Update mcp/orchestrator.py

mcp/orchestrator.py

@@ -21,7 +21,7 @@ from mcp.umls            import lookup_umls
 from mcp.openfda         import fetch_drug_safety
 from mcp.disgenet        import disease_to_genes
 from mcp.clinicaltrials  import fetch_clinical_trials
-from mcp.cbio            import fetch_cbio_variants
+from mcp.cbio            import fetch_cbio
 from mcp.openai_utils    import ai_summarize, ai_qa
 from mcp.gemini          import gemini_summarize, gemini_qa
 
@@ -117,7 +117,7 @@ async def orchestrate_search(query: str, llm: str = _DEFAULT_LLM) -> Dict[str, A
     gene_task  = asyncio.create_task(_gene_enrichment(seeds))
     trials_t   = asyncio.create_task(fetch_clinical_trials(query, max_studies=10))
     cbio_t     = asyncio.create_task(
-        fetch_cbio_variants(seeds[0]) if seeds else asyncio.sleep(0, result=[])
+        fetch_cbio(seeds[0]) if seeds else asyncio.sleep(0, result=[])
     )
 
     umls_list, fda_list, gene_data, trials, variants = await asyncio.gather(
@@ -173,7 +173,7 @@ async def orchestrate_search(query: str, llm: str = _DEFAULT_LLM) -> Dict[str, A
 
 async def answer_ai_question(question: str, context: str, llm: str = _DEFAULT_LLM) -> Dict[str, str]:
     """
-    Follow-up QA: uses the designated QA function from the LLM router.
+    Follow-up QA: uses the chosen LLM’s QA function.
     """
     _, qa_fn, _ = _llm_router(llm)
     prompt = f"Q: {question}\nContext: {context}\nA:"