0aa31cd3cb
Add centralized call_llm() and async_call_llm() functions that own the full LLM request lifecycle: 1. Resolve provider + model from task config or explicit args 2. Get or create a cached client for that provider 3. Format request args (max_tokens handling, provider extra_body) 4. Make the API call with max_tokens/max_completion_tokens retry 5. Return the response Config: expanded auxiliary section with provider:model slots for all tasks (compression, vision, web_extract, session_search, skills_hub, mcp, flush_memories). Config version bumped to 7. Migrated all auxiliary consumers: - context_compressor.py: uses call_llm(task='compression') - vision_tools.py: uses async_call_llm(task='vision') - web_tools.py: uses async_call_llm(task='web_extract') - session_search_tool.py: uses async_call_llm(task='session_search') - browser_tool.py: uses call_llm(task='vision'/'web_extract') - mcp_tool.py: uses call_llm(task='mcp') - skills_guard.py: uses call_llm(provider='openrouter') - run_agent.py flush_memories: uses call_llm(task='flush_memories') Tests updated for context_compressor and MCP tool. Some test mocks still need updating (15 remaining failures from mock pattern changes, 2 pre-existing).
389 lines
15 KiB
Python
389 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, Union
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from agent.auxiliary_client import async_call_llm
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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)
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except Exception as e:
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logging.debug("Unexpected error formatting timestamp %s: %s", ts, e)
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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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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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return response.choices[0].message.content.strip()
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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(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: 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("Error resolving parent for session %s: %s", sid, e)
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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() -> 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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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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# No event loop running, create a new one
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results = asyncio.run(_summarize_all())
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except concurrent.futures.TimeoutError:
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logging.warning("Session summarization timed out after 60 seconds")
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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, _, _), 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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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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