Teknium
466720c2f3
When session_search is called without a query (or with an empty query), it now returns metadata for the most recent sessions instead of erroring. This lets the agent quickly see what was worked on recently without needing specific keywords. Returns for each session: session_id, title, source, started_at, last_active, message_count, preview (first user message). Zero LLM cost — pure DB query. Current session lineage and child delegation sessions are excluded. The agent can then keyword-search specific sessions if it needs deeper context from any of them.
476 lines
19 KiB
Python
476 lines
19 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Session Search Tool - Long-Term Conversation Recall
|
|
|
|
Searches past session transcripts in SQLite via FTS5, then summarizes the top
|
|
matching sessions using a cheap/fast model (same pattern as web_extract).
|
|
Returns focused summaries of past conversations rather than raw transcripts,
|
|
keeping the main model's context window clean.
|
|
|
|
Flow:
|
|
1. FTS5 search finds matching messages ranked by relevance
|
|
2. Groups by session, takes the top N unique sessions (default 3)
|
|
3. Loads each session's conversation, truncates to ~100k chars centered on matches
|
|
4. Sends to Gemini Flash with a focused summarization prompt
|
|
5. Returns per-session summaries with metadata
|
|
"""
|
|
|
|
import asyncio
|
|
import concurrent.futures
|
|
import json
|
|
import os
|
|
import logging
|
|
from typing import Dict, Any, List, Optional, Union
|
|
|
|
from agent.auxiliary_client import async_call_llm
|
|
MAX_SESSION_CHARS = 100_000
|
|
MAX_SUMMARY_TOKENS = 10000
|
|
|
|
|
|
def _format_timestamp(ts: Union[int, float, str, None]) -> str:
|
|
"""Convert a Unix timestamp (float/int) or ISO string to a human-readable date.
|
|
|
|
Returns "unknown" for None, str(ts) if conversion fails.
|
|
"""
|
|
if ts is None:
|
|
return "unknown"
|
|
try:
|
|
if isinstance(ts, (int, float)):
|
|
from datetime import datetime
|
|
dt = datetime.fromtimestamp(ts)
|
|
return dt.strftime("%B %d, %Y at %I:%M %p")
|
|
if isinstance(ts, str):
|
|
if ts.replace(".", "").replace("-", "").isdigit():
|
|
from datetime import datetime
|
|
dt = datetime.fromtimestamp(float(ts))
|
|
return dt.strftime("%B %d, %Y at %I:%M %p")
|
|
return ts
|
|
except (ValueError, OSError, OverflowError) as e:
|
|
# Log specific errors for debugging while gracefully handling edge cases
|
|
logging.debug("Failed to format timestamp %s: %s", ts, e, exc_info=True)
|
|
except Exception as e:
|
|
logging.debug("Unexpected error formatting timestamp %s: %s", ts, e, exc_info=True)
|
|
return str(ts)
|
|
|
|
|
|
def _format_conversation(messages: List[Dict[str, Any]]) -> str:
|
|
"""Format session messages into a readable transcript for summarization."""
|
|
parts = []
|
|
for msg in messages:
|
|
role = msg.get("role", "unknown").upper()
|
|
content = msg.get("content") or ""
|
|
tool_name = msg.get("tool_name")
|
|
|
|
if role == "TOOL" and tool_name:
|
|
# Truncate long tool outputs
|
|
if len(content) > 500:
|
|
content = content[:250] + "\n...[truncated]...\n" + content[-250:]
|
|
parts.append(f"[TOOL:{tool_name}]: {content}")
|
|
elif role == "ASSISTANT":
|
|
# Include tool call names if present
|
|
tool_calls = msg.get("tool_calls")
|
|
if tool_calls and isinstance(tool_calls, list):
|
|
tc_names = []
|
|
for tc in tool_calls:
|
|
if isinstance(tc, dict):
|
|
name = tc.get("name") or tc.get("function", {}).get("name", "?")
|
|
tc_names.append(name)
|
|
if tc_names:
|
|
parts.append(f"[ASSISTANT]: [Called: {', '.join(tc_names)}]")
|
|
if content:
|
|
parts.append(f"[ASSISTANT]: {content}")
|
|
else:
|
|
parts.append(f"[ASSISTANT]: {content}")
|
|
else:
|
|
parts.append(f"[{role}]: {content}")
|
|
|
|
return "\n\n".join(parts)
|
|
|
|
|
|
def _truncate_around_matches(
|
|
full_text: str, query: str, max_chars: int = MAX_SESSION_CHARS
|
|
) -> str:
|
|
"""
|
|
Truncate a conversation transcript to max_chars, centered around
|
|
where the query terms appear. Keeps content near matches, trims the edges.
|
|
"""
|
|
if len(full_text) <= max_chars:
|
|
return full_text
|
|
|
|
# Find the first occurrence of any query term
|
|
query_terms = query.lower().split()
|
|
text_lower = full_text.lower()
|
|
first_match = len(full_text)
|
|
for term in query_terms:
|
|
pos = text_lower.find(term)
|
|
if pos != -1 and pos < first_match:
|
|
first_match = pos
|
|
|
|
if first_match == len(full_text):
|
|
# No match found, take from the start
|
|
first_match = 0
|
|
|
|
# Center the window around the first match
|
|
half = max_chars // 2
|
|
start = max(0, first_match - half)
|
|
end = min(len(full_text), start + max_chars)
|
|
if end - start < max_chars:
|
|
start = max(0, end - max_chars)
|
|
|
|
truncated = full_text[start:end]
|
|
prefix = "...[earlier conversation truncated]...\n\n" if start > 0 else ""
|
|
suffix = "\n\n...[later conversation truncated]..." if end < len(full_text) else ""
|
|
return prefix + truncated + suffix
|
|
|
|
|
|
async def _summarize_session(
|
|
conversation_text: str, query: str, session_meta: Dict[str, Any]
|
|
) -> Optional[str]:
|
|
"""Summarize a single session conversation focused on the search query."""
|
|
system_prompt = (
|
|
"You are reviewing a past conversation transcript to help recall what happened. "
|
|
"Summarize the conversation with a focus on the search topic. Include:\n"
|
|
"1. What the user asked about or wanted to accomplish\n"
|
|
"2. What actions were taken and what the outcomes were\n"
|
|
"3. Key decisions, solutions found, or conclusions reached\n"
|
|
"4. Any specific commands, files, URLs, or technical details that were important\n"
|
|
"5. Anything left unresolved or notable\n\n"
|
|
"Be thorough but concise. Preserve specific details (commands, paths, error messages) "
|
|
"that would be useful to recall. Write in past tense as a factual recap."
|
|
)
|
|
|
|
source = session_meta.get("source", "unknown")
|
|
started = _format_timestamp(session_meta.get("started_at"))
|
|
|
|
user_prompt = (
|
|
f"Search topic: {query}\n"
|
|
f"Session source: {source}\n"
|
|
f"Session date: {started}\n\n"
|
|
f"CONVERSATION TRANSCRIPT:\n{conversation_text}\n\n"
|
|
f"Summarize this conversation with focus on: {query}"
|
|
)
|
|
|
|
max_retries = 3
|
|
for attempt in range(max_retries):
|
|
try:
|
|
response = await async_call_llm(
|
|
task="session_search",
|
|
messages=[
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": user_prompt},
|
|
],
|
|
temperature=0.1,
|
|
max_tokens=MAX_SUMMARY_TOKENS,
|
|
)
|
|
return response.choices[0].message.content.strip()
|
|
except RuntimeError:
|
|
logging.warning("No auxiliary model available for session summarization")
|
|
return None
|
|
except Exception as e:
|
|
if attempt < max_retries - 1:
|
|
await asyncio.sleep(1 * (attempt + 1))
|
|
else:
|
|
logging.warning(
|
|
"Session summarization failed after %d attempts: %s",
|
|
max_retries,
|
|
e,
|
|
exc_info=True,
|
|
)
|
|
return None
|
|
|
|
|
|
def _list_recent_sessions(db, limit: int, current_session_id: str = None) -> str:
|
|
"""Return metadata for the most recent sessions (no LLM calls)."""
|
|
try:
|
|
sessions = db.list_sessions_rich(limit=limit + 5) # fetch extra to skip current
|
|
|
|
# Resolve current session lineage to exclude it
|
|
current_root = None
|
|
if current_session_id:
|
|
try:
|
|
sid = current_session_id
|
|
visited = set()
|
|
while sid and sid not in visited:
|
|
visited.add(sid)
|
|
s = db.get_session(sid)
|
|
parent = s.get("parent_session_id") if s else None
|
|
sid = parent if parent else None
|
|
current_root = max(visited, key=len) if visited else current_session_id
|
|
except Exception:
|
|
current_root = current_session_id
|
|
|
|
results = []
|
|
for s in sessions:
|
|
sid = s.get("id", "")
|
|
if current_root and (sid == current_root or sid == current_session_id):
|
|
continue
|
|
# Skip child/delegation sessions (they have parent_session_id)
|
|
if s.get("parent_session_id"):
|
|
continue
|
|
results.append({
|
|
"session_id": sid,
|
|
"title": s.get("title") or None,
|
|
"source": s.get("source", ""),
|
|
"started_at": s.get("started_at", ""),
|
|
"last_active": s.get("last_active", ""),
|
|
"message_count": s.get("message_count", 0),
|
|
"preview": s.get("preview", ""),
|
|
})
|
|
if len(results) >= limit:
|
|
break
|
|
|
|
return json.dumps({
|
|
"success": True,
|
|
"mode": "recent",
|
|
"results": results,
|
|
"count": len(results),
|
|
"message": f"Showing {len(results)} most recent sessions. Use a keyword query to search specific topics.",
|
|
}, ensure_ascii=False)
|
|
except Exception as e:
|
|
logging.error("Error listing recent sessions: %s", e, exc_info=True)
|
|
return json.dumps({"success": False, "error": f"Failed to list recent sessions: {e}"}, ensure_ascii=False)
|
|
|
|
|
|
def session_search(
|
|
query: str,
|
|
role_filter: str = None,
|
|
limit: int = 3,
|
|
db=None,
|
|
current_session_id: str = None,
|
|
) -> str:
|
|
"""
|
|
Search past sessions and return focused summaries of matching conversations.
|
|
|
|
Uses FTS5 to find matches, then summarizes the top sessions with Gemini Flash.
|
|
The current session is excluded from results since the agent already has that context.
|
|
"""
|
|
if db is None:
|
|
return json.dumps({"success": False, "error": "Session database not available."}, ensure_ascii=False)
|
|
|
|
limit = min(limit, 5) # Cap at 5 sessions to avoid excessive LLM calls
|
|
|
|
# Recent sessions mode: when query is empty, return metadata for recent sessions.
|
|
# No LLM calls — just DB queries for titles, previews, timestamps.
|
|
if not query or not query.strip():
|
|
return _list_recent_sessions(db, limit, current_session_id)
|
|
|
|
query = query.strip()
|
|
|
|
try:
|
|
# Parse role filter
|
|
role_list = None
|
|
if role_filter and role_filter.strip():
|
|
role_list = [r.strip() for r in role_filter.split(",") if r.strip()]
|
|
|
|
# FTS5 search -- get matches ranked by relevance
|
|
raw_results = db.search_messages(
|
|
query=query,
|
|
role_filter=role_list,
|
|
limit=50, # Get more matches to find unique sessions
|
|
offset=0,
|
|
)
|
|
|
|
if not raw_results:
|
|
return json.dumps({
|
|
"success": True,
|
|
"query": query,
|
|
"results": [],
|
|
"count": 0,
|
|
"message": "No matching sessions found.",
|
|
}, ensure_ascii=False)
|
|
|
|
# Resolve child sessions to their parent — delegation stores detailed
|
|
# content in child sessions, but the user's conversation is the parent.
|
|
def _resolve_to_parent(session_id: str) -> str:
|
|
"""Walk delegation chain to find the root parent session ID."""
|
|
visited = set()
|
|
sid = session_id
|
|
while sid and sid not in visited:
|
|
visited.add(sid)
|
|
try:
|
|
session = db.get_session(sid)
|
|
if not session:
|
|
break
|
|
parent = session.get("parent_session_id")
|
|
if parent:
|
|
sid = parent
|
|
else:
|
|
break
|
|
except Exception as e:
|
|
logging.debug(
|
|
"Error resolving parent for session %s: %s",
|
|
sid,
|
|
e,
|
|
exc_info=True,
|
|
)
|
|
break
|
|
return sid
|
|
|
|
current_lineage_root = (
|
|
_resolve_to_parent(current_session_id) if current_session_id else None
|
|
)
|
|
|
|
# Group by resolved (parent) session_id, dedup, skip the current
|
|
# session lineage. Compression and delegation create child sessions
|
|
# that still belong to the same active conversation.
|
|
seen_sessions = {}
|
|
for result in raw_results:
|
|
raw_sid = result["session_id"]
|
|
resolved_sid = _resolve_to_parent(raw_sid)
|
|
# Skip the current session lineage — the agent already has that
|
|
# context, even if older turns live in parent fragments.
|
|
if current_lineage_root and resolved_sid == current_lineage_root:
|
|
continue
|
|
if current_session_id and raw_sid == current_session_id:
|
|
continue
|
|
if resolved_sid not in seen_sessions:
|
|
result = dict(result)
|
|
result["session_id"] = resolved_sid
|
|
seen_sessions[resolved_sid] = result
|
|
if len(seen_sessions) >= limit:
|
|
break
|
|
|
|
# Prepare all sessions for parallel summarization
|
|
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 in parallel."""
|
|
coros = [
|
|
_summarize_session(text, query, meta)
|
|
for _, _, text, meta in tasks
|
|
]
|
|
return await asyncio.gather(*coros, return_exceptions=True)
|
|
|
|
try:
|
|
asyncio.get_running_loop()
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
|
|
results = pool.submit(lambda: asyncio.run(_summarize_all())).result(timeout=60)
|
|
except RuntimeError:
|
|
# No event loop running, create a new one
|
|
results = asyncio.run(_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, _, _), result in zip(tasks, results):
|
|
if isinstance(result, Exception):
|
|
logging.warning(
|
|
"Failed to summarize session %s: %s",
|
|
session_id,
|
|
result,
|
|
exc_info=True,
|
|
)
|
|
continue
|
|
if result:
|
|
summaries.append({
|
|
"session_id": session_id,
|
|
"when": _format_timestamp(match_info.get("session_started")),
|
|
"source": match_info.get("source", "unknown"),
|
|
"model": match_info.get("model"),
|
|
"summary": result,
|
|
})
|
|
|
|
return json.dumps({
|
|
"success": True,
|
|
"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 json.dumps({"success": False, "error": f"Search failed: {str(e)}"}, ensure_ascii=False)
|
|
|
|
|
|
def check_session_search_requirements() -> bool:
|
|
"""Requires SQLite state database and an auxiliary text 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. This is your recall -- "
|
|
"every past session is searchable, and this tool summarizes what happened.\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 to list recent sessions instead (no search, just metadata).",
|
|
},
|
|
"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
|
|
|
|
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="🔍",
|
|
)
|