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# mcp/orchestrator.py
import asyncio
from mcp.pubmed import fetch_pubmed
from mcp.arxiv import fetch_arxiv
from mcp.umls import extract_umls_concepts
from mcp.openfda import fetch_drug_safety
from mcp.ncbi import search_gene, get_mesh_definition
from mcp.mygene import fetch_gene_info
from mcp.ensembl import fetch_ensembl
from mcp.opentargets import fetch_ot
from mcp.clinicaltrials import search_trials
from mcp.cbio import fetch_cbio
from mcp.gemini import gemini_summarize, gemini_qa
from mcp.openai_utils import ai_summarize, ai_qa
from mcp.disgenet import disease_to_genes
async def orchestrate_search(query, llm="openai"):
# --- Literature: PubMed + arXiv
pubmed_task = asyncio.create_task(fetch_pubmed(query, max_results=7))
arxiv_task = asyncio.create_task(fetch_arxiv(query, max_results=7))
# --- UMLS, OpenFDA, Gene, Mesh
umls_task = asyncio.create_task(extract_umls_concepts(query))
fda_task = asyncio.create_task(fetch_drug_safety(query))
gene_ncbi_task = asyncio.create_task(search_gene(query))
mygene_task = asyncio.create_task(fetch_gene_info(query))
ensembl_task = asyncio.create_task(fetch_ensembl(query))
ot_task = asyncio.create_task(fetch_ot(query))
mesh_task = asyncio.create_task(get_mesh_definition(query))
# --- Trials, cBio, DisGeNET
trials_task = asyncio.create_task(search_trials(query, max_studies=10))
cbio_task = asyncio.create_task(fetch_cbio(query))
disgenet_task = asyncio.create_task(disease_to_genes(query))
# Run
pubmed, arxiv, umls, fda, ncbi, mygene, ensembl, ot, mesh, trials, cbio, disgenet = await asyncio.gather(
pubmed_task, arxiv_task, umls_task, fda_task, gene_ncbi_task,
mygene_task, ensembl_task, ot_task, mesh_task, trials_task, cbio_task, disgenet_task
)
# Genes: flatten and deduplicate
genes = []
for g in (ncbi, mygene, ensembl, ot):
if isinstance(g, list):
genes.extend(g)
elif isinstance(g, dict) and g:
genes.append(g)
genes = [g for i, g in enumerate(genes) if g and genes.index(g) == i] # dedup
# --- AI summary (LLM engine select)
papers = (pubmed or []) + (arxiv or [])
if llm == "gemini":
ai_summary = await gemini_summarize(" ".join([p.get("summary", "") for p in papers]))
llm_used = "gemini"
else:
ai_summary = await ai_summarize(" ".join([p.get("summary", "") for p in papers]))
llm_used = "openai"
return {
"papers": papers,
"genes": genes,
"umls": umls or [],
"gene_disease": disgenet if isinstance(disgenet, list) else [],
"mesh_defs": [mesh] if isinstance(mesh, str) and mesh else [],
"drug_safety": fda or [],
"clinical_trials": trials or [],
"variants": cbio if isinstance(cbio, list) else [],
"ai_summary": ai_summary,
"llm_used": llm_used
}
async def answer_ai_question(question, context="", llm="openai"):
# Gemini fallback if OpenAI quota is exceeded
try:
if llm == "gemini":
answer = await gemini_qa(question, context)
else:
answer = await ai_qa(question, context)
except Exception as e:
answer = f"LLM unavailable or quota exceeded. ({e})"
return {"answer": answer}
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