Remove config input models

.semversioner/next-release/patch-20250102170720512799.json

@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Remove config input models."
+}

graphrag/config/create_graphrag_config.py

@@ -6,11 +6,10 @@
 import os
 from enum import Enum
 from pathlib import Path
-from typing import cast
+from typing import Any, cast
 
 from datashaper import AsyncType
 from environs import Env
-from pydantic import TypeAdapter
 
 import graphrag.config.defaults as defs
 from graphrag.config.enums import (
@@ -28,8 +27,6 @@
     AzureApiBaseMissingError,
     AzureDeploymentNameMissingError,
 )
-from graphrag.config.input_models.graphrag_config_input import GraphRagConfigInput
-from graphrag.config.input_models.llm_config_input import LLMConfigInput
 from graphrag.config.models.basic_search_config import BasicSearchConfig
 from graphrag.config.models.cache_config import CacheConfig
 from graphrag.config.models.chunking_config import ChunkingConfig, ChunkStrategyType
@@ -55,27 +52,24 @@
 from graphrag.config.models.umap_config import UmapConfig
 from graphrag.config.read_dotenv import read_dotenv
 
-InputModelValidator = TypeAdapter(GraphRagConfigInput)
-
 
 def create_graphrag_config(
-    values: GraphRagConfigInput | None = None, root_dir: str | None = None
+    values: dict[str, Any] | None = None, root_dir: str | None = None
 ) -> GraphRagConfig:
     """Load Configuration Parameters from a dictionary."""
     values = values or {}
     root_dir = root_dir or str(Path.cwd())
     env = _make_env(root_dir)
     _token_replace(cast("dict", values))
-    InputModelValidator.validate_python(values, strict=True)
 
     reader = EnvironmentReader(env)
 
-    def hydrate_async_type(input: LLMConfigInput, base: AsyncType) -> AsyncType:
+    def hydrate_async_type(input: dict[str, Any], base: AsyncType) -> AsyncType:
         value = input.get(Fragment.async_mode)
         return AsyncType(value) if value else base
 
     def hydrate_llm_params(
-        config: LLMConfigInput, base: LLMParameters
+        config: dict[str, Any], base: LLMParameters
     ) -> LLMParameters:
         with reader.use(config.get("llm")):
             llm_type = reader.str(Fragment.type)
@@ -132,7 +126,7 @@ def hydrate_llm_params(
             )
 
     def hydrate_embeddings_params(
-        config: LLMConfigInput, base: LLMParameters
+        config: dict[str, Any], base: LLMParameters
     ) -> LLMParameters:
         with reader.use(config.get("llm")):
             api_type = reader.str(Fragment.type) or defs.EMBEDDING_TYPE
@@ -198,7 +192,7 @@ def hydrate_embeddings_params(
             )
 
     def hydrate_parallelization_params(
-        config: LLMConfigInput, base: ParallelizationParameters
+        config: dict[str, Any], base: ParallelizationParameters
     ) -> ParallelizationParameters:
         with reader.use(config.get("parallelization")):
             return ParallelizationParameters(