a5bc698b9a
Three-tier match strategy for _truncate_around_matches(): 1. Full-phrase search (exact query string positions) 2. Proximity co-occurrence (all terms within 200 chars) 3. Individual terms (fallback, preserves existing behavior) Sliding window picks the start offset covering the most matches. Moved inline import re to module level. Co-authored-by: Al Sayed Hoota <78100282+AlsayedHoota@users.noreply.github.com>
555 lines
22 KiB
Python
555 lines
22 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, then summarizes the top
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matching sessions using a cheap/fast model (same pattern as web_extract).
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Returns focused summaries of past conversations rather than raw transcripts,
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keeping the main model's context window clean.
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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. Loads each session's conversation, truncates to ~100k chars centered on matches
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4. Sends to Gemini Flash with a focused summarization prompt
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5. Returns per-session 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 _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(limit=limit + 5, exclude_sources=list(_HIDDEN_SESSION_SOURCES)) # 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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while sid and sid not in visited:
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visited.add(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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current_root = max(visited, key=len) if visited else current_session_id
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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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) -> str:
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"""
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Search past sessions and return focused summaries of matching conversations.
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Uses FTS5 to find matches, then summarizes the top sessions with Gemini Flash.
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The current session is excluded from results since the agent already has that context.
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"""
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if db is None:
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return tool_error("Session database not available.", success=False)
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limit = min(limit, 5) # Cap at 5 sessions to avoid excessive LLM calls
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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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"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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# Prepare all sessions for parallel summarization
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tasks = []
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for session_id, match_info in seen_sessions.items():
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try:
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messages = db.get_messages_as_conversation(session_id)
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if not messages:
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continue
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session_meta = db.get_session(session_id) or {}
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conversation_text = _format_conversation(messages)
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conversation_text = _truncate_around_matches(conversation_text, query)
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tasks.append((session_id, match_info, conversation_text, session_meta))
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except Exception as e:
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logging.warning(
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"Failed to prepare session %s: %s",
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session_id,
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e,
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exc_info=True,
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)
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# Summarize all sessions in parallel
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async def _summarize_all() -> List[Union[str, Exception]]:
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"""Summarize all sessions in parallel."""
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coros = [
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_summarize_session(text, query, meta)
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for _, _, text, meta in tasks
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]
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return await asyncio.gather(*coros, return_exceptions=True)
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try:
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# Use _run_async() which properly manages event loops across
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# CLI, gateway, and worker-thread contexts. The previous
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# pattern (asyncio.run() in a ThreadPoolExecutor) created a
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# disposable event loop that conflicted with cached
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# AsyncOpenAI/httpx clients bound to a different loop,
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# causing deadlocks in gateway mode (#2681).
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from model_tools import _run_async
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results = _run_async(_summarize_all())
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except concurrent.futures.TimeoutError:
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logging.warning(
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"Session summarization timed out after 60 seconds",
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exc_info=True,
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)
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return json.dumps({
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"success": False,
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"error": "Session summarization timed out. Try a more specific query or reduce the limit.",
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}, ensure_ascii=False)
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summaries = []
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for (session_id, match_info, conversation_text, _), result in zip(tasks, results):
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if isinstance(result, Exception):
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logging.warning(
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"Failed to summarize session %s: %s",
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session_id, result, exc_info=True,
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)
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result = None
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entry = {
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"session_id": session_id,
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"when": _format_timestamp(match_info.get("session_started")),
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"source": match_info.get("source", "unknown"),
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"model": match_info.get("model"),
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}
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if result:
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entry["summary"] = result
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else:
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# Fallback: raw preview so matched sessions aren't silently
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# dropped when the summarizer is unavailable (fixes #3409).
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preview = (conversation_text[:500] + "\n…[truncated]") if conversation_text else "No preview available."
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entry["summary"] = f"[Raw preview — summarization unavailable]\n{preview}"
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summaries.append(entry)
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return json.dumps({
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"success": True,
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"query": query,
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"results": summaries,
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"count": len(summaries),
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"sessions_searched": len(seen_sessions),
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}, ensure_ascii=False)
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except Exception as e:
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logging.error("Session search failed: %s", e, exc_info=True)
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return tool_error(f"Search failed: {str(e)}", success=False)
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def check_session_search_requirements() -> bool:
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"""Requires SQLite state database and an auxiliary text model."""
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try:
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from hermes_state import DEFAULT_DB_PATH
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return DEFAULT_DB_PATH.parent.exists()
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except ImportError:
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return False
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SESSION_SEARCH_SCHEMA = {
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"name": "session_search",
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"description": (
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"Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- "
|
|
"every past session is searchable, and this tool summarizes what happened.\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. "
|
|
"Returns LLM-generated summaries of matching sessions.\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 -- it's fast and cheap. "
|
|
"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. Returns summaries of the top matching sessions."
|
|
),
|
|
"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 summarize (default: 3, max: 5).",
|
|
"default": 3,
|
|
},
|
|
},
|
|
"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),
|
|
db=kw.get("db"),
|
|
current_session_id=kw.get("current_session_id")),
|
|
check_fn=check_session_search_requirements,
|
|
emoji="🔍",
|
|
)
|