Update src/api/agent.py

src/api/agent.py

@@ -114,6 +114,8 @@ class AgentResponse(BaseModel):
     primo_search_url: Optional[str] = None
     primo_ai_url: Optional[str] = None
     pubmed_search_url: Optional[str] = None
+    primo_ai_url: Optional[str] = None
+    pubmed_search_url: Optional[str] = None
 
 
 # ── Constants ─────────────────────────────────────────────────────────────────
@@ -1024,12 +1026,16 @@ async def _search_pubmed_articles(context: SearchContextPayload, limit: int = 5)
 def _tool_urls(context: SearchContextPayload) -> Dict[str, str]:
     ai_query = quote(context.ai_tool_query or context.display_topic or context.topic)
     clean_topic = _strip_resource_noise(context.topic)
+    semantic_q = quote((context.ai_tool_query or context.display_topic or context.topic).replace(" ", "+"))
     return {
         "leapspace": f"https://www.sciencedirect.com/leapspace?query={ai_query}",
         "scopus_ai": f"https://www-scopus-com.khalifa.idm.oclc.org/pages/ai?query={ai_query}",
         "ebsco_ai": f"https://research-ebsco-com.khalifa.idm.oclc.org/c/ntdlkp/search?q={ai_query}",
         "primo_ai": f"{PRIMO_AI_URL}{ai_query}",
         "consensus": f"https://consensus.app/results/?q={ai_query}",
+        "openalex": f"https://openalex.org/works?page=1&sort=relevance_score:desc&search.semantic={semantic_q}",
+        "scispace": f"https://scispace.com/search?q={ai_query}",
+        "perplexity": f"https://www.perplexity.ai/search?q={ai_query}",
         "primo_discovery": _primo_clean_url(context),
         "pubmed": build_pubmed_url(clean_topic, context.year_from, context.year_to) if context.intent == "search_medical" else "",
     }
@@ -1043,7 +1049,8 @@ def _database_guidance_for_topic(context: SearchContextPayload) -> str:
         f'<a href="{urls["scopus_ai"]}" target="_blank" style="color:#1e40af;font-weight:700;text-decoration:none">Scopus AI</a> · '
         f'<a href="{urls["ebsco_ai"]}" target="_blank" style="color:#9d174d;font-weight:700;text-decoration:none">EBSCO AI</a> · '
         f'<a href="{urls["primo_ai"]}" target="_blank" style="color:#5b21b6;font-weight:700;text-decoration:none">PRIMO AI Assistant</a> · '
-        f'<a href="{urls["consensus"]}" target="_blank" style="color:#86198f;font-weight:700;text-decoration:none">Consensus</a>'
+        f'<a href="{urls["consensus"]}" target="_blank" style="color:#86198f;font-weight:700;text-decoration:none">Consensus</a> · '
+        f'<a href="{urls["openalex"]}" target="_blank" style="color:#0c4a6e;font-weight:700;text-decoration:none">OpenAlex</a>'
     )
     more_results = f'<a href="{urls["primo_discovery"]}" target="_blank" style="color:#5b21b6;font-weight:700;text-decoration:none">PRIMO Library Discovery</a>'
     if context.intent == "search_medical" and urls["pubmed"]:
@@ -1308,28 +1315,24 @@ def _apply_follow_up_action(base_context: SearchContextPayload, action: Optional
 
 async def _generate_topic_follow_ups(topic: str, model: str) -> List[dict]:
     """
-    FIX: Generate 3 topic-specific intellectual follow-up questions using LLM.
-    These are ASPECT-based (related subtopics, methodological angles, applications)
-    not generic filter suggestions like "peer reviewed only".
+    Generate 3 topic-specific follow-up questions that ALWAYS preserve the original topic anchor.
+    These should start a fresh contextual search, not a refinement action.
     """
     settings = get_settings()
     if not settings.openai_api_key:
         return []
     try:
-        llm = _get_llm(model, temperature=0.5, max_tokens=200)
+        llm = _get_llm(model, temperature=0.5, max_tokens=220)
         response = await llm.ainvoke([
             {"role": "system", "content": (
                 "You are a research librarian helping a user explore a topic more deeply. "
-                "Given a research topic, generate exactly 3 short follow-up questions that explore "
-                "DIFFERENT ASPECTS of the topic — such as subtopics, methodological angles, "
-                "applications, comparisons, or related fields. "
-                "Do NOT suggest filter actions like peer-reviewed, date ranges, or format changes. "
-                "Return ONLY a JSON array of 3 strings, each under 12 words. "
-                "No explanation, no preamble, no markdown.\n"
-                "Example for 'quantum computing':\n"
-                '[\"What are the main hardware approaches in quantum computing?\", '
-                '"How does quantum error correction work?\", '
-                '"Applications of quantum computing in cryptography\"]'
+                "Given a research topic, generate exactly 3 short follow-up questions that explore different aspects of the SAME topic. "
+                "Every suggestion must keep the original topic explicitly present. "
+                "For example, if the topic is 'global warming', acceptable suggestions include "
+                "'What are the main adaptation strategies for global warming?' and "
+                "'How does global warming affect water security?'. "
+                "Do NOT suggest generic filters like peer reviewed or last 5 years. "
+                "Return ONLY a JSON array of 3 strings."
             )},
             {"role": "user", "content": f"Topic: {topic}"},
         ])
@@ -1339,16 +1342,26 @@ async def _generate_topic_follow_ups(topic: str, model: str) -> List[dict]:
         questions = json.loads(raw)
         if not isinstance(questions, list):
             return []
-        return [
-            {"label": q.strip("?") + "?", "question": q.strip("?") + "?"}
-            for q in questions[:3]
-            if isinstance(q, str) and q.strip()
-        ]
+        out = []
+        topic_l = (topic or "").lower()
+        for q in questions[:3]:
+            if not isinstance(q, str) or not q.strip():
+                continue
+            clean = q.strip().rstrip('?')
+            if topic_l not in clean.lower():
+                clean = f"{clean} for {topic}"
+            full_q = clean + "?"
+            out.append({
+                "label": full_q,
+                "question": f"Find research articles on {full_q}",
+            })
+        return out
     except Exception as e:
         logger.warning(f"Topic follow-up generation failed: {e}")
         return []
 
 
+
 async def _search_follow_up(
     context: SearchContextPayload, model: str, summary_mode: bool = False
 ) -> Tuple[str, List[dict]]: