perf: improve speed

custom_agent.py

@@ -1,5 +1,6 @@
 import logging
 import os
+import time
 from collections.abc import AsyncIterable
 from pathlib import Path
 
@@ -61,11 +62,40 @@ class CustomAgent(Agent):
         tools: list[llm.Tool],
         model_settings: ModelSettings,
     ) -> AsyncIterable[llm.ChatChunk | str | FlushSentinel]:
+        llm_node_started_at = time.perf_counter()
         memory_context = await self._recall_room_memory(chat_ctx)
         if memory_context:
             chat_ctx = _with_memory_as_latest_user_message(chat_ctx, memory_context)
 
-        return Agent.default.llm_node(self, chat_ctx, tools, model_settings)
+        llm_result = Agent.default.llm_node(self, chat_ctx, tools, model_settings)
+        if not hasattr(llm_result, "__aiter__"):
+            elapsed = time.perf_counter() - llm_node_started_at
+            logger.info("LLM node completed without streaming in %.3fs", elapsed)
+            return llm_result
+
+        async def _instrumented_stream() -> AsyncIterable[llm.ChatChunk | str | FlushSentinel]:
+            first_chunk_at: float | None = None
+            chunk_count = 0
+            try:
+                async for chunk in llm_result:
+                    chunk_count += 1
+                    if first_chunk_at is None:
+                        first_chunk_at = time.perf_counter()
+                        logger.info(
+                            "LLM first chunk after %.3fs",
+                            first_chunk_at - llm_node_started_at,
+                        )
+                    yield chunk
+            finally:
+                finished_at = time.perf_counter()
+                logger.info(
+                    "LLM stream completed in %.3fs (first_chunk=%.3fs, chunks=%s)",
+                    finished_at - llm_node_started_at,
+                    (first_chunk_at - llm_node_started_at) if first_chunk_at else -1.0,
+                    chunk_count,
+                )
+
+        return _instrumented_stream()
 
     async def _recall_room_memory(self, chat_ctx: ChatContext) -> str:
         if self._memory_client is None:
@@ -75,10 +105,22 @@ class CustomAgent(Agent):
         if not user_query:
             return ""
 
+        started_at = time.perf_counter()
         try:
-            return await self._memory_client.recall(user_query)
+            recalled = await self._memory_client.recall(user_query)
+            elapsed = time.perf_counter() - started_at
+            logger.info(
+                "Memory recall completed in %.3fs (query_len=%s, memory_len=%s)",
+                elapsed,
+                len(user_query),
+                len(recalled),
+            )
+            return recalled
         except Exception:
-            logger.exception("Unexpected memory recall failure")
+            logger.exception(
+                "Unexpected memory recall failure after %.3fs",
+                time.perf_counter() - started_at,
+            )
             return ""
 
 
@@ -140,8 +182,8 @@ async def entrypoint(ctx: JobContext) -> None:
     TTS_NUM_CHANNELS = _env_int("CUSTOM_TTS_NUM_CHANNELS", 1)
     OUTPUT_SAMPLE_RATE = _env_int("CUSTOM_OUTPUT_SAMPLE_RATE", TTS_SAMPLE_RATE)
     MEMORY_URL = os.getenv("CUSTOM_MEMORY_URL", "").strip()
-    MEMORY_TIMEOUT = _env_float("CUSTOM_MEMORY_TIMEOUT", 10.0)
+    MEMORY_TIMEOUT = _env_float("CUSTOM_MEMORY_TIMEOUT", 2.0)
-    MEMORY_MAX_CHARS = _env_int("CUSTOM_MEMORY_MAX_CHARS", 8000)
+    MEMORY_MAX_CHARS = _env_int("CUSTOM_MEMORY_MAX_CHARS", 2000)
     MEMORY_API_KEY = os.getenv("CUSTOM_MEMORY_API_KEY") or None
 
     blackbox_stt = BlackboxSTT(
@@ -199,7 +241,7 @@ async def entrypoint(ctx: JobContext) -> None:
                 "false_interruption_timeout": 1.0,
             },
         ),
-        preemptive_generation=False,
+        preemptive_generation=_env_bool("CUSTOM_PREEMPTIVE_GENERATION", True),
         aec_warmup_duration=3.0,
         tts_text_transforms=[
             "filter_emoji",
@@ -211,6 +253,17 @@ async def entrypoint(ctx: JobContext) -> None:
     def _on_metrics_collected(ev: MetricsCollectedEvent) -> None:
         metrics.log_metrics(ev.metrics)
 
+    @session.on("conversation_item_added")
+    def _on_conversation_item_added(event) -> None:
+        item = getattr(event, "item", None)
+        if not isinstance(item, ChatMessage):
+            return
+
+        if item.role == "user" and item.metrics:
+            logger.info("User turn metrics: %s", item.metrics)
+        elif item.role == "assistant" and item.metrics:
+            logger.info("Assistant turn metrics: %s", item.metrics)
+
     memory_client = (
         MemoryRecallClient(
             url=MEMORY_URL,

memory.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
+import re
 from typing import Any
 
 import aiohttp
@@ -11,6 +12,23 @@ from livekit.agents import APIConnectionError, APIStatusError, APITimeoutError,
 
 logger = logging.getLogger("memory-recall")
 
+_LOCATION_STOPWORDS = {
+    "哪里",
+    "在哪",
+    "在哪里",
+    "哪儿",
+    "位置",
+    "什么地方",
+    "帮我找",
+    "帮我寻找",
+    "找一下",
+    "找",
+    "请问",
+    "请",
+    "吗",
+    "呢",
+}
+
 
 class MemoryRecallClient:
     def __init__(
@@ -100,27 +118,31 @@ def _format_room_graph_memory(payload: Any, query: str) -> str:
     if not objects and not relations and not summary:
         return ""
 
-    objects_text = json.dumps(objects, ensure_ascii=False, indent=2)
+    query_terms = _query_terms(query)
-    relations_text = json.dumps(relations, ensure_ascii=False, indent=2)
+    relevant_objects, relevant_relations = _relevant_room_graph(
+        objects=objects,
+        relations=relations,
+        query_terms=query_terms,
+    )
+
+    objects_text = json.dumps(
+        relevant_objects or _compact_items(objects, limit=12),
+        ensure_ascii=False,
+        separators=(",", ":"),
+    )
+    relations_text = json.dumps(
+        relevant_relations or _compact_items(relations, limit=24),
+        ensure_ascii=False,
+        separators=(",", ":"),
+    )
 
     prompt = f"""
 你是一个物品定位助手。
 
-我的房间内有以下物品信息：
+目标物品：{query}
-
+相关物品：{objects_text}
-{objects_text}
+相关空间关系：{relations_text}
-
+房间概览：{summary}
-这些物品之间的空间关系如下：
-
-{relations_text}
-
-房间概览如下：
-
-{summary}
-
-现在我要找的目标物品是：{query}
-
-请根据上面的 objects、relations 和 summary，告诉我它在哪里。
 
 回答要求：
 1. 只说明它和其他物品的位置关系。
@@ -131,5 +153,140 @@ def _format_room_graph_memory(payload: Any, query: str) -> str:
 6. 不要回答 right-near-low、left-far-high 这类区域标签，只回答“在……的左边/右边/上方/下方/前面/后面/附近”等相对关系。
 7. 如果用户当前输入不是找物品或问位置，可以忽略这段房间记忆。
 """.strip()
-    
+
+    logger.info(
+        "Formatted room memory: query_terms=%s, objects=%s/%s, relations=%s/%s, chars=%s",
+        query_terms,
+        len(relevant_objects),
+        len(objects) if isinstance(objects, list) else 0,
+        len(relevant_relations),
+        len(relations) if isinstance(relations, list) else 0,
+        len(prompt),
+    )
     return prompt
+
+
+def _query_terms(query: str) -> list[str]:
+    normalized = re.sub(r"[\s？?。！，、,.!]", "", query)
+    for word in _LOCATION_STOPWORDS:
+        normalized = normalized.replace(word, "")
+
+    terms = [normalized] if normalized else []
+    for token in re.findall(r"[\u4e00-\u9fffA-Za-z0-9_-]{2,}", query):
+        if token not in _LOCATION_STOPWORDS and token not in terms:
+            terms.append(token)
+    return terms[:4]
+
+
+def _relevant_room_graph(
+    *,
+    objects: Any,
+    relations: Any,
+    query_terms: list[str],
+) -> tuple[list[Any], list[Any]]:
+    if not isinstance(objects, list) or not isinstance(relations, list) or not query_terms:
+        return [], []
+
+    matched_ids: set[str] = set()
+    matched_objects: list[Any] = []
+    object_by_id: dict[str, Any] = {}
+
+    for obj in objects:
+        obj_id = _object_id(obj)
+        if obj_id:
+            object_by_id[obj_id] = obj
+
+        obj_text = _compact_text(obj)
+        if any(term and term in obj_text for term in query_terms):
+            matched_objects.append(obj)
+            if obj_id:
+                matched_ids.add(obj_id)
+
+    relevant_relations: list[Any] = []
+    related_ids: set[str] = set(matched_ids)
+    for relation in relations:
+        relation_text = _compact_text(relation)
+        relation_ids = _ids_in_value(relation)
+        if (
+            any(term and term in relation_text for term in query_terms)
+            or bool(matched_ids.intersection(relation_ids))
+        ):
+            relevant_relations.append(relation)
+            related_ids.update(relation_ids)
+
+    relevant_objects = list(matched_objects)
+    seen_object_keys = {_object_key(obj) for obj in relevant_objects}
+    for obj_id in related_ids:
+        obj = object_by_id.get(obj_id)
+        key = _object_key(obj)
+        if obj is not None and key not in seen_object_keys:
+            relevant_objects.append(obj)
+            seen_object_keys.add(key)
+
+    return _compact_items(relevant_objects, limit=16), _compact_items(relevant_relations, limit=32)
+
+
+def _compact_items(items: Any, *, limit: int) -> list[Any]:
+    if not isinstance(items, list):
+        return []
+    return [_compact_item(item) for item in items[:limit]]
+
+
+def _compact_item(item: Any) -> Any:
+    if not isinstance(item, dict):
+        return item
+
+    preferred_keys = (
+        "id",
+        "name",
+        "label",
+        "class",
+        "category",
+        "type",
+        "text",
+        "source",
+        "target",
+        "subject",
+        "object",
+        "relation",
+        "predicate",
+        "description",
+    )
+    compact = {key: item[key] for key in preferred_keys if key in item and item[key] not in (None, "")}
+    return compact or item
+
+
+def _object_id(obj: Any) -> str | None:
+    if not isinstance(obj, dict):
+        return None
+    for key in ("id", "object_id", "uuid", "name", "label"):
+        value = obj.get(key)
+        if isinstance(value, (str, int)):
+            return str(value)
+    return None
+
+
+def _object_key(obj: Any) -> str:
+    return _object_id(obj) or _compact_text(obj)
+
+
+def _ids_in_value(value: Any) -> set[str]:
+    ids: set[str] = set()
+    if isinstance(value, dict):
+        for key, item in value.items():
+            if key in {"id", "object_id", "source", "target", "subject", "object", "from", "to"}:
+                if isinstance(item, (str, int)):
+                    ids.add(str(item))
+                elif isinstance(item, dict):
+                    obj_id = _object_id(item)
+                    if obj_id:
+                        ids.add(obj_id)
+            ids.update(_ids_in_value(item))
+    elif isinstance(value, list):
+        for item in value:
+            ids.update(_ids_in_value(item))
+    return ids
+
+
+def _compact_text(value: Any) -> str:
+    return json.dumps(value, ensure_ascii=False, separators=(",", ":"))

tts.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import asyncio
 import logging
 import os
+import time
 import wave
 from collections.abc import Mapping
 from io import BytesIO
@@ -88,6 +89,7 @@ class BlackboxTTSStream(tts.ChunkedStream):
         self._tts: BlackboxTTS = tts
 
     async def _run(self, output_emitter: tts.AudioEmitter) -> None:
+        started_at = time.perf_counter()
         form = aiohttp.FormData(default_to_multipart=True)
         form.add_field("text", self.input_text)
         form.add_field("model_name", self._tts._model_name)
@@ -131,6 +133,9 @@ class BlackboxTTSStream(tts.ChunkedStream):
                 content_type = resp.headers.get("Content-Type", "audio/wav")
                 logged_wav_format = False
                 wav_header_probe = bytearray()
+                first_audio_at: float | None = None
+                chunk_count = 0
+                total_bytes = 0
                 output_emitter.initialize(
                     request_id=utils.shortuuid(),
                     sample_rate=self._tts.sample_rate,
@@ -140,6 +145,16 @@ class BlackboxTTSStream(tts.ChunkedStream):
 
                 async for data, _ in resp.content.iter_chunks():
                     if data:
+                        chunk_count += 1
+                        total_bytes += len(data)
+                        if first_audio_at is None:
+                            first_audio_at = time.perf_counter()
+                            logger.info(
+                                "TTS first audio chunk after %.3fs (text_len=%s, bytes=%s)",
+                                first_audio_at - started_at,
+                                len(self.input_text),
+                                len(data),
+                            )
                         if not logged_wav_format:
                             wav_header_probe.extend(data)
                             logged_wav_format = _log_wav_format(
@@ -156,6 +171,15 @@ class BlackboxTTSStream(tts.ChunkedStream):
                                 logged_wav_format = True
                         output_emitter.push(data)
                 output_emitter.flush()
+                finished_at = time.perf_counter()
+                logger.info(
+                    "TTS stream completed in %.3fs (first_chunk=%.3fs, chunks=%s, bytes=%s, text_len=%s)",
+                    finished_at - started_at,
+                    (first_audio_at - started_at) if first_audio_at else -1.0,
+                    chunk_count,
+                    total_bytes,
+                    len(self.input_text),
+                )
         except asyncio.TimeoutError as e:
             raise APITimeoutError("TTS blackbox request timed out") from e
         except aiohttp.ClientError as e: