feat:Reduce unnecessary LLM calls from the respective channels

backend/app/classification/cache_helpers.py

@@ -0,0 +1,222 @@
+import re
+import time
+import asyncio
+import logging
+from typing import Optional, Dict, Any
+from cachetools import TTLCache
+from collections import Counter
+from langchain_core.messages import HumanMessage
+import xxhash
+import hashlib
+import json
+
+logger = logging.getLogger(__name__)
+
+try:
+    _HAS_XXHASH = True
+except Exception:
+    xxhash = None
+    _HAS_XXHASH = False
+
+# Config
+CACHE_MAXSIZE = 4096
+CACHE_TTL_SECONDS = 60 * 60
+MAX_MESSAGE_LENGTH = 10000  # Max message length to process (prevents DoS via large payloads)
+
+# Patterns for fast-path classification (concise to reduce memory)
+# Merge related intents into fewer regexes and add common Discord patterns
+_PATTERNS = {
+    # common salutations
+    "greeting": re.compile(r"^\s*(?:hi|hello|hey|good\s+morning|good\s+afternoon|good\s+evening)\b", re.I),
+    # explicit help / action requests
+    "action_request": re.compile(r".*\b(?:help|please\s+help|plz\s+help|need\s+help|support|assist|request)\b", re.I),
+    # bug / error reports
+    "bug_report": re.compile(r".*\b(?:bug|error|exception|stack\s*trace|crash|failed|traceback|segfault)\b", re.I),
+    # thanks and short acknowledgements (shared fast-path)
+    "thanks_ack": re.compile(r"^\s*(?:thanks|thank\s+you|thx|ty|ok|okay|got\s+it|roger|ack)\b", re.I),
+    # modern short responses / slang that are non-actionable
+    "slang": re.compile(r"^\s*(?:brb|lol|lmao|rofl|omg|wtf|smh|idk|np|yw|pls|plz|bump|ping|fyi|imo|idc)\b", re.I),
+    # general intent bucket for optimization/performance/docs/feature keywords
+    "intent_general": re.compile(
+        r".*\b(?:optimi[sz]e|improve|speed\s*up|performance|memory|resource|efficient|documentation|docs|guide|tutorial|example|feature|suggest|idea)\b",
+        re.I,
+    ),
+    # Discord-specific: user mentions (@user)
+    "discord_mention": re.compile(r"(?:<@!?\d+>|@\w+)\b"),
+    # Channel mentions (#channel or <#123456>)
+    "channel_mention": re.compile(r"(?:<#\d+>|#\w+)\b"),
+    # Bot/CLI-like commands commonly used on Discord (prefix-based)
+    "command": re.compile(r"^\s*(?:/|!|\?|\.|\$)[A-Za-z0-9_\-]+"),
+    # Code snippets or blocks (inline or triple backticks)
+    "code_block": re.compile(r"```[\s\S]*?```|`[^`]+`", re.S),
+    # URLs (simple detection)
+    "url": re.compile(r"https?://\S+|www\.\S+"),
+    # GitHub/issue/PR references (#123, owner/repo#123, PR #123)
+    "pr_issue_ref": re.compile(r"(?:\b#\d+\b|\b[A-Za-z0-9_.-]+/[A-Za-z0-9_.-]+#\d+\b|\bPR\s*#\d+\b)", re.I),
+    # Emoji shortname like :emoji:
+    "emoji_short": re.compile(r":[a-zA-Z0-9_+\-]+:"),
+}
+
+# Simple deterministic classifications for the patterns
+# Keep mapping concise and reflect combined pattern keys
+_PATTERN_CLASSIFICATION = {
+    "greeting": {"needs_devrel": False, "priority": "low", "reasoning": "greeting"},
+    "thanks_ack": {"needs_devrel": False, "priority": "low", "reasoning": "thanks/acknowledgement"},
+    "slang": {"needs_devrel": False, "priority": "low", "reasoning": "short/slang response"},
+    "action_request": {"needs_devrel": True, "priority": "high", "reasoning": "explicit help/request keywords"},
+    "bug_report": {"needs_devrel": True, "priority": "high", "reasoning": "error or bug report"},
+    "integration": {"needs_devrel": True, "priority": "high", "reasoning": "Discord/GitHub/integration requests (OAuth, commands, threads, repo ops)"},
+    "architecture": {"needs_devrel": True, "priority": "medium", "reasoning": "architecture/infra mentions (queues, DBs, LLMs)"},
+    "intent_general": {"needs_devrel": True, "priority": "medium", "reasoning": "optimization/docs/feature requests"},
+
+    # Discord/GitHub specific quick classifications
+    "discord_mention": {"needs_devrel": False, "priority": "low", "reasoning": "user mention"},
+    "channel_mention": {"needs_devrel": False, "priority": "low", "reasoning": "channel mention"},
+    "command": {"needs_devrel": False, "priority": "medium", "reasoning": "bot/CLI command invocation"},
+    "code_block": {"needs_devrel": False, "priority": "low", "reasoning": "code snippet or block"},
+    "url": {"needs_devrel": False, "priority": "low", "reasoning": "contains URL"},
+    "pr_issue_ref": {"needs_devrel": True, "priority": "medium", "reasoning": "reference to issue or PR"},
+    "emoji_short": {"needs_devrel": False, "priority": "low", "reasoning": "emoji shortname"},
+}
+
+_cache = TTLCache(maxsize=CACHE_MAXSIZE, ttl=CACHE_TTL_SECONDS)
+# In-flight calls to dedupe concurrent identical requests (bounded with TTL to prevent leaks)
+_inflight: TTLCache = TTLCache(maxsize=1000, ttl=120)  # Max 1000 concurrent, 2min timeout
+
+# Simple metrics
+metrics = Counter({"total": 0, "cache_hits": 0, "cache_misses": 0, "skipped_llm": 0})
+
+
+# Simple cache key generation
+def make_key(model: str, prompt: str, params: Dict[str, Any]) -> str:
+    """
+    Create a stable cache key using XXHash128 for speed.
+    - normalize prompt to reduce trivial differences
+    - serialize params with sorted keys and compact separators
+    - use blake2b as a fallback if xxhash unavailable
+    """
+    norm_prompt = normalize_message(prompt)
+
+    # Serialize params once; for very large params consider hashing only relevant fields
+    try:
+        params_blob = json.dumps(params or {}, sort_keys=True, separators=(",", ":"), default=str).encode("utf-8")
+    except Exception:
+        params_blob = str(params).encode("utf-8")
+
+    payload = b"|".join([model.encode("utf-8"), norm_prompt.encode("utf-8"), params_blob])
+
+    # Use XXHash128 for better collision resistance (if available), otherwise fallback
+    if _HAS_XXHASH:
+        return xxhash.xxh3_128_hexdigest(payload)
+    else:
+        return hashlib.blake2b(payload, digest_size=16).hexdigest()
+
+
+def _compose_prompt_with_context(normalized: str, context_id: Optional[str]) -> str:
+    if context_id:
+        return f"{normalized}|ctx:{context_id}"
+    return normalized
+
+
+def key_for_normalized(normalized: str, context_id: Optional[str], model: str, params: Dict[str, Any]) -> str:
+    """
+    Compute cache key from a normalized message and optional context id.
+    """
+    prompt = _compose_prompt_with_context(normalized, context_id)
+    return make_key(model, prompt, params)
+
+
+def get_cached_by_normalized(normalized: str, context_id: Optional[str], model: str, params: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """Retrieve cached payload for a normalized message + context."""
+    key = key_for_normalized(normalized, context_id, model, params)
+    return cache_get(key)
+
+
+def set_cached_by_normalized(normalized: str, context_id: Optional[str], model: str, params: Dict[str, Any], payload: Dict[str, Any]) -> None:
+    """Store payload for normalized message + context."""
+    key = key_for_normalized(normalized, context_id, model, params)
+    cache_set(key, payload)
+
+
+# Cache wrapper for LLM calls (async - uses llm.ainvoke)
+async def cached_llm_call(prompt: str, model: str, params: Dict[str, Any], llm):
+    """
+    Cached wrapper for async LLM calls with:
+    - fast-path simple pattern classification to avoid LLM cost
+    - cache hit/miss metrics
+    - in-flight deduplication so concurrent identical requests share one LLM call
+    """
+    # Fast-path: simple deterministic classification (avoid LLM)
+    normalized = normalize_message(prompt)
+    simple = is_simple_message(normalized)
+    if simple is not None:
+        metrics["skipped_llm"] += 1
+        return simple
+
+    metrics["total"] += 1
+    key = make_key(model, prompt, params)
+
+    # Quick cache check
+    cached = cache_get(key)
+    if cached is not None:
+        metrics["cache_hits"] += 1
+        return cached
+
+    metrics["cache_misses"] += 1
+
+    # Deduplicate in-flight identical calls so only one LLM request is made
+    loop = asyncio.get_running_loop()
+    # Attempt to install a future atomically to dedupe concurrent callers
+    future = loop.create_future()
+    prev = _inflight.setdefault(key, future)
+    if prev is not future:
+        # another caller is in-flight; await its result/failure
+        return await prev
+
+    # we are the owner; perform the fetch and set the future result/exception
+    async def _owner_fetch():
+        try:
+            start = time.time()
+            response = await llm.ainvoke([HumanMessage(content=prompt)])
+            elapsed = time.time() - start
+            # store response content or small payload rather than full object
+            result = response.content if hasattr(response, "content") else response
+            _cache[key] = result
+            future.set_result(result)
+            return result
+        except asyncio.CancelledError:
+            future.cancel()
+            raise
+        except Exception as e:
+            future.set_exception(e)
+            raise
+        finally:
+            # ensure inflight entry removed
+            _inflight.pop(key, None)
+
+    # schedule owner fetch and await its result
+    loop.create_task(_owner_fetch())
+    return await future
+
+def normalize_message(msg: str) -> str:
+    """Normalize message for caching. Truncates to MAX_MESSAGE_LENGTH to prevent DoS."""
+    s = (msg or "")[:MAX_MESSAGE_LENGTH].strip().lower()
+    s = re.sub(r"\s+", " ", s)
+    return s
+
+def is_simple_message(normalized: str) -> Optional[Dict[str, Any]]:
+    for name, pattern in _PATTERNS.items():
+        if pattern.match(normalized):
+            return dict(_PATTERN_CLASSIFICATION[name], original_message=normalized)
+    return None
+
+def cache_get(key: str) -> Optional[Dict[str, Any]]:
+    try:
+        return _cache[key]
+    except KeyError:
+        return None
+
+
+def cache_set(key: str, value: Dict[str, Any]) -> None:
+    """Store value in cache."""
+    _cache[key] = value

backend/app/classification/classification_router.py

@@ -1,12 +1,26 @@
+import asyncio
 import logging
+import json
 from typing import Dict, Any
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_core.messages import HumanMessage
 from app.core.config import settings
 from .prompt import DEVREL_TRIAGE_PROMPT
+from app.classification.cache_helpers import (
+    normalize_message,
+    is_simple_message,
+    get_cached_by_normalized,
+    set_cached_by_normalized,
+    metrics,
+    MAX_MESSAGE_LENGTH,
+)
 
 logger = logging.getLogger(__name__)
 
+# Limit concurrent LLM calls to prevent rate limiting and cost explosions
+_LLM_SEMAPHORE = asyncio.Semaphore(10)
+
+
 class ClassificationRouter:
     """Simple DevRel triage - determines if message needs DevRel assistance"""
 
@@ -20,28 +34,59 @@ def __init__(self, llm_client=None):
     async def should_process_message(self, message: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
         """Simple triage: Does this message need DevRel assistance?"""
         try:
+            # Early return for oversized messages to prevent DoS
+            if len(message) > MAX_MESSAGE_LENGTH:
+                logger.warning(f"Message exceeds max length ({len(message)} > {MAX_MESSAGE_LENGTH}), using fallback")
+                return self._fallback_triage(message[:MAX_MESSAGE_LENGTH])
+
+            metrics["total"] += 1
+            normalized = normalize_message(message)
+
+            # fast-path: simple pattern match (no LLM)
+            simple = is_simple_message(normalized)
+
+            if simple is not None:
+                metrics["skipped_llm"] += 1
+                return simple
+
+            # cache lookup (include a light context fingerprint if present)
+            ctx_id = None
+            if context:
+                ctx_id = context.get("channel_id") or context.get("thread_id") or ""
+                if not ctx_id:
+                    ctx_id = None
+
+            cached = get_cached_by_normalized(normalized, ctx_id, settings.classification_agent_model, {"temperature": 0.1})
+            if cached is not None:
+                metrics["cache_hits"] += 1
+                return cached
+
+            metrics["cache_misses"] += 1
+
             triage_prompt = DEVREL_TRIAGE_PROMPT.format(
                 message=message,
                 context=context or 'No additional context'
             )
 
-            response = await self.llm.ainvoke([HumanMessage(content=triage_prompt)])
-
+            # Use semaphore to limit concurrent LLM calls
+            async with _LLM_SEMAPHORE:
+                response = await self.llm.ainvoke([HumanMessage(content=triage_prompt)])
             response_text = response.content.strip()
+
             if '{' in response_text:
                 json_start = response_text.find('{')
                 json_end = response_text.rfind('}') + 1
                 json_str = response_text[json_start:json_end]
-
-                import json
                 result = json.loads(json_str)
 
-                return {
+                payload = {
                     "needs_devrel": result.get("needs_devrel", True),
                     "priority": result.get("priority", "medium"),
                     "reasoning": result.get("reasoning", "LLM classification"),
                     "original_message": message
                 }
+                set_cached_by_normalized(normalized, ctx_id, settings.classification_agent_model, {"temperature": 0.1}, payload)
+                return payload
 
             return self._fallback_triage(message)