# Pre-Alpha Requirements Document

## Introduction

Pre-Alpha - техническая валидация core data processing pipeline Kenoma. Доказываем что можем собирать, обрабатывать и предоставлять доступ к multi-modal данным из внешних источников. Никакого UI - чисто technical proof-of-concept для валидации foundational architecture.

## Alignment with Product Vision

Валидирует фундаментальную предпосылку Kenoma: автоматическая агрегация и обработка fragmented digital information в unified, queryable формат. Pre-Alpha закладывает техническую основу для "AI-powered second brain" доказывая что можем собирать данные real-time, обрабатывать multi-modal контент и предоставлять intelligent access через semantic search.

## Requirements

### Requirement 1: Telegram Connector Integration

**User Story:** As an engineer, I want to connect to Telegram account and forward messages to the ingest pipeline, so that I can validate the data collection flow

#### Acceptance Criteria

1. Connector SHALL be configurable via CLI with account credentials and chat selection
2. WHEN message received in monitored chats THEN connector SHALL forward to ingest pipeline  
3. Connector SHALL support historical data export via CLI for initial data loading
4. Connector SHALL capture all message types (text, image, audio, video, documents)
5. Connector SHALL include metadata (sender, timestamp, chat ID, message type)

### Requirement 2: Multi-Modal Data Processing

**User Story:** As a system, I want to process different content types into normalized text representation, so that all information can be queried uniformly

#### Acceptance Criteria

1. WHEN text content received THEN system SHALL store in searchable format
2. WHEN image received THEN system SHALL generate description using vision AI
3. WHEN audio/video received THEN system SHALL transcribe to text using STT
4. WHEN documents received THEN system SHALL extract text (PDF, DOCX, XLSX, TXT, CSV)
5. System SHALL store ONLY text representation and metadata, NOT original files

### Requirement 3: Unified Data Storage

**User Story:** As an engineer, I want all processed data stored in unified schema, so that I can query across content types consistently

#### Acceptance Criteria

1. System SHALL store content with schema: ID, source, timestamp, content_type, text_content, metadata
2. System SHALL maintain relationships between messages (replies, threads)
3. System SHALL support full-text search and vector embeddings for semantic search
4. System SHALL generate and store embeddings for all text content

### Requirement 4: Data Access API

**User Story:** As a developer, I want to query stored data through API, so that I can build applications on processed context

#### Acceptance Criteria

1. API SHALL provide filtering by date range, source, content type, metadata
2. API SHALL support semantic search with relevance scores  
3. API SHALL return unified JSON format regardless of original content type
4. API SHALL support pagination for large result sets

## Non-Functional Requirements

### Performance
- Support parallel processing (up to 20 concurrent threads)
- Handle bulk historical data import efficiently

### Reliability  
- System restarts without data loss
- Processing errors logged with full context
- Failed attempts retry with exponential backoff

### Security
- No authentication required for pre-alpha
- Data stored unencrypted (encryption planned for alpha)
- Single tenant only

## Scope Definition

### In Scope
- Telegram connector with CLI configuration
- Multi-modal data processing pipeline
- Unified text-only storage with vector embeddings
- REST API with search and filter endpoints
- Manual configuration via CLI

### Out of Scope
- User interface (web/mobile)
- Authentication system
- Multiple connectors
- Production deployment
- Data encryption
- Original media storage

### Dependencies
- Telegram Bot API access
- OpenAI API for OCR/vision processing
- Vector database (Qdrant/Weaviate)
- PostgreSQL for structured data

### Assumptions
- Manual configuration acceptable
- Single tenant for testing
- External AI services (OpenAI) usage allowed

## Success Metrics

### Technical Metrics
- Process 1000+ Telegram messages without critical errors
- All content types (text, image, audio, video, docs) processed successfully
- Vector search returns relevant results for test queries

### Functional Metrics
- All 4 requirements fully implemented and tested
- Historical data import works
- API supports all specified query types

### Quality Metrics
- No data loss during processing
- Comprehensive error logging
- Technical documentation complete

## Risks & Mitigations

### Risk: AI Service Dependencies
**Description:** OpenAI service failures could break processing
**Impact:** High  
**Mitigation:** Implement fallback providers and graceful degradation

### Risk: Vector Database Performance
**Description:** Search might not scale with data volume
**Impact:** Medium
**Mitigation:** Use proven vector DB, implement pagination, test realistic volumes

## Deliverables

1. **Ingest Pipeline** - Multi-modal content processing flow
2. **Telegram Connector** - CLI-configurable with historical import  
3. **Data Storage** - Text-only with vector embeddings
4. **REST API** - Search and filter endpoints
5. **Documentation** - Setup guide and architecture docs
6. **Test Results** - Success metrics validation report