Update tools_additions.py

tools_additions.py

@@ -4,6 +4,7 @@
 #   GROUP 8: DBSCAN Clustering (title + abstract separate vectors)
 #   GROUP 9: Cluster Splitting and Min-Membership Enforcement
 #   GROUP 10: LLM Cluster Labeling (Anthropic API)
+#   GROUP 11: Agentic Council (Mistral + Gemini panels, Gemini synthesis judge)
 # =============================================================================
 
 import os
@@ -366,7 +367,6 @@ def split_large_clusters(
         next_cid += len(unique_sub)
 
         indices = sub_df.index.tolist()
-        noise_mask = sub_labels == -1
         for local_i, (orig_idx, sl) in enumerate(zip(indices, sub_labels)):
             if sl == -1:
                 # Absorb noise into nearest non-noise neighbour's cluster
@@ -406,7 +406,7 @@ def split_large_clusters(
 
 
 # =============================================================================
-# GROUP 10: LLM CLUSTER LABELING (ANTHROPIC API + MISTRAL + GEMINI)
+# GROUP 10: LLM CLUSTER LABELING (ANTHROPIC API)
 # =============================================================================
 
 def _top3_sentences(group_df: pd.DataFrame) -> str:
@@ -510,35 +510,28 @@ def label_clusters_with_llm(
 
 
 # =============================================================================
-# GROUP 11: AGENTIC COUNCIL (MISTRAL + GEMINI + ANTHROPIC SYNTHESIS)
+# GROUP 11: AGENTIC COUNCIL (MISTRAL + GEMINI PANELS, GEMINI SYNTHESIS JUDGE)
 # =============================================================================
 
 COUNCIL_PROMPT_TEMPLATE = """You are a senior Information Systems research analyst.
 You have been given a PAJAIS research gap analysis report with the following findings:
-
 {findings}
-
 Based on this analysis, provide your expert assessment covering:
 1. The 3 most strategically important research gaps for the field
 2. Which novel topics have the highest publication impact potential
 3. Recommended methodologies for addressing the top gap
 4. Any risks or caveats in the analysis
-
 Be specific, cite topic names from the report, and limit your response to 300 words."""
 
 SYNTHESIS_PROMPT_TEMPLATE = """You are the Chief Research Officer synthesizing advice from two expert panels.
-
 Panel A (Mistral) said:
 {mistral_response}
-
 Panel B (Gemini) said:
 {gemini_response}
-
 Your task:
 1. Identify the 2-3 points both panels AGREE on (consensus insights)
 2. Identify where they DIVERGE and explain which view is more defensible
 3. Produce a final 200-word synthesis recommendation
-
 Structure your response as:
 ### Consensus
 <points>
@@ -606,56 +599,30 @@ def _call_gemini(
         return f"[Gemini unavailable: {e}]"
 
 
-def _call_anthropic_synthesis(
-    prompt: str,
-    api_key: str,
-    model: str = "claude-sonnet-4-20250514",
-) -> str:
-    """Call Anthropic for synthesis."""
-    try:
-        resp = httpx.post(
-            "https://api.anthropic.com/v1/messages",
-            headers={
-                "x-api-key": api_key,
-                "anthropic-version": "2023-06-01",
-                "content-type": "application/json",
-            },
-            json={
-                "model": model,
-                "max_tokens": 600,
-                "messages": [{"role": "user", "content": prompt}],
-            },
-            timeout=30.0,
-        )
-        resp.raise_for_status()
-        return resp.json()["content"][0]["text"].strip()
-    except Exception as e:
-        logger.error(f"Anthropic synthesis call failed: {e}")
-        return f"[Anthropic synthesis unavailable: {e}]"
-
-
 def run_agentic_council(
     taxonomy_map: Dict[str, Any],
     topic_df: Optional[pd.DataFrame],
     mistral_api_key: str = "",
     gemini_api_key: str = "",
-    anthropic_api_key: str = "",
+    anthropic_api_key: str = "",          # retained for signature compatibility
 ) -> Dict[str, str]:
     """
-    Run the three-model agentic council:
-    Mistral → Gemini → Anthropic (synthesis judge).
+    Run the three-stage agentic council:
+      Stage 1 — Mistral panel: independent analysis of findings.
+      Stage 2 — Gemini panel: independent analysis of findings.
+      Stage 3 — Gemini synthesis judge: reconciles both panels (gemini-1.5-pro).
 
     Parameters
     ----------
     taxonomy_map      : Output of generate_taxonomy_map.
     topic_df          : Topic DataFrame (used to build findings summary).
     mistral_api_key   : Mistral API key.
-    gemini_api_key    : Google AI Studio API key.
-    anthropic_api_key : Anthropic API key. Falls back to ANTHROPIC_API_KEY env var.
+    gemini_api_key    : Google AI Studio API key (used for both panel and judge).
+    anthropic_api_key : Ignored. Kept for backward-compatible call sites.
 
     Returns
     -------
-    Dict with keys: 'mistral', 'gemini', 'synthesis', 'findings_summary'
+    Dict with keys: 'findings_summary', 'mistral', 'gemini', 'synthesis'
     """
     # Build findings summary
     gap = taxonomy_map.get("gap_analysis", {})
@@ -681,44 +648,45 @@ def run_agentic_council(
 PAJAIS Coverage: {gap.get('coverage_pct', 0):.1f}% ({gap.get('mapped_count', 0)} mapped, {gap.get('novel_count', 0)} novel)
 Covered themes (sample): {covered_str}
 Uncovered themes (sample): {uncovered_str}
-
 Top discovered topics: {top_topics_str}
-
 Novel research themes (top 5):
 {novel_str}
-
 Publishable gap candidates:
 {pub_str}
 """.strip()
 
     council_prompt = COUNCIL_PROMPT_TEMPLATE.format(findings=findings)
 
-    logger.info("Council: calling Mistral…")
+    # Stage 1 — Mistral panel
+    logger.info("Council: calling Mistral (panel)…")
     mistral_resp = (
         _call_mistral(council_prompt, mistral_api_key)
         if mistral_api_key.strip()
         else "[Mistral API key not provided]"
     )
 
-    logger.info("Council: calling Gemini…")
+    # Stage 2 — Gemini panel
+    logger.info("Council: calling Gemini (panel)…")
     gemini_resp = (
         _call_gemini(council_prompt, gemini_api_key)
         if gemini_api_key.strip()
         else "[Gemini API key not provided]"
     )
 
+    # Stage 3 — Gemini synthesis judge (upgraded model)
     synthesis_prompt = SYNTHESIS_PROMPT_TEMPLATE.format(
         mistral_response=mistral_resp,
         gemini_response=gemini_resp,
     )
-
-    anth_key = anthropic_api_key or os.environ.get("ANTHROPIC_API_KEY", "")
-    logger.info("Council: calling Anthropic synthesis judge…")
-    synthesis_resp = (
-        _call_anthropic_synthesis(synthesis_prompt, anth_key)
-        if anth_key.strip()
-        else "[Anthropic API key not provided]"
-    )
+    logger.info("Council: calling Gemini (synthesis judge)…")
+    if gemini_api_key.strip():
+        synthesis_resp = _call_gemini(
+            synthesis_prompt,
+            gemini_api_key,
+            model="gemini-1.5-pro",
+        )
+    else:
+        synthesis_resp = "[Gemini API key not provided — synthesis skipped]"
 
     return {
         "findings_summary": findings,