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multiple: pydantic 2 compatibility, v0.3 (#26443)

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Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com>
Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com>
Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com>
Co-authored-by: ZhangShenao <15201440436@163.com>
Co-authored-by: Friso H. Kingma <fhkingma@gmail.com>
Co-authored-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Co-authored-by: Morgante Pell <morgantep@google.com>
This commit is contained in:
Erick Friis
2024-09-13 14:38:45 -07:00
committed by GitHub
parent d9813bdbbc
commit c2a3021bb0
1402 changed files with 38318 additions and 30410 deletions
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18
libs/experimental/langchain_experimental/rl_chain/base.py
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@@ -25,8 +25,8 @@ from langchain_core.prompts import (
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from pydantic import BaseModel, ConfigDict, model_validator
from langchain_experimental.pydantic_v1 import BaseModel, root_validator
from langchain_experimental.rl_chain.helpers import _Embed
from langchain_experimental.rl_chain.metrics import (
MetricsTrackerAverage,
@@ -279,8 +279,9 @@ class AutoSelectionScorer(SelectionScorer[Event], BaseModel):
)
return chat_prompt
@root_validator(pre=True)
def set_prompt_and_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]:
@model_validator(mode="before")
@classmethod
def set_prompt_and_llm_chain(cls, values: Dict[str, Any]) -> Any:
llm = values.get("llm")
prompt = values.get("prompt")
scoring_criteria_template_str = values.get("scoring_criteria_template_str")
@@ -358,8 +359,8 @@ class RLChain(Chain, Generic[TEvent]):
active_policy: Policy = _NoOpPolicy()
auto_embed: bool = False
selection_scorer_activated: bool = True
selected_input_key = "rl_chain_selected"
selected_based_on_input_key = "rl_chain_selected_based_on"
selected_input_key: str = "rl_chain_selected"
selected_based_on_input_key: str = "rl_chain_selected_based_on"
metrics: Optional[Union[MetricsTrackerRollingWindow, MetricsTrackerAverage]] = None
def __init__(
@@ -400,9 +401,10 @@ class RLChain(Chain, Generic[TEvent]):
else:
self.metrics = MetricsTrackerAverage(step=metrics_step)
class Config:
arbitrary_types_allowed = True
extra = "forbid"
model_config = ConfigDict(
arbitrary_types_allowed=True,
extra="forbid",
)
@property
def input_keys(self) -> List[str]: