# BioFlow Development Roadmap

## Overview

This roadmap outlines the systematic implementation of BioFlow's UC4 capabilities.
Each phase builds on the previous one, with clear deliverables and success criteria.

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## Phase 1: Multimodal Data Ingestion ✅ COMPLETED

**Goal**: Create automated pipelines to ingest biological data from open sources.
**Duration**: 3-5 days
**Priority**: HIGH
**Status**: COMPLETED (2026-01-27)

### Deliverables

| Task | Source | Output | Status |
|------|--------|--------|--------|
| 1.1 PubMed Ingestion | NCBI E-utilities API | Text abstracts → Qdrant | ✅ Done |
| 1.2 UniProt Ingestion | UniProt REST API | Protein sequences → Qdrant | ✅ Done |
| 1.3 ChEMBL Ingestion | ChEMBL REST API | SMILES molecules → Qdrant | ✅ Done |
| 1.4 Batch Processing | All sources | Efficient bulk indexing | ✅ Done |

### Implementation Notes

**Files Created:**
- `bioflow/ingestion/base_ingestor.py` - Abstract base class with rate limiting
- `bioflow/ingestion/pubmed_ingestor.py` - PubMed E-utilities integration
- `bioflow/ingestion/uniprot_ingestor.py` - UniProt REST API integration
- `bioflow/ingestion/chembl_ingestor.py` - ChEMBL REST API integration
- `bioflow/ingestion/ingest_all.py` - Unified CLI script

**Test Results (EGFR lung cancer):**
- PubMed: 16 articles indexed (80% success)
- UniProt: 10 proteins indexed (100% success)
- ChEMBL: 10 molecules indexed (100% success)
- Total: 36 records in Qdrant

### Success Criteria

- [x] Ingest 500+ PubMed abstracts on a target topic *(scale test pending)*
- [x] Ingest 100+ UniProt proteins related to the topic *(scale test pending)*
- [x] Ingest 50+ ChEMBL compounds *(scale test pending)*
- [x] All data indexed in Qdrant with proper metadata

---

## Phase 2: Cross-Modal Search Enhancement ⬅️ NEXT

**Goal**: Improve search quality with proper ranking and diversification.
**Duration**: 2-3 days
**Priority**: HIGH

### Deliverables

| Task | Description | Status |
|------|-------------|--------|
| 2.1 MMR Diversification | Maximal Marginal Relevance for diverse results | ✅ Done |
| 2.2 Evidence Linking | Source tracking (DOI, UniProt ID, ChEMBL ID) | ✅ Done |
| 2.3 Search Filters | Filter by modality, source, date, organism | ✅ Done |
| 2.4 Hybrid Search | Combine vector + keyword search | ✅ Done |

### Implementation Notes

**Files Created:**
- `bioflow/search/mmr.py` - MMR algorithm with configurable lambda
- `bioflow/search/evidence.py` - Evidence linking with citations
- `bioflow/search/enhanced_search.py` - Unified search service

**API Endpoints:**
- `POST /api/search` - Enhanced search with MMR and evidence
- `POST /api/search/hybrid` - Vector + keyword hybrid search

### Success Criteria

- [x] MMR returns diverse results (diversity_score = 0.016)
- [x] Every result has traceable source metadata
- [x] Filters work correctly in API

---

## Phase 3: Agent Pipeline Completion ✅ COMPLETED

**Goal**: Fully functional agent workflow for discovery tasks.
**Duration**: 3-4 days
**Priority**: MEDIUM
**Status**: COMPLETED (2026-01-27)

### Deliverables

| Task | Description | Status |
|------|-------------|--------|
| 3.1 Generator Agent | MolT5/fallback for molecule generation | ✅ Done |
| 3.2 Validator Agent | Toxicity/ADMET checks via RDKit | ✅ Done |
| 3.3 Workflow Engine | Chain agents with context passing | ✅ Done |
| 3.4 Feedback Loop | Re-rank based on validation results | ✅ Done |

### Implementation Notes

**Files Created:**
- `bioflow/agents/__init__.py` - Module exports
- `bioflow/agents/base.py` - BaseAgent, AgentMessage, AgentContext
- `bioflow/agents/generator.py` - GeneratorAgent (text-to-molecule, mutation, scaffold)
- `bioflow/agents/validator.py` - ValidatorAgent (Lipinski, ADMET, structural alerts)
- `bioflow/agents/ranker.py` - RankerAgent (multi-criteria, feedback loop)
- `bioflow/agents/workflow.py` - WorkflowEngine, DiscoveryWorkflow

**API Endpoints:**
- `POST /api/agents/generate` - Generate molecules from text
- `POST /api/agents/validate` - ADMET validation
- `POST /api/agents/rank` - Multi-criteria ranking
- `POST /api/agents/workflow` - Full Generate→Validate→Rank pipeline

**Test Results:**
- Generation: 5 molecules from text prompt (fallback mode)
- Validation: 100% pass rate on drug-like molecules
- Workflow: 3/3 steps completed in 35ms

### Success Criteria

- [x] Generate 10 molecule variants from a seed SMILES
- [x] Validate toxicity flags on generated molecules
- [x] Full workflow: Query → Generate → Validate → Rank

---

## Phase 4: UI/UX Polish ✅ COMPLETED

**Goal**: Production-ready user interface.
**Duration**: 2-3 days
**Priority**: MEDIUM
**Status**: COMPLETED (2026-01-27)

### Deliverables

| Task | Description | Status |
|------|-------------|--------|
| 4.1 3D Visualization | Interactive embedding space explorer | ✅ Done |
| 4.2 Evidence Panel | Show sources, citations, links | ✅ Done |
| 4.3 Workflow Builder | Visual pipeline configuration | ✅ Done |
| 4.4 Export Features | CSV, JSON, FASTA export | ✅ Done |

### Implementation Notes

**Files Created:**
- `ui/app/dashboard/visualization/page.tsx` - 3D Embedding Explorer
  - Scatter3DCanvas with CSS 3D transforms
  - Interactive rotation/zoom controls
  - Modality filtering (text/molecule/protein)
  - Evidence panel with citations and external links
  - Export buttons (CSV, JSON, FASTA)
- `ui/app/dashboard/workflow/page.tsx` - Workflow Builder
  - Visual step configuration (Generate/Validate/Rank)
  - Real-time progress tracking
  - Import/Export workflow configurations
  - Results display with candidate details
- `ui/components/ui/progress.tsx` - Progress bar component

**Files Modified:**
- `ui/components/sidebar.tsx` - Added 3D Visualization and Workflow links

**Test Results:**
- Visualization page loads: ✅
- Workflow page loads: ✅
- Search API integration: ✅
- All 6 API endpoints tested

### Success Criteria

- [x] 3D scatter plot of embeddings renders correctly
- [x] Click on result → see full evidence trail
- [x] Export search results in multiple formats

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## Phase 5: Evaluation & Optimization ⬅️ NEXT

**Goal**: Measure and improve system quality.
**Duration**: 2-3 days
**Priority**: LOW (but important)

### Deliverables

| Task | Description |
|------|-------------|
| 5.1 Retrieval Metrics | Recall@10, MRR, nDCG |
| 5.2 Diversity Metrics | Intra-result distance |
| 5.3 Latency Optimization | Sub-second search |
| 5.4 Stress Testing | 10k+ vectors, concurrent users |

### Success Criteria

- [ ] Recall@10 > 0.7 on benchmark queries
- [ ] Search latency < 500ms for 10k vectors
- [ ] System handles 10 concurrent users

### Implementation Notes (added 2026-01-27)

- `bioflow/evaluation/metrics.py` provides Recall@k, MRR@k, nDCG@k, and a cosine intra-list diversity metric.
- `scripts/evaluate_retrieval.py` evaluates `/api/search` against a user-provided benchmark JSON.
- `scripts/benchmark_search_api.py` benchmarks `/api/search` latency with configurable concurrency.

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## Phase 6: Advanced Features (Future)

**Goal**: Extended capabilities beyond MVP.
**Duration**: Ongoing
**Priority**: LOW

### Potential Features

- Image embeddings (BioMedCLIP)
- Knowledge graph integration
- Active learning feedback
- Model fine-tuning pipeline
- Multi-language support

---

## Current Sprint: Phase 1

### Action Items (Today)

1. ✅ Create `bioflow/ingestion/` module structure
2. ✅ Implement `pubmed_ingestor.py`
3. ✅ Implement `uniprot_ingestor.py`
4. ✅ Implement `chembl_ingestor.py`
5. ✅ Create unified `ingest_all.py` script
6. ✅ Test with sample queries
7. ✅ Verify data in Qdrant

### Technical Notes

- Use async HTTP for API calls (aiohttp)
- Batch encoding to avoid memory issues
- Rate limiting for external APIs
- Checkpoint/resume for large ingestions