| # ARIA Project Status |
| _Last updated: May 5, 2026_ |
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| ## What Was Built |
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| Multi-agent pipeline in agent/agent.py with 5 stages: |
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| 1. Query Architect: generates 5 MeSH-optimised PubMed queries via Qwen2.5-72B on AMD MI300X |
| 2. Literature Scout: fetches from PubMed and Europe PMC in parallel, deduplicates by PMID |
| 3. PRISMA Filter: automatic inclusion/exclusion screening with one-line reasons, user can override any decision |
| 4. Evidence Synthesiser: structured synthesis with Background, Key Findings, Level of Evidence, Conflicting Evidence, Research Gaps, Clinical Implications |
| 5. Citation Builder: formatted references with PMID, synthesis runs on PRISMA-included papers only |
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| ## Additional Features |
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| * SSE streaming: real-time 5-stage progress bar with percentage |
| * PRISMA-style paper screening: automated include/exclude with rationale, user override buttons |
| * Evidence confidence scoring: green/yellow/red badges on each synthesis section with hover tooltips |
| * Abstract viewer: click any citation to expand full abstract inline |
| * PDF export: download formatted report via ReportLab |
| * Selective literature review: checkboxes on citations, user picks papers, generates focused academic paragraph |
| * Predictive model: constructive and destructive forecasts as a post-synthesis stage |
| * Evidence comparison table: LLM extracts structured data table with real author names, no duplicates |
| * Follow-up Q&A: ask follow-up questions after synthesis, answered using already-fetched papers |
| * Query refinement suggestions: 3 AI-generated follow-up research questions based on synthesis gaps |
| * Session history: queries saved to sessions.json, reloadable from sidebar |
| * Rate limit retry logic: automatic backoff on API errors |
| * SSL patch for PubMed and Europe PMC Entrez on corporate/university networks |
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| ## Tech Stack |
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| * LLM: Qwen2.5-72B-Instruct on AMD MI300X via vLLM 0.17.1 |
| * Agent Framework: LangGraph + LangChain |
| * Literature Retrieval: BioPython Entrez / PubMed NCBI + Europe PMC |
| * Web Framework: Flask with SSE streaming |
| * PDF: ReportLab |
| * Frontend: HTML, CSS, vanilla JS |
| * Runtime: Windows 11, Python 3.11, RTX 3060 12GB (local) + AMD MI300X 192GB (inference) |
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| ## API Endpoints |
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| * GET / � main UI |
| * GET /stream � SSE pipeline stream |
| * POST /query � standard pipeline (fallback) |
| * POST /score � confidence scoring |
| * POST /predict � predictive model |
| * POST /selective-review � focused literature review from selected papers |
| * POST /extract-table � evidence comparison table |
| * POST /export-pdf � PDF report download |
| * POST /followup � follow-up question against existing synthesis |
| * POST /suggest-queries � 3 AI-generated follow-up research questions |
| * GET /sessions � load query history |
| * POST /sessions/save � save completed query |
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| ## Environment |
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| * VLLM_BASE_URL: set via HF Space environment variable |
| * VLLM_API_KEY: EMPTY |
| * Python venv at ./venv |
| * Start server: venv\Scripts\activate && python app.py |
| * AMD MI300X: DigitalOcean droplet, 192GB VRAM |
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| ## Deployment |
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| * HF Space: https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/glitch-squad-biomedical-assistant |
| * GitHub: https://github.com/azlaan428/glitch-squad-biomedical-assistant |
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| ## What Remains |
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| 1. Demo video (under 5 minutes) |
| 2. Final lablab.ai submission |
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