70a0a5ff4a
session_search was returning the current session if it matched the query, which is redundant — the agent already has the current conversation context. This wasted an LLM summarization call and a result slot. Added current_session_id parameter to session_search(). The agent passes self.session_id and the search filters out any results where either the raw or parent-resolved session ID matches. Both the raw match and the parent-resolved match are checked to handle child sessions from delegation. Two tests added verifying the exclusion works and that other sessions are still returned.
382 lines
15 KiB
Python
382 lines
15 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 os
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import logging
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from typing import Dict, Any, List, Optional
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from openai import AsyncOpenAI, OpenAI
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from agent.auxiliary_client import get_async_text_auxiliary_client
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# Resolve the async auxiliary client at import time so we have the model slug.
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# Handles Codex Responses API adapter transparently.
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_async_aux_client, _SUMMARIZER_MODEL = get_async_text_auxiliary_client()
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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) -> str:
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"""Convert a Unix timestamp (float/int) or ISO string to a human-readable date."""
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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 Exception:
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pass
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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, centered around
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where the query terms appear. Keeps content near matches, trims the edges.
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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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# Find the first occurrence of any query term
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query_terms = query.lower().split()
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text_lower = full_text.lower()
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first_match = len(full_text)
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for term in query_terms:
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pos = text_lower.find(term)
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if pos != -1 and pos < first_match:
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first_match = pos
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if first_match == len(full_text):
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# No match found, take from the start
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first_match = 0
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# Center the window around the first match
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half = max_chars // 2
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start = max(0, first_match - half)
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end = min(len(full_text), start + max_chars)
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if end - start < max_chars:
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start = max(0, end - 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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if _async_aux_client is None or _SUMMARIZER_MODEL is None:
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logging.warning("No auxiliary model available for session summarization")
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return None
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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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from agent.auxiliary_client import get_auxiliary_extra_body, auxiliary_max_tokens_param
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_extra = get_auxiliary_extra_body()
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response = await _async_aux_client.chat.completions.create(
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model=_SUMMARIZER_MODEL,
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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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**({} if not _extra else {"extra_body": _extra}),
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temperature=0.1,
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**auxiliary_max_tokens_param(MAX_SUMMARY_TOKENS),
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)
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return response.choices[0].message.content.strip()
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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(f"Session summarization failed after {max_retries} attempts: {e}")
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return None
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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 json.dumps({"success": False, "error": "Session database not available."}, ensure_ascii=False)
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if not query or not query.strip():
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return json.dumps({"success": False, "error": "Query cannot be empty."}, ensure_ascii=False)
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query = query.strip()
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limit = min(limit, 5) # Cap at 5 sessions to avoid excessive LLM calls
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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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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):
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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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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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return sid
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# Group by resolved (parent) session_id, dedup, skip current session
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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 — the agent already has that context
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if current_session_id and resolved_sid == current_session_id:
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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(f"Failed to prepare session {session_id}: {e}")
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# Summarize all sessions in parallel
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async def _summarize_all():
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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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asyncio.get_running_loop()
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with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
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results = pool.submit(lambda: asyncio.run(_summarize_all())).result(timeout=60)
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except RuntimeError:
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results = asyncio.run(_summarize_all())
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summaries = []
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for (session_id, match_info, _, _), result in zip(tasks, results):
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if isinstance(result, Exception):
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logging.warning(f"Failed to summarize session {session_id}: {result}")
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continue
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if result:
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summaries.append({
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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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"summary": result,
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})
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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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return json.dumps({"success": False, "error": f"Search failed: {str(e)}"}, ensure_ascii=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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if _async_aux_client is None:
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return False
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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. This is your recall -- "
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"every past session is searchable, and this tool summarizes what happened.\n\n"
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"USE THIS PROACTIVELY when:\n"
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"- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n"
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"- The user asks about a topic you worked on before but don't have in current context\n"
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"- The user references a project, person, or concept that seems familiar but isn't in memory\n"
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"- You want to check if you've solved a similar problem before\n"
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"- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\n"
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"Don't hesitate to search -- it's fast and cheap. Better to search and confirm "
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"than to guess or ask the user to repeat themselves.\n\n"
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"Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), "
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"phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). "
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"IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses "
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"sessions that only mention some terms. If a broad OR query returns nothing, try individual "
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"keyword searches in parallel. Returns summaries of the top matching sessions."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions.",
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},
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"role_filter": {
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"type": "string",
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"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.",
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},
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"limit": {
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"type": "integer",
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"description": "Max sessions to summarize (default: 3, max: 5).",
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"default": 3,
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},
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},
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"required": ["query"],
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},
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}
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# --- Registry ---
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from tools.registry import registry
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registry.register(
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name="session_search",
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toolset="session_search",
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schema=SESSION_SEARCH_SCHEMA,
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handler=lambda args, **kw: session_search(
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query=args.get("query", ""),
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role_filter=args.get("role_filter"),
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limit=args.get("limit", 3),
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db=kw.get("db"),
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current_session_id=kw.get("current_session_id")),
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check_fn=check_session_search_requirements,
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)
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