# Step 2: Embed query
embeddings = self.embedding_model.embed(query, "search")

# Step 3: Semantic search (over-fetch for scoring pool)
internal_limit = max(limit * 4, 60)
semantic_results = self.vector_store.search(
    query=query, vectors=embeddings, top_k=internal_limit, filters=filters
)

# Step 4: Keyword search (if store supports it)
keyword_results = self.vector_store.keyword_search(
    query=query_lemmatized, top_k=internal_limit, filters=filters
)