mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-28 06:51:16 +08:00
feat: add persistent memory system + SQLite session store
Two-part implementation: Part A - Curated Bounded Memory: - New memory tool (tools/memory_tool.py) with MEMORY.md + USER.md stores - Character-limited (2200/1375 chars), § delimited entries - Frozen snapshot injected into system prompt at session start - Model manages pruning via replace/remove with substring matching - Usage indicator shown in system prompt header Part B - SQLite Session Store: - New hermes_state.py with SessionDB class, FTS5 full-text search - Gateway session.py rewritten to dual-write SQLite + legacy JSONL - Compression-triggered session splitting with parent_session_id chains - New session_search tool with Gemini Flash summarization of matched sessions - CLI session lifecycle (create on launch, close on exit) Also: - System prompt now cached per session, only rebuilt on compression (fixes prefix cache invalidation from date/time changes every turn) - Config version bumped to 3, hermes doctor checks for new artifacts - Disabled in batch_runner and RL environments
This commit is contained in:
teknium1
319
tools/session_search_tool.py
Normal file
319
tools/session_search_tool.py
Normal file
@@ -0,0 +1,319 @@
|
||||
#!/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
|
||||
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
SUMMARIZER_MODEL = "google/gemini-3-flash-preview"
|
||||
MAX_SESSION_CHARS = 100_000
|
||||
MAX_SUMMARY_TOKENS = 2000
|
||||
|
||||
_summarizer_client = None
|
||||
|
||||
|
||||
def _get_client() -> AsyncOpenAI:
|
||||
"""Lazy-init the summarizer client (shared with web_tools pattern)."""
|
||||
global _summarizer_client
|
||||
if _summarizer_client is None:
|
||||
api_key = os.getenv("OPENROUTER_API_KEY")
|
||||
if not api_key:
|
||||
raise ValueError("OPENROUTER_API_KEY not set")
|
||||
_summarizer_client = AsyncOpenAI(
|
||||
api_key=api_key,
|
||||
base_url="https://openrouter.ai/api/v1",
|
||||
)
|
||||
return _summarizer_client
|
||||
|
||||
|
||||
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 = session_meta.get("started_at", "unknown")
|
||||
|
||||
user_prompt = (
|
||||
f"Search topic: {query}\n"
|
||||
f"Session source: {source}\n"
|
||||
f"Session started: {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 _get_client().chat.completions.create(
|
||||
model=SUMMARIZER_MODEL,
|
||||
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 Exception as e:
|
||||
if attempt < max_retries - 1:
|
||||
await asyncio.sleep(1 * (attempt + 1))
|
||||
else:
|
||||
logging.warning(f"Session summarization failed after {max_retries} attempts: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def session_search(
|
||||
query: str,
|
||||
role_filter: str = None,
|
||||
limit: int = 3,
|
||||
db=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.
|
||||
"""
|
||||
if db is None:
|
||||
return json.dumps({"success": False, "error": "Session database not available."}, ensure_ascii=False)
|
||||
|
||||
if not query or not query.strip():
|
||||
return json.dumps({"success": False, "error": "Query cannot be empty."}, ensure_ascii=False)
|
||||
|
||||
query = query.strip()
|
||||
limit = min(limit, 5) # Cap at 5 sessions to avoid excessive LLM calls
|
||||
|
||||
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)
|
||||
|
||||
# Group by session_id, keep order (highest ranked first)
|
||||
seen_sessions = {}
|
||||
for result in raw_results:
|
||||
sid = result["session_id"]
|
||||
if sid not in seen_sessions:
|
||||
seen_sessions[sid] = result
|
||||
if len(seen_sessions) >= limit:
|
||||
break
|
||||
|
||||
# Summarize each matching session
|
||||
summaries = []
|
||||
for session_id, match_info in seen_sessions.items():
|
||||
try:
|
||||
# Load full conversation
|
||||
messages = db.get_messages_as_conversation(session_id)
|
||||
if not messages:
|
||||
continue
|
||||
|
||||
# Get session metadata
|
||||
session_meta = db.get_session(session_id) or {}
|
||||
|
||||
# Format and truncate
|
||||
conversation_text = _format_conversation(messages)
|
||||
conversation_text = _truncate_around_matches(conversation_text, query)
|
||||
|
||||
# Summarize with Gemini Flash (handle both async and sync contexts)
|
||||
coro = _summarize_session(conversation_text, query, session_meta)
|
||||
try:
|
||||
asyncio.get_running_loop()
|
||||
# Already in an async context (gateway) -- run in a thread
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
|
||||
summary = pool.submit(lambda: asyncio.run(coro)).result(timeout=30)
|
||||
except RuntimeError:
|
||||
# No running loop (normal CLI) -- use asyncio.run directly
|
||||
summary = asyncio.run(coro)
|
||||
|
||||
if summary:
|
||||
summaries.append({
|
||||
"session_id": session_id,
|
||||
"source": match_info.get("source", "unknown"),
|
||||
"model": match_info.get("model"),
|
||||
"session_started": match_info.get("session_started"),
|
||||
"summary": summary,
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logging.warning(f"Failed to summarize session {session_id}: {e}")
|
||||
continue
|
||||
|
||||
return json.dumps({
|
||||
"success": True,
|
||||
"query": query,
|
||||
"results": summaries,
|
||||
"count": len(summaries),
|
||||
"sessions_searched": len(seen_sessions),
|
||||
}, ensure_ascii=False)
|
||||
|
||||
except Exception as e:
|
||||
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 OpenRouter API key."""
|
||||
if not os.getenv("OPENROUTER_API_KEY"):
|
||||
return False
|
||||
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 and recall past conversations. Finds matching sessions using "
|
||||
"full-text search, then provides a focused summary of each matching "
|
||||
"conversation.\n\n"
|
||||
"Use this when you need to recall:\n"
|
||||
"- A solution or approach from a previous session\n"
|
||||
"- Something the user said or asked about before\n"
|
||||
"- A command, file path, or technical detail from past work\n"
|
||||
"- The outcome of a previous task\n\n"
|
||||
"Supports search syntax:\n"
|
||||
" Keywords: docker deployment\n"
|
||||
" Phrases: '\"exact phrase\"'\n"
|
||||
" Boolean: docker OR kubernetes, python NOT java\n"
|
||||
" Prefix: deploy*\n\n"
|
||||
"Returns summaries (not raw transcripts) of the top matching sessions, "
|
||||
"focused on your search topic. Max 3 sessions per search."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions.",
|
||||
},
|
||||
"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": ["query"],
|
||||
},
|
||||
}
|
||||
Reference in New Issue
Block a user