Create orchestrator.py

mcp/orchestrator.py

@@ -0,0 +1,37 @@
+# mcp/orchestrator.py
+
+from mcp.arxiv import fetch_arxiv
+from mcp.pubmed import fetch_pubmed
+from mcp.nlp import extract_keywords
+from mcp.umls import lookup_umls
+from mcp.openfda import fetch_drug_safety
+from mcp.openai_utils import ai_summarize, ai_qa
+
+async def orchestrate_search(query: str) -> dict:
+    # Fetch results in parallel (use asyncio.gather for speed)
+    arxiv_results = await fetch_arxiv(query)
+    pubmed_results = await fetch_pubmed(query)
+    all_papers = arxiv_results + pubmed_results
+    # Semantic ranking (use OpenAI embeddings or similar)
+    # ...
+    # NLP: extract keywords/drugs
+    keywords = extract_keywords(" ".join([p['summary'] for p in all_papers]))
+    # UMLS enrichment
+    umls_results = [await lookup_umls(k) for k in keywords]
+    # Drug safety
+    drug_data = [await fetch_drug_safety(k) for k in keywords]
+    # Summarization
+    summary = await ai_summarize(" ".join([p['summary'] for p in all_papers]))
+    # Suggest reading (top links)
+    links = [p['link'] for p in all_papers[:3]]
+    return {
+        "papers": all_papers,
+        "umls": umls_results,
+        "drug_safety": drug_data,
+        "ai_summary": summary,
+        "suggested_reading": links,
+    }
+
+async def answer_ai_question(question: str, context: str = "") -> dict:
+    answer = await ai_qa(question, context)
+    return {"answer": answer}