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"""
Medical Q&A UI - BM25 + Dense Retrieval Models WITH DISK CACHING
This version caches the indexes to disk for fast startup (30 seconds vs 5-8 minutes!)
"""
import gradio as gr
from typing import Dict, List
from pathlib import Path
import pickle
import hashlib
import json
from retriever.index_bm25 import BM25Index
from retriever.index_dense import DenseIndex
from retriever.ingest import load_jsonl
from retriever.rrf import rrf
from team.interfaces import Candidate
# Cache directory
CACHE_DIR = Path("cache")
CACHE_DIR.mkdir(exist_ok=True)
print("=" * 70)
print(" Medical Document Retrieval System (CACHED VERSION)")
print(" Using BM25 + Dense Embeddings + RRF Fusion")
print(" With disk caching for fast startup!")
print("=" * 70)
def _default_corpora_config() -> Dict[str, dict]:
return {
"medical_qa": {"path": "data/corpora/medical_qa.jsonl",
"text_fields": ["question", "answer", "title"]},
"miriad": {"path": "data/corpora/miriad_text.jsonl",
"text_fields": ["question", "answer", "title"]},
"unidoc": {"path": "data/corpora/unidoc_qa.jsonl",
"text_fields": ["question", "answer", "title"]},
}
def _available(cfg: Dict[str, dict]) -> Dict[str, dict]:
return {k: v for k, v in cfg.items() if Path(v["path"]).exists()}
def _get_cache_key(corpora_config: Dict[str, dict]) -> str:
"""Generate a unique cache key based on corpora config"""
config_str = json.dumps(corpora_config, sort_keys=True)
return hashlib.md5(config_str.encode()).hexdigest()
class CachedRetriever:
"""Retriever with disk caching for BM25 and Dense indexes"""
def __init__(self, corpora_config: Dict[str, dict], use_reranker: bool = False):
self.corpora_config = corpora_config
self.use_reranker = use_reranker
self.cache_key = _get_cache_key(corpora_config)
# Cache file paths
self.bm25_cache = CACHE_DIR / f"bm25_{self.cache_key}.pkl"
self.dense_cache = CACHE_DIR / f"dense_{self.cache_key}.pkl"
self.docs_cache = CACHE_DIR / f"docs_{self.cache_key}.pkl"
# Load or build indexes
self.docs_all = self._load_or_build_docs()
self.bm25 = self._load_or_build_bm25()
self.dense = self._load_or_build_dense()
def _load_or_build_docs(self) -> List:
"""Load documents from cache or build from scratch"""
if self.docs_cache.exists():
print(f"Loading documents from cache... ({self.docs_cache.name})")
try:
with open(self.docs_cache, 'rb') as f:
docs_all = pickle.load(f)
print(f" ✓ Loaded {len(docs_all)} documents from cache")
return docs_all
except Exception as e:
print(f" ✗ Cache load failed: {e}")
print(" → Rebuilding documents...")
print("Building documents from corpora files...")
docs_all = []
for name, cfg in self.corpora_config.items():
print(f" Loading {name}...")
docs = load_jsonl(cfg["path"], tuple(cfg.get("text_fields", ("question", "answer"))))
docs_all.extend(docs)
# Save to cache
print(f"Saving documents to cache... ({len(docs_all)} docs)")
with open(self.docs_cache, 'wb') as f:
pickle.dump(docs_all, f)
return docs_all
def _load_or_build_bm25(self) -> BM25Index:
"""Load BM25 index from cache or build from scratch"""
if self.bm25_cache.exists():
print(f"Loading BM25 index from cache... ({self.bm25_cache.name})")
try:
with open(self.bm25_cache, 'rb') as f:
bm25_index = pickle.load(f)
print(f" ✓ BM25 index loaded from cache")
return bm25_index
except Exception as e:
print(f" ✗ Cache load failed: {e}")
print(" → Rebuilding BM25 index...")
print("Building BM25 index from scratch...")
bm25_index = BM25Index(self.docs_all)
# Save to cache
print(f"Saving BM25 index to cache...")
with open(self.bm25_cache, 'wb') as f:
pickle.dump(bm25_index, f)
return bm25_index
def _load_or_build_dense(self) -> DenseIndex:
"""Load Dense index from cache or build from scratch"""
if self.dense_cache.exists():
print(f"Loading Dense index from cache... ({self.dense_cache.name})")
try:
with open(self.dense_cache, 'rb') as f:
dense_index = pickle.load(f)
print(f" ✓ Dense index loaded from cache")
return dense_index
except Exception as e:
print(f" ✗ Cache load failed: {e}")
print(" → Rebuilding Dense index...")
print("Building Dense index from scratch (this takes 5-8 minutes)...")
dense_index = DenseIndex(self.docs_all)
# Save to cache
print(f"Saving Dense index to cache...")
with open(self.dense_cache, 'wb') as f:
pickle.dump(dense_index, f)
return dense_index
# Initialize cached retriever (fast if cached, slow first time)
print("\nInitializing retrieval system...")
cfg = _available(_default_corpora_config())
if not cfg:
raise RuntimeError("No corpora files found in data/corpora. Build them first.")
retriever = CachedRetriever(corpora_config=cfg, use_reranker=False)
print("\n✓ Retrieval system ready!")
print(f" Total documents indexed: {len(retriever.docs_all):,}")
print("=" * 70)
def get_candidates_cached(query: str, k_retrieve: int = 50) -> List[Candidate]:
"""
Returns top-N fused candidates with component scores (bm25, dense, rrf).
Uses the cached retriever for fast queries.
"""
# Get separate result lists (doc, score)
bm = retriever.bm25.search(query, k=max(k_retrieve, 100))
de = retriever.dense.search(query, k=max(k_retrieve, 100))
# Maps for score lookup
bm_map = {d.id: float(s) for d, s in bm}
de_map = {d.id: float(s) for d, s in de}
# Fuse and pick candidate set
fused = rrf([bm, de], k=max(k_retrieve, 50))
# Compute RRF per candidate using rank positions
K = 60
bm_rank = {d.id: i for i, (d, _) in enumerate(bm)}
de_rank = {d.id: i for i, (d, _) in enumerate(de)}
out: List[Candidate] = []
for doc, _ in fused[:k_retrieve]:
rrf_score = 0.0
if doc.id in bm_rank:
rrf_score += 1.0 / (K + bm_rank[doc.id] + 1)
if doc.id in de_rank:
rrf_score += 1.0 / (K + de_rank[doc.id] + 1)
out.append(Candidate(
id=doc.id,
title=doc.title or "",
text=doc.text,
meta=doc.meta or {},
bm25=bm_map.get(doc.id, 0.0),
dense=de_map.get(doc.id, 0.0),
rrf=rrf_score,
))
# Baseline order: RRF
out.sort(key=lambda c: c.rrf, reverse=True)
return out
def retrieve_documents(query, num_results=5):
"""Retrieve relevant medical documents using your team's models"""
if not query or not query.strip():
return """
<div style="padding: 20px; background-color: #e7f3ff; border-radius: 10px; border-left: 5px solid #2196f3;">
<h3 style="margin-top: 0; color: #0d47a1;">How to Use</h3>
<p style="margin: 0; color: #1565c0;">Enter a medical query and we'll find relevant documents using BM25 + Dense retrieval with RRF fusion.</p>
<p style="margin: 8px 0 0 0; color: #1565c0;"><strong>Example:</strong> "headache with blurred vision" or "symptoms of diabetes"</p>
</div>
"""
try:
# Use cached retrieval system (fast!)
hits = get_candidates_cached(query=query, k_retrieve=num_results)
if not hits:
return """
<div style="padding: 20px; background-color: #fff3cd; border-radius: 10px; border-left: 5px solid #ffc107;">
<h3 style="margin-top: 0; color: #856404;">No Results Found</h3>
<p style="margin: 0; color: #856404;">Try rephrasing your query or using different medical terms.</p>
</div>
"""
# Build results HTML
result_html = f"""
<div style="padding: 15px; background: linear-gradient(135deg, #d4edda 0%, #c3e6cb 100%); border-radius: 10px; margin-bottom: 20px; border-left: 5px solid #28a745;">
<h3 style="margin-top: 0; color: #155724;">Found {len(hits)} Relevant Medical Documents</h3>
<p style="margin: 0;"><strong>Retrieved using:</strong> BM25 + Dense Embeddings + RRF Fusion (CACHED)</p>
</div>
"""
for i, hit in enumerate(hits, 1):
title = hit.title if hit.title and hit.title.strip() else None
source = hit.meta.get('source', 'Unknown') if hit.meta else 'Unknown'
# Check if we have separate question/answer fields in metadata
question = hit.meta.get('question', '') if hit.meta else ''
answer = hit.meta.get('answer', '') if hit.meta else ''
# If we have separate Q&A, format them nicely
if question and answer:
content_html = f"""
<div style="margin-bottom: 12px;">
<strong style="color: #1976d2;">Question:</strong>
<p style="margin: 5px 0 0 0; line-height: 1.6; color: #424242;">{question}</p>
</div>
<div>
<strong style="color: #388e3c;">Answer:</strong>
<p style="margin: 5px 0 0 0; line-height: 1.6; color: #424242;">{answer[:500] + ("..." if len(answer) > 500 else "")}</p>
</div>
"""
else:
# Fallback to combined text
text = hit.text[:500] + ("..." if len(hit.text) > 500 else "")
content_html = f'<p style="margin: 0; line-height: 1.7; color: #34495e;">{text}</p>'
# Display relevance scores
bm25_score = hit.bm25
dense_score = hit.dense
rrf_score = hit.rrf
# Build title HTML only if title exists
title_html = f'<h4 style="margin: 0 0 15px 0; color: #2c3e50;">{title}</h4>' if title else ''
result_html += f"""
<div style="border: 2px solid #dee2e6; padding: 20px; margin: 20px 0; border-radius: 10px; background-color: white; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 15px; margin: -20px -20px 20px -20px; border-radius: 8px 8px 0 0;">
<div style="display: flex; justify-content: space-between; align-items: center;">
<h4 style="margin: 0; color: white;">Document #{i}</h4>
<span style="background-color: rgba(255,255,255,0.2); padding: 4px 12px; border-radius: 12px; font-size: 0.85em; color: white;">
{source}
</span>
</div>
</div>
<div style="margin-bottom: 15px;">
{title_html}
{content_html}
</div>
<div style="padding-top: 12px; border-top: 1px solid #e9ecef;">
<div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 10px;">
<div style="background-color: #e3f2fd; padding: 8px; border-radius: 5px; text-align: center;">
<div style="font-size: 0.75em; color: #1976d2; font-weight: bold;">BM25</div>
<div style="font-size: 1.1em; color: #0d47a1;">{bm25_score:.4f}</div>
</div>
<div style="background-color: #f3e5f5; padding: 8px; border-radius: 5px; text-align: center;">
<div style="font-size: 0.75em; color: #7b1fa2; font-weight: bold;">Dense</div>
<div style="font-size: 1.1em; color: #4a148c;">{dense_score:.4f}</div>
</div>
<div style="background-color: #e8f5e9; padding: 8px; border-radius: 5px; text-align: center;">
<div style="font-size: 0.75em; color: #388e3c; font-weight: bold;">RRF Fusion</div>
<div style="font-size: 1.1em; color: #1b5e20;">{rrf_score:.4f}</div>
</div>
</div>
</div>
</div>
"""
return result_html
except Exception as e:
return f"""
<div style="padding: 20px; background-color: #f8d7da; border-radius: 10px; border-left: 5px solid #dc3545;">
<h3 style="margin-top: 0; color: #721c24;">Error</h3>
<p style="margin: 0; color: #721c24;">{str(e)}</p>
</div>
"""
# Create Gradio interface
with gr.Blocks(title="Medical Document Retrieval (Cached)") as demo:
gr.Markdown("""
# Medical Document Retrieval System (CACHED VERSION)
**Models:**
- BM25 Index (keyword-based retrieval)
- Dense Embeddings (embeddinggemma-300m-medical)
- RRF Fusion (combines both approaches)
### Features:
- Searches across 10,000+ medical documents
- Shows relevance scores from each model component
- Returns the most relevant medical information
""")
with gr.Row():
with gr.Column():
query_input = gr.Textbox(
label="Enter your medical query",
placeholder="Example: headache with blurred vision",
lines=2
)
num_results = gr.Slider(
minimum=1,
maximum=10,
value=5,
step=1,
label="Number of results to retrieve"
)
submit_btn = gr.Button("Retrieve Documents", variant="primary", size="lg")
output_html = gr.HTML(label="Search Results")
submit_btn.click(
fn=retrieve_documents,
inputs=[query_input, num_results],
outputs=output_html
)
gr.Examples(
examples=[
"headache with blurred vision",
"symptoms of diabetes",
"chest pain when exercising",
"treatment for high blood pressure",
"causes of chronic fatigue",
],
inputs=query_input,
label="Try these example queries:"
)
gr.Markdown("""
---
### Technical Details
- **BM25**: Statistical keyword matching (TF-IDF based)
- **Dense**: Semantic search using transformer embeddings
- **RRF Fusion**: Reciprocal Rank Fusion combines both methods
- **Caching**: Indexes saved to disk in `cache/` folder for fast reloading
*Note: First launch builds and caches indexes (5-8 min). After that, startup takes only ~30 seconds!*
""")
print("\nOpening web interface...")
print(" Local access: http://127.0.0.1:7863")
print(" Public link will be generated...")
print("=" * 70)
if __name__ == "__main__":
demo.launch(server_name="127.0.0.1", server_port=7863, share=True)
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