community[patch]: Remove usage of @root_validator(allow_reuse=True) (#25235)

Remove usage of @root_validator(allow_reuse=True)

libs/community/langchain_community/chat_models/gpt_router.py

@@ -167,7 +167,7 @@ class GPTRouter(BaseChatModel):
     """Number of chat completions to generate for each prompt."""
     max_tokens: int = 256
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         values["gpt_router_api_base"] = get_from_dict_or_env(
             values,
@@ -183,7 +183,10 @@ class GPTRouter(BaseChatModel):
                 "GPT_ROUTER_API_KEY",
             )
         )
+        return values
 
+    @root_validator(pre=True, skip_on_failure=True)
+    def post_init(cls, values: Dict) -> Dict:
         try:
             from gpt_router.client import GPTRouterClient
 

libs/community/langchain_community/chat_models/perplexity.py

@@ -88,7 +88,7 @@ class ChatPerplexity(BaseChatModel):
     def lc_secrets(self) -> Dict[str, str]:
         return {"pplx_api_key": "PPLX_API_KEY"}
 
-    @root_validator(pre=True, allow_reuse=True)
+    @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
@@ -114,7 +114,7 @@ class ChatPerplexity(BaseChatModel):
         values["model_kwargs"] = extra
         return values
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=False, skip_on_failure=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         values["pplx_api_key"] = get_from_dict_or_env(

libs/community/langchain_community/embeddings/baichuan.py

@@ -3,7 +3,11 @@ from typing import Any, Dict, List, Optional
 import requests
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
-from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from langchain_core.utils import (
+    convert_to_secret_str,
+    get_from_dict_or_env,
+    secret_from_env,
+)
 from requests import RequestException
 
 BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
@@ -53,7 +57,10 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
     session: Any  #: :meta private:
     model_name: str = Field(default="Baichuan-Text-Embedding", alias="model")
     """The model used to embed the documents."""
-    baichuan_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    baichuan_api_key: Optional[SecretStr] = Field(
+        alias="api_key",
+        default_factory=secret_from_env("BAICHUAN_API_KEY", default=None),
+    )
     """Automatically inferred from env var `BAICHUAN_API_KEY` if not provided."""
     chunk_size: int = 16
     """Chunk size when multiple texts are input"""
@@ -61,22 +68,21 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
     class Config:
         allow_population_by_field_name = True
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=False, skip_on_failure=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that auth token exists in environment."""
-        try:
+        if values["baichuan_api_key"] is None:
+            # This is likely here for some backwards compatibility with
+            # BAICHUAN_AUTH_TOKEN
             baichuan_api_key = convert_to_secret_str(
-                get_from_dict_or_env(values, "baichuan_api_key", "BAICHUAN_API_KEY")
-            )
-        except ValueError as original_exc:
-            try:
-                baichuan_api_key = convert_to_secret_str(
-                    get_from_dict_or_env(
-                        values, "baichuan_auth_token", "BAICHUAN_AUTH_TOKEN"
-                    )
+                get_from_dict_or_env(
+                    values, "baichuan_auth_token", "BAICHUAN_AUTH_TOKEN"
                 )
-            except ValueError:
-                raise original_exc
+            )
+            values["baichuan_api_key"] = baichuan_api_key
+        else:
+            baichuan_api_key = values["baichuan_api_key"]
+
         session = requests.Session()
         session.headers.update(
             {

libs/community/langchain_community/embeddings/gradient_ai.py

@@ -53,7 +53,7 @@ class GradientEmbeddings(BaseModel, Embeddings):
     class Config:
         extra = "forbid"
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
 
@@ -65,8 +65,15 @@ class GradientEmbeddings(BaseModel, Embeddings):
         )
 
         values["gradient_api_url"] = get_from_dict_or_env(
-            values, "gradient_api_url", "GRADIENT_API_URL"
+            values,
+            "gradient_api_url",
+            "GRADIENT_API_URL",
+            default="https://api.gradient.ai/api",
         )
+        return values
+
+    @root_validator(pre=False, skip_on_failure=True)
+    def post_init(cls, values: Dict) -> Dict:
         try:
             import gradientai
         except ImportError:
@@ -85,7 +92,6 @@ class GradientEmbeddings(BaseModel, Embeddings):
             host=values["gradient_api_url"],
         )
         values["client"] = gradient.get_embeddings_model(slug=values["model"])
-
         return values
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:

libs/community/langchain_community/embeddings/infinity.py

@@ -47,7 +47,7 @@ class InfinityEmbeddings(BaseModel, Embeddings):
     class Config:
         extra = "forbid"
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
 

libs/community/langchain_community/embeddings/infinity_local.py

@@ -60,7 +60,7 @@ class InfinityEmbeddingsLocal(BaseModel, Embeddings):
     class Config:
         extra = "forbid"
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=False, skip_on_failure=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
 

libs/community/langchain_community/embeddings/sparkllm.py

@@ -12,8 +12,10 @@ from wsgiref.handlers import format_date_time
 import numpy as np
 import requests
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
-from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
+from langchain_core.utils import (
+    secret_from_env,
+)
 from numpy import ndarray
 
 # SparkLLMTextEmbeddings is an embedding model provided by iFLYTEK Co., Ltd.. (https://iflytek.com/en/).
@@ -102,11 +104,18 @@ class SparkLLMTextEmbeddings(BaseModel, Embeddings):
             ]
     """  # noqa: E501
 
-    spark_app_id: Optional[SecretStr] = Field(default=None, alias="app_id")
+    spark_app_id: SecretStr = Field(
+        alias="app_id", default_factory=secret_from_env("SPARK_APP_ID")
+    )
     """Automatically inferred from env var `SPARK_APP_ID` if not provided."""
-    spark_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    spark_api_key: Optional[SecretStr] = Field(
+        alias="api_key", default_factory=secret_from_env("SPARK_API_KEY", default=None)
+    )
     """Automatically inferred from env var `SPARK_API_KEY` if not provided."""
-    spark_api_secret: Optional[SecretStr] = Field(default=None, alias="api_secret")
+    spark_api_secret: Optional[SecretStr] = Field(
+        alias="api_secret",
+        default_factory=secret_from_env("SPARK_API_SECRET", default=None),
+    )
     """Automatically inferred from env var `SPARK_API_SECRET` if not provided."""
     base_url: str = Field(default="https://emb-cn-huabei-1.xf-yun.com/")
     """Base URL path for API requests"""
@@ -118,20 +127,6 @@ class SparkLLMTextEmbeddings(BaseModel, Embeddings):
     class Config:
         allow_population_by_field_name = True
 
-    @root_validator(allow_reuse=True)
-    def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that auth token exists in environment."""
-        values["spark_app_id"] = convert_to_secret_str(
-            get_from_dict_or_env(values, "spark_app_id", "SPARK_APP_ID")
-        )
-        values["spark_api_key"] = convert_to_secret_str(
-            get_from_dict_or_env(values, "spark_api_key", "SPARK_API_KEY")
-        )
-        values["spark_api_secret"] = convert_to_secret_str(
-            get_from_dict_or_env(values, "spark_api_secret", "SPARK_API_SECRET")
-        )
-        return values
-
     def _embed(self, texts: List[str], host: str) -> Optional[List[List[float]]]:
         """Internal method to call Spark Embedding API and return embeddings.
 

libs/community/langchain_community/llms/gradient_ai.py

@@ -77,7 +77,7 @@ class GradientLLM(BaseLLM):
         allow_population_by_field_name = True
         extra = "forbid"
 
-    @root_validator(allow_reuse=True)
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
 
@@ -88,6 +88,26 @@ class GradientLLM(BaseLLM):
             values, "gradient_workspace_id", "GRADIENT_WORKSPACE_ID"
         )
 
+        values["gradient_api_url"] = get_from_dict_or_env(
+            values, "gradient_api_url", "GRADIENT_API_URL"
+        )
+        return values
+
+    @root_validator(pre=False, skip_on_failure=True)
+    def post_init(cls, values: Dict) -> Dict:
+        """Post init validation."""
+        # Can be most to post_init_validation
+        try:
+            import gradientai  # noqa
+        except ImportError:
+            logging.warning(
+                "DeprecationWarning: `GradientLLM` will use "
+                "`pip install gradientai` in future releases of langchain."
+            )
+        except Exception:
+            pass
+
+        # Can be most to post_init_validation
         if (
             values["gradient_access_token"] is None
             or len(values["gradient_access_token"]) < 10
@@ -114,20 +134,6 @@ class GradientLLM(BaseLLM):
             if 0 >= kw.get("max_generated_token_count", 1):
                 raise ValueError("`max_generated_token_count` must be positive")
 
-        values["gradient_api_url"] = get_from_dict_or_env(
-            values, "gradient_api_url", "GRADIENT_API_URL"
-        )
-
-        try:
-            import gradientai  # noqa
-        except ImportError:
-            logging.warning(
-                "DeprecationWarning: `GradientLLM` will use "
-                "`pip install gradientai` in future releases of langchain."
-            )
-        except Exception:
-            pass
-
         return values
 
     @property

libs/community/langchain_community/vectorstores/thirdai_neuraldb.py

@@ -6,8 +6,6 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import root_validator
-from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from langchain_core.vectorstores import VectorStore
 
 
@@ -164,18 +162,6 @@ class NeuralDBVectorStore(VectorStore):
         offset = self.db._savable_state.documents.get_source_by_id(source_id)[1]
         return [str(offset + i) for i in range(len(texts))]  # type: ignore[arg-type]
 
-    @root_validator(allow_reuse=True)
-    def validate_environments(cls, values: Dict) -> Dict:
-        """Validate ThirdAI environment variables."""
-        values["thirdai_key"] = convert_to_secret_str(
-            get_from_dict_or_env(
-                values,
-                "thirdai_key",
-                "THIRDAI_KEY",
-            )
-        )
-        return values
-
     def insert(  # type: ignore[no-untyped-def, no-untyped-def]
         self,
         sources: List[Any],

libs/community/scripts/check_pydantic.sh

@@ -20,7 +20,7 @@ count=$(git grep -E '(@root_validator)|(@validator)|(@pre_init)' -- "*.py" | wc
 # PRs that increase the current count will not be accepted.
 # PRs that decrease update the code in the repository
 # and allow decreasing the count of are welcome!
-current_count=336
+current_count=337
 
 if [ "$count" -gt "$current_count" ]; then
   echo "The PR seems to be introducing new usage of @root_validator and/or @field_validator."

libs/community/tests/unit_tests/embeddings/test_baichuan.py

@@ -8,7 +8,7 @@ from langchain_community.embeddings import BaichuanTextEmbeddings
 def test_sparkllm_initialization_by_alias() -> None:
     # Effective initialization
     embeddings = BaichuanTextEmbeddings(  # type: ignore[call-arg]
-        model="embedding_model",  # type: ignore[arg-type]
+        model="embedding_model",
         api_key="your-api-key",  # type: ignore[arg-type]
     )
     assert embeddings.model_name == "embedding_model"

libs/community/tests/unit_tests/embeddings/test_sparkllm.py

@@ -2,7 +2,7 @@ import os
 from typing import cast
 
 import pytest
-from langchain_core.pydantic_v1 import SecretStr, ValidationError
+from langchain_core.pydantic_v1 import SecretStr
 
 from langchain_community.embeddings import SparkLLMTextEmbeddings
 
@@ -43,5 +43,5 @@ def test_initialization_parameters_from_env() -> None:
 
     # Environment variable missing
     del os.environ["SPARK_APP_ID"]
-    with pytest.raises(ValidationError):
+    with pytest.raises(ValueError):
         SparkLLMTextEmbeddings()