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MediGuard AI — Retrieve Node
Performs hybrid search (BM25 + vector KNN) and merges results.
"""
from __future__ import annotations
import logging
from typing import Any
logger = logging.getLogger(__name__)
def retrieve_node(state: dict, *, context: Any) -> dict:
"""Retrieve documents from OpenSearch via hybrid search."""
query = state.get("rewritten_query") or state.get("query", "")
# 1. Try cache first
cache_key = f"retrieve:{query}"
if context.cache:
cached = context.cache.get(cache_key)
if cached is not None:
logger.debug("Cache hit for retrieve query")
return {"retrieved_documents": cached}
# 2. Embed the query
try:
query_embedding = context.embedding_service.embed_query(query)
except Exception as exc:
logger.error("Embedding failed: %s", exc)
return {"retrieved_documents": [], "errors": [str(exc)]}
# 3. Hybrid search
try:
results = context.opensearch_client.search_hybrid(
query_text=query,
query_vector=query_embedding,
top_k=10,
)
except Exception as exc:
logger.error("OpenSearch hybrid search failed: %s — falling back to BM25", exc)
try:
results = context.opensearch_client.search_bm25(
query_text=query,
top_k=10,
)
except Exception as exc2:
logger.error("BM25 fallback also failed: %s", exc2)
return {"retrieved_documents": [], "errors": [str(exc), str(exc2)]}
documents = [
{
"id": hit.get("_id", ""),
"score": hit.get("_score", 0.0),
"text": hit.get("_source", {}).get("chunk_text", ""),
"title": hit.get("_source", {}).get("title", ""),
"section": hit.get("_source", {}).get("section_title", ""),
"metadata": hit.get("_source", {}),
}
for hit in results
]
# 4. Store in cache (5 min TTL)
if context.cache:
context.cache.set(cache_key, documents, ttl=300)
return {"retrieved_documents": documents}
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