Feature Specification: Conversational Q&A
1. Overview & Vision
Conversational Q&A is the primary interface for interacting with organizational knowledge. It provides a natural language chatbot experience that leverages Retrieval-Augmented Generation (RAG) to provide grounded, accurate, and cited answers, effectively acting as an intelligent expert who has read every document in the company.
2. Personas & Stakeholders
| Persona | Goal |
|---|---|
| Member | Get instant, accurate answers to complex work questions. |
| Knowledge Admin | Monitor chat quality and identify knowledge gaps. |
| Executive | Quickly summarize internal reports and strategy docs. |
3. User Stories
- As a member, I want to ask "What is our remote work policy for 2025?" and get a direct summary instead of reading a PDF.
- As a user, I want to ask follow-up questions (e.g., "What about contractors?") and have the assistant remember the previous context.
- As a new hire, I want to ask "How do I set up my VPN?" and get step-by-step instructions from our technical docs.
4. Functional Requirements (FR)
- REQ-CHAT-001: Natural language understanding and response generation.
- REQ-CHAT-002: Persistent multi-session chat history per user.
- REQ-CHAT-003: Streaming response generation (Server-Sent Events).
- REQ-CHAT-004: Contextual memory (last 10 messages) within a session.
5. Non-Functional Requirements (NFR)
- Latency: Time to First Token (TTFT) < 2 seconds.
- Accuracy: AI MUST NOT hallucinate; it MUST state if information is missing from the KB.
- Privacy: Conversations are strictly private to the user and their organization.
6. Business Logic & Rules
- Retrieval Scope: Every query triggers a vector search scoped strictly to the user's
organizationId. - System Prompting: Strict instructions to only answer from provided context and include citations.
- Session Management: Sessions are auto-titled based on the first user query.
7. User Interface (UI/UX)
- Main View: Messaging interface with distinct user/assistant bubbles.
- Sidebar: List of recent chat sessions with "Delete" and "Rename" actions.
- Streaming UI: Dynamic token rendering with a "Stop" button.
8. Information Architecture
- Primary module landing page.
- "Chat History" accessible via the left sidebar.
9. Data Model & Persistence
- Table:
chat_sessions(Metadata). - Table:
chat_messages(Content & Citations). - Vector Store: Semantic retrieval from
kb_chunks.
10. API & Service Layer
POST /sessions/:id/messages(Streaming endpoint).GET /sessions(History list).ChatServiceorchestrates the RAG pipeline.
11. Integration Patterns
- OpenAI API: Uses GPT-4o / GPT-4o-mini for generation.
- Shell Identity: Extracts user preferences and names for personalized greetings.
12. Security & Permissions
- RBAC:
ai_assistant:chatrequired to use the feature. - Isolation: Users cannot see or access chat sessions belonging to other users.
13. Error Handling & Resilience
- LLM Timeout: Graceful retry or error message ("AI is currently busy").
- No Context: Fallback response: "I couldn't find relevant information in our knowledge base."
14. Performance & Scalability
- Optimized context window management (truncation of long histories).
- Background indexing ensuring vector search is always against the latest data.
15. Globalization & i18n
- Support for Vietnamese and English queries/responses.
- Automatic language detection (the AI responds in the language used by the user).
16. Accessibility (a11y)
- Aria-live regions for streaming text announcements.
- Keyboard-navigable chat history and input area.
17. Observability & Analytics
- Tracking of "Queries per Session" to measure user engagement.
- Monitoring of "Token Consumption" per organization.
18. Testing & Quality
- Evaluation datasets (Golden Sets) to measure AI accuracy.
- Load testing for high-concurrency streaming.
19. Constraints & Assumptions
- Assumes organization has added at least one valid source to the Knowledge Base.
20. Future Enhancements
- Shared sessions (collaborative chat).
- Image/Document upload directly into the chat for temporary context.