ForcePilot/backend/package/yuxi/knowledge/chunking/ragflow_like/presets.py
Wenjie Zhang 410dd47c14 refactor: 将后端代码迁移至 backend 目录
- 将 server/, src/, scripts/, test/ 等目录移动到 backend/ 目录下
- 使用 git rename 保留文件历史记录
- 更新 docker-compose.yml 和 api.Dockerfile 配置

WIP: 项目结构重构进行中
2026-03-24 11:08:12 +08:00

239 lines
8.6 KiB
Python

from __future__ import annotations
from copy import deepcopy
from typing import Any
from yuxi.utils import logger
CHUNK_PRESET_GENERAL = "general"
CHUNK_PRESET_QA = "qa"
CHUNK_PRESET_BOOK = "book"
CHUNK_PRESET_LAWS = "laws"
CHUNK_PRESET_IDS = {
CHUNK_PRESET_GENERAL,
CHUNK_PRESET_QA,
CHUNK_PRESET_BOOK,
CHUNK_PRESET_LAWS,
}
CHUNK_PRESET_DESCRIPTIONS: dict[str, str] = {
CHUNK_PRESET_GENERAL: "通用分块:按分隔符和长度切分,适合大多数普通文档。",
CHUNK_PRESET_QA: "问答分块:优先抽取问题-回答结构,适合 FAQ、题库、问答手册。",
CHUNK_PRESET_BOOK: "书籍分块:强化章节标题识别并做层级合并,适合教材、手册、长章节文档。",
CHUNK_PRESET_LAWS: "法规分块:按法条层级组织与合并,适合法律法规、制度规范类文本。",
}
CHUNK_ENGINE_VERSION = "ragflow_like_v1"
GENERAL_INTERNAL_PARSER_ID = "naive"
_BASE_DEFAULTS: dict[str, Any] = {
"table_context_size": 0,
"image_context_size": 0,
}
_PRESET_DEFAULTS: dict[str, dict[str, Any] | None] = {
CHUNK_PRESET_GENERAL: {
"layout_recognize": "DeepDOC",
"chunk_token_num": 512,
"delimiter": "\n",
"auto_keywords": 0,
"auto_questions": 0,
"html4excel": False,
"topn_tags": 3,
"raptor": {
"use_raptor": True,
"prompt": (
"Please summarize the following paragraphs. Be careful with the "
"numbers, do not make things up. Paragraphs as following:\n"
" {cluster_content}\n"
"The above is the content you need to summarize."
),
"max_token": 256,
"threshold": 0.1,
"max_cluster": 64,
"random_seed": 0,
},
"graphrag": {
"use_graphrag": True,
"entity_types": ["organization", "person", "geo", "event", "category"],
"method": "light",
},
},
CHUNK_PRESET_QA: {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
CHUNK_PRESET_BOOK: {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
CHUNK_PRESET_LAWS: {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
}
def deep_merge(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]:
result = deepcopy(base)
for key, value in (override or {}).items():
if isinstance(value, dict) and isinstance(result.get(key), dict):
result[key] = deep_merge(result[key], value)
else:
result[key] = value
return result
def normalize_chunk_preset_id(value: str | None) -> str:
if not value:
return CHUNK_PRESET_GENERAL
normalized = str(value).strip().lower()
if normalized == GENERAL_INTERNAL_PARSER_ID:
return CHUNK_PRESET_GENERAL
if normalized in CHUNK_PRESET_IDS:
return normalized
logger.warning(f"Unknown chunk preset id '{value}', fallback to general")
return CHUNK_PRESET_GENERAL
def map_to_internal_parser_id(preset_id: str) -> str:
normalized = normalize_chunk_preset_id(preset_id)
if normalized == CHUNK_PRESET_GENERAL:
return GENERAL_INTERNAL_PARSER_ID
return normalized
def get_default_chunk_parser_config(preset_id: str) -> dict[str, Any]:
normalized = normalize_chunk_preset_id(preset_id)
default_config = deepcopy(_PRESET_DEFAULTS.get(normalized) or {})
return deep_merge(_BASE_DEFAULTS, default_config)
def _safe_int(value: Any) -> int | None:
if value is None:
return None
try:
return int(value)
except (TypeError, ValueError):
return None
def _legacy_params_to_parser_config(params: dict[str, Any] | None) -> dict[str, Any]:
if not isinstance(params, dict):
return {}
parser_config: dict[str, Any] = {}
chunk_size = _safe_int(params.get("chunk_size"))
chunk_overlap = _safe_int(params.get("chunk_overlap"))
if chunk_size and chunk_size > 0:
parser_config["chunk_token_num"] = chunk_size
if chunk_size and chunk_size > 0 and chunk_overlap is not None:
overlap_percent = round(max(0, min(chunk_overlap, chunk_size - 1)) * 100 / chunk_size)
parser_config["overlapped_percent"] = max(0, min(overlap_percent, 99))
if isinstance(params.get("qa_separator"), str) and params.get("qa_separator"):
parser_config["delimiter"] = params["qa_separator"]
if isinstance(params.get("delimiter"), str) and params.get("delimiter"):
parser_config["delimiter"] = params["delimiter"]
if "chunk_token_num" in params:
normalized_chunk_token_num = _safe_int(params.get("chunk_token_num"))
if normalized_chunk_token_num is not None:
parser_config["chunk_token_num"] = normalized_chunk_token_num
if "overlapped_percent" in params:
normalized_overlapped_percent = _safe_int(params.get("overlapped_percent"))
if normalized_overlapped_percent is not None:
parser_config["overlapped_percent"] = max(0, min(normalized_overlapped_percent, 99))
return parser_config
def ensure_chunk_defaults_in_additional_params(additional_params: dict[str, Any] | None) -> dict[str, Any]:
params = dict(additional_params or {})
params["chunk_preset_id"] = normalize_chunk_preset_id(params.get("chunk_preset_id"))
if "chunk_parser_config" in params and not isinstance(params.get("chunk_parser_config"), dict):
logger.warning("Invalid chunk_parser_config in additional_params, fallback to empty dict")
params["chunk_parser_config"] = {}
return params
def resolve_chunk_processing_params(
kb_additional_params: dict[str, Any] | None,
file_processing_params: dict[str, Any] | None,
request_params: dict[str, Any] | None = None,
) -> dict[str, Any]:
kb_additional = ensure_chunk_defaults_in_additional_params(kb_additional_params)
file_params = dict(file_processing_params or {})
request = dict(request_params or {})
preset_id = normalize_chunk_preset_id(
request.get("chunk_preset_id") or file_params.get("chunk_preset_id") or kb_additional.get("chunk_preset_id")
)
parser_config = get_default_chunk_parser_config(preset_id)
kb_parser_config = kb_additional.get("chunk_parser_config")
if isinstance(kb_parser_config, dict):
parser_config = deep_merge(parser_config, kb_parser_config)
file_parser_config = file_params.get("chunk_parser_config")
if isinstance(file_parser_config, dict):
parser_config = deep_merge(parser_config, file_parser_config)
req_parser_config = request.get("chunk_parser_config")
if isinstance(req_parser_config, dict):
parser_config = deep_merge(parser_config, req_parser_config)
merged_legacy = {}
merged_legacy.update(file_params)
merged_legacy.update(request)
parser_config = deep_merge(parser_config, _legacy_params_to_parser_config(merged_legacy))
# Build processing params snapshot (keep existing + request overrides for non-chunk fields)
snapshot: dict[str, Any] = {}
snapshot.update(file_params)
snapshot.update(request)
snapshot["chunk_preset_id"] = preset_id
snapshot["chunk_parser_config"] = parser_config
snapshot["chunk_engine_version"] = CHUNK_ENGINE_VERSION
# Keep backward-compatible fields for current UI
if "chunk_size" not in snapshot and isinstance(parser_config.get("chunk_token_num"), int):
snapshot["chunk_size"] = parser_config["chunk_token_num"]
if "chunk_overlap" not in snapshot and isinstance(parser_config.get("overlapped_percent"), int):
token_num = parser_config.get("chunk_token_num")
if isinstance(token_num, int) and token_num > 0:
snapshot["chunk_overlap"] = int(token_num * parser_config["overlapped_percent"] / 100)
if "qa_separator" not in snapshot and isinstance(parser_config.get("delimiter"), str):
snapshot["qa_separator"] = parser_config["delimiter"]
return snapshot
def get_chunk_preset_options() -> list[dict[str, str]]:
return [
{
"value": CHUNK_PRESET_GENERAL,
"label": "General",
"description": CHUNK_PRESET_DESCRIPTIONS[CHUNK_PRESET_GENERAL],
},
{
"value": CHUNK_PRESET_QA,
"label": "QA",
"description": CHUNK_PRESET_DESCRIPTIONS[CHUNK_PRESET_QA],
},
{
"value": CHUNK_PRESET_BOOK,
"label": "Book",
"description": CHUNK_PRESET_DESCRIPTIONS[CHUNK_PRESET_BOOK],
},
{
"value": CHUNK_PRESET_LAWS,
"label": "Laws",
"description": CHUNK_PRESET_DESCRIPTIONS[CHUNK_PRESET_LAWS],
},
]