yoniebans
3ac750ec07
Reverses the default introduced by the salvaged dual-mode commit. Why: profiled four representative queries against a real 280-session state.db (workspace harness, not committed). Summary mode is 1,299x-6,293x slower than fast (median ~30s vs ~10ms; 99%+ in the auxiliary LLM call) and produces 2.9x-3.9x larger result blobs, but it answers a materially different question. The user's typical 'what did we work on for X?' is the summary question — fast surfaces only what FTS5 directly matched while summary surfaces cross-session synthesis (e.g. work sessions referenced inside the matched cron jobs). Backwards-compatible default; fast remains opt-in for cheap discovery via mode='fast'. Changes: - tools/session_search_tool.py: default parameter, defensive coercion fallbacks, and registry handler all default to 'summary'. Schema description rewritten with measured trade-offs and the 'use fast for discovery, summary for recall' framing. - run_agent.py: both direct call sites mirror the new default. - tests/tools/test_session_search.py: split the old default-test into test_default_search_returns_summary_mode_recap (asserts new default) and test_explicit_fast_mode_returns_snippets... (covers fast path without mocking the default away). Invalid-mode test now asserts fallback to summary. Source-grep test updated.
672 lines
28 KiB
Python
672 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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Session Search Tool - Long-Term Conversation Recall
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Searches past session transcripts in SQLite via FTS5. Keyword search defaults
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to fast snippet/context hits without any LLM call; callers can opt into focused
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LLM summaries with mode="summary" when deeper recall is worth the latency.
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Flow:
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1. FTS5 search finds matching messages ranked by relevance
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2. Groups by session, takes the top N unique sessions (default 3)
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3. Fast mode returns snippets and nearby context immediately
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4. Summary mode loads each session, truncates around matches, and calls an LLM
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5. Returns per-session hits/summaries with metadata
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"""
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import asyncio
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import concurrent.futures
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import json
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import logging
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import re
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from typing import Dict, Any, List, Optional, Union
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from agent.auxiliary_client import async_call_llm, extract_content_or_reasoning
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MAX_SESSION_CHARS = 100_000
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MAX_SUMMARY_TOKENS = 10000
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def _get_session_search_max_concurrency(default: int = 3) -> int:
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"""Read auxiliary.session_search.max_concurrency with sane bounds."""
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try:
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from hermes_cli.config import load_config
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config = load_config()
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except ImportError:
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return default
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aux = config.get("auxiliary", {}) if isinstance(config, dict) else {}
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task_config = aux.get("session_search", {}) if isinstance(aux, dict) else {}
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if not isinstance(task_config, dict):
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return default
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raw = task_config.get("max_concurrency")
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if raw is None:
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return default
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try:
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value = int(raw)
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except (TypeError, ValueError):
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return default
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return max(1, min(value, 5))
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def _format_timestamp(ts: Union[int, float, str, None]) -> str:
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"""Convert a Unix timestamp (float/int) or ISO string to a human-readable date.
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Returns "unknown" for None, str(ts) if conversion fails.
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"""
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if ts is None:
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return "unknown"
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try:
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if isinstance(ts, (int, float)):
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from datetime import datetime
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dt = datetime.fromtimestamp(ts)
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return dt.strftime("%B %d, %Y at %I:%M %p")
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if isinstance(ts, str):
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if ts.replace(".", "").replace("-", "").isdigit():
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from datetime import datetime
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dt = datetime.fromtimestamp(float(ts))
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return dt.strftime("%B %d, %Y at %I:%M %p")
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return ts
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except (ValueError, OSError, OverflowError) as e:
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# Log specific errors for debugging while gracefully handling edge cases
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logging.debug("Failed to format timestamp %s: %s", ts, e, exc_info=True)
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except Exception as e:
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logging.debug("Unexpected error formatting timestamp %s: %s", ts, e, exc_info=True)
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return str(ts)
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def _format_conversation(messages: List[Dict[str, Any]]) -> str:
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"""Format session messages into a readable transcript for summarization."""
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parts = []
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for msg in messages:
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role = msg.get("role", "unknown").upper()
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content = msg.get("content") or ""
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tool_name = msg.get("tool_name")
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if role == "TOOL" and tool_name:
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# Truncate long tool outputs
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if len(content) > 500:
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content = content[:250] + "\n...[truncated]...\n" + content[-250:]
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parts.append(f"[TOOL:{tool_name}]: {content}")
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elif role == "ASSISTANT":
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# Include tool call names if present
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tool_calls = msg.get("tool_calls")
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if tool_calls and isinstance(tool_calls, list):
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tc_names = []
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for tc in tool_calls:
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if isinstance(tc, dict):
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name = tc.get("name") or tc.get("function", {}).get("name", "?")
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tc_names.append(name)
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if tc_names:
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parts.append(f"[ASSISTANT]: [Called: {', '.join(tc_names)}]")
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if content:
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parts.append(f"[ASSISTANT]: {content}")
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else:
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parts.append(f"[ASSISTANT]: {content}")
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else:
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parts.append(f"[{role}]: {content}")
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return "\n\n".join(parts)
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def _truncate_around_matches(
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full_text: str, query: str, max_chars: int = MAX_SESSION_CHARS
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) -> str:
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"""
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Truncate a conversation transcript to *max_chars*, choosing a window
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that maximises coverage of positions where the *query* actually appears.
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Strategy (in priority order):
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1. Try to find the full query as a phrase (case-insensitive).
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2. If no phrase hit, look for positions where all query terms appear
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within a 200-char proximity window (co-occurrence).
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3. Fall back to individual term positions.
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Once candidate positions are collected the function picks the window
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start that covers the most of them.
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"""
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if len(full_text) <= max_chars:
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return full_text
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text_lower = full_text.lower()
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query_lower = query.lower().strip()
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match_positions: list[int] = []
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# --- 1. Full-phrase search ------------------------------------------------
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phrase_pat = re.compile(re.escape(query_lower))
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match_positions = [m.start() for m in phrase_pat.finditer(text_lower)]
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# --- 2. Proximity co-occurrence of all terms (within 200 chars) -----------
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if not match_positions:
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terms = query_lower.split()
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if len(terms) > 1:
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# Collect every occurrence of each term
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term_positions: dict[str, list[int]] = {}
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for t in terms:
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term_positions[t] = [
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m.start() for m in re.finditer(re.escape(t), text_lower)
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]
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# Slide through positions of the rarest term and check proximity
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rarest = min(terms, key=lambda t: len(term_positions.get(t, [])))
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for pos in term_positions.get(rarest, []):
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if all(
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any(abs(p - pos) < 200 for p in term_positions.get(t, []))
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for t in terms
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if t != rarest
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):
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match_positions.append(pos)
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# --- 3. Individual term positions (last resort) ---------------------------
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if not match_positions:
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terms = query_lower.split()
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for t in terms:
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for m in re.finditer(re.escape(t), text_lower):
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match_positions.append(m.start())
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if not match_positions:
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# Nothing at all — take from the start
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truncated = full_text[:max_chars]
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suffix = "\n\n...[later conversation truncated]..." if max_chars < len(full_text) else ""
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return truncated + suffix
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# --- Pick window that covers the most match positions ---------------------
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match_positions.sort()
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best_start = 0
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best_count = 0
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for candidate in match_positions:
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ws = max(0, candidate - max_chars // 4) # bias: 25% before, 75% after
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we = ws + max_chars
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if we > len(full_text):
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ws = max(0, len(full_text) - max_chars)
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we = len(full_text)
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count = sum(1 for p in match_positions if ws <= p < we)
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if count > best_count:
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best_count = count
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best_start = ws
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start = best_start
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end = min(len(full_text), start + max_chars)
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truncated = full_text[start:end]
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prefix = "...[earlier conversation truncated]...\n\n" if start > 0 else ""
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suffix = "\n\n...[later conversation truncated]..." if end < len(full_text) else ""
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return prefix + truncated + suffix
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async def _summarize_session(
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conversation_text: str, query: str, session_meta: Dict[str, Any]
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) -> Optional[str]:
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"""Summarize a single session conversation focused on the search query."""
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system_prompt = (
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"You are reviewing a past conversation transcript to help recall what happened. "
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"Summarize the conversation with a focus on the search topic. Include:\n"
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"1. What the user asked about or wanted to accomplish\n"
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"2. What actions were taken and what the outcomes were\n"
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"3. Key decisions, solutions found, or conclusions reached\n"
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"4. Any specific commands, files, URLs, or technical details that were important\n"
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"5. Anything left unresolved or notable\n\n"
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"Be thorough but concise. Preserve specific details (commands, paths, error messages) "
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"that would be useful to recall. Write in past tense as a factual recap."
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)
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source = session_meta.get("source", "unknown")
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started = _format_timestamp(session_meta.get("started_at"))
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user_prompt = (
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f"Search topic: {query}\n"
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f"Session source: {source}\n"
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f"Session date: {started}\n\n"
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f"CONVERSATION TRANSCRIPT:\n{conversation_text}\n\n"
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f"Summarize this conversation with focus on: {query}"
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)
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max_retries = 3
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for attempt in range(max_retries):
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try:
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response = await async_call_llm(
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task="session_search",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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temperature=0.1,
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max_tokens=MAX_SUMMARY_TOKENS,
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)
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content = extract_content_or_reasoning(response)
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if content:
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return content
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# Reasoning-only / empty — let the retry loop handle it
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logging.warning("Session search LLM returned empty content (attempt %d/%d)", attempt + 1, max_retries)
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if attempt < max_retries - 1:
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await asyncio.sleep(1 * (attempt + 1))
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continue
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return content
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except RuntimeError:
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logging.warning("No auxiliary model available for session summarization")
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return None
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except Exception as e:
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if attempt < max_retries - 1:
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await asyncio.sleep(1 * (attempt + 1))
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else:
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logging.warning(
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"Session summarization failed after %d attempts: %s",
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max_retries,
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e,
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exc_info=True,
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)
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return None
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# Sources that are excluded from session browsing/searching by default.
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# Third-party integrations (Paperclip agents, etc.) tag their sessions with
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# HERMES_SESSION_SOURCE=tool so they don't clutter the user's session history.
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_HIDDEN_SESSION_SOURCES = ("tool",)
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def _list_recent_sessions(db, limit: int, current_session_id: str = None) -> str:
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"""Return metadata for the most recent sessions (no LLM calls)."""
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try:
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sessions = db.list_sessions_rich(
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limit=limit + 5,
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exclude_sources=list(_HIDDEN_SESSION_SOURCES),
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order_by_last_active=True,
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) # fetch extra to skip current
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# Resolve current session lineage to exclude it
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current_root = None
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if current_session_id:
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try:
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sid = current_session_id
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visited = set()
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current_root = current_session_id
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while sid and sid not in visited:
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visited.add(sid)
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current_root = sid
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s = db.get_session(sid)
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parent = s.get("parent_session_id") if s else None
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sid = parent if parent else None
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except Exception:
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current_root = current_session_id
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results = []
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for s in sessions:
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sid = s.get("id", "")
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if current_root and (sid == current_root or sid == current_session_id):
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continue
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# Skip child/delegation sessions (they have parent_session_id)
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if s.get("parent_session_id"):
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continue
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results.append({
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"session_id": sid,
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"title": s.get("title") or None,
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"source": s.get("source", ""),
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"started_at": s.get("started_at", ""),
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"last_active": s.get("last_active", ""),
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"message_count": s.get("message_count", 0),
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"preview": s.get("preview", ""),
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})
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if len(results) >= limit:
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break
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return json.dumps({
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"success": True,
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"mode": "recent",
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"results": results,
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"count": len(results),
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"message": f"Showing {len(results)} most recent sessions. Use a keyword query to search specific topics.",
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}, ensure_ascii=False)
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except Exception as e:
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logging.error("Error listing recent sessions: %s", e, exc_info=True)
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return tool_error(f"Failed to list recent sessions: {e}", success=False)
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def session_search(
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query: str,
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role_filter: str = None,
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limit: int = 3,
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db=None,
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current_session_id: str = None,
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mode: str = "summary",
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) -> str:
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"""
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Search past sessions. Summary mode (default) returns LLM-generated recaps;
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fast mode returns FTS snippets without LLM calls for cheap discovery.
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"""
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if db is None:
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try:
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from hermes_state import SessionDB
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db = SessionDB()
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except Exception:
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logging.debug("SessionDB unavailable for session_search", exc_info=True)
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from hermes_state import format_session_db_unavailable
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return tool_error(format_session_db_unavailable(), success=False)
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mode = (mode or "summary").strip().lower() if isinstance(mode, str) else "summary"
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if mode in ("summarized", "summarise", "summarize", "deep"):
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mode = "summary"
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if mode not in ("fast", "summary"):
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mode = "summary"
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# Defensive: models (especially open-source) may send non-int limit values
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# (None when JSON null, string "int", or even a type object). Coerce to a
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# safe integer before any arithmetic/comparison to prevent TypeError.
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if not isinstance(limit, int):
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try:
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limit = int(limit)
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except (TypeError, ValueError):
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limit = 3
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limit = max(1, min(limit, 5)) # Clamp to [1, 5]
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# Recent sessions mode: when query is empty, return metadata for recent sessions.
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# No LLM calls — just DB queries for titles, previews, timestamps.
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if not query or not query.strip():
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return _list_recent_sessions(db, limit, current_session_id)
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query = query.strip()
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try:
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# Parse role filter
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role_list = None
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if role_filter and role_filter.strip():
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role_list = [r.strip() for r in role_filter.split(",") if r.strip()]
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# FTS5 search -- get matches ranked by relevance
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raw_results = db.search_messages(
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query=query,
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role_filter=role_list,
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exclude_sources=list(_HIDDEN_SESSION_SOURCES),
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limit=50, # Get more matches to find unique sessions
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offset=0,
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)
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if not raw_results:
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return json.dumps({
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"success": True,
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"mode": mode,
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"query": query,
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"results": [],
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"count": 0,
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"message": "No matching sessions found.",
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}, ensure_ascii=False)
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# Resolve child sessions to their parent — delegation stores detailed
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# content in child sessions, but the user's conversation is the parent.
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def _resolve_to_parent(session_id: str) -> str:
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"""Walk delegation chain to find the root parent session ID."""
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visited = set()
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sid = session_id
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while sid and sid not in visited:
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visited.add(sid)
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try:
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session = db.get_session(sid)
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if not session:
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break
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parent = session.get("parent_session_id")
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if parent:
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sid = parent
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else:
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break
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except Exception as e:
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logging.debug(
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"Error resolving parent for session %s: %s",
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sid,
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e,
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exc_info=True,
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)
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break
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return sid
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current_lineage_root = (
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_resolve_to_parent(current_session_id) if current_session_id else None
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)
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# Group by resolved (parent) session_id, dedup, skip the current
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# session lineage. Compression and delegation create child sessions
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# that still belong to the same active conversation.
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seen_sessions = {}
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for result in raw_results:
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raw_sid = result["session_id"]
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resolved_sid = _resolve_to_parent(raw_sid)
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# Skip the current session lineage — the agent already has that
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# context, even if older turns live in parent fragments.
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if current_lineage_root and resolved_sid == current_lineage_root:
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continue
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if current_session_id and raw_sid == current_session_id:
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continue
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if resolved_sid not in seen_sessions:
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result = dict(result)
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result["session_id"] = resolved_sid
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seen_sessions[resolved_sid] = result
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if len(seen_sessions) >= limit:
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break
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if mode == "fast":
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results = []
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for session_id, match_info in seen_sessions.items():
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try:
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session_meta = db.get_session(session_id) or {}
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except Exception:
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session_meta = {}
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snippet = match_info.get("snippet") or ""
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context = match_info.get("context") or []
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if not isinstance(context, list):
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context = []
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results.append({
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"session_id": session_id,
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"when": _format_timestamp(
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session_meta.get("started_at") or match_info.get("session_started")
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),
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"source": session_meta.get("source") or match_info.get("source", "unknown"),
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"model": session_meta.get("model") or match_info.get("model") or "unknown",
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"matched_role": match_info.get("role"),
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"title": session_meta.get("title") or None,
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"snippet": snippet,
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"context": context,
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"summary": "[Search hit — summary not generated in fast mode] Use snippet/context fields, or set mode='summary' for LLM-generated recall.",
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})
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return json.dumps({
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"success": True,
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"mode": "fast",
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"query": query,
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"results": results,
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"count": len(results),
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"sessions_searched": len(seen_sessions),
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"message": "Fast search returned FTS snippets without LLM summarization. Use mode='summary' for focused summaries when needed.",
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}, ensure_ascii=False)
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# Prepare all sessions for parallel summarization
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|
tasks = []
|
|
for session_id, match_info in seen_sessions.items():
|
|
try:
|
|
messages = db.get_messages_as_conversation(session_id)
|
|
if not messages:
|
|
continue
|
|
session_meta = db.get_session(session_id) or {}
|
|
conversation_text = _format_conversation(messages)
|
|
conversation_text = _truncate_around_matches(conversation_text, query)
|
|
tasks.append((session_id, match_info, conversation_text, session_meta))
|
|
except Exception as e:
|
|
logging.warning(
|
|
"Failed to prepare session %s: %s",
|
|
session_id,
|
|
e,
|
|
exc_info=True,
|
|
)
|
|
|
|
# Summarize all sessions in parallel
|
|
async def _summarize_all() -> List[Union[str, Exception]]:
|
|
"""Summarize all sessions with bounded concurrency."""
|
|
max_concurrency = min(_get_session_search_max_concurrency(), max(1, len(tasks)))
|
|
semaphore = asyncio.Semaphore(max_concurrency)
|
|
|
|
async def _bounded_summary(text: str, meta: Dict[str, Any]) -> Optional[str]:
|
|
async with semaphore:
|
|
return await _summarize_session(text, query, meta)
|
|
|
|
coros = [
|
|
_bounded_summary(text, meta)
|
|
for _, _, text, meta in tasks
|
|
]
|
|
return await asyncio.gather(*coros, return_exceptions=True)
|
|
|
|
try:
|
|
# Use _run_async() which properly manages event loops across
|
|
# CLI, gateway, and worker-thread contexts. The previous
|
|
# pattern (asyncio.run() in a ThreadPoolExecutor) created a
|
|
# disposable event loop that conflicted with cached
|
|
# AsyncOpenAI/httpx clients bound to a different loop,
|
|
# causing deadlocks in gateway mode (#2681).
|
|
from model_tools import _run_async
|
|
results = _run_async(_summarize_all())
|
|
except concurrent.futures.TimeoutError:
|
|
logging.warning(
|
|
"Session summarization timed out after 60 seconds",
|
|
exc_info=True,
|
|
)
|
|
return json.dumps({
|
|
"success": False,
|
|
"error": "Session summarization timed out. Try a more specific query or reduce the limit.",
|
|
}, ensure_ascii=False)
|
|
|
|
summaries = []
|
|
for (session_id, match_info, conversation_text, session_meta), result in zip(tasks, results):
|
|
if isinstance(result, Exception):
|
|
logging.warning(
|
|
"Failed to summarize session %s: %s",
|
|
session_id, result, exc_info=True,
|
|
)
|
|
result = None
|
|
|
|
# Prefer resolved parent session metadata over FTS5 match metadata.
|
|
# match_info carries source/model from the *child* session that contained
|
|
# the FTS5 hit; after _resolve_to_parent() the session_id points to the
|
|
# root, so session_meta has the authoritative platform/source for the
|
|
# session the user actually cares about (#15909).
|
|
entry = {
|
|
"session_id": session_id,
|
|
"when": _format_timestamp(
|
|
session_meta.get("started_at") or match_info.get("session_started")
|
|
),
|
|
"source": session_meta.get("source") or match_info.get("source", "unknown"),
|
|
"model": session_meta.get("model") or match_info.get("model"),
|
|
}
|
|
|
|
if result:
|
|
entry["summary"] = result
|
|
else:
|
|
# Fallback: raw preview so matched sessions aren't silently
|
|
# dropped when the summarizer is unavailable (fixes #3409).
|
|
preview = (conversation_text[:500] + "\n…[truncated]") if conversation_text else "No preview available."
|
|
entry["summary"] = f"[Raw preview — summarization unavailable]\n{preview}"
|
|
|
|
summaries.append(entry)
|
|
|
|
return json.dumps({
|
|
"success": True,
|
|
"mode": "summary",
|
|
"query": query,
|
|
"results": summaries,
|
|
"count": len(summaries),
|
|
"sessions_searched": len(seen_sessions),
|
|
}, ensure_ascii=False)
|
|
|
|
except Exception as e:
|
|
logging.error("Session search failed: %s", e, exc_info=True)
|
|
return tool_error(f"Search failed: {str(e)}", success=False)
|
|
|
|
|
|
def check_session_search_requirements() -> bool:
|
|
"""Requires SQLite state database; summary mode also needs an auxiliary model."""
|
|
try:
|
|
from hermes_state import DEFAULT_DB_PATH
|
|
return DEFAULT_DB_PATH.parent.exists()
|
|
except ImportError:
|
|
return False
|
|
|
|
|
|
SESSION_SEARCH_SCHEMA = {
|
|
"name": "session_search",
|
|
"description": (
|
|
"Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- "
|
|
"every past session is searchable.\n\n"
|
|
"TWO MODES:\n"
|
|
"1. Recent sessions (no query): Call with no arguments to see what was worked on recently. "
|
|
"Returns titles, previews, and timestamps. Zero LLM cost, instant. "
|
|
"Start here when the user asks what were we working on or what did we do recently.\n"
|
|
"2. Keyword search (with query): Search for specific topics across all past sessions. "
|
|
"Defaults to mode='summary', returning LLM-generated recaps of the matched sessions (the recall "
|
|
"you usually want). Set mode='fast' for cheap, instant FTS snippet hits when you only need to "
|
|
"discover which sessions touched a topic.\n\n"
|
|
"USE THIS PROACTIVELY when:\n"
|
|
"- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n"
|
|
"- The user asks about a topic you worked on before but don't have in current context\n"
|
|
"- The user references a project, person, or concept that seems familiar but isn't in memory\n"
|
|
"- You want to check if you've solved a similar problem before\n"
|
|
"- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\n"
|
|
"Don't hesitate to search when it is actually cross-session -- summary mode is one tool call away. "
|
|
"Better to search and confirm than to guess or ask the user to repeat themselves.\n\n"
|
|
"Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), "
|
|
"phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). "
|
|
"IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses "
|
|
"sessions that only mention some terms. If a broad OR query returns nothing, try individual "
|
|
"keyword searches in parallel."
|
|
),
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"query": {
|
|
"type": "string",
|
|
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost).",
|
|
},
|
|
"role_filter": {
|
|
"type": "string",
|
|
"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.",
|
|
},
|
|
"limit": {
|
|
"type": "integer",
|
|
"description": "Max sessions to return (default: 3, max: 5).",
|
|
"default": 3,
|
|
},
|
|
"mode": {
|
|
"type": "string",
|
|
"enum": ["fast", "summary"],
|
|
"description": (
|
|
"summary (default) loads each matched session's transcript and runs the LLM "
|
|
"summariser to produce a focused recap — ~30s, ~3-4 KB returned per session, "
|
|
"surfaces cross-session synthesis (e.g. references to work sessions that didn't "
|
|
"themselves match FTS5). Use this when the user wants to know WHAT HAPPENED in "
|
|
"past sessions about a topic. "
|
|
"fast returns FTS5 snippets + 1-message context without any LLM call — ~10ms, "
|
|
"~1 KB per session, surfaces only what FTS5 directly matched. Use this when the "
|
|
"user only needs to discover WHICH SESSIONS touched a topic, or when you'll "
|
|
"drill into specific sessions yourself afterwards. "
|
|
"If a fast result looks promising but lacks detail, you can call again with "
|
|
"mode='summary' on the same query."
|
|
),
|
|
"default": "summary",
|
|
},
|
|
},
|
|
"required": [],
|
|
},
|
|
}
|
|
|
|
|
|
# --- Registry ---
|
|
from tools.registry import registry, tool_error
|
|
|
|
registry.register(
|
|
name="session_search",
|
|
toolset="session_search",
|
|
schema=SESSION_SEARCH_SCHEMA,
|
|
handler=lambda args, **kw: session_search(
|
|
query=args.get("query") or "",
|
|
role_filter=args.get("role_filter"),
|
|
limit=args.get("limit", 3),
|
|
mode=args.get("mode", "summary"),
|
|
db=kw.get("db"),
|
|
current_session_id=kw.get("current_session_id")),
|
|
check_fn=check_session_search_requirements,
|
|
emoji="🔍",
|
|
)
|