assistant serves as an intelligent protocol companion that helps contributors navigate claims, understand governance proposals, interpret protocol activity, analyse verification history, and discover relevant documentation.

The AI Assistant must never modify protocol state or bypass governance decisions. Instead, it consumes indexed blockchain data, backend services, documentation, and public knowledge to generate contextual responses while preserving the blockchain as the single source of truth.

The AI Assistant API will become the dependency for:

• Frontend AI Assistant
• Mobile Application
• Documentation Portal
• Governance Dashboard
• Contributor Workspace
• Analytics Platform
• Administration Portal

Every downstream service assumes AI-generated responses are contextual, traceable, and based on verified protocol data.

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🧠 Background

TruthBounty V2 introduces a large and interconnected protocol consisting of:

• smart contracts
• governance
• reputation
• staking
• verification
• disputes
• settlements
• rewards
• analytics

As the protocol expands, contributors require intelligent assistance for:

• understanding protocol behaviour
• navigating documentation
• locating claims
• interpreting governance proposals
• analysing verification activity
• understanding reputation changes

The AI Assistant simplifies protocol interaction without becoming an authority over protocol state.

All authoritative information must originate from indexed blockchain events and official documentation.

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🎯 Objectives

Implement a production-ready AI Assistant API that:

• answers protocol questions
• retrieves protocol knowledge
• summarises protocol activity
• analyses indexed data
• assists contributors
• supports contextual conversations
• integrates Retrieval-Augmented Generation (RAG)
• returns standardised API responses

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🧩 Technical Scope

1. Conversation API

Implement endpoints supporting:

• new conversations
• conversation history
• follow-up questions
• conversation deletion
• session management

Support authenticated and anonymous sessions where appropriate.

---

2. Knowledge Retrieval

Implement retrieval from:

• protocol documentation
• indexed blockchain data
• governance proposals
• claim metadata
• verification history
• reputation data
• dispute history
• analytics

The AI must retrieve information rather than memorise protocol state.

---

3. Retrieval-Augmented Generation (RAG)

Implement a RAG pipeline supporting:

• vector search
• semantic search
• document ranking
• context assembly
• citation generation

Retrieved context should always precede language model generation.

---

4. Protocol Queries

Support intelligent questions including:

• claim status
• reputation summaries
• governance proposals
• dispute history
• contributor activity
• reward distribution
• protocol statistics

Responses must reference authoritative indexed data.

---

5. Conversation Context

Maintain conversation context including:

• previous interactions
• referenced claims
• governance discussions
• contributor history
• selected protocol modules

Support configurable context windows.

---

6. Security

Ensure:

• authenticated sessions
• rate limiting
• prompt injection protection
• input validation
• data isolation
• secure document retrieval

Sensitive administrative information must never be exposed.

---

7. Performance

Implement:

• Redis caching
• streaming responses
• asynchronous retrieval
• embedding cache
• efficient vector search

The assistant should respond with low latency while maintaining retrieval accuracy.

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8. Monitoring

Expose operational metrics including:

• conversations started
• average response latency
• retrieval accuracy
• cache hit ratio
• token usage
• failed requests

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9. API Standardisation

Return consistent response objects containing:

• generated response
• supporting citations
• retrieved documents
• metadata
• timestamps
• request identifier

Errors must follow backend API standards.

---

10. Future Extensibility

Design the architecture to support:

• multilingual assistance
• voice interaction
• agent workflows
• governance copilots
• developer assistants
• moderator assistants

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✅ Acceptance Criteria

• Conversations can be created successfully.
• Knowledge retrieval functions correctly.
• RAG pipeline retrieves relevant documentation.
• Protocol queries return accurate information.
• Conversation context is maintained.
• Redis caching improves performance.
• Monitoring metrics are available.
• Unit and integration tests pass.

---

🧪 Testing Requirements

Implement tests covering:

• conversation lifecycle
• knowledge retrieval
• semantic search
• RAG pipeline
• protocol queries
• authentication
• prompt injection protection
• caching behaviour
• concurrent sessions
• performance benchmarking

---

📚 Documentation

Update:

• Backend API Documentation
• AI Assistant Architecture
• RAG Pipeline Documentation
• OpenAPI Specification
• Operational Runbook

---

🔗 Dependencies

Depends on:

• BE-002 — Implement PostgreSQL Database Infrastructure
• BE-003 — Implement Blockchain Indexing Service
• BE-004 — Implement Redis Cache & Distributed Session Layer
• BE-011 — Implement Governance API
• BE-012 — Implement Analytics API

Blocks:

• Frontend AI Assistant
• Mobile AI Assistant
• Documentation Portal
• Governance Copilot
• Contributor Workspace
• Administration Portal

---

🏷 Labels

• backend
• ai
• architecture
• protocol-critical
• complexity-high
• stellar-wave

---

📊 Complexity

High

The AI Assistant API integrates retrieval pipelines, indexed blockchain data, documentation, and conversational interfaces into a unified service. While it never modifies protocol state, it must provide accurate, contextual, and traceable responses using Retrieval-Augmented Generation and secure backend infrastructure.

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⏱ Estimated Effort

6–8 days

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🚀 Definition of Done

• Conversation API implemented.
• RAG pipeline operational.
• Knowledge retrieval integrated.
• Protocol query support completed.
• Conversation context management implemented.
• Redis caching enabled.
• Monitoring operational.
• Tests passing.
• Documentation updated.
• Ready for integration with the Frontend AI Assistant, Mobile Application, Governance Dashboard, and Documentation Portal.

[frank0277]

@frank0277 has applied to work on this issue as part of the Stellar Wave Program's 7th wave.

Hi maintainer i would like to work on this

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[OutstandingVick]

@OutstandingVick has applied to work on this issue as part of the Stellar Wave Program's 7th wave.

Hi! I'd love to contribute to this project. I have experience building and maintaining production applications and enjoy working on well-scoped tasks. I focus on writing clean, maintainable code, communicating clearly, and delivering quality work on time. I'm excited about the opportunity to contribute and collaborate with the team.

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[VictorEzenma]

@VictorEzenma has applied to work on this issue as part of the Stellar Wave Program's 7th wave.

Hi maintainer, I would like to contribute to this issue

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[Guddy1-tech]

@Guddy1-tech has applied to work on this issue as part of the Stellar Wave Program's 7th wave.

Hi! I'm a full-stack developer with experience in backend (Go, Java, SQL, REST APIs) and frontend (JavaScript/TypeScript, React). I'd like to take on this issue. I'll review the existing implementation, develop a clean and maintainable solution, verify it through testing, and submit a well-documented PR that follows the project's contribution guidelines.

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