langchain[minor]: Upgrade ambiguous root_validator to @pre_init (#24895)

The @pre_init validator is a temporary solution for base models. It has
similar (but not identical) semantics to @root_validator(), but it works
strictly as a pre-init validator.

It'll work as expected as long as the pydantic model type hints were
correct.

libs/langchain/langchain/evaluation/embedding_distance/base.py

@@ -10,7 +10,8 @@ from langchain_core.callbacks.manager import (
     Callbacks,
 )
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import Field, root_validator
+from langchain_core.pydantic_v1 import Field
+from langchain_core.utils import pre_init
 
 from langchain.chains.base import Chain
 from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
@@ -68,7 +69,7 @@ class _EmbeddingDistanceChainMixin(Chain):
     embeddings: Embeddings = Field(default_factory=_embedding_factory)
     distance_metric: EmbeddingDistance = Field(default=EmbeddingDistance.COSINE)
 
-    @root_validator(pre=False)
+    @pre_init
     def _validate_tiktoken_installed(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Validate that the TikTok library is installed.
 

libs/langchain/langchain/evaluation/string_distance/base.py

@@ -8,7 +8,8 @@ from langchain_core.callbacks.manager import (
     CallbackManagerForChainRun,
     Callbacks,
 )
-from langchain_core.pydantic_v1 import Field, root_validator
+from langchain_core.pydantic_v1 import Field
+from langchain_core.utils import pre_init
 
 from langchain.chains.base import Chain
 from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
@@ -63,7 +64,7 @@ class _RapidFuzzChainMixin(Chain):
     """Whether to normalize the score to a value between 0 and 1.
     Applies only to the Levenshtein and Damerau-Levenshtein distances."""
 
-    @root_validator
+    @pre_init
     def validate_dependencies(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """
         Validate that the rapidfuzz library is installed.

libs/langchain/langchain/memory/buffer.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, List, Optional
 
 from langchain_core.messages import BaseMessage, get_buffer_string
-from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils import pre_init
 
 from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
 from langchain.memory.utils import get_prompt_input_key
@@ -82,7 +82,7 @@ class ConversationStringBufferMemory(BaseMemory):
     input_key: Optional[str] = None
     memory_key: str = "history"  #: :meta private:
 
-    @root_validator()
+    @pre_init
     def validate_chains(cls, values: Dict) -> Dict:
         """Validate that return messages is not True."""
         if values.get("return_messages", False):

libs/langchain/langchain/memory/summary.py

@@ -6,7 +6,8 @@ from langchain_core.chat_history import BaseChatMessageHistory
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import BaseModel, root_validator
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.utils import pre_init
 
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
@@ -85,7 +86,7 @@ class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin):
             buffer = self.buffer
         return {self.memory_key: buffer}
 
-    @root_validator()
+    @pre_init
     def validate_prompt_input_variables(cls, values: Dict) -> Dict:
         """Validate that prompt input variables are consistent."""
         prompt_variables = values["prompt"].input_variables

libs/langchain/langchain/memory/summary_buffer.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, List, Union
 
 from langchain_core.messages import BaseMessage, get_buffer_string
-from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils import pre_init
 
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.summary import SummarizerMixin
@@ -64,7 +64,7 @@ class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin):
             )
         return {self.memory_key: final_buffer}
 
-    @root_validator()
+    @pre_init
     def validate_prompt_input_variables(cls, values: Dict) -> Dict:
         """Validate that prompt input variables are consistent."""
         prompt_variables = values["prompt"].input_variables

libs/langchain/langchain/output_parsers/combining.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from typing import Any, Dict, List
 
 from langchain_core.output_parsers import BaseOutputParser
-from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils import pre_init
 
 
 class CombiningOutputParser(BaseOutputParser[Dict[str, Any]]):
@@ -15,7 +15,7 @@ class CombiningOutputParser(BaseOutputParser[Dict[str, Any]]):
     def is_lc_serializable(cls) -> bool:
         return True
 
-    @root_validator()
+    @pre_init
     def validate_parsers(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Validate the parsers."""
         parsers = values["parsers"]

libs/langchain/langchain/output_parsers/enum.py

@@ -3,7 +3,7 @@ from typing import Dict, List, Type
 
 from langchain_core.exceptions import OutputParserException
 from langchain_core.output_parsers import BaseOutputParser
-from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils import pre_init
 
 
 class EnumOutputParser(BaseOutputParser[Enum]):
@@ -12,7 +12,7 @@ class EnumOutputParser(BaseOutputParser[Enum]):
     enum: Type[Enum]
     """The enum to parse. Its values must be strings."""
 
-    @root_validator()
+    @pre_init
     def raise_deprecation(cls, values: Dict) -> Dict:
         enum = values["enum"]
         if not all(isinstance(e.value, str) for e in enum):

libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py

@@ -4,7 +4,8 @@ import numpy as np
 from langchain_core.callbacks.manager import Callbacks
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import Field, root_validator
+from langchain_core.pydantic_v1 import Field
+from langchain_core.utils import pre_init
 
 from langchain.retrievers.document_compressors.base import (
     BaseDocumentCompressor,
@@ -45,7 +46,7 @@ class EmbeddingsFilter(BaseDocumentCompressor):
 
         arbitrary_types_allowed = True
 
-    @root_validator()
+    @pre_init
     def validate_params(cls, values: Dict) -> Dict:
         """Validate similarity parameters."""
         if values["k"] is None and values["similarity_threshold"] is None: