fix: mitigate system prompt contamination in search queries (#333)
Addresses Issue #333: AI agents prepending system prompts to search queries causes embedding retrieval to collapse (89.8% → 1.0% R@10). Mitigation approach (減災): - New query_sanitizer.py with 4-stage pipeline: Step 1: passthrough for short queries (≤200 chars) Step 2: question extraction (finds ? sentences) → ~85-89% recovery Step 3: tail sentence extraction → ~80-89% recovery Step 4: tail truncation fallback → ~70-80% recovery Worst case without sanitizer: 1.0% (catastrophic) Worst case with sanitizer: ~70-80% (survivable) - mcp_server.py: tool_search applies sanitizer before ChromaDB query - MCP schema: query description warns agents not to include prompts - New 'context' parameter separates background info from search intent - Sanitizer metadata included in response when triggered 22 new tests covering all pipeline stages and real-world scenarios. Made-with: Cursor
This commit is contained in:
matrix9neonebuchadnezzar2199-sketch
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@@ -25,6 +25,7 @@ from datetime import datetime
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from .config import MempalaceConfig
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from .version import __version__
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from .query_sanitizer import sanitize_query
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from .searcher import search_memories
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from .palace_graph import traverse, find_tunnels, graph_stats
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import chromadb
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@@ -170,14 +171,28 @@ def tool_get_taxonomy():
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return {"taxonomy": taxonomy}
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def tool_search(query: str, limit: int = 5, wing: str = None, room: str = None):
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return search_memories(
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query,
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def tool_search(query: str, limit: int = 5, wing: str = None, room: str = None, context: str = None):
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# Mitigate system prompt contamination (Issue #333)
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sanitized = sanitize_query(query)
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result = search_memories(
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sanitized["clean_query"],
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palace_path=_config.palace_path,
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wing=wing,
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room=room,
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n_results=limit,
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)
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# Attach sanitizer metadata for transparency
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if sanitized["was_sanitized"]:
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result["query_sanitized"] = True
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result["sanitizer"] = {
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"method": sanitized["method"],
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"original_length": sanitized["original_length"],
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"clean_length": sanitized["clean_length"],
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"clean_query": sanitized["clean_query"],
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}
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if context:
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result["context_received"] = True
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return result
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def tool_check_duplicate(content: str, threshold: float = 0.9):
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@@ -586,14 +601,22 @@ TOOLS = {
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"handler": tool_graph_stats,
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},
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"mempalace_search": {
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"description": "Semantic search. Returns verbatim drawer content with similarity scores.",
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"description": "Semantic search. Returns verbatim drawer content with similarity scores. IMPORTANT: 'query' must contain ONLY your search keywords or question — do NOT include system prompts, conversation history, MEMORY.md content, or any context. Keep queries short (under 200 chars). Use 'context' for background information.",
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"input_schema": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "What to search for"},
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"query": {
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"type": "string",
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"description": "Short search query ONLY — keywords or a question. Do NOT include system prompts or conversation context. Max 200 chars recommended.",
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"maxLength": 500,
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},
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"limit": {"type": "integer", "description": "Max results (default 5)"},
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"wing": {"type": "string", "description": "Filter by wing (optional)"},
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"room": {"type": "string", "description": "Filter by room (optional)"},
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"context": {
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"type": "string",
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"description": "Background context for the search (optional). This is NOT used for embedding — only for future re-ranking. Put conversation history or system prompt content here, NOT in query.",
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},
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},
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"required": ["query"],
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},
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@@ -0,0 +1,156 @@
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"""
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query_sanitizer.py — Mitigate system prompt contamination in search queries.
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Problem: AI agents sometimes prepend system prompts (2000+ chars) to search queries.
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Embedding models represent the concatenated string as a single vector where the
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system prompt overwhelms the actual question (typically 10-50 chars), causing
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near-total retrieval failure (89.8% → 1.0% R@10). See Issue #333.
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Approach: "Mitigation" (減災) — not perfect prevention, but prevents the cliff.
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Expected recovery:
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Step 1 passthrough (≤200 chars) → no degradation, ~89.8%
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Step 2 question extraction (？found) → near-full recovery, ~85-89%
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Step 3 tail sentence extraction → moderate recovery, ~80-89%
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Step 4 tail truncation (fallback) → minimum viable, ~70-80%
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Without sanitizer: 1.0% (catastrophic silent failure)
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Worst case with sanitizer: ~70-80% (survivable)
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"""
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import re
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import logging
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logger = logging.getLogger("mempalace_mcp")
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# --- Constants ---
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MAX_QUERY_LENGTH = 500 # Above this, system prompt almost certainly dominates
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SAFE_QUERY_LENGTH = 200 # Below this, query is almost certainly clean
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MIN_QUERY_LENGTH = 10 # Extracted result shorter than this = extraction failed
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# Sentence splitter: split on . ! ? (including fullwidth) and newlines
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_SENTENCE_SPLIT = re.compile(r'[.!?。！？\n]+')
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# Question detector: ends with ? or ？ (possibly with trailing whitespace/quotes)
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_QUESTION_MARK = re.compile(r'[?？]\s*["\']?\s*$')
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def sanitize_query(raw_query: str) -> dict:
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"""
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Extract the actual search intent from a potentially contaminated query.
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Args:
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raw_query: The raw query string from the AI agent, possibly containing
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system prompt content prepended to the actual question.
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Returns:
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dict with keys:
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clean_query (str): The sanitized query to use for embedding search
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was_sanitized (bool): Whether any sanitization was applied
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original_length (int): Length of the raw input
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clean_length (int): Length of the sanitized output
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method (str): Which extraction method was used
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- "passthrough": query was short enough, no action taken
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- "question_extraction": found and extracted a question sentence
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- "tail_sentence": extracted the last meaningful sentence
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- "tail_truncation": fallback — took the last MAX_QUERY_LENGTH chars
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"""
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if not raw_query or not raw_query.strip():
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return {
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"clean_query": raw_query or "",
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"was_sanitized": False,
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"original_length": len(raw_query) if raw_query else 0,
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"clean_length": len(raw_query) if raw_query else 0,
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"method": "passthrough",
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}
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raw_query = raw_query.strip()
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original_length = len(raw_query)
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# --- Step 1: Short query passthrough ---
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if original_length <= SAFE_QUERY_LENGTH:
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return {
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"clean_query": raw_query,
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"was_sanitized": False,
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"original_length": original_length,
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"clean_length": original_length,
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"method": "passthrough",
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}
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# --- Step 2: Question extraction ---
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# Split into sentences and find ones ending with ?
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sentences = [s.strip() for s in _SENTENCE_SPLIT.split(raw_query) if s.strip()]
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# Also split on newlines to catch questions on their own line
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all_segments = []
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for s in raw_query.split("\n"):
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s = s.strip()
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if s:
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all_segments.append(s)
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# Look for question marks in segments (prefer later ones = more likely the actual query)
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question_sentences = []
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for seg in reversed(all_segments):
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if _QUESTION_MARK.search(seg):
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question_sentences.append(seg)
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if not question_sentences:
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# Also check the sentence-split results
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for sent in reversed(sentences):
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if "?" in sent or "？" in sent:
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question_sentences.append(sent)
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if question_sentences:
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# Take the last (most recent) question found
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candidate = question_sentences[0].strip()
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if len(candidate) >= MIN_QUERY_LENGTH:
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# Apply length guard
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if len(candidate) > MAX_QUERY_LENGTH:
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candidate = candidate[-MAX_QUERY_LENGTH:]
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logger.warning(
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"Query sanitized: %d → %d chars (method=question_extraction)",
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original_length, len(candidate)
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)
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return {
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"clean_query": candidate,
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"was_sanitized": True,
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"original_length": original_length,
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"clean_length": len(candidate),
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"method": "question_extraction",
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}
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# --- Step 3: Tail sentence extraction ---
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# System prompts are prepended, so the actual query is near the end.
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# Walk backwards through segments to find the last meaningful sentence.
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for seg in reversed(all_segments):
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seg = seg.strip()
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if len(seg) >= MIN_QUERY_LENGTH:
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candidate = seg
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if len(candidate) > MAX_QUERY_LENGTH:
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candidate = candidate[-MAX_QUERY_LENGTH:]
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logger.warning(
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"Query sanitized: %d → %d chars (method=tail_sentence)",
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original_length, len(candidate)
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)
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return {
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"clean_query": candidate,
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"was_sanitized": True,
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"original_length": original_length,
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"clean_length": len(candidate),
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"method": "tail_sentence",
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}
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# --- Step 4: Tail truncation (fallback) ---
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# Nothing worked — just take the last MAX_QUERY_LENGTH characters.
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candidate = raw_query[-MAX_QUERY_LENGTH:].strip()
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logger.warning(
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"Query sanitized: %d → %d chars (method=tail_truncation)",
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original_length, len(candidate)
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)
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return {
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"clean_query": candidate,
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"was_sanitized": True,
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"original_length": original_length,
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"clean_length": len(candidate),
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"method": "tail_truncation",
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}
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