71b3a828c6
引入 A_Memorix 插件 v2.0.0：新增大量运行时组件、存储/模式更新、检索能力提升、管理工具、导入/调优工作流以及相关文档。关键新增内容包括：lifecycle_orchestrator、SDKMemoryKernel/运行时初始化器、新的存储层与 metadata_store 变更（SCHEMA_VERSION v8）、检索增强（双路径检索、图关系召回、稀疏 BM25），以及多种工具服务（episode/person_profile/relation/segmentation/tuning/search execution）。同时新增 Web 导入/摘要导入器及大量维护脚本。还更新了插件清单、embedding API 适配器、plugin.py、requirements/pyproject，以及主入口文件，使新插件接入项目。该变更为 2.0.0 版本发布做好准备，实现统一的 SDK Tool 接口并扩展整体运行能力。
729 lines
29 KiB
Python
729 lines
29 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
知识库自动导入脚本 (Strategy-Aware Version)
|
||
|
||
功能：
|
||
1. 扫描 plugins/A_memorix/data/raw 下的 .txt 文件
|
||
2. 检查 data/import_manifest.json 确认是否已导入
|
||
3. 使用 Strategy 模式处理文件 (Narrative/Factual/Quote)
|
||
4. 将生成的数据直接存入 VectorStore/GraphStore/MetadataStore
|
||
5. 更新 manifest
|
||
"""
|
||
|
||
import sys
|
||
import os
|
||
import json
|
||
import asyncio
|
||
import time
|
||
import random
|
||
import hashlib
|
||
import tomlkit
|
||
import argparse
|
||
from pathlib import Path
|
||
from datetime import datetime
|
||
from typing import List, Dict, Any, Optional
|
||
from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
|
||
from rich.console import Console
|
||
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
|
||
|
||
console = Console()
|
||
|
||
class LLMGenerationError(Exception):
|
||
pass
|
||
|
||
# 路径设置
|
||
current_dir = Path(__file__).resolve().parent
|
||
plugin_root = current_dir.parent
|
||
workspace_root = plugin_root.parent
|
||
maibot_root = workspace_root / "MaiBot"
|
||
for path in (workspace_root, maibot_root, plugin_root):
|
||
path_str = str(path)
|
||
if path_str not in sys.path:
|
||
sys.path.insert(0, path_str)
|
||
|
||
# 数据目录
|
||
DATA_DIR = plugin_root / "data"
|
||
RAW_DIR = DATA_DIR / "raw"
|
||
PROCESSED_DIR = DATA_DIR / "processed"
|
||
MANIFEST_PATH = DATA_DIR / "import_manifest.json"
|
||
|
||
|
||
def _build_arg_parser() -> argparse.ArgumentParser:
|
||
parser = argparse.ArgumentParser(description="A_Memorix Knowledge Importer (Strategy-Aware)")
|
||
parser.add_argument("--force", action="store_true", help="Force re-import")
|
||
parser.add_argument("--clear-manifest", action="store_true", help="Clear manifest")
|
||
parser.add_argument(
|
||
"--type",
|
||
"-t",
|
||
default="auto",
|
||
help="Target import strategy override (auto/narrative/factual/quote)",
|
||
)
|
||
parser.add_argument("--concurrency", "-c", type=int, default=5)
|
||
parser.add_argument(
|
||
"--chat-log",
|
||
action="store_true",
|
||
help="聊天记录导入模式：强制 narrative 策略，并使用 LLM 语义抽取 event_time/event_time_range",
|
||
)
|
||
parser.add_argument(
|
||
"--chat-reference-time",
|
||
default=None,
|
||
help="chat_log 模式的相对时间参考点（如 2026/02/12 10:30）；不传则使用当前本地时间",
|
||
)
|
||
return parser
|
||
|
||
|
||
# --help/-h fast path: avoid heavy host/plugin bootstrap
|
||
if any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
|
||
_build_arg_parser().print_help()
|
||
sys.exit(0)
|
||
|
||
|
||
try:
|
||
import A_memorix.core as core_module
|
||
import A_memorix.core.storage as storage_module
|
||
from src.common.logger import get_logger
|
||
from src.services import llm_service as llm_api
|
||
from src.config.config import global_config, model_config
|
||
|
||
VectorStore = core_module.VectorStore
|
||
GraphStore = core_module.GraphStore
|
||
MetadataStore = core_module.MetadataStore
|
||
ImportStrategy = core_module.ImportStrategy
|
||
create_embedding_api_adapter = core_module.create_embedding_api_adapter
|
||
RelationWriteService = getattr(core_module, "RelationWriteService", None)
|
||
|
||
looks_like_quote_text = storage_module.looks_like_quote_text
|
||
parse_import_strategy = storage_module.parse_import_strategy
|
||
resolve_stored_knowledge_type = storage_module.resolve_stored_knowledge_type
|
||
select_import_strategy = storage_module.select_import_strategy
|
||
|
||
from A_memorix.core.utils.time_parser import normalize_time_meta
|
||
from A_memorix.core.utils.import_payloads import normalize_paragraph_import_item
|
||
from A_memorix.core.strategies.base import BaseStrategy, ProcessedChunk, KnowledgeType as StratKnowledgeType
|
||
from A_memorix.core.strategies.narrative import NarrativeStrategy
|
||
from A_memorix.core.strategies.factual import FactualStrategy
|
||
from A_memorix.core.strategies.quote import QuoteStrategy
|
||
|
||
except ImportError as e:
|
||
print(f"❌ 无法导入模块: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
sys.exit(1)
|
||
|
||
logger = get_logger("A_Memorix.AutoImport")
|
||
|
||
|
||
def _log_before_retry(retry_state) -> None:
|
||
"""使用项目统一日志风格记录重试信息。"""
|
||
exc = None
|
||
if getattr(retry_state, "outcome", None) is not None and retry_state.outcome.failed:
|
||
exc = retry_state.outcome.exception()
|
||
next_sleep = getattr(getattr(retry_state, "next_action", None), "sleep", None)
|
||
logger.warning(
|
||
"LLM 调用即将重试: "
|
||
f"attempt={getattr(retry_state, 'attempt_number', '?')} "
|
||
f"next_sleep={next_sleep} "
|
||
f"error={exc}"
|
||
)
|
||
|
||
class AutoImporter:
|
||
def __init__(
|
||
self,
|
||
force: bool = False,
|
||
clear_manifest: bool = False,
|
||
target_type: str = "auto",
|
||
concurrency: int = 5,
|
||
chat_log: bool = False,
|
||
chat_reference_time: Optional[str] = None,
|
||
):
|
||
self.vector_store: Optional[VectorStore] = None
|
||
self.graph_store: Optional[GraphStore] = None
|
||
self.metadata_store: Optional[MetadataStore] = None
|
||
self.embedding_manager = None
|
||
self.relation_write_service = None
|
||
self.plugin_config = {}
|
||
self.manifest = {}
|
||
self.force = force
|
||
self.clear_manifest = clear_manifest
|
||
self.chat_log = chat_log
|
||
parsed_target_type = parse_import_strategy(target_type, default=ImportStrategy.AUTO)
|
||
self.target_type = ImportStrategy.NARRATIVE.value if chat_log else parsed_target_type.value
|
||
self.chat_reference_dt = self._parse_reference_time(chat_reference_time)
|
||
if self.chat_log and parsed_target_type not in {ImportStrategy.AUTO, ImportStrategy.NARRATIVE}:
|
||
logger.warning(
|
||
f"chat_log 模式已启用，target_type={target_type} 将被覆盖为 narrative"
|
||
)
|
||
self.concurrency_limit = concurrency
|
||
self.semaphore = None
|
||
self.storage_lock = None
|
||
|
||
async def initialize(self):
|
||
logger.info(f"正在初始化... (并发数: {self.concurrency_limit})")
|
||
self.semaphore = asyncio.Semaphore(self.concurrency_limit)
|
||
self.storage_lock = asyncio.Lock()
|
||
|
||
RAW_DIR.mkdir(parents=True, exist_ok=True)
|
||
PROCESSED_DIR.mkdir(parents=True, exist_ok=True)
|
||
|
||
if self.clear_manifest:
|
||
logger.info("🧹 清理 Mainfest")
|
||
self.manifest = {}
|
||
self._save_manifest()
|
||
elif MANIFEST_PATH.exists():
|
||
try:
|
||
with open(MANIFEST_PATH, "r", encoding="utf-8") as f:
|
||
self.manifest = json.load(f)
|
||
except Exception:
|
||
self.manifest = {}
|
||
|
||
config_path = plugin_root / "config.toml"
|
||
try:
|
||
with open(config_path, "r", encoding="utf-8") as f:
|
||
self.plugin_config = tomlkit.load(f)
|
||
except Exception as e:
|
||
logger.error(f"加载插件配置失败: {e}")
|
||
return False
|
||
|
||
try:
|
||
await self._init_stores()
|
||
except Exception as e:
|
||
logger.error(f"初始化存储失败: {e}")
|
||
return False
|
||
|
||
return True
|
||
|
||
async def _init_stores(self):
|
||
# ... (Same as original)
|
||
self.embedding_manager = create_embedding_api_adapter(
|
||
batch_size=self.plugin_config.get("embedding", {}).get("batch_size", 32),
|
||
default_dimension=self.plugin_config.get("embedding", {}).get("dimension", 384),
|
||
model_name=self.plugin_config.get("embedding", {}).get("model_name", "auto"),
|
||
retry_config=self.plugin_config.get("embedding", {}).get("retry", {}),
|
||
)
|
||
try:
|
||
dim = await self.embedding_manager._detect_dimension()
|
||
except:
|
||
dim = self.embedding_manager.default_dimension
|
||
|
||
q_type_str = str(self.plugin_config.get("embedding", {}).get("quantization_type", "int8") or "int8").lower()
|
||
# Need to access QuantizationType from storage_module if not imported globally
|
||
QuantizationType = storage_module.QuantizationType
|
||
if q_type_str != "int8":
|
||
raise ValueError(
|
||
"embedding.quantization_type 在 vNext 仅允许 int8(SQ8)。"
|
||
" 请先执行 scripts/release_vnext_migrate.py migrate。"
|
||
)
|
||
|
||
self.vector_store = VectorStore(
|
||
dimension=dim,
|
||
quantization_type=QuantizationType.INT8,
|
||
data_dir=DATA_DIR / "vectors"
|
||
)
|
||
|
||
SparseMatrixFormat = storage_module.SparseMatrixFormat
|
||
m_fmt_str = self.plugin_config.get("graph", {}).get("sparse_matrix_format", "csr")
|
||
m_map = {"csr": SparseMatrixFormat.CSR, "csc": SparseMatrixFormat.CSC}
|
||
|
||
self.graph_store = GraphStore(
|
||
matrix_format=m_map.get(m_fmt_str, SparseMatrixFormat.CSR),
|
||
data_dir=DATA_DIR / "graph"
|
||
)
|
||
|
||
self.metadata_store = MetadataStore(data_dir=DATA_DIR / "metadata")
|
||
self.metadata_store.connect()
|
||
|
||
if RelationWriteService is not None:
|
||
self.relation_write_service = RelationWriteService(
|
||
metadata_store=self.metadata_store,
|
||
graph_store=self.graph_store,
|
||
vector_store=self.vector_store,
|
||
embedding_manager=self.embedding_manager,
|
||
)
|
||
|
||
if self.vector_store.has_data(): self.vector_store.load()
|
||
if self.graph_store.has_data(): self.graph_store.load()
|
||
|
||
def _should_write_relation_vectors(self) -> bool:
|
||
retrieval_cfg = self.plugin_config.get("retrieval", {})
|
||
if not isinstance(retrieval_cfg, dict):
|
||
return False
|
||
rv_cfg = retrieval_cfg.get("relation_vectorization", {})
|
||
if not isinstance(rv_cfg, dict):
|
||
return False
|
||
return bool(rv_cfg.get("enabled", False)) and bool(rv_cfg.get("write_on_import", True))
|
||
|
||
def load_file(self, file_path: Path) -> str:
|
||
with open(file_path, "r", encoding="utf-8") as f:
|
||
return f.read()
|
||
|
||
def get_file_hash(self, content: str) -> str:
|
||
return hashlib.md5(content.encode("utf-8")).hexdigest()
|
||
|
||
def _parse_reference_time(self, value: Optional[str]) -> datetime:
|
||
"""解析 chat_log 模式的参考时间（用于相对时间语义解析）。"""
|
||
if not value:
|
||
return datetime.now()
|
||
formats = [
|
||
"%Y/%m/%d %H:%M:%S",
|
||
"%Y/%m/%d %H:%M",
|
||
"%Y-%m-%d %H:%M:%S",
|
||
"%Y-%m-%d %H:%M",
|
||
"%Y/%m/%d",
|
||
"%Y-%m-%d",
|
||
]
|
||
text = str(value).strip()
|
||
for fmt in formats:
|
||
try:
|
||
return datetime.strptime(text, fmt)
|
||
except ValueError:
|
||
continue
|
||
logger.warning(
|
||
f"无法解析 chat_reference_time={value}，将回退为当前本地时间"
|
||
)
|
||
return datetime.now()
|
||
|
||
async def _extract_chat_time_meta_with_llm(
|
||
self,
|
||
text: str,
|
||
model_config: Any,
|
||
) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
使用 LLM 从聊天文本语义中抽取时间信息。
|
||
支持将相对时间表达转换为绝对时间。
|
||
"""
|
||
if not text.strip():
|
||
return None
|
||
|
||
reference_now = self.chat_reference_dt.strftime("%Y/%m/%d %H:%M")
|
||
prompt = f"""You are a time extraction engine for chat logs.
|
||
Extract temporal information from the following chat paragraph.
|
||
|
||
Rules:
|
||
1. Use semantic understanding, not regex matching.
|
||
2. Convert relative expressions (e.g., yesterday evening, last Friday morning) to absolute local datetime using reference_now.
|
||
3. If a time span exists, return event_time_start/event_time_end.
|
||
4. If only one point in time exists, return event_time.
|
||
5. If no reliable time can be inferred, return all time fields as null.
|
||
6. Output ONLY valid JSON. No markdown, no explanation.
|
||
|
||
reference_now: {reference_now}
|
||
timezone: local system timezone
|
||
|
||
Allowed output formats for time values:
|
||
- "YYYY/MM/DD"
|
||
- "YYYY/MM/DD HH:mm"
|
||
|
||
JSON schema:
|
||
{{
|
||
"event_time": null,
|
||
"event_time_start": null,
|
||
"event_time_end": null,
|
||
"time_range": null,
|
||
"time_granularity": "day",
|
||
"time_confidence": 0.0
|
||
}}
|
||
|
||
Chat paragraph:
|
||
\"\"\"{text}\"\"\"
|
||
"""
|
||
try:
|
||
result = await self._llm_call(prompt, model_config)
|
||
except Exception as e:
|
||
logger.warning(f"chat_log 时间语义抽取失败: {e}")
|
||
return None
|
||
|
||
if not isinstance(result, dict):
|
||
return None
|
||
|
||
raw_time_meta = {
|
||
"event_time": result.get("event_time"),
|
||
"event_time_start": result.get("event_time_start"),
|
||
"event_time_end": result.get("event_time_end"),
|
||
"time_range": result.get("time_range"),
|
||
"time_granularity": result.get("time_granularity"),
|
||
"time_confidence": result.get("time_confidence"),
|
||
}
|
||
try:
|
||
normalized = normalize_time_meta(raw_time_meta)
|
||
except Exception as e:
|
||
logger.warning(f"chat_log 时间语义抽取结果不可用，已忽略: {e}")
|
||
return None
|
||
|
||
has_effective_time = any(
|
||
key in normalized
|
||
for key in ("event_time", "event_time_start", "event_time_end")
|
||
)
|
||
if not has_effective_time:
|
||
return None
|
||
|
||
return normalized
|
||
|
||
def _determine_strategy(self, filename: str, content: str) -> BaseStrategy:
|
||
"""Layer 1: Global Strategy Routing"""
|
||
strategy = select_import_strategy(
|
||
content,
|
||
override=self.target_type,
|
||
chat_log=self.chat_log,
|
||
)
|
||
if self.chat_log:
|
||
logger.info(f"chat_log 模式: {filename} 强制使用 NarrativeStrategy")
|
||
elif strategy == ImportStrategy.QUOTE:
|
||
logger.info(f"Auto-detected Quote/Lyric type for {filename}")
|
||
|
||
if strategy == ImportStrategy.FACTUAL:
|
||
return FactualStrategy(filename)
|
||
if strategy == ImportStrategy.QUOTE:
|
||
return QuoteStrategy(filename)
|
||
return NarrativeStrategy(filename)
|
||
|
||
def _chunk_rescue(self, chunk: ProcessedChunk, filename: str) -> Optional[BaseStrategy]:
|
||
"""Layer 2: Chunk-level rescue strategies"""
|
||
# If we are already in Quote strategy, no need to rescue
|
||
if chunk.type == StratKnowledgeType.QUOTE:
|
||
return None
|
||
|
||
if looks_like_quote_text(chunk.chunk.text):
|
||
logger.info(f" > Rescuing chunk {chunk.chunk.index} as Quote")
|
||
return QuoteStrategy(filename)
|
||
|
||
return None
|
||
|
||
async def process_and_import(self):
|
||
if not await self.initialize(): return
|
||
|
||
files = list(RAW_DIR.glob("*.txt"))
|
||
logger.info(f"扫描到 {len(files)} 个文件 in {RAW_DIR}")
|
||
|
||
if not files: return
|
||
|
||
tasks = []
|
||
for file_path in files:
|
||
tasks.append(asyncio.create_task(self._process_single_file(file_path)))
|
||
|
||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||
|
||
success_count = sum(1 for r in results if r is True)
|
||
logger.info(f"本次主处理完成，共成功处理 {success_count}/{len(files)} 个文件")
|
||
|
||
if self.vector_store: self.vector_store.save()
|
||
if self.graph_store: self.graph_store.save()
|
||
|
||
async def _process_single_file(self, file_path: Path) -> bool:
|
||
filename = file_path.name
|
||
async with self.semaphore:
|
||
try:
|
||
content = self.load_file(file_path)
|
||
file_hash = self.get_file_hash(content)
|
||
|
||
if not self.force and filename in self.manifest:
|
||
record = self.manifest[filename]
|
||
if record.get("hash") == file_hash and record.get("imported"):
|
||
logger.info(f"跳过已导入文件: {filename}")
|
||
return False
|
||
|
||
logger.info(f">>> 开始处理: {filename}")
|
||
|
||
# 1. Strategy Selection
|
||
strategy = self._determine_strategy(filename, content)
|
||
logger.info(f" 策略: {strategy.__class__.__name__}")
|
||
|
||
# 2. Split (Strategy-Aware)
|
||
initial_chunks = strategy.split(content)
|
||
logger.info(f" 初步分块: {len(initial_chunks)}")
|
||
|
||
processed_data = {"paragraphs": [], "entities": [], "relations": []}
|
||
|
||
# 3. Extract Loop
|
||
model_config = await self._select_model()
|
||
|
||
for i, chunk in enumerate(initial_chunks):
|
||
current_strategy = strategy
|
||
# Layer 2: Chunk Rescue
|
||
rescue_strategy = self._chunk_rescue(chunk, filename)
|
||
if rescue_strategy:
|
||
# Re-split? No, just re-process this text as a single chunk using the rescue strategy
|
||
# But rescue strategy might want to split it further?
|
||
# Simplification: Treat the whole chunk text as one block for the rescue strategy
|
||
# OR create a single chunk object for it.
|
||
# Creating a new chunk using rescue strategy logic might be complex if split behavior differs.
|
||
# Let's just instantiate a chunk of the new type manually
|
||
chunk.type = StratKnowledgeType.QUOTE
|
||
chunk.flags.verbatim = True
|
||
chunk.flags.requires_llm = False # Quotes don't usually need LLM
|
||
current_strategy = rescue_strategy
|
||
|
||
# Extraction
|
||
if chunk.flags.requires_llm:
|
||
result_chunk = await current_strategy.extract(chunk, lambda p: self._llm_call(p, model_config))
|
||
else:
|
||
# For quotes, extract might be just pass through or regex
|
||
result_chunk = await current_strategy.extract(chunk)
|
||
|
||
time_meta = None
|
||
if self.chat_log:
|
||
time_meta = await self._extract_chat_time_meta_with_llm(
|
||
result_chunk.chunk.text,
|
||
model_config,
|
||
)
|
||
|
||
# Normalize Data
|
||
self._normalize_and_aggregate(
|
||
result_chunk,
|
||
processed_data,
|
||
time_meta=time_meta,
|
||
)
|
||
|
||
logger.info(f" 已处理块 {i+1}/{len(initial_chunks)}")
|
||
|
||
# 4. Save Json
|
||
json_path = PROCESSED_DIR / f"{file_path.stem}.json"
|
||
with open(json_path, "w", encoding="utf-8") as f:
|
||
json.dump(processed_data, f, ensure_ascii=False, indent=2)
|
||
|
||
# 5. Import to DB
|
||
async with self.storage_lock:
|
||
await self._import_to_db(processed_data)
|
||
|
||
self.manifest[filename] = {
|
||
"hash": file_hash,
|
||
"timestamp": time.time(),
|
||
"imported": True
|
||
}
|
||
self._save_manifest()
|
||
self.vector_store.save()
|
||
self.graph_store.save()
|
||
logger.info(f"✅ 文件 {filename} 处理并导入完成")
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ 处理失败 {filename}: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return False
|
||
|
||
def _normalize_and_aggregate(
|
||
self,
|
||
chunk: ProcessedChunk,
|
||
all_data: Dict,
|
||
time_meta: Optional[Dict[str, Any]] = None,
|
||
):
|
||
"""Convert strategy-specific data to unified generic format for storage."""
|
||
# Generic fields
|
||
para_item = {
|
||
"content": chunk.chunk.text,
|
||
"source": chunk.source.file,
|
||
"knowledge_type": resolve_stored_knowledge_type(
|
||
chunk.type.value,
|
||
content=chunk.chunk.text,
|
||
).value,
|
||
"entities": [],
|
||
"relations": []
|
||
}
|
||
|
||
data = chunk.data
|
||
|
||
# 1. Triples (Factual)
|
||
if "triples" in data:
|
||
for t in data["triples"]:
|
||
para_item["relations"].append({
|
||
"subject": t.get("subject"),
|
||
"predicate": t.get("predicate"),
|
||
"object": t.get("object")
|
||
})
|
||
# Auto-add entities from triples
|
||
para_item["entities"].extend([t.get("subject"), t.get("object")])
|
||
|
||
# 2. Events & Relations (Narrative)
|
||
if "events" in data:
|
||
# Store events as content/metadata? Or entities?
|
||
# For now maybe just keep them in logic, or add as 'Event' entities?
|
||
# Creating entities for events is good.
|
||
para_item["entities"].extend(data["events"])
|
||
|
||
if "relations" in data: # Narrative also outputs relations list
|
||
para_item["relations"].extend(data["relations"])
|
||
for r in data["relations"]:
|
||
para_item["entities"].extend([r.get("subject"), r.get("object")])
|
||
|
||
# 3. Verbatim Entities (Quote)
|
||
if "verbatim_entities" in data:
|
||
para_item["entities"].extend(data["verbatim_entities"])
|
||
|
||
# Dedupe per paragraph
|
||
para_item["entities"] = list(set([e for e in para_item["entities"] if e]))
|
||
|
||
if time_meta:
|
||
para_item["time_meta"] = time_meta
|
||
|
||
all_data["paragraphs"].append(para_item)
|
||
all_data["entities"].extend(para_item["entities"])
|
||
if "relations" in para_item:
|
||
all_data["relations"].extend(para_item["relations"])
|
||
|
||
@retry(
|
||
retry=retry_if_exception_type((LLMGenerationError, json.JSONDecodeError)),
|
||
stop=stop_after_attempt(3),
|
||
wait=wait_exponential(multiplier=1, min=2, max=10),
|
||
before_sleep=_log_before_retry
|
||
)
|
||
async def _llm_call(self, prompt: str, model_config: Any) -> Dict:
|
||
"""Generic LLM Caller"""
|
||
success, response, _, _ = await llm_api.generate_with_model(
|
||
prompt=prompt,
|
||
model_config=model_config,
|
||
request_type="Script.ProcessKnowledge"
|
||
)
|
||
if success:
|
||
txt = response.strip()
|
||
if "```" in txt:
|
||
txt = txt.split("```json")[-1].split("```")[0].strip()
|
||
try:
|
||
return json.loads(txt)
|
||
except json.JSONDecodeError:
|
||
# Fallback: try to find first { and last }
|
||
start = txt.find('{')
|
||
end = txt.rfind('}')
|
||
if start != -1 and end != -1:
|
||
return json.loads(txt[start:end+1])
|
||
raise
|
||
else:
|
||
raise LLMGenerationError("LLM generation failed")
|
||
|
||
async def _select_model(self) -> Any:
|
||
models = llm_api.get_available_models()
|
||
if not models: raise ValueError("No LLM models")
|
||
|
||
config_model = self.plugin_config.get("advanced", {}).get("extraction_model", "auto")
|
||
if config_model != "auto" and config_model in models:
|
||
return models[config_model]
|
||
|
||
for task_key in ["lpmm_entity_extract", "lpmm_rdf_build", "embedding"]:
|
||
if task_key in models: return models[task_key]
|
||
|
||
return models[list(models.keys())[0]]
|
||
|
||
# Re-use existing methods
|
||
async def _add_entity_with_vector(self, name: str, source_paragraph: Optional[str] = None) -> str:
|
||
# Same as before
|
||
hash_value = self.metadata_store.add_entity(name, source_paragraph=source_paragraph)
|
||
self.graph_store.add_nodes([name])
|
||
try:
|
||
emb = await self.embedding_manager.encode(name)
|
||
try:
|
||
self.vector_store.add(emb.reshape(1, -1), [hash_value])
|
||
except ValueError: pass
|
||
except Exception: pass
|
||
return hash_value
|
||
|
||
async def import_json_data(self, data: Dict, filename: str = "script_import", progress_callback=None):
|
||
"""Public import entrypoint for pre-processed JSON payloads."""
|
||
if not self.storage_lock:
|
||
raise RuntimeError("Importer is not initialized. Call initialize() first.")
|
||
|
||
async with self.storage_lock:
|
||
await self._import_to_db(data, progress_callback=progress_callback)
|
||
self.manifest[filename] = {
|
||
"hash": self.get_file_hash(json.dumps(data, ensure_ascii=False, sort_keys=True)),
|
||
"timestamp": time.time(),
|
||
"imported": True,
|
||
}
|
||
self._save_manifest()
|
||
self.vector_store.save()
|
||
self.graph_store.save()
|
||
|
||
async def _import_to_db(self, data: Dict, progress_callback=None):
|
||
# Same logic, but ensure robust
|
||
with self.graph_store.batch_update():
|
||
for item in data.get("paragraphs", []):
|
||
paragraph = normalize_paragraph_import_item(
|
||
item,
|
||
default_source="script",
|
||
)
|
||
content = paragraph["content"]
|
||
source = paragraph["source"]
|
||
k_type_val = paragraph["knowledge_type"]
|
||
|
||
h_val = self.metadata_store.add_paragraph(
|
||
content=content,
|
||
source=source,
|
||
knowledge_type=k_type_val,
|
||
time_meta=paragraph["time_meta"],
|
||
)
|
||
|
||
if h_val not in self.vector_store:
|
||
try:
|
||
emb = await self.embedding_manager.encode(content)
|
||
self.vector_store.add(emb.reshape(1, -1), [h_val])
|
||
except Exception as e:
|
||
logger.error(f" Vector fail: {e}")
|
||
|
||
para_entities = paragraph["entities"]
|
||
for entity in para_entities:
|
||
if entity:
|
||
await self._add_entity_with_vector(entity, source_paragraph=h_val)
|
||
|
||
para_relations = paragraph["relations"]
|
||
for rel in para_relations:
|
||
s, p, o = rel.get("subject"), rel.get("predicate"), rel.get("object")
|
||
if s and p and o:
|
||
await self._add_entity_with_vector(s, source_paragraph=h_val)
|
||
await self._add_entity_with_vector(o, source_paragraph=h_val)
|
||
confidence = float(rel.get("confidence", 1.0) or 1.0)
|
||
rel_meta = rel.get("metadata", {})
|
||
write_vector = self._should_write_relation_vectors()
|
||
if self.relation_write_service is not None:
|
||
await self.relation_write_service.upsert_relation_with_vector(
|
||
subject=s,
|
||
predicate=p,
|
||
obj=o,
|
||
confidence=confidence,
|
||
source_paragraph=h_val,
|
||
metadata=rel_meta if isinstance(rel_meta, dict) else {},
|
||
write_vector=write_vector,
|
||
)
|
||
else:
|
||
rel_hash = self.metadata_store.add_relation(
|
||
s,
|
||
p,
|
||
o,
|
||
confidence=confidence,
|
||
source_paragraph=h_val,
|
||
metadata=rel_meta if isinstance(rel_meta, dict) else {},
|
||
)
|
||
self.graph_store.add_edges([(s, o)], relation_hashes=[rel_hash])
|
||
try:
|
||
self.metadata_store.set_relation_vector_state(rel_hash, "none")
|
||
except Exception:
|
||
pass
|
||
|
||
if progress_callback: progress_callback(1)
|
||
|
||
async def close(self):
|
||
if self.metadata_store: self.metadata_store.close()
|
||
|
||
def _save_manifest(self):
|
||
with open(MANIFEST_PATH, "w", encoding="utf-8") as f:
|
||
json.dump(self.manifest, f, ensure_ascii=False, indent=2)
|
||
|
||
async def main():
|
||
parser = _build_arg_parser()
|
||
args = parser.parse_args()
|
||
|
||
if not global_config: return
|
||
|
||
importer = AutoImporter(
|
||
force=args.force,
|
||
clear_manifest=args.clear_manifest,
|
||
target_type=args.type,
|
||
concurrency=args.concurrency,
|
||
chat_log=args.chat_log,
|
||
chat_reference_time=args.chat_reference_time,
|
||
)
|
||
await importer.process_and_import()
|
||
await importer.close()
|
||
|
||
if __name__ == "__main__":
|
||
if sys.platform == "win32":
|
||
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
|
||
asyncio.run(main())
|