Introduced beartype (#3460)

### What problem does this PR solve?

Introduced [beartype](https://github.com/beartype/beartype) for runtime
type-checking.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

rag/llm/embedding_model.py

@@ -15,7 +15,6 @@
 #
 import logging
 import re
-from typing import Optional
 import threading
 import requests
 from huggingface_hub import snapshot_download
@@ -242,10 +241,10 @@ class FastEmbed(Base):
 
     def __init__(
             self,
-            key: Optional[str] = None,
+            key: str | None = None,
             model_name: str = "BAAI/bge-small-en-v1.5",
-            cache_dir: Optional[str] = None,
-            threads: Optional[int] = None,
+            cache_dir: str | None = None,
+            threads: int | None = None,
             **kwargs,
     ):
         if not settings.LIGHTEN and not FastEmbed._model:

rag/nlp/search.py

@@ -17,7 +17,6 @@
 import logging
 import re
 import json
-from typing import List, Optional, Dict, Union
 from dataclasses import dataclass
 
 from rag.utils import rmSpace
@@ -37,13 +36,13 @@ class Dealer:
     @dataclass
     class SearchResult:
         total: int
-        ids: List[str]
-        query_vector: List[float] = None
-        field: Optional[Dict] = None
-        highlight: Optional[Dict] = None
-        aggregation: Union[List, Dict, None] = None
-        keywords: Optional[List[str]] = None
-        group_docs: List[List] = None
+        ids: list[str]
+        query_vector: list[float] | None = None
+        field: dict | None = None
+        highlight: dict | None = None
+        aggregation: list | dict | None = None
+        keywords: list[str] | None = None
+        group_docs: list[list] | None = None
 
     def get_vector(self, txt, emb_mdl, topk=10, similarity=0.1):
         qv, _ = emb_mdl.encode_queries(txt)

rag/raptor.py

@@ -17,7 +17,6 @@ import logging
 import re
 from concurrent.futures import ThreadPoolExecutor, ALL_COMPLETED, wait
 from threading import Lock
-from typing import Tuple
 import umap
 import numpy as np
 from sklearn.mixture import GaussianMixture
@@ -45,7 +44,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
         optimal_clusters = n_clusters[np.argmin(bics)]
         return optimal_clusters
 
-    def __call__(self, chunks: Tuple[str, np.ndarray], random_state, callback=None):
+    def __call__(self, chunks: tuple[str, np.ndarray], random_state, callback=None):
         layers = [(0, len(chunks))]
         start, end = 0, len(chunks)
         if len(chunks) <= 1: return

rag/svr/task_executor.py

@@ -13,6 +13,9 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+from beartype.claw import beartype_packages
+beartype_packages(["agent", "api", "deepdoc", "plugins", "rag", "ragflow_sdk"])  # <-- raise exceptions in your code
+
 import logging
 import sys
 from api.utils.log_utils import initRootLogger

rag/utils/doc_store_conn.py

@@ -1,19 +1,17 @@
 from abc import ABC, abstractmethod
-from typing import Optional, Union
 from dataclasses import dataclass
 import numpy as np
 import polars as pl
-from typing import List, Dict
 
 DEFAULT_MATCH_VECTOR_TOPN = 10
 DEFAULT_MATCH_SPARSE_TOPN = 10
-VEC = Union[list, np.ndarray]
+VEC = list | np.ndarray
 
 
 @dataclass
 class SparseVector:
     indices: list[int]
-    values: Union[list[float], list[int], None] = None
+    values: list[float] | list[int] | None = None
 
     def __post_init__(self):
         assert (self.values is None) or (len(self.indices) == len(self.values))
@@ -82,7 +80,7 @@ class MatchSparseExpr(ABC):
         sparse_data: SparseVector | dict,
         distance_type: str,
         topn: int,
-        opt_params: Optional[dict] = None,
+        opt_params: dict | None = None,
     ):
         self.vector_column_name = vector_column_name
         self.sparse_data = sparse_data
@@ -98,7 +96,7 @@ class MatchTensorExpr(ABC):
         query_data: VEC,
         query_data_type: str,
         topn: int,
-        extra_option: Optional[dict] = None,
+        extra_option: dict | None = None,
     ):
         self.column_name = column_name
         self.query_data = query_data
@@ -108,16 +106,13 @@ class MatchTensorExpr(ABC):
 
 
 class FusionExpr(ABC):
-    def __init__(self, method: str, topn: int, fusion_params: Optional[dict] = None):
+    def __init__(self, method: str, topn: int, fusion_params: dict | None = None):
         self.method = method
         self.topn = topn
         self.fusion_params = fusion_params
 
 
-MatchExpr = Union[
-    MatchTextExpr, MatchDenseExpr, MatchSparseExpr, MatchTensorExpr, FusionExpr
-]
-
+MatchExpr = MatchTextExpr | MatchDenseExpr | MatchSparseExpr | MatchTensorExpr | FusionExpr
 
 class OrderByExpr(ABC):
     def __init__(self):
@@ -229,11 +224,11 @@ class DocStoreConnection(ABC):
         raise NotImplementedError("Not implemented")
 
     @abstractmethod
-    def getFields(self, res, fields: List[str]) -> Dict[str, dict]:
+    def getFields(self, res, fields: list[str]) -> dict[str, dict]:
         raise NotImplementedError("Not implemented")
 
     @abstractmethod
-    def getHighlight(self, res, keywords: List[str], fieldnm: str):
+    def getHighlight(self, res, keywords: list[str], fieldnm: str):
         raise NotImplementedError("Not implemented")
 
     @abstractmethod

rag/utils/es_conn.py

@@ -3,7 +3,6 @@ import re
 import json
 import time
 import os
-from typing import List, Dict
 
 import copy
 from elasticsearch import Elasticsearch
@@ -363,7 +362,7 @@ class ESConnection(DocStoreConnection):
             rr.append(d["_source"])
         return rr
 
-    def getFields(self, res, fields: List[str]) -> Dict[str, dict]:
+    def getFields(self, res, fields: list[str]) -> dict[str, dict]:
         res_fields = {}
         if not fields:
             return {}
@@ -382,7 +381,7 @@ class ESConnection(DocStoreConnection):
                 res_fields[d["id"]] = m
         return res_fields
 
-    def getHighlight(self, res, keywords: List[str], fieldnm: str):
+    def getHighlight(self, res, keywords: list[str], fieldnm: str):
         ans = {}
         for d in res["hits"]["hits"]:
             hlts = d.get("highlight")

rag/utils/infinity_conn.py

@@ -3,7 +3,6 @@ import os
 import re
 import json
 import time
-from typing import List, Dict
 import infinity
 from infinity.common import ConflictType, InfinityException
 from infinity.index import IndexInfo, IndexType
@@ -384,7 +383,7 @@ class InfinityConnection(DocStoreConnection):
     def getChunkIds(self, res):
         return list(res["id"])
 
-    def getFields(self, res, fields: List[str]) -> Dict[str, dict]:
+    def getFields(self, res, fields: list[str]) -> list[str, dict]:
         res_fields = {}
         if not fields:
             return {}
@@ -412,7 +411,7 @@ class InfinityConnection(DocStoreConnection):
             res_fields[id] = m
         return res_fields
 
-    def getHighlight(self, res, keywords: List[str], fieldnm: str):
+    def getHighlight(self, res, keywords: list[str], fieldnm: str):
         ans = {}
         num_rows = len(res)
         column_id = res["id"]