diff --git a/config/chat/tests/test_agent_runtime.py b/config/chat/tests/test_agent_runtime.py index f0e673c..fb4ce80 100644 --- a/config/chat/tests/test_agent_runtime.py +++ b/config/chat/tests/test_agent_runtime.py @@ -89,8 +89,10 @@ class FakeMemory: def __init__(self): self.added_messages = None self.added_context = None + self.recall_rerank = None - def recall(self, *, query, context, limit): + def recall(self, *, query, context, limit, rerank=False): + self.recall_rerank = rerank return [ MemorySearchResult( id="memory-1", @@ -162,6 +164,7 @@ def add(self, *, messages, context, prompt=None): first_request = create.call_args_list[0].kwargs self.assertIn("[user] User prefers concise answers.", first_request["system"]) + self.assertIs(memory.recall_rerank, True) remember_tool = next( tool for tool in first_request["tools"] if tool["name"] == "remember" ) @@ -188,7 +191,7 @@ def add(self, *, messages, context, prompt=None): def test_memory_failures_do_not_abort_the_agent_turn(self): class BrokenMemory: - def recall(self, *, query, context, limit): + def recall(self, *, query, context, limit, rerank=False): return [] def add(self, *, messages, context, prompt=None): diff --git a/config/core/agent_runtime.py b/config/core/agent_runtime.py index f5a940f..61fc338 100644 --- a/config/core/agent_runtime.py +++ b/config/core/agent_runtime.py @@ -42,6 +42,7 @@ def recall( query: str, context: MemoryContext, limit: int = 5, + rerank: bool = False, ) -> list[MemorySearchResult]: ... def add( @@ -65,6 +66,7 @@ class AgentRuntimeConfig: base_url: str | None = None max_tokens: int = 8_000 max_rounds: int = MAX_ROUNDS + memory_rerank: bool = True def __post_init__(self) -> None: if not self.model: @@ -285,6 +287,7 @@ def _system_with_recall(self, latest_user_query: str) -> str: query=latest_user_query, context=self.memory_context, limit=5, + rerank=self.config.memory_rerank, ) except Exception: logger.warning( diff --git a/config/core/memory/bge_reranker.py b/config/core/memory/bge_reranker.py new file mode 100644 index 0000000..311f72d --- /dev/null +++ b/config/core/memory/bge_reranker.py @@ -0,0 +1,73 @@ +from __future__ import annotations + +from collections.abc import Callable, Sequence +from dataclasses import replace +from typing import Protocol + +from core.memory.vector_store import MemorySearchResult + +DEFAULT_BGE_RERANKER_MODEL = "BAAI/bge-reranker-v2-m3" + + +class BGEScoringModel(Protocol): + def compute_score( + self, + sentence_pairs: list[list[str]], + *, + normalize: bool, + ) -> Sequence[float] | float: ... + + +def _load_bge_model(model_name: str) -> BGEScoringModel: + from FlagEmbedding import FlagReranker # type: ignore[import-untyped] + + return FlagReranker(model_name, use_fp16=False) + + +class BGEReranker: + """Lazily load BGE and rerank Memory retrieval candidates.""" + + def __init__( + self, + model_name: str = DEFAULT_BGE_RERANKER_MODEL, + *, + model_factory: Callable[[str], BGEScoringModel] = _load_bge_model, + ) -> None: + self.model_name = model_name + self._model_factory = model_factory + self._model: BGEScoringModel | None = None + + def rerank( + self, + *, + query: str, + candidates: Sequence[MemorySearchResult], + limit: int, + ) -> list[MemorySearchResult]: + if limit <= 0 or not candidates: + return [] + + raw_scores = self._get_model().compute_score( + [[query, candidate.data] for candidate in candidates], + normalize=True, + ) + if isinstance(raw_scores, (float, int)): + scores = [float(raw_scores)] + else: + scores = [float(score) for score in raw_scores] + if len(scores) != len(candidates): + raise RuntimeError( + "BGE returned a score count that does not match candidates" + ) + + ranked = sorted( + zip(candidates, scores, strict=True), + key=lambda item: item[1], + reverse=True, + ) + return [replace(candidate, score=score) for candidate, score in ranked[:limit]] + + def _get_model(self) -> BGEScoringModel: + if self._model is None: + self._model = self._model_factory(self.model_name) + return self._model diff --git a/config/core/memory/composition.py b/config/core/memory/composition.py index 4927de6..a425564 100644 --- a/config/core/memory/composition.py +++ b/config/core/memory/composition.py @@ -7,6 +7,7 @@ from fastembed import SparseTextEmbedding from qdrant_client import QdrantClient +from core.memory.bge_reranker import BGEReranker from core.memory.config import AnthropicLLMConfig from core.memory.embedder import ( MULTILINGUAL_E5_BASE_DIMENSION, @@ -62,4 +63,5 @@ def build_memory(*, config: MemoryCompositionConfig) -> Memory: extractor=extractor, dense_encoder=dense_encoder, vector_store=vector_store, + reranker=BGEReranker(), ) diff --git a/config/core/memory/flashrank_reranker.py b/config/core/memory/flashrank_reranker.py new file mode 100644 index 0000000..bd0de36 --- /dev/null +++ b/config/core/memory/flashrank_reranker.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +from collections.abc import Callable, Mapping, Sequence +from dataclasses import replace +from importlib import import_module +from pathlib import Path +from typing import Any, Protocol, cast + +from core.memory.vector_store import MemorySearchResult + +DEFAULT_FLASHRANK_MODEL = "ms-marco-MultiBERT-L-12" +DEFAULT_FLASHRANK_CACHE_DIR = Path.home() / ".mini-code-agent" / "models" / "flashrank" + + +class FlashRankBackend(Protocol): + def __call__( + self, + *, + query: str, + passages: list[dict[str, object]], + ) -> Sequence[Mapping[str, object]]: ... + + +def _load_flashrank_backend( + model_name: str, + cache_dir: Path, +) -> FlashRankBackend: + flashrank = import_module("flashrank") + ranker = flashrank.Ranker(model_name=model_name, cache_dir=str(cache_dir)) + + def rerank( + *, + query: str, + passages: list[dict[str, object]], + ) -> Sequence[Mapping[str, object]]: + request = flashrank.RerankRequest(query=query, passages=passages) + return cast(Sequence[Mapping[str, object]], ranker.rerank(request)) + + return rerank + + +class FlashRankReranker: + """Lazily load FlashRank and rerank Memory retrieval candidates.""" + + def __init__( + self, + model_name: str = DEFAULT_FLASHRANK_MODEL, + *, + cache_dir: Path = DEFAULT_FLASHRANK_CACHE_DIR, + backend_factory: Callable[[str, Path], FlashRankBackend] = ( + _load_flashrank_backend + ), + ) -> None: + self.model_name = model_name + self.cache_dir = cache_dir + self._backend_factory = backend_factory + self._backend: FlashRankBackend | None = None + + def rerank( + self, + *, + query: str, + candidates: Sequence[MemorySearchResult], + limit: int, + ) -> list[MemorySearchResult]: + if limit <= 0 or not candidates: + return [] + + raw_results = self._get_backend()( + query=query, + passages=[ + {"id": index, "text": candidate.data} + for index, candidate in enumerate(candidates) + ], + ) + if len(raw_results) != len(candidates): + raise RuntimeError( + "FlashRank returned a result count that does not match candidates" + ) + + try: + ranked = [ + replace( + candidates[int(cast(Any, result["id"]))], + score=float(cast(Any, result["score"])), + ) + for result in raw_results + ] + except (KeyError, TypeError, ValueError, IndexError) as error: + raise RuntimeError("FlashRank returned an invalid result") from error + return ranked[:limit] + + def _get_backend(self) -> FlashRankBackend: + if self._backend is None: + self._backend = self._backend_factory( + self.model_name, + self.cache_dir, + ) + return self._backend diff --git a/config/core/memory/memory.py b/config/core/memory/memory.py index bbc1554..be9c9f7 100644 --- a/config/core/memory/memory.py +++ b/config/core/memory/memory.py @@ -12,6 +12,7 @@ MemoryExtractor, MemoryMessage, ) +from core.memory.reranker import MemoryReranker from core.memory.vector_store import MemorySearchResult, MemoryVectorStore @@ -37,16 +38,20 @@ def __post_init__(self) -> None: class Memory: + _RERANK_CANDIDATES_PER_SCOPE = 10 + def __init__( self, *, extractor: MemoryExtractor, dense_encoder: DenseEncoder, vector_store: MemoryVectorStore, + reranker: MemoryReranker | None = None, ) -> None: self.extractor = extractor self.dense_encoder = dense_encoder self.vector_store = vector_store + self.reranker = reranker def close(self) -> None: """Release resources owned by the configured vector-store adapter.""" @@ -174,12 +179,14 @@ def recall( query: str, context: MemoryContext, limit: int = 5, + rerank: bool = False, ) -> builtins.list[MemorySearchResult]: """Recall User and current Space Memories for one agent Turn.""" + scope_limit = self._RERANK_CANDIDATES_PER_SCOPE if rerank else limit user_results = self._search_scope( query=query, filters={"user_id": context.user_id, "space_id": None}, - limit=limit, + limit=scope_limit, ) space_results = self._search_scope( query=query, @@ -187,7 +194,7 @@ def recall( "user_id": context.user_id, "space_id": context.space_id, }, - limit=limit, + limit=scope_limit, ) result_by_text: dict[str, MemorySearchResult] = {} @@ -208,11 +215,20 @@ def recall( score_by_text, key=score_by_text.__getitem__, reverse=True, - )[:limit] - return [ + )[: scope_limit * 2] + candidates = [ replace(result_by_text[text], score=score_by_text[text]) for text in ranked_texts ] + if not rerank: + return candidates[:limit] + if self.reranker is None: + raise RuntimeError("rerank=True requires a configured MemoryReranker") + return self.reranker.rerank( + query=query, + candidates=candidates, + limit=limit, + ) def _reciprocal_rank_fusion( self, diff --git a/config/core/memory/reranker.py b/config/core/memory/reranker.py new file mode 100644 index 0000000..f270072 --- /dev/null +++ b/config/core/memory/reranker.py @@ -0,0 +1,16 @@ +from collections.abc import Sequence +from typing import Protocol + +from core.memory.vector_store import MemorySearchResult + + +class MemoryReranker(Protocol): + """Reorder retrieval candidates and return at most ``limit`` results.""" + + def rerank( + self, + *, + query: str, + candidates: Sequence[MemorySearchResult], + limit: int, + ) -> list[MemorySearchResult]: ... diff --git a/config/evals/memory_retrieval/cases.json b/config/evals/memory_retrieval/cases.json new file mode 100644 index 0000000..d59677a --- /dev/null +++ b/config/evals/memory_retrieval/cases.json @@ -0,0 +1,240 @@ +{ + "version": 1, + "description": "Hand-authored retrieval cases for the Memory.recall interface.", + "memories": [ + { + "id": "00000000-0000-0000-0000-000000000001", + "text": "修改检索算分后，提交前必须运行 python -m pytest tests/utils/test_scoring.py -q。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000002", + "text": "提交前运行 uv run ruff check . 检查代码规范。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000003", + "text": "提交前运行 uv run mypy 检查静态类型。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000004", + "text": "部署前运行 uv run python config/manage.py check --deploy。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000005", + "text": "CLI 只能恢复当前工作区中的 Conversation。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000006", + "text": "每个新 Turn 都会自动召回当前 User 的 User Memory，以及当前 Memory Space 的 Space Memory。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000007", + "text": "用户偏好简洁回答。", + "user_id": "user-a", + "space_id": null + }, + { + "id": "00000000-0000-0000-0000-000000000008", + "text": "用户偏好先给结论，再解释原因。", + "user_id": "user-a", + "space_id": null + }, + { + "id": "00000000-0000-0000-0000-000000000009", + "text": "支持工单 ticket-4821 的状态是等待客户确认。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000010", + "text": "This repository uses Ruff as its Python formatter and linter.", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000011", + "text": "这个前端项目提交前运行 npm test。", + "user_id": "user-a", + "space_id": "space-beta" + }, + { + "id": "00000000-0000-0000-0000-000000000012", + "text": "支持工单 ticket-4821 已经关闭。", + "user_id": "user-b", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000013", + "text": "用户偏好详细、长篇的回答。", + "user_id": "user-b", + "space_id": null + }, + { + "id": "00000000-0000-0000-0000-000000000014", + "text": "Conversation metadata is stored in SQLite.", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000015", + "text": "Memory retrieval vectors are stored in a local Qdrant collection.", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000016", + "text": "支持工单 ticket-4281 的状态是等待工程师处理。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000017", + "text": "包含 sudo 的 shell 命令会被权限 hook 阻止。", + "user_id": "user-a", + "space_id": "space-alpha" + }, + { + "id": "00000000-0000-0000-0000-000000000018", + "text": "Start the local Web interface with uv run python config/manage.py runserver.", + "user_id": "user-a", + "space_id": "space-alpha" + } + ], + "queries": [ + { + "id": "q01-semantic-test-command", + "query": "检索打分逻辑改完以后，提交代码之前该跑什么？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000001" + ], + "tags": ["single", "semantic", "zh"] + }, + { + "id": "q02-exact-ticket", + "query": "ticket-4821 现在是什么状态？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000009" + ], + "tags": ["single", "keyword", "mixed"] + }, + { + "id": "q03-user-length-preference", + "query": "用户对回答长度有什么偏好？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000007" + ], + "tags": ["single", "user-scope", "zh"] + }, + { + "id": "q04-english-formatter", + "query": "Which formatter and linter does this repository use?", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000010" + ], + "tags": ["single", "semantic", "en"] + }, + { + "id": "q05-mixed-resume-scope", + "query": "CLI resume Conversation 时可以跨 workspace 吗？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000005" + ], + "tags": ["single", "scope", "mixed"] + }, + { + "id": "q06-recall-timing", + "query": "记忆是在什么时候自动找回来的？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000006" + ], + "tags": ["single", "semantic", "zh"] + }, + { + "id": "q07-space-specific-test-command", + "query": "当前这个 Python 项目应该运行哪条测试命令？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000001" + ], + "tags": ["single", "space-isolation", "zh"] + }, + { + "id": "q08-all-precommit-checks", + "query": "提交前需要完成哪些测试、规范和类型检查？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000001", + "00000000-0000-0000-0000-000000000002", + "00000000-0000-0000-0000-000000000003" + ], + "tags": ["multi", "semantic", "zh"] + }, + { + "id": "q09-answer-style", + "query": "How should responses be structured and sized for this user?", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000007", + "00000000-0000-0000-0000-000000000008" + ], + "tags": ["multi", "user-scope", "en"] + }, + { + "id": "q10-resume-and-memory", + "query": "恢复旧聊天和在新 Turn 中使用记忆分别遵循什么规则？", + "context": { + "user_id": "user-a", + "space_id": "space-alpha" + }, + "relevant_memory_ids": [ + "00000000-0000-0000-0000-000000000005", + "00000000-0000-0000-0000-000000000006" + ], + "tags": ["multi", "semantic", "zh"] + } + ] +} diff --git a/config/evals/memory_retrieval/download_longmemeval.py b/config/evals/memory_retrieval/download_longmemeval.py new file mode 100644 index 0000000..04b0b0e --- /dev/null +++ b/config/evals/memory_retrieval/download_longmemeval.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import argparse +import hashlib +import tempfile +import urllib.request +from pathlib import Path + +REVISION = "98d7416c24c778c2fee6e6f3006e7a073259d48f" +FILE_NAME = "longmemeval_s_cleaned.json" +DOWNLOAD_URL = ( + "https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/" + f"resolve/{REVISION}/{FILE_NAME}" +) +EXPECTED_SIZE = 277_383_467 +EXPECTED_SHA256 = "d6f21ea9d60a0d56f34a05b609c79c88a451d2ae03597821ea3d5a9678c3a442" +DEFAULT_OUTPUT = Path(__file__).resolve().parent / "data" / FILE_NAME +CHUNK_SIZE = 1024 * 1024 +PROGRESS_INTERVAL = 25 * 1024 * 1024 + + +def _verify(path: Path) -> None: + size = path.stat().st_size + if size != EXPECTED_SIZE: + raise RuntimeError( + f"unexpected file size for {path}: {size}, expected {EXPECTED_SIZE}" + ) + + digest = hashlib.sha256() + with path.open("rb") as dataset_file: + while chunk := dataset_file.read(CHUNK_SIZE): + digest.update(chunk) + actual_sha256 = digest.hexdigest() + if actual_sha256 != EXPECTED_SHA256: + raise RuntimeError( + f"unexpected SHA-256 for {path}: {actual_sha256}, " + f"expected {EXPECTED_SHA256}" + ) + + +def download(*, output: Path) -> None: + if output.exists(): + _verify(output) + print(f"Verified existing dataset: {output}") + return + + output.parent.mkdir(parents=True, exist_ok=True) + temporary_path: Path | None = None + digest = hashlib.sha256() + downloaded = 0 + next_progress = PROGRESS_INTERVAL + + try: + request = urllib.request.Request( + DOWNLOAD_URL, + headers={"User-Agent": "mini-code-agent-memory-eval/0.2"}, + ) + with ( + urllib.request.urlopen(request, timeout=60) as response, + tempfile.NamedTemporaryFile( + dir=output.parent, + prefix=f".{FILE_NAME}.", + suffix=".part", + delete=False, + ) as temporary_file, + ): + temporary_path = Path(temporary_file.name) + while chunk := response.read(CHUNK_SIZE): + temporary_file.write(chunk) + digest.update(chunk) + downloaded += len(chunk) + if downloaded >= next_progress: + print( + f"Downloaded {downloaded / 1024 / 1024:.0f} MiB...", flush=True + ) + next_progress += PROGRESS_INTERVAL + + if downloaded != EXPECTED_SIZE: + raise RuntimeError( + f"unexpected download size: {downloaded}, expected {EXPECTED_SIZE}" + ) + actual_sha256 = digest.hexdigest() + if actual_sha256 != EXPECTED_SHA256: + raise RuntimeError( + f"unexpected download SHA-256: {actual_sha256}, " + f"expected {EXPECTED_SHA256}" + ) + + temporary_path.replace(output) + print(f"Downloaded and verified dataset: {output}") + finally: + if temporary_path is not None and temporary_path.exists(): + temporary_path.unlink() + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Download the pinned LongMemEval-S retrieval dataset" + ) + parser.add_argument( + "--output", + type=Path, + default=DEFAULT_OUTPUT, + help=f"dataset path (default: {DEFAULT_OUTPUT})", + ) + args = parser.parse_args() + download(output=args.output) + + +if __name__ == "__main__": + main() diff --git a/config/evals/memory_retrieval/longmemeval_adapter.py b/config/evals/memory_retrieval/longmemeval_adapter.py new file mode 100644 index 0000000..876a8e2 --- /dev/null +++ b/config/evals/memory_retrieval/longmemeval_adapter.py @@ -0,0 +1,197 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any +from uuid import NAMESPACE_URL, uuid5 + +LONGMEMEVAL_USER_ID = "longmemeval" + + +def _memory_id( + *, + question_id: str, + session_index: int, + session_id: str, +) -> str: + return str( + uuid5( + NAMESPACE_URL, + "https://github.com/xiaowu0162/LongMemEval/" + f"{question_id}/{session_index}/{session_id}", + ) + ) + + +def _user_text(*, question_id: str, session: list[dict[str, Any]]) -> str: + contents: list[str] = [] + for turn in session: + if turn.get("role") != "user": + continue + content = turn.get("content") + if not isinstance(content, str): + raise ValueError(f"{question_id}: user turn content must be a string") + contents.append(content) + return " ".join(contents) + + +def load_longmemeval( + path: str | Path, + *, + max_cases: int | None = None, +) -> dict[str, Any]: + """Adapt LongMemEval into the dataset shape consumed by retrieval evals. + + Sessions are indexed at the official flat-session granularity: only user + messages are joined into each candidate document. Abstention questions and + questions without user-side evidence are excluded from retrieval scoring. + """ + if max_cases is not None and max_cases <= 0: + raise ValueError("max_cases must be greater than zero") + + source_path = Path(path) + with source_path.open(encoding="utf-8") as source_file: + source_cases = json.load(source_file) + if not isinstance(source_cases, list): + raise ValueError("LongMemEval dataset must contain a JSON list") + + memories: list[dict[str, Any]] = [] + queries: list[dict[str, Any]] = [] + seen_question_ids: set[str] = set() + skipped_abstention = 0 + skipped_without_user_evidence = 0 + + for source_case in source_cases: + question_id = source_case.get("question_id") + if not isinstance(question_id, str) or not question_id: + raise ValueError("LongMemEval question_id must be a non-empty string") + if question_id in seen_question_ids: + raise ValueError(f"duplicate LongMemEval question_id: {question_id}") + seen_question_ids.add(question_id) + + if question_id.endswith("_abs"): + skipped_abstention += 1 + continue + + session_ids = source_case.get("haystack_session_ids") + sessions = source_case.get("haystack_sessions") + session_dates = source_case.get("haystack_dates") + if not all( + isinstance(value, list) for value in (session_ids, sessions, session_dates) + ): + raise ValueError(f"{question_id}: haystack fields must be lists") + if not len(session_ids) == len(sessions) == len(session_dates): + raise ValueError(f"{question_id}: haystack fields must have equal lengths") + answer_session_ids = source_case.get("answer_session_ids") + if not isinstance(answer_session_ids, list): + raise ValueError(f"{question_id}: answer_session_ids must be a list") + answer_session_id_set = set(answer_session_ids) + if not answer_session_id_set.issubset(set(session_ids)): + raise ValueError( + f"{question_id}: answer_session_ids must exist in the haystack" + ) + + user_evidence_indices = [ + session_index + for session_index, session in enumerate(sessions) + if any( + turn.get("role") == "user" and turn.get("has_answer") is True + for turn in session + ) + ] + if not user_evidence_indices: + skipped_without_user_evidence += 1 + continue + user_evidence_session_ids = { + session_ids[session_index] for session_index in user_evidence_indices + } + if not user_evidence_session_ids.issubset(answer_session_id_set): + raise ValueError( + f"{question_id}: user-side evidence must belong to answer sessions" + ) + + question = source_case.get("question") + question_type = source_case.get("question_type") + if not isinstance(question, str) or not question: + raise ValueError(f"{question_id}: question must be a non-empty string") + if not isinstance(question_type, str) or not question_type: + raise ValueError(f"{question_id}: question_type must be a non-empty string") + + space_id = f"longmemeval:{question_id}" + memory_ids: list[str] = [] + for session_index, (session_id, session, session_date) in enumerate( + zip( + session_ids, + sessions, + session_dates, + strict=True, + ) + ): + if not isinstance(session_id, str) or not session_id: + raise ValueError( + f"{question_id}: session ids must be non-empty strings" + ) + if not isinstance(session, list): + raise ValueError(f"{question_id}: each session must be a list") + memory_id = _memory_id( + question_id=question_id, + session_index=session_index, + session_id=session_id, + ) + memory_ids.append(memory_id) + memories.append( + { + "id": memory_id, + "text": _user_text( + question_id=question_id, + session=session, + ), + "user_id": LONGMEMEVAL_USER_ID, + "space_id": space_id, + "source_session_id": session_id, + "source_date": session_date, + } + ) + + relevant_memory_ids = [ + memory_ids[session_index] for session_index in user_evidence_indices + ] + queries.append( + { + "id": question_id, + "query": question, + "context": { + "user_id": LONGMEMEVAL_USER_ID, + "space_id": space_id, + }, + "relevant_memory_ids": relevant_memory_ids, + "tags": [ + "longmemeval", + question_type, + "single" if len(relevant_memory_ids) == 1 else "multi", + ], + "question_date": source_case.get("question_date"), + "reference_answer": source_case.get("answer"), + } + ) + + if max_cases is not None and len(queries) >= max_cases: + break + + return { + "version": 1, + "description": "LongMemEval flat-session retrieval cases.", + "source": { + "name": "LongMemEval", + "path": str(source_path), + "granularity": "session", + "indexed_roles": ["user"], + }, + "stats": { + "adapted_cases": len(queries), + "skipped_abstention": skipped_abstention, + "skipped_without_user_evidence": skipped_without_user_evidence, + }, + "memories": memories, + "queries": queries, + } diff --git a/config/evals/memory_retrieval/run.py b/config/evals/memory_retrieval/run.py new file mode 100644 index 0000000..e625b0e --- /dev/null +++ b/config/evals/memory_retrieval/run.py @@ -0,0 +1,383 @@ +from __future__ import annotations + +import argparse +import json +import logging +import math +import sys +from collections import defaultdict +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from tqdm import tqdm + +EVAL_ROOT = Path(__file__).resolve().parent +CONFIG_ROOT = EVAL_ROOT.parents[1] +for import_root in (EVAL_ROOT, CONFIG_ROOT): + if str(import_root) not in sys.path: + sys.path.insert(0, str(import_root)) + +import jieba # type: ignore[import-untyped] # noqa: E402 +from core.memory.bge_reranker import ( # noqa: E402 + DEFAULT_BGE_RERANKER_MODEL, + BGEReranker, +) +from core.memory.embedder import ( # noqa: E402 + MULTILINGUAL_E5_BASE_DIMENSION, + FastEmbedBM25Encoder, + build_multilingual_e5_base_encoder, +) +from core.memory.flashrank_reranker import ( # noqa: E402 + DEFAULT_FLASHRANK_MODEL, + FlashRankReranker, +) +from core.memory.memory import Memory, MemoryContext # noqa: E402 +from core.memory.qdrant_store import QdrantStore # noqa: E402 +from core.memory.reranker import MemoryReranker # noqa: E402 +from fastembed import SparseTextEmbedding # noqa: E402 +from longmemeval_adapter import load_longmemeval # noqa: E402 +from qdrant_client import QdrantClient # noqa: E402 + +CASES_PATH = Path(__file__).with_name("cases.json") +LIMIT = 5 + + +class UnusedMemoryExtractor: + def extract(self, **_: Any) -> list[Any]: + raise AssertionError("retrieval evaluation must not extract memories") + + +@dataclass(frozen=True, slots=True) +class CaseResult: + case_id: str + tags: tuple[str, ...] + relevant_ids: tuple[str, ...] + retrieved_ids: tuple[str, ...] + hit_at_1: float + recall_at_5: float + mrr_at_5: float + scope_leak_rate: float + + +@dataclass(frozen=True, slots=True) +class EvalReranker: + name: str + model_name: str | None + adapter: MemoryReranker | None + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Evaluate Memory.recall retrieval") + parser.add_argument( + "--longmemeval", + type=Path, + metavar="PATH", + help="load a LongMemEval JSON dataset instead of the built-in cases", + ) + parser.add_argument( + "--max-cases", + type=int, + metavar="N", + help="adapt at most N scored LongMemEval cases", + ) + parser.add_argument( + "--reranker", + choices=("none", "bge", "flashrank"), + action="append", + dest="rerankers", + help="reranking backend; repeat to compare (default: none)", + ) + args = parser.parse_args() + if args.max_cases is not None and args.longmemeval is None: + parser.error("--max-cases requires --longmemeval") + if args.max_cases is not None and args.max_cases <= 0: + parser.error("--max-cases must be greater than zero") + args.rerankers = args.rerankers or ["none"] + if len(args.rerankers) != len(set(args.rerankers)): + parser.error("--reranker values must not be repeated") + return args + + +def _load_dataset(args: argparse.Namespace) -> dict[str, Any]: + if args.longmemeval is not None: + return load_longmemeval( + args.longmemeval, + max_cases=args.max_cases, + ) + return json.loads(CASES_PATH.read_text(encoding="utf-8")) + + +def _select_reranker(name: str) -> EvalReranker: + if name == "none": + return EvalReranker(name="none", model_name=None, adapter=None) + if name == "bge": + return EvalReranker( + name="bge", + model_name=DEFAULT_BGE_RERANKER_MODEL, + adapter=BGEReranker(), + ) + if name == "flashrank": + return EvalReranker( + name="flashrank", + model_name=DEFAULT_FLASHRANK_MODEL, + adapter=FlashRankReranker(), + ) + raise ValueError(f"unsupported reranker: {name}") + + +def _build_eval_memory(*, reranker: MemoryReranker | None) -> Memory: + jieba.setLogLevel(logging.WARNING) + dense_encoder = build_multilingual_e5_base_encoder() + bm25_encoder = FastEmbedBM25Encoder( + model=SparseTextEmbedding(model_name="Qdrant/bm25"), + chinese_segmenter=jieba.cut, + ) + vector_store = QdrantStore( + client=QdrantClient(":memory:"), + collection_name="memory_retrieval_eval", + dimension=MULTILINGUAL_E5_BASE_DIMENSION, + bm25_encoder=bm25_encoder, + ) + return Memory( + extractor=UnusedMemoryExtractor(), + dense_encoder=dense_encoder, + vector_store=vector_store, + reranker=reranker, + ) + + +def _is_in_scope(eval_memory: dict[str, Any], context: MemoryContext) -> bool: + return eval_memory["user_id"] == context.user_id and ( + eval_memory["space_id"] is None or eval_memory["space_id"] == context.space_id + ) + + +def _evaluate_case( + *, + memory: Memory, + case: dict[str, Any], + memory_by_id: dict[str, dict[str, Any]], + rerank: bool, +) -> CaseResult: + context = MemoryContext(**case["context"]) + results = memory.recall( + query=case["query"], + context=context, + limit=LIMIT, + rerank=rerank, + ) + retrieved_ids = tuple(result.id for result in results) + relevant_ids = tuple(case["relevant_memory_ids"]) + relevant_set = set(relevant_ids) + + first_relevant_rank = next( + ( + rank + for rank, memory_id in enumerate(retrieved_ids, start=1) + if memory_id in relevant_set + ), + None, + ) + leaked_count = sum( + memory_id not in memory_by_id + or not _is_in_scope(memory_by_id[memory_id], context) + for memory_id in retrieved_ids + ) + + return CaseResult( + case_id=case["id"], + tags=tuple(case["tags"]), + relevant_ids=relevant_ids, + retrieved_ids=retrieved_ids, + hit_at_1=float(bool(retrieved_ids and retrieved_ids[0] in relevant_set)), + recall_at_5=len(relevant_set.intersection(retrieved_ids)) / len(relevant_set), + mrr_at_5=0.0 if first_relevant_rank is None else 1 / first_relevant_rank, + scope_leak_rate=(leaked_count / len(retrieved_ids) if retrieved_ids else 0.0), + ) + + +def _mean_metrics(results: list[CaseResult]) -> dict[str, float]: + return { + "Hit@1": sum(result.hit_at_1 for result in results) / len(results), + "Recall@5": sum(result.recall_at_5 for result in results) / len(results), + "MRR@5": sum(result.mrr_at_5 for result in results) / len(results), + "ScopeLeakRate": sum(result.scope_leak_rate for result in results) + / len(results), + } + + +def _dcg(relevances: list[float]) -> float: + if not relevances: + return 0.0 + return relevances[0] + sum( + relevance / math.log2(rank) + for rank, relevance in enumerate(relevances[1:], start=2) + ) + + +def _longmemeval_metrics(results: list[CaseResult]) -> dict[str, float]: + recall_any_values: list[float] = [] + recall_all_values: list[float] = [] + ndcg_values: list[float] = [] + + for result in results: + relevant_ids = set(result.relevant_ids) + retrieved_ids = result.retrieved_ids[:LIMIT] + retrieved_id_set = set(retrieved_ids) + recall_any_values.append(float(bool(relevant_ids & retrieved_id_set))) + recall_all_values.append(float(relevant_ids <= retrieved_id_set)) + + actual_relevances = [ + float(memory_id in relevant_ids) for memory_id in retrieved_ids + ] + ideal_relevances = [1.0] * min(len(relevant_ids), LIMIT) + ideal_dcg = _dcg(ideal_relevances) + ndcg_values.append(_dcg(actual_relevances) / ideal_dcg if ideal_dcg else 0.0) + + count = len(results) + return { + "RecallAny@5": sum(recall_any_values) / count, + "RecallAll@5": sum(recall_all_values) / count, + "NDCG@5": sum(ndcg_values) / count, + } + + +def _print_report( + results: list[CaseResult], + *, + reranker: EvalReranker, +) -> None: + groups: dict[str, list[CaseResult]] = defaultdict(list) + groups["overall"] = results + for result in results: + for tag in result.tags: + groups[tag].append(result) + + model_name = reranker.model_name or "none" + print( + f"Memory retrieval eval: {len(results)} cases, limit={LIMIT}, " + f"reranker={reranker.name}, model={model_name}" + ) + print( + f"{'group':<18} {'cases':>5} {'Hit@1':>9} {'Recall@5':>10} " + f"{'MRR@5':>9} {'ScopeLeak':>11}" + ) + for group_name in ["overall", *sorted(groups.keys() - {"overall"})]: + group_results = groups[group_name] + metrics = _mean_metrics(group_results) + print( + f"{group_name:<18} {len(group_results):>5} " + f"{metrics['Hit@1']:>8.1%} {metrics['Recall@5']:>9.1%} " + f"{metrics['MRR@5']:>8.1%} " + f"{metrics['ScopeLeakRate']:>10.1%}" + ) + + imperfect = [ + result + for result in results + if result.recall_at_5 < 1.0 or result.scope_leak_rate > 0.0 + ] + if imperfect: + print("\nCases needing inspection:") + for result in imperfect: + print( + f"- {result.case_id}: relevant={list(result.relevant_ids)}, " + f"retrieved={list(result.retrieved_ids)}" + ) + + +def _print_longmemeval_report(results: list[CaseResult]) -> None: + groups: dict[str, list[CaseResult]] = defaultdict(list) + groups["overall"] = results + non_question_type_tags = {"longmemeval", "single", "multi"} + for result in results: + for tag in result.tags: + if tag not in non_question_type_tags: + groups[tag].append(result) + + print(f"\nLongMemEval official session metrics: k={LIMIT}") + print( + f"{'group':<30} {'cases':>5} {'RecallAny@5':>13} " + f"{'RecallAll@5':>13} {'NDCG@5':>9}" + ) + for group_name in ["overall", *sorted(groups.keys() - {"overall"})]: + group_results = groups[group_name] + metrics = _longmemeval_metrics(group_results) + print( + f"{group_name:<30} {len(group_results):>5} " + f"{metrics['RecallAny@5']:>12.1%} " + f"{metrics['RecallAll@5']:>12.1%} " + f"{metrics['NDCG@5']:>8.1%}" + ) + + +def main() -> None: + args = _parse_args() + rerankers = [_select_reranker(name) for name in args.rerankers] + dataset = _load_dataset(args) + memories = dataset["memories"] + if not dataset["queries"]: + raise ValueError("dataset contains no retrieval cases") + + if "stats" in dataset: + stats = dataset["stats"] + print( + "LongMemEval adapter: " + f"adapted={stats['adapted_cases']}, " + f"skipped_abstention={stats['skipped_abstention']}, " + "skipped_without_user_evidence=" + f"{stats['skipped_without_user_evidence']}" + ) + + memory_by_id = {memory["id"]: memory for memory in memories} + memory = _build_eval_memory(reranker=None) + + try: + for record in tqdm( + memories, + desc="Indexing memories", + unit="memory", + dynamic_ncols=True, + disable=None, + ): + payload = { + "data": record["text"], + "user_id": record["user_id"], + } + if record["space_id"] is not None: + payload["space_id"] = record["space_id"] + memory.vector_store.upsert( + memory_id=record["id"], + vector=memory.dense_encoder.encode_document(record["text"]), + payload=payload, + ) + + for index, reranker in enumerate(rerankers): + if index: + print() + memory.reranker = reranker.adapter + results = [ + _evaluate_case( + memory=memory, + case=case, + memory_by_id=memory_by_id, + rerank=reranker.adapter is not None, + ) + for case in tqdm( + dataset["queries"], + desc=f"Evaluating {reranker.name}", + unit="case", + dynamic_ncols=True, + disable=None, + ) + ] + _print_report(results, reranker=reranker) + if args.longmemeval is not None: + _print_longmemeval_report(results) + finally: + memory.close() + + +if __name__ == "__main__": + main() diff --git a/config/tests/memory/test_bge_reranker.py b/config/tests/memory/test_bge_reranker.py new file mode 100644 index 0000000..9f1e2ed --- /dev/null +++ b/config/tests/memory/test_bge_reranker.py @@ -0,0 +1,63 @@ +from unittest import TestCase + +from core.memory.bge_reranker import ( + DEFAULT_BGE_RERANKER_MODEL, + BGEReranker, +) +from core.memory.vector_store import MemorySearchResult + + +class BGERerankerTests(TestCase): + def test_lazily_loads_once_and_maps_scores_back_to_ranked_results(self): + factory_calls = [] + + class FakeModel: + calls = [] + + def compute_score(self, sentence_pairs, *, normalize): + self.calls.append((sentence_pairs, normalize)) + return [0.1, 0.9, 0.4] + + model = FakeModel() + + def build_model(model_name): + factory_calls.append(model_name) + return model + + candidates = [ + MemorySearchResult( + id=str(index), + data=text, + scope="user", + score=0.0, + metadata={}, + ) + for index, text in enumerate(("first", "second", "third")) + ] + reranker = BGEReranker(model_factory=build_model) + + self.assertEqual(factory_calls, []) + + first_results = reranker.rerank( + query="query", + candidates=candidates, + limit=2, + ) + reranker.rerank(query="another query", candidates=candidates, limit=1) + + self.assertEqual(factory_calls, [DEFAULT_BGE_RERANKER_MODEL]) + self.assertEqual( + model.calls[0], + ( + [ + ["query", "first"], + ["query", "second"], + ["query", "third"], + ], + True, + ), + ) + self.assertEqual( + [(result.id, result.score) for result in first_results], + [("1", 0.9), ("2", 0.4)], + ) diff --git a/config/tests/memory/test_composition.py b/config/tests/memory/test_composition.py index 91ec9d4..9adb903 100644 --- a/config/tests/memory/test_composition.py +++ b/config/tests/memory/test_composition.py @@ -3,6 +3,7 @@ from unittest import TestCase from unittest.mock import patch +from core.memory.bge_reranker import BGEReranker from core.memory.composition import MemoryCompositionConfig, build_memory from core.memory.config import AnthropicLLMConfig from core.memory.extraction import LLMMemoryExtractor @@ -38,6 +39,7 @@ def test_builds_memory_with_persistent_adapter_seams( self.assertIs(memory.dense_encoder, dense_encoder) self.assertIsInstance(memory.extractor, LLMMemoryExtractor) self.assertIsInstance(memory.vector_store, QdrantStore) + self.assertIsInstance(memory.reranker, BGEReranker) set_jieba_log_level.assert_called_once_with(logging.WARNING) finally: memory.close() diff --git a/config/tests/memory/test_flashrank_reranker.py b/config/tests/memory/test_flashrank_reranker.py new file mode 100644 index 0000000..dbd31ac --- /dev/null +++ b/config/tests/memory/test_flashrank_reranker.py @@ -0,0 +1,68 @@ +from pathlib import Path +from unittest import TestCase + +from core.memory.flashrank_reranker import ( + DEFAULT_FLASHRANK_MODEL, + FlashRankReranker, +) +from core.memory.vector_store import MemorySearchResult + + +class FlashRankRerankerTests(TestCase): + def test_lazily_loads_once_and_maps_ranked_results(self): + factory_calls = [] + backend_calls = [] + + def backend(*, query, passages): + backend_calls.append((query, passages)) + return [ + {**passages[1], "score": 0.9}, + {**passages[2], "score": 0.4}, + {**passages[0], "score": 0.1}, + ] + + def build_backend(model_name, cache_dir): + factory_calls.append((model_name, cache_dir)) + return backend + + candidates = [ + MemorySearchResult( + id=str(index), + data=text, + scope="user", + score=0.0, + metadata={}, + ) + for index, text in enumerate(("first", "second", "third")) + ] + cache_dir = Path("test-cache") + reranker = FlashRankReranker( + cache_dir=cache_dir, + backend_factory=build_backend, + ) + + self.assertEqual(factory_calls, []) + + first_results = reranker.rerank( + query="query", + candidates=candidates, + limit=2, + ) + reranker.rerank(query="another query", candidates=candidates, limit=1) + + self.assertEqual(factory_calls, [(DEFAULT_FLASHRANK_MODEL, cache_dir)]) + self.assertEqual( + backend_calls[0], + ( + "query", + [ + {"id": 0, "text": "first"}, + {"id": 1, "text": "second"}, + {"id": 2, "text": "third"}, + ], + ), + ) + self.assertEqual( + [(result.id, result.score) for result in first_results], + [("1", 0.9), ("2", 0.4)], + ) diff --git a/config/tests/memory/test_memory_search.py b/config/tests/memory/test_memory_search.py index f4fdeda..b5ba361 100644 --- a/config/tests/memory/test_memory_search.py +++ b/config/tests/memory/test_memory_search.py @@ -167,3 +167,50 @@ def keyword_search(self, *, query, top_k, filters): {result.data for result in results[1:]}, {"User writes Python.", "This repository uses Django."}, ) + + def test_reranks_twenty_candidates_down_to_requested_limit(self): + requested_top_ks = [] + + class CandidateStore: + def dense_search(self, *, query_vector, top_k, filters): + requested_top_ks.append(top_k) + scope = "user" if filters["space_id"] is None else "space" + return [ + MemorySearchResult( + id=f"{scope}-{index}", + data=f"{scope} memory {index}", + scope=scope, + score=1 / (index + 1), + metadata=dict(filters), + ) + for index in range(top_k) + ] + + def keyword_search(self, *, query, top_k, filters): + return None + + class RecordingReranker: + candidates = [] + + def rerank(self, *, query, candidates, limit): + self.candidates = list(candidates) + return list(reversed(candidates))[:limit] + + reranker = RecordingReranker() + memory = Memory( + extractor=UnusedMemoryExtractor(), + dense_encoder=FakeDenseEncoder(), + vector_store=CandidateStore(), + reranker=reranker, + ) + + results = memory.recall( + query="Which memory matters?", + context=MemoryContext(user_id="u1", space_id="s1"), + limit=5, + rerank=True, + ) + + self.assertEqual(requested_top_ks, [10, 10]) + self.assertEqual(len(reranker.candidates), 20) + self.assertEqual(results, list(reversed(reranker.candidates))[:5]) diff --git a/pyproject.toml b/pyproject.toml index 0b663ef..74c5f4e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,11 +9,18 @@ dependencies = [ "anthropic>=0.45", "django>=5.2,<6.0", "fastembed>=0.8.0", + "flagembedding>=1.4.0", "jieba>=0.42.1", "prompt-toolkit>=3.0.53", "python-dotenv>=1.0", "pyyaml>=6.0.3", "qdrant-client>=1.19.0", + 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