From b7661762592cb3230ff7f866cb1272bd9767e0f5 Mon Sep 17 00:00:00 2001
From: m3taversal <m3taversal@gmail.com>
Date: Wed, 1 Apr 2026 15:27:13 +0100
Subject: [PATCH] =?UTF-8?q?Wire=20RRF=20merge=20into=20bot.py=20=E2=80=94?=
 =?UTF-8?q?=20replace=20keyword+vector=20concatenation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

bot.py now calls orchestrate_retrieval() from retrieval.py instead of
doing keyword retrieval and vector search separately then concatenating.
Claims found by both systems get RRF-boosted to the top. Query
decomposition (multi-part → sub-queries) is now active.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 telegram/bot.py | 56 ++++++++++++++++++-------------------------------
 1 file changed, 20 insertions(+), 36 deletions(-)

diff --git a/telegram/bot.py b/telegram/bot.py
index 521972b..79fa921 100644
--- a/telegram/bot.py
+++ b/telegram/bot.py
@@ -42,7 +42,8 @@ from telegram.ext import (
 
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 import json as _json
-from kb_retrieval import KBIndex, format_context_for_prompt, retrieve_context
+from kb_retrieval import KBIndex, retrieve_context, retrieve_vector_context
+from retrieval import orchestrate_retrieval
 from market_data import get_token_price, format_price_context
 from worktree_lock import main_worktree_lock
 from x_client import search_tweets, fetch_from_url, check_research_rate_limit, record_research_usage, get_research_remaining
@@ -994,41 +995,24 @@ async def handle_tagged(update: Update, context: ContextTypes.DEFAULT_TYPE):
             logger.warning("Query reformulation failed: %s", e)
             # Fall through — use raw text
 
-    # Retrieve full KB context (entity resolution + claim search + agent positions)
-    t_kb = time.monotonic()
-    kb_ctx = retrieve_context(search_query_text, KB_READ_DIR, index=kb_index)
-    kb_context_text = format_context_for_prompt(kb_ctx)
-    kb_duration = int((time.monotonic() - t_kb) * 1000)
-    retrieval_layers = ["keyword"] if (kb_ctx and (kb_ctx.entities or kb_ctx.claims)) else []
-    tool_calls.append({
-        "tool": "retrieve_context",
-        "input": {"query": search_query_text[:200], "original_query": text[:200] if search_query_text != text else None},
-        "output": {"entities": len(kb_ctx.entities) if kb_ctx else 0,
-                   "claims": len(kb_ctx.claims) if kb_ctx else 0},
-        "duration_ms": kb_duration,
-    })
-
-    # Layer 1+2: Qdrant vector search + graph expansion (semantic, complements keyword)
-    # Pass keyword-matched paths to exclude duplicates at Qdrant query level
-    # Normalize: KBIndex stores absolute paths, Qdrant stores repo-relative paths
-    keyword_paths = []
-    if kb_ctx and kb_ctx.claims:
-        for c in kb_ctx.claims:
-            p = c.path
-            if KB_READ_DIR and p.startswith(KB_READ_DIR):
-                p = p[len(KB_READ_DIR):].lstrip("/")
-            keyword_paths.append(p)
-    from kb_retrieval import retrieve_vector_context
-    vector_context, vector_meta = retrieve_vector_context(search_query_text, keyword_paths=keyword_paths)
-    if vector_context:
-        kb_context_text = kb_context_text + "\n\n" + vector_context
-    retrieval_layers.extend(vector_meta.get("layers_hit", []))
-    tool_calls.append({
-        "tool": "retrieve_qdrant_context", "input": {"query": text[:200]},
-        "output": {"direct_hits": len(vector_meta.get("direct_results", [])),
-                   "expanded": len(vector_meta.get("expanded_results", []))},
-        "duration_ms": vector_meta.get("duration_ms", 0),
-    })
+    # Unified retrieval: keyword → decompose → vector → RRF merge
+    # Both systems search independently; RRF boosts claims found by both.
+    def _vector_fn(q):
+        return retrieve_vector_context(q)  # no keyword_paths exclusion — RRF deduplicates
+    retrieval_result = await orchestrate_retrieval(
+        text=text,
+        search_query=search_query_text,
+        kb_read_dir=KB_READ_DIR,
+        kb_index=kb_index,
+        llm_fn=call_openrouter,
+        triage_model=TRIAGE_MODEL,
+        retrieve_context_fn=retrieve_context,
+        retrieve_vector_fn=_vector_fn,
+    )
+    kb_context_text = retrieval_result["kb_context_text"]
+    kb_ctx = retrieval_result["kb_ctx"]
+    retrieval_layers = retrieval_result["retrieval_layers"]
+    tool_calls.extend(retrieval_result["tool_calls"])
 
     stats = get_db_stats()