Upload 2 files

src/api/agent.py

@@ -213,6 +213,11 @@ SUMMARY_RE = re.compile(
     re.IGNORECASE,
 )
 
+BEST_DATABASES_RE = re.compile(
+    r"\b(best databases?|which databases?|which database|best database|recommended databases?)\b",
+    re.IGNORECASE,
+)
+
 MEDICAL_KEYWORDS = [
     "medicine", "medical", "health", "clinical", "nursing", "pharmacy", "biomedical",
     "anatomy", "physiology", "epidemiology", "oncology", "cardiology", "neuroscience",
@@ -1022,6 +1027,7 @@ 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)
     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}",
@@ -1030,119 +1036,164 @@ def _tool_urls(context: SearchContextPayload) -> Dict[str, str]:
     }
 
 
-_BOOLEAN_PHRASE_SYNONYMS: Dict[str, List[str]] = {
-    "global warming": ["climate change"],
-    "artificial intelligence": ["AI", "machine learning"],
-    "machine learning": ["artificial intelligence", "AI"],
-    "healthcare": ["clinical", "medicine"],
-    "energy transition": ["decarbonization", "low carbon"],
-}
-_BOOLEAN_TOKEN_SYNONYMS: Dict[str, List[str]] = {
-    "india": ["indian"],
-    "adaptation": ["resilience"],
-    "mitigation": ["emissions reduction"],
-    "transport": ["transportation"],
-    "climate": ["warming"],
-}
-
-
-def _boolean_or_group(terms: List[str]) -> str:
-    clean=[]
-    seen=set()
-    for term in terms:
-        t=_normalize_whitespace(term).strip('"').strip()
-        if not t:
-            continue
-        key=t.lower()
-        if key in seen:
-            continue
-        seen.add(key)
-        if re.search(r"\s", t):
-            clean.append(f'"{t}"')
-        else:
-            clean.append(t)
-    if not clean:
-        return '"library search"'
-    if len(clean)==1:
-        return clean[0]
-    return '(' + ' OR '.join(clean) + ')'
-
-
-def _make_primo_boolean_query(context: SearchContextPayload) -> str:
-    topic = _strip_resource_noise(_title_case_topic(context.display_topic or context.topic))
-    if not topic:
-        return '"library search"'
-    lower = topic.lower()
-    groups: List[str] = []
-    consumed = lower
-    for phrase, syns in sorted(_BOOLEAN_PHRASE_SYNONYMS.items(), key=lambda kv: len(kv[0]), reverse=True):
-        if phrase in consumed:
-            groups.append(_boolean_or_group([phrase] + syns))
-            consumed = consumed.replace(phrase, ' ')
-    for token in re.findall(r"[a-z0-9-]+", consumed):
-        if len(token) < 3:
-            continue
-        groups.append(_boolean_or_group([token] + _BOOLEAN_TOKEN_SYNONYMS.get(token, [])))
-    if not groups:
-        groups = [_boolean_or_group([topic])]
-    return ' AND '.join(groups)
-
-
-def _primo_clean_url(context: SearchContextPayload) -> str:
-    clean_topic = _strip_resource_noise(context.topic) or context.topic
-    return f"{PRIMO_BASE_URL}&query=any,contains,{quote(clean_topic)}&lang=en&mode=advanced&offset=0"
+def _ai_source_links_html(context: SearchContextPayload) -> str:
+    urls = _tool_urls(context)
+    return (
+        f'<a href="{urls["leapspace"]}" target="_blank" style="color:#9a3412;font-weight:700;text-decoration:none">LeapSpace</a> · '
+        f'<a href="{urls["scopus_ai"]}" target="_blank" style="color:#1e40af;font-weight:700;text-decoration:none">Scopus AI</a> · '
+        f'<a href="{urls["consensus"]}" target="_blank" style="color:#86198f;font-weight:700;text-decoration:none">Consensus</a> · '
+        f'<a href="{urls["primo_discovery"]}" target="_blank" style="color:#5b21b6;font-weight:700;text-decoration:none">PRIMO Library Discovery</a>'
+    )
 
 
-def _build_trace_block(user_question: str, context: SearchContextPayload, used_summary: bool) -> str:
+def _database_guidance_for_topic(context: SearchContextPayload) -> str:
     topic = _escape(context.display_topic or context.topic)
-    q = _escape(_normalize_whitespace(user_question))
-    boolean_q = _escape(context.primo_boolean_query or _make_primo_boolean_query(context))
-    if used_summary:
-        how = (
-            f"I interpreted your question as <strong>{topic}</strong>, used Semantic Scholar abstracts to prepare a brief literature snapshot when available, "
-            f"and searched PRIMO for KU-accessible results."
-        )
-    else:
-        how = (
-            f"I interpreted your question as <strong>{topic}</strong> and searched PRIMO for KU-accessible results using the Boolean query below."
+    links = _ai_source_links_html(context)
+    if context.intent == "search_medical":
+        return (
+            f"For a stronger evidence trail on <strong>{topic}</strong>, start with <strong>PubMed</strong> or <strong>Embase</strong> for the core medical literature, "
+            f"then continue with {links}."
         )
     return (
-        '<div style="margin:10px 0 14px 0;padding:12px 14px;background:#f8fafc;border:1px solid #dbeafe;border-radius:12px">'
-        f"<strong>Your question:</strong> {q}<br>"
-        f"<strong>Interpreted topic:</strong> {topic}<br>"
-        f"<strong>PRIMO query used:</strong> <code>{boolean_q}</code><br>"
-        f"<strong>How I found this:</strong> {how}"
-        '</div>'
+        f"For deeper work on <strong>{topic}</strong>, continue with {links}. "
+        "Use Scopus AI for broader scholarly mapping, LeapSpace for guided evidence exploration, "
+        "Consensus for quick paper-grounded answers, and PRIMO Library Discovery for KU-accessible material."
     )
 
 
-def _research_end_note() -> str:
-    return "<br><br><em>For deeper research, recommended AI research tools are listed at the end of this answer.</em>"
+def _looks_database_recommendation_question(question: str) -> bool:
+    q = (question or "").lower()
+    return bool(BEST_DATABASES_RE.search(q)) and any(kw in q for kw in [
+        "database", "databases", "subject", "engineering", "medicine", "medical", "ai", "computer",
+        "physics", "chemistry", "chemical", "civil", "mechanical", "electrical", "business",
+        "biomedical", "nursing", "systematic review", "health"
+    ])
 
 
-def _contact_footer_for_query(intent: str, question: str, context: Optional[SearchContextPayload] = None) -> str:
-    q = (question or '').lower()
-    blocks = [
-        '<br><br><strong>Library contact points</strong><br>'
-        f'General help: <a href="{ASK_LIBRARIAN_URL}" target="_blank">Ask a Librarian</a> · '
-        '<a href="mailto:libse@ku.ac.ae">libse@ku.ac.ae</a>'
+def _database_recommendation_payload(question: str) -> Dict[str, Any]:
+    q = (question or "").lower()
+    general = {
+        "title": "General / multidisciplinary research",
+        "intro": "These are the best starting databases for broad academic searching at KU.",
+        "items": [
+            {"name": "PRIMO Library Discovery", "url": "https://khalifa.primo.exlibrisgroup.com/discovery/search?vid=971KUOSTAR_INST:KU", "why": "Best first stop for KU-accessible books, articles, journals, and holdings."},
+            {"name": "Scopus", "url": "https://khalifa.idm.oclc.org/login?url=https://www.scopus.com", "why": "Broad multidisciplinary database for scholarly discovery and citation tracking."},
+            {"name": "Web of Science", "url": "https://khalifa.idm.oclc.org/login?url=https://www.webofscience.com", "why": "Strong citation database for high-level literature mapping and journal evaluation."},
+            {"name": "ScienceDirect", "url": "https://khalifa.idm.oclc.org/login?url=https://www.sciencedirect.com", "why": "Large full-text platform across science, engineering, and medicine."},
+        ],
+    }
+    subjects = [
+        (["systematic review", "medicine", "medical", "health", "nursing", "clinical", "biomedical", "pharmacy"], {
+            "title": "Medical and health sciences",
+            "intro": "For medical evidence and subject searching, start with these databases.",
+            "items": [
+                {"name": "PubMed@KU", "url": "http://www.ncbi.nlm.nih.gov/pubmed/?otool=iaekustlib", "why": "Core biomedical literature with KU full-text linking."},
+                {"name": "Embase", "url": "https://khalifa.idm.oclc.org/login?url=https://www.embase.com", "why": "Excellent for biomedical, drugs, devices, and evidence searching."},
+                {"name": "CINAHL Ultimate", "url": "https://khalifa.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=cul", "why": "Best for nursing and allied health literature."},
+                {"name": "Cochrane Library", "url": "https://khalifa.idm.oclc.org/login?url=https://www.cochranelibrary.com", "why": "Key source for systematic reviews and evidence-based medicine."},
+                {"name": "UpToDate", "url": "https://khalifa.idm.oclc.org/login?url=https://www.uptodate.com", "why": "Clinical point-of-care resource for evidence summaries."},
+            ],
+        }),
+        (["computer science", "machine learning", "artificial intelligence", " ai", "cyber", "software", "algorithm", "computer", "data science"], {
+            "title": "Computer science and AI",
+            "intro": "For computing, AI, software, and algorithms, these are the best starting points.",
+            "items": [
+                {"name": "ACM Digital Library", "url": "https://khalifa.idm.oclc.org/login?url=https://dl.acm.org", "why": "Core computer science literature."},
+                {"name": "IEEE Xplore", "url": "https://khalifa.idm.oclc.org/login?url=https://ieeexplore.ieee.org", "why": "Strong for AI, computing systems, networks, and engineering."},
+                {"name": "arXiv", "url": "https://arxiv.org", "why": "Useful for fast-moving preprints in AI and computer science."},
+                {"name": "Scopus", "url": "https://khalifa.idm.oclc.org/login?url=https://www.scopus.com", "why": "Good for citation tracking and broader discovery."},
+            ],
+        }),
+        (["electrical", "electronics", "signals", "power systems", "communications"], {
+            "title": "Electrical engineering",
+            "intro": "For electrical engineering topics, start here.",
+            "items": [
+                {"name": "IEEE Xplore", "url": "https://khalifa.idm.oclc.org/login?url=https://ieeexplore.ieee.org", "why": "Core database for electrical engineering literature and standards."},
+                {"name": "INSPEC", "url": "https://khalifa.idm.oclc.org/login?url=https://www.theiet.org/publishing/inspec", "why": "Strong indexing for EE, electronics, communications, and computing."},
+                {"name": "ScienceDirect", "url": "https://khalifa.idm.oclc.org/login?url=https://www.sciencedirect.com", "why": "Good full-text coverage across electrical engineering and controls."},
+            ],
+        }),
+        (["mechanical", "thermo", "fluids", "manufacturing"], {
+            "title": "Mechanical engineering",
+            "intro": "For mechanical engineering, these are the strongest starts.",
+            "items": [
+                {"name": "ASME Digital Library", "url": "https://khalifa.idm.oclc.org/login?url=https://asmedigitalcollection.asme.org", "why": "Core mechanical engineering journals and conference papers."},
+                {"name": "Knovel", "url": "https://khalifa.idm.oclc.org/login?url=https://app.knovel.com", "why": "Engineering reference books and technical data."},
+                {"name": "ScienceDirect", "url": "https://khalifa.idm.oclc.org/login?url=https://www.sciencedirect.com", "why": "Strong full-text support for applied engineering topics."},
+            ],
+        }),
+        (["civil", "geotechnical", "transportation", "construction", "structures"], {
+            "title": "Civil engineering",
+            "intro": "For civil engineering and infrastructure topics, start here.",
+            "items": [
+                {"name": "ASCE Library", "url": "https://khalifa.idm.oclc.org/login?url=https://ascelibrary.org", "why": "Core civil engineering journals and conference papers."},
+                {"name": "ASTM Compass", "url": "https://khalifa.idm.oclc.org/login?url=https://compass.astm.org", "why": "Important standards and testing information for materials and engineering."},
+                {"name": "ScienceDirect", "url": "https://khalifa.idm.oclc.org/login?url=https://www.sciencedirect.com", "why": "Useful full-text coverage across civil engineering subjects."},
+            ],
+        }),
+        (["aerospace", "aerodynamics", "propulsion", "aircraft"], {
+            "title": "Aerospace engineering",
+            "intro": "For aerospace topics, these are the best databases to begin with.",
+            "items": [
+                {"name": "AIAA Aerospace Research Central", "url": "https://khalifa.idm.oclc.org/login?url=https://arc.aiaa.org/", "why": "Core aerospace journals and conference literature."},
+                {"name": "ASME Digital Library", "url": "https://khalifa.idm.oclc.org/login?url=https://asmedigitalcollection.asme.org", "why": "Useful for thermal, mechanical, and fluid systems relevant to aerospace."},
+                {"name": "ScienceDirect", "url": "https://khalifa.idm.oclc.org/login?url=https://www.sciencedirect.com", "why": "Good full-text support for aerospace materials and systems."},
+            ],
+        }),
+        (["chemical engineering", "catalysis", "separations", "chemistry", "chemical"], {
+            "title": "Chemistry and chemical engineering",
+            "intro": "For chemistry and chemical engineering, these are the strongest starting points.",
+            "items": [
+                {"name": "ACS Publications", "url": "https://khalifa.idm.oclc.org/login?url=https://pubs.acs.org", "why": "Core chemistry journals."},
+                {"name": "SciFindern", "url": "https://khalifa.idm.oclc.org/login?url=https://scifinder-n.cas.org", "why": "Best for substances, reactions, and chemistry references."},
+                {"name": "Reaxys", "url": "https://khalifa.idm.oclc.org/login?url=https://www.reaxys.com", "why": "Useful for chemistry reactions and property searching."},
+                {"name": "ScienceDirect", "url": "https://khalifa.idm.oclc.org/login?url=https://www.sciencedirect.com", "why": "Strong full-text source for chemical engineering journals."},
+            ],
+        }),
+        (["physics", "quantum", "optics", "plasma"], {
+            "title": "Physics",
+            "intro": "For physics topics, start with these databases and platforms.",
+            "items": [
+                {"name": "APS Journals", "url": "https://khalifa.idm.oclc.org/login?url=https://journals.aps.org", "why": "Core American Physical Society journals."},
+                {"name": "AIP", "url": "https://khalifa.idm.oclc.org/login?url=https://aip.scitation.org", "why": "Major physics and applied physics content."},
+                {"name": "arXiv", "url": "https://arxiv.org", "why": "Open preprints in physics and related areas."},
+            ],
+        }),
+        (["business", "management", "marketing", "economics", "finance"], {
+            "title": "Business and management",
+            "intro": "For business-related topics, start with these databases.",
+            "items": [
+                {"name": "Business Source Complete", "url": "https://khalifa.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=bsh", "why": "Comprehensive business database."},
+                {"name": "Emerald Insight", "url": "https://khalifa.idm.oclc.org/login?url=https://www.emerald.com/insight", "why": "Strong for management, marketing, and business journals."},
+                {"name": "ProQuest Databases", "url": "https://khalifa.idm.oclc.org/login?url=https://search.proquest.com", "why": "Broad business and dissertation coverage."},
+            ],
+        }),
     ]
-    if intent == 'search_medical' or 'pubmed' in q or 'clinical' in q or 'medical' in q or 'nursing' in q:
-        blocks.append('Medical support: <strong>Jason Fetty</strong> · <a href="mailto:jason.fetty@ku.ac.ae">jason.fetty@ku.ac.ae</a> · <a href="tel:+97123124722">+971 2 312 4722</a>')
-    elif any(tok in q for tok in ['database access', 'full text', 'proxy', 'login', 'eresource', 'e-resource', 'access problem']):
-        blocks.append('E-resources support: <strong>Rani Anand</strong> · <a href="mailto:rani.anand@ku.ac.ae">rani.anand@ku.ac.ae</a> · <a href="tel:+97123123935">+971 2 312 3935</a>')
-    elif any(tok in q for tok in ['website', 'system', 'technical', 'error', 'bug']):
-        blocks.append('Systems support: <strong>Walter Brian Hall</strong> · <a href="mailto:walter.hall@ku.ac.ae">walter.hall@ku.ac.ae</a> · <a href="tel:+97123123163">+971 2 312 3163</a>')
-    elif intent in {'search_academic', 'search_medical'} or any(tok in q for tok in ['research', 'review', 'literature', 'citation', 'orcid', 'scopus', 'bibliometric']):
-        blocks.append('Research support: <strong>Nikesh Narayanan</strong> · <a href="mailto:nikesh.narayanan@ku.ac.ae">nikesh.narayanan@ku.ac.ae</a> · <a href="tel:+97123123980">+971 2 312 3980</a>')
-    else:
-        blocks.append('Public services: <strong>Muna Al Blooshi</strong> · <a href="mailto:muna.alblooshi@ku.ac.ae">muna.alblooshi@ku.ac.ae</a> · <a href="tel:+97123124212">+971 2 312 4212</a>')
-    return '<div style="margin-top:14px;padding-top:10px;border-top:1px solid #e5e7eb;font-size:.95rem">' + '<br>'.join(blocks) + '</div>'
+    for keywords, payload in subjects:
+        if any(k in q for k in keywords):
+            return payload
+    return general
+
+
+def _database_recommendation_answer(question: str) -> str:
+    payload = _database_recommendation_payload(question)
+    answer = f"<strong>Best databases for { _escape(payload['title']) }</strong><br><br>{_escape(payload['intro'])}<br>"
+    for item in payload['items']:
+        answer += (
+            f'<br>• <a href="{item["url"]}" target="_blank" style="color:#003366;font-weight:600">{_escape(item["name"])}</a><br>'
+            f'<span style="font-size:.9rem;color:#4b5563">{_escape(item["why"])}</span>'
+        )
+    answer += (
+        f'<br><br>You can also start in <a href="{PRIMO_BASE_URL}" target="_blank">PRIMO Library Discovery</a> '
+        'and then narrow by resource type, date, and peer-reviewed content.'
+    )
+    answer += _source_badge("ku_library", "https://library.ku.ac.ae/eresources")
+    return answer
 
 
 # ── Research Snapshot ─────────────────────────────────────────────────────────
 
-async def _research_snapshot(question: str, context: SearchContextPayload, model: str) -> Tuple[str, List[dict], List[dict]]:
+async def _research_snapshot(context: SearchContextPayload, model: str) -> Tuple[str, List[dict], List[dict]]:
     """
     Full research snapshot:
     1. Semantic Scholar → cited summary with hyperlinked [Author, Year] citations
@@ -1153,7 +1204,6 @@ async def _research_snapshot(question: str, context: SearchContextPayload, model
     references: List[dict] = []
     primo_result, primo_url = await _search_primo_catalog(context, limit=6)
     search_results = primo_result.get("results", []) or []
-    trace = _build_trace_block(question, context, used_summary=bool(papers))
 
     if papers:
         summary = await _generate_cited_summary(topic, papers, model)
@@ -1161,13 +1211,13 @@ async def _research_snapshot(question: str, context: SearchContextPayload, model
             _safe_metrics_increment("summary_fallbacks")
             summary = _extractive_snapshot_from_papers(topic, papers)
 
-        answer = f"{trace}<strong>📖 Research Snapshot: {_escape(topic)}</strong><br><br>{summary}"
+        answer = f"<strong>📖 Research Snapshot: {_escape(topic)}</strong><br><br>{summary}"
         if search_results:
             answer += (
                 "<br><br><strong>KU Library starting points:</strong> "
                 "I also pulled KU-accessible results below so you can move directly from the snapshot to subscribed material."
             )
-        answer += _research_end_note()
+        answer += f"<br><br><strong>AI sources:</strong> {_database_guidance_for_topic(context)}"
         answer += "<br><br><strong>Selected references:</strong><br>"
         for i, paper in enumerate(papers[:6], 1):
             answer += (
@@ -1180,24 +1230,21 @@ async def _research_snapshot(question: str, context: SearchContextPayload, model
         answer += _source_badge("semantic", "https://www.semanticscholar.org/")
         if search_results:
             answer += _source_badge("primo", primo_url)
-        answer += _contact_footer_for_query(context.intent, question, context)
         references = [{"title": p["title"], "url": p["link"]} for p in papers[:6]]
         return answer, search_results, references
 
     if search_results:
         answer = (
-            f"{trace}<strong>📚 KU Library starting points on {_escape(topic)}</strong><br><br>"
-            "I found KU-accessible library results you can open immediately."
-            f"{_research_end_note()}"
+            f"<strong>📚 KU Library starting points on {_escape(topic)}</strong><br><br>"
+            "I couldn't build a Semantic Scholar snapshot right now, but here are KU-accessible library results you can open immediately. "
+            f"<br><br><strong>AI sources:</strong> {_database_guidance_for_topic(context)}"
         )
     else:
         answer = (
-            f"{trace}<strong>Research starting points for {_escape(topic)}</strong><br><br>"
-            "I prepared a library discovery path for this topic."
-            f"{_research_end_note()}"
+            f"I couldn't retrieve a research snapshot for <strong>{_escape(topic)}</strong> right now.<br><br>"
+            f"<strong>AI sources:</strong> {_database_guidance_for_topic(context)}"
         )
     answer += _source_badge("primo", primo_url)
-    answer += _contact_footer_for_query(context.intent, question, context)
     return answer, search_results, []
 
 
@@ -1332,8 +1379,9 @@ def _apply_follow_up_action(base_context: SearchContextPayload, action: Optional
 
 async def _generate_topic_follow_ups(topic: str, model: str) -> List[dict]:
     """
-    Generate 3 topic-specific follow-up questions, but preserve the original anchor topic
-    in the next clicked search query.
+    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".
     """
     settings = get_settings()
     if not settings.openai_api_key:
@@ -1344,11 +1392,15 @@ async def _generate_topic_follow_ups(topic: str, model: str) -> List[dict]:
             {"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, "
+                "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."
+                "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\"]'
             )},
             {"role": "user", "content": f"Topic: {topic}"},
         ])
@@ -1358,19 +1410,11 @@ async def _generate_topic_follow_ups(topic: str, model: str) -> List[dict]:
         questions = json.loads(raw)
         if not isinstance(questions, list):
             return []
-        out=[]
-        topic_l = topic.lower()
-        for q in questions[:3]:
-            if not isinstance(q, str) or not q.strip():
-                continue
-            label = q.strip().rstrip('?') + '?'
-            q_clean = _normalize_whitespace(q.strip().rstrip('?'))
-            if topic_l not in q_clean.lower():
-                full_q = f"Find research articles on {q_clean} in the context of {topic}"
-            else:
-                full_q = f"Find research articles on {q_clean}"
-            out.append({"label": label, "question": full_q, "action": None})
-        return out
+        return [
+            {"label": q.strip("?") + "?", "question": q.strip("?") + " — find articles", "action": "new_search"}
+            for q in questions[:3]
+            if isinstance(q, str) and q.strip()
+        ]
     except Exception as e:
         logger.warning(f"Topic follow-up generation failed: {e}")
         return []
@@ -1527,19 +1571,20 @@ async def _run_search_mode(
     search_results = result.get("results", []) or []
     total = result.get("total", len(search_results))
 
-    answer = _build_trace_block(question, context, used_summary=False) + _search_answer_intro(context, is_follow_up)
+    answer = _search_answer_intro(context, is_follow_up)
     if filters:
         answer += f"<br><br><strong>Current filters:</strong> {_escape(filters)}."
     if search_results:
         answer += f"<br><br>I found <strong>{total}</strong> result(s). The top items are shown below."
     else:
-        answer += "<br><br>I prepared a direct discovery path for this topic using the search shown above."
+        answer += (
+            "<br><br>I couldn't retrieve matching results right now. "
+            "You can still try the direct library links and AI research tools below."
+        )
     if context.intent == "search_medical" and context.source != "pubmed":
         answer += "<br><br>For the strongest medical evidence trail, I also recommend checking <strong>PubMed</strong> or <strong>Embase</strong>."
 
-    answer += _research_end_note()
     answer += _source_badge(source_name, source_url)
-    answer += _contact_footer_for_query(context.intent, question, context)
     sources = []
     for item in search_results[:6]:
         link = item.get("primo_url") or item.get("link")
@@ -1660,6 +1705,22 @@ async def agent_query(request: Request, req: AgentRequest):
             follow_up_suggestions=follow_up_suggestions,
         )
 
+    if _looks_database_recommendation_question(question):
+        follow_up_question = "Would you like me to search one of these databases for you next?"
+        follow_up_suggestions = [
+            {"label": "Search PRIMO on this topic", "question": question.replace("best databases", "find articles").replace("which database should I use for", "find articles on")},
+            {"label": "Show off-campus access help", "question": "How do I access databases off campus?"},
+        ]
+        return AgentResponse(
+            answer=_database_recommendation_answer(question),
+            intent="library_info",
+            tools_used=["database_recommendation"],
+            model_used=model,
+            response_time=time.time() - start_time,
+            follow_up_question=follow_up_question,
+            follow_up_suggestions=follow_up_suggestions,
+        )
+
     is_follow_up, follow_up_action, base_context = _detect_follow_up(question, client_state)
 
     if is_follow_up and base_context:
@@ -1669,7 +1730,11 @@ async def agent_query(request: Request, req: AgentRequest):
         if follow_up_action in {"summarize_topic", "deep_research_tools", "best_databases"}:
             context = await _prepare_queries(question, context, model, True)
             if follow_up_action == "summarize_topic":
-                answer, search_results, sources = await _research_snapshot(question, context, model)
+                answer, search_results, sources = await _research_snapshot(context, model)
+            elif follow_up_action == "best_databases":
+                answer = _database_recommendation_answer(context.display_topic or context.topic)
+                search_results = []
+                sources = []
             else:
                 answer = (
                     f"<strong>Research guide for {_escape(context.display_topic or context.topic)}</strong><br><br>"
@@ -1766,7 +1831,7 @@ async def agent_query(request: Request, req: AgentRequest):
 
         follow_up_question, follow_up_suggestions = _library_follow_up(question)
         return AgentResponse(
-            answer=answer + _contact_footer_for_query("library_info", question),
+            answer=answer,
             intent="library_info",
             tools_used=tools_used,
             sources=sources,
@@ -1802,7 +1867,7 @@ async def agent_query(request: Request, req: AgentRequest):
         ))
 
         if summary_mode or deep_research_mode:
-            answer, search_results, sources = await _research_snapshot(question, context, model)
+            answer, search_results, sources = await _research_snapshot(context, model)
             if deep_research_mode and "Research guide" not in answer:
                 answer += f"<br><br><strong>Research guide:</strong> {_database_guidance_for_topic(context)}"
             follow_up_question, follow_up_suggestions = await _search_follow_up(context, model, summary_mode=True)
@@ -1851,7 +1916,7 @@ async def agent_query(request: Request, req: AgentRequest):
         answer = await _web_search_answer(question, history, model) + _source_badge("web_live")
         follow_up_question, follow_up_suggestions = _general_follow_up(question)
         return AgentResponse(
-            answer=answer + _contact_footer_for_query("general_recent", question),
+            answer=answer,
             intent="general_recent",
             tools_used=["web_search"],
             model_used=model,
@@ -1863,7 +1928,7 @@ async def agent_query(request: Request, req: AgentRequest):
     answer = await _web_search_answer(question, history, model) + _source_badge("web")
     follow_up_question, follow_up_suggestions = _general_follow_up(question)
     return AgentResponse(
-        answer=answer + _contact_footer_for_query("general", question),
+        answer=answer,
         intent="general",
         tools_used=["web_search"],
         model_used=model,

src/api/search.py

@@ -94,6 +94,36 @@ async def search_endpoint(req: SearchRequest):
     return await search_scholar(req)
 
 
+def _build_exact_primo_url(doc: dict, query_encoded: str) -> str:
+    pnx = doc.get("pnx", {}) or {}
+    control = pnx.get("control", {}) or {}
+    search = pnx.get("search", {}) or {}
+    recordid = ""
+    for candidate in [
+        control.get("recordid"),
+        control.get("sourcerecordid"),
+        search.get("recordid"),
+        search.get("addsrcrecordid"),
+    ]:
+        if isinstance(candidate, list) and candidate:
+            recordid = str(candidate[0]).strip()
+            if recordid:
+                break
+        elif isinstance(candidate, str) and candidate.strip():
+            recordid = candidate.strip()
+            break
+    if recordid:
+        return (
+            "https://khalifa.primo.exlibrisgroup.com/discovery/fulldisplay"
+            f"?docid={quote(recordid)}&context=L&vid=971KUOSTAR_INST:KU&lang=en"
+            f"&search_scope=MyInst_and_CI&tab=Everything&query=any,contains,{query_encoded}&offset=0"
+        )
+    return (
+        f"https://khalifa.primo.exlibrisgroup.com/discovery/search"
+        f"?query=any,contains,{query_encoded}&vid=971KUOSTAR_INST:KU"
+    )
+
+
 # ── PRIMO ──────────────────────────────────────────────────────────────────────
 
 async def search_primo(req: SearchRequest, api_key: str):
@@ -178,10 +208,7 @@ async def search_primo(req: SearchRequest, api_key: str):
                     "date": (d.get("date") or [""])[0],
                     "type": (d.get("type") or [""])[0],
                     "source": (d.get("source") or [""])[0],
-                    "primo_url": (
-                        f"https://khalifa.primo.exlibrisgroup.com/discovery/search"
-                        f"?query=any,contains,{query_encoded}&vid=971KUOSTAR_INST:KU"
-                    ),
+                    "primo_url": _build_exact_primo_url(doc, query_encoded),
                     "_source": "PRIMO",
                 })
             return {