stages dict | eval dict | storage dict | samples dict |
|---|---|---|---|
{
"model_load": {
"seconds": 54.8586,
"embedder": "Qwen/Qwen3-VL-Embedding-2B"
},
"ingest_text": {
"seconds": 8.0462,
"chunks": 38
},
"ingest_images": {
"seconds": 2.7866,
"images": 12
},
"ingest_3d_views": {
"seconds": 0.2765,
"views": 6
},
"ingest_video": {
"secon... | {
"text_recall": {
"n": 10,
"ann_binary_top200_recall@5": 1,
"float_mrl512_top20_recall@5": 1,
"cross_encoder_recall@5": 1
},
"text_to_image_top5": {
"n": 4,
"hits": 4
},
"text_to_audio_top1": {
"n": 4,
"hits": 4
},
"asset_view_grouping": {
"views_in_top6": 4,
"expe... | {
"chunks": 68,
"binary_index_bytes": 4352,
"float_store_bytes": 139264,
"full_float_would_be_bytes": 557056,
"storage_compression_vs_full_float": 3.88
} | {
"audio_sr": 44100,
"stt_transcript": "Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.",
"text_qa_example": {
"question": "Which planet has the hottest surface in the solar system and why?",
"gold": "text:corpus:0",
"ann_top1": "text:corpus:0",
"reranked_to... |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
AnyModal RAG — validated end-to-end prototype
Reference run: job 6ab1136251992417dfccf413 (a10g-small, 4m35s wall-clock, inference only).
Stack (all open weights)
- Unified embedder:
Qwen/Qwen3-VL-Embedding-2B— one 2048-d space for text, images, video keyframes, 3D-proxy views - Reranker:
Qwen/Qwen3-Reranker-0.6B(cross-encoder) - Audio:
laion/larger_clap_general(Apache-2.0 audio/text space), faster-whispertinyfor STT - Generator:
Qwen/Qwen3-1.7B(decoder-only, citations pinned to chunk ids) - Index: sqlite-vec, binary (1-bit/dim) top-200 → float MRL-512 top-20 → cross-encoder top-5
Measured results
| Metric | Value |
|---|---|
| Text retrieval: binary ANN top-200 recall@5 | 1.0 |
| Float MRL-512 top-20 recall@5 | 1.0 |
| Cross-encoder recall@5 | 1.0 |
| Text→image hits@5 | 4/4 |
| Text→audio hits@1 | 4/4 |
| Storage compression vs full float | 3.88× (68 chunks) |
| Query pipeline latency (embed→ANN→float→cross-encoder) | 146 ms total (binary ANN 1.1 ms, float rerank 9.7 ms, cross-encoder 135 ms) |
| Query embedding | 6.4 ms |
| STT (faster-whisper tiny) | correct LibriSpeech transcript |
| RAG answers | 3/3 correct, grounded, with citations |
Files
anyrag_demo.py— full pipeline (ingest → embed → index → retrieve → rerank → generate), all modalities in one scriptmetrics.json— raw metrics from the passing v6 run
Full design doc: see conversation blueprint artifact (stage × modality matrix, architecture rationale, cost analysis).
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