project.json

{
  "name": "Multimodal Document Retrieval Baseline",
  "problem": "Business documents contain meaning in text, tables, layout, and imagery that text-only retrieval can miss.",
  "domain": "multimodal-ai",
  "architecture": "retrieval",
  "hugging_face_tasks": [
    "visual-document-retrieval",
    "document-question-answering",
    "image-to-text",
    "feature-extraction"
  ],
  "recommended_stack": [
    "FastAPI for ingestion and multimodal search",
    "Transformers with LayoutLMv3 or ColPali-style encoders",
    "OCR adapter plus image captioning",
    "PostgreSQL plus pgvector for fused embeddings",
    "Object storage for source documents",
    "OpenTelemetry plus retrieval ablation reports"
  ],
  "real_world_data_sources": [
    {
      "name": "SEC EDGAR company facts API",
      "url": "https://data.sec.gov/api/xbrl/companyfacts/CIK0000320193.json",
      "purpose": "Real tabular filing facts and document metadata"
    },
    {
      "name": "arXiv API",
      "url": "https://export.arxiv.org/api/query?search_query=all:document%20understanding&start=0&max_results=5",
      "purpose": "Public papers and document-layout retrieval scenarios"
    }
  ],
  "job_description_skills": [
    "Multimodal embeddings and document understanding",
    "OCR, layout, image, and text feature fusion",
    "Vector retrieval and modality-aware ranking",
    "Ablation testing and retrieval evaluation",
    "Scalable document ingestion and object storage"
  ],
  "impact_targets": [
    "Improve Recall@5 by >= 20% over text-only retrieval",
    "Report modality ablations for OCR, layout, and image signals",
    "Index 100,000 documents with resumable ingestion",
    "Keep p95 retrieval latency below 500 ms"
  ],
  "baseline_evaluation": {
    "test_examples": 4,
    "accuracy": 1,
    "synthetic_evaluation": true
  },
  "estimated_delivery": "8-12 weeks for one engineer",
  "generated_baseline_is_production_ready": false
}