rag-api-node-1 / src /infrastructure /adapters /query_expander.py
Peterase's picture
feat: Add query enhancements and flexible prompting (v2.1)
6246bba
"""
Query Expander & Rewriter
Improves query quality by:
- Expanding short/vague queries
- Fixing typos
- Adding context
- Clarifying ambiguous queries
Uses LLM only for short queries (<4 words) to minimize latency.
"""
import logging
import re
from typing import Dict, Any, Optional
from dataclasses import dataclass
logger = logging.getLogger(__name__)
@dataclass
class ExpandedQuery:
"""Result of query expansion"""
original: str
expanded: str
was_expanded: bool
expansion_reason: str
confidence: float
class QueryExpander:
"""
Expands and rewrites queries for better search results.
Strategy:
1. Check if expansion needed (short, vague, typos)
2. Use LLM to expand (only for queries that need it)
3. Cache expansions to avoid repeated LLM calls
"""
# Queries that are too vague and need expansion
VAGUE_PATTERNS = [
r"^news$",
r"^today'?s?\s+news$",
r"^latest$",
r"^breaking$",
r"^updates?$",
r"^ethiopia$",
r"^africa$",
]
# Common typos to fix
TYPO_FIXES = {
"ethopia": "ethiopia",
"etiopia": "ethiopia",
"ethiopa": "ethiopia",
"todays": "today's",
"whats": "what's",
"wheres": "where's",
"hows": "how's",
"breakin": "breaking",
"lates": "latest",
"updat": "update",
}
def __init__(self, llm_adapter=None, cache=None):
"""
Initialize query expander.
Args:
llm_adapter: LLM adapter for query expansion
cache: Cache adapter for storing expansions
"""
self.llm = llm_adapter
self.cache = cache
def expand(self, query: str) -> ExpandedQuery:
"""
Expand query if needed.
Args:
query: Original user query
Returns:
ExpandedQuery with original and expanded versions
"""
original = query.strip()
# Step 1: Check cache first
if self.cache:
cache_key = f"query_expansion:{original.lower()}"
cached = self.cache.get(cache_key)
if cached:
logger.debug(f"Query expansion cache hit: {original}")
return ExpandedQuery(
original=original,
expanded=cached["expanded"],
was_expanded=cached["was_expanded"],
expansion_reason=cached["reason"],
confidence=cached["confidence"]
)
# Step 2: Fix typos first
fixed_query = self._fix_typos(original)
if fixed_query != original:
logger.info(f"Fixed typos: '{original}' β†’ '{fixed_query}'")
# Step 3: Check if expansion needed
needs_expansion, reason = self._needs_expansion(fixed_query)
if not needs_expansion:
result = ExpandedQuery(
original=original,
expanded=fixed_query,
was_expanded=False,
expansion_reason="No expansion needed",
confidence=1.0
)
self._cache_result(original, result)
return result
# Step 4: Expand using LLM
if self.llm:
try:
expanded = self._expand_with_llm(fixed_query, reason)
result = ExpandedQuery(
original=original,
expanded=expanded,
was_expanded=True,
expansion_reason=reason,
confidence=0.85
)
logger.info(f"Expanded query: '{original}' β†’ '{expanded}'")
self._cache_result(original, result)
return result
except Exception as e:
logger.error(f"Query expansion failed: {e}")
# Step 5: Fallback - use fixed query
result = ExpandedQuery(
original=original,
expanded=fixed_query,
was_expanded=False,
expansion_reason="LLM expansion failed",
confidence=0.7
)
self._cache_result(original, result)
return result
def _fix_typos(self, query: str) -> str:
"""Fix common typos in query"""
words = query.lower().split()
fixed_words = []
for word in words:
# Remove punctuation for matching
clean_word = re.sub(r'[^\w\s]', '', word)
if clean_word in self.TYPO_FIXES:
fixed_words.append(self.TYPO_FIXES[clean_word])
else:
fixed_words.append(word)
return ' '.join(fixed_words)
def _needs_expansion(self, query: str) -> tuple[bool, str]:
"""
Check if query needs expansion.
Returns:
(needs_expansion, reason)
"""
query_lower = query.lower().strip()
word_count = len(query.split())
# Check if too vague
for pattern in self.VAGUE_PATTERNS:
if re.match(pattern, query_lower, re.IGNORECASE):
return True, "Vague query"
# Check if too short (but not a proper noun)
if word_count <= 2:
# Don't expand if it's a location or proper noun
if not self._is_proper_noun(query):
return True, "Too short"
# Check if missing context
if word_count <= 3 and not any(
kw in query_lower
for kw in ["news", "latest", "today", "breaking", "what", "when", "where", "who", "how", "why"]
):
return True, "Missing context"
return False, "No expansion needed"
def _is_proper_noun(self, query: str) -> bool:
"""Check if query is a proper noun (location, name, etc.)"""
# Simple heuristic: starts with capital letter
words = query.split()
return all(word[0].isupper() for word in words if word)
def _expand_with_llm(self, query: str, reason: str) -> str:
"""
Expand query using LLM.
Args:
query: Query to expand
reason: Reason for expansion
Returns:
Expanded query
"""
prompt = f"""You are a query expansion assistant for a news search system.
Task: Expand this short/vague query into a clear, specific news search query.
Rules:
1. Keep it concise (max 15 words)
2. Add context about what news the user wants
3. Preserve the original intent
4. Add temporal context if missing (e.g., "latest", "today")
5. Make it a natural question or statement
Original query: "{query}"
Reason for expansion: {reason}
Expanded query:"""
try:
expanded = self.llm.generate(prompt, max_tokens=50).strip()
# Clean up the response
expanded = expanded.strip('"\'')
# Validate expansion
if len(expanded.split()) > 20:
# Too long, truncate
expanded = ' '.join(expanded.split()[:15])
if len(expanded.split()) < 3:
# Expansion failed, return original
return query
return expanded
except Exception as e:
logger.error(f"LLM expansion error: {e}")
return query
def _cache_result(self, original: str, result: ExpandedQuery):
"""Cache expansion result"""
if self.cache:
cache_key = f"query_expansion:{original.lower()}"
self.cache.set(
cache_key,
{
"expanded": result.expanded,
"was_expanded": result.was_expanded,
"reason": result.expansion_reason,
"confidence": result.confidence
},
expiration=3600 # 1 hour
)
# ═══════════════════════════════════════════════════════════════════════════
# SINGLETON INSTANCE
# ═══════════════════════════════════════════════════════════════════════════
# Will be initialized with dependencies in main.py
query_expander: Optional[QueryExpander] = None
def initialize_query_expander(llm_adapter, cache=None):
"""Initialize global query expander instance"""
global query_expander
query_expander = QueryExpander(llm_adapter, cache)
logger.info("Query expander initialized")