fix(types): revert azure search, milvus and onyx response validation to main

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

litellm/llms/azure_ai/vector_stores/transformation.py

@@ -4,8 +4,6 @@ from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
-from pydantic import ConfigDict, TypeAdapter, with_config
-from typing_extensions import NotRequired, ReadOnly, TypedDict
 
 from litellm.llms.azure.common_utils import BaseAzureLLM
 from litellm.llms.base_llm.vector_store.transformation import (
@@ -32,26 +30,6 @@ else:
     LiteLLMLoggingObj = Any
 
 
-_AzureSearchHit = with_config(ConfigDict(extra="allow", strict=True))(
-    TypedDict(
-        "_AzureSearchHit",
-        {
-            "id": NotRequired[ReadOnly[str | None]],
-            "content": NotRequired[ReadOnly[str | None]],
-            "@search.score": NotRequired[ReadOnly[int | float | None]],
-        },
-    )
-)
-
-
-@with_config(ConfigDict(extra="allow", strict=True))
-class _AzureSearchResponse(TypedDict):
-    value: NotRequired[ReadOnly[Sequence[_AzureSearchHit]]]
-
-
-_AZURE_SEARCH_RESPONSE: Final = TypeAdapter(_AzureSearchResponse)
-
-
 class AzureAIVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, BaseAzureLLM):
     """
     Configuration for Azure AI Search Vector Store
@@ -220,7 +198,7 @@ class AzureAIVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, BaseAzureLLM
         }
         """
         try:
-            response_json: Final = _AZURE_SEARCH_RESPONSE.validate_python(response.json())
+            response_json: Final = response.json()
 
             # Extract results from Azure AI Search API response
             results: Final = response_json.get("value", [])

litellm/llms/milvus/vector_stores/transformation.py

@@ -4,8 +4,6 @@ from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
-from pydantic import ConfigDict, TypeAdapter, with_config
-from typing_extensions import NotRequired, ReadOnly, TypedDict
 
 from litellm.llms.base_llm.vector_store.transformation import (
     BaseQueryEmbeddingVectorStoreConfig,
@@ -31,20 +29,6 @@ if TYPE_CHECKING:
 else:
     LiteLLMLoggingObj = Any
 
-
-@with_config(ConfigDict(extra="allow", strict=True))
-class _MilvusSearchResponse(TypedDict):
-    data: ReadOnly[NotRequired[Sequence[Mapping[str, object]]]]
-
-
-_MILVUS_SEARCH_RESPONSE: Final = TypeAdapter(_MilvusSearchResponse)
-_MILVUS_SEARCH_TEXT: Final[TypeAdapter[str | None]] = TypeAdapter(
-    str | None, config=ConfigDict(strict=True, hide_input_in_errors=True)
-)
-_MILVUS_SEARCH_SCORE: Final[TypeAdapter[int | float | None]] = TypeAdapter(
-    int | float | None, config=ConfigDict(strict=True, hide_input_in_errors=True)
-)
-
 MILVUS_OPTIONAL_PARAMS: Final = {
     "annsField",
     "limit",
@@ -227,7 +211,7 @@ class MilvusVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig):
         }
         """
         try:
-            response_json: Final = _MILVUS_SEARCH_RESPONSE.validate_python(response.json())
+            response_json: Final = response.json()
 
             # Extract results from Azure AI Search API response
             results: Final = response_json.get("data", [])
@@ -247,7 +231,7 @@ class MilvusVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig):
             search_results: Final[list[VectorStoreSearchResult]] = []
             for result in results:
                 # Extract text content
-                text_content = _MILVUS_SEARCH_TEXT.validate_python(result.get(text_field, ""))
+                text_content = result.get(text_field, "")
 
                 content = [
                     VectorStoreResultContent(
@@ -257,13 +241,14 @@ class MilvusVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig):
                 ]
 
                 # Get the search score (distance from the query vector)
-                score = _MILVUS_SEARCH_SCORE.validate_python(result.get("distance", 0.0))
+                score = result.get("distance", 0.0)
 
                 # Build attributes with all available metadata
                 # Exclude system fields and already-processed fields
-                attributes = {
-                    key: value for key, value in result.items() if key not in ["id", "content", "distance", text_field]
-                }
+                attributes = {}
+                for key, value in result.items():
+                    if key not in ["id", "content", "distance", text_field]:
+                        attributes[key] = value
 
                 result_obj = VectorStoreSearchResult(
                     score=score,

litellm/proxy/guardrails/guardrail_hooks/onyx/onyx.py

@@ -10,8 +10,6 @@ from typing import TYPE_CHECKING, Final, Literal, Optional
 
 import httpx
 from fastapi import HTTPException
-from pydantic import ConfigDict, TypeAdapter, with_config
-from typing_extensions import NotRequired, ReadOnly, TypedDict
 
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_guardrail import (
@@ -30,21 +28,6 @@ if TYPE_CHECKING:
     from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
 
 
-@with_config(ConfigDict(extra="allow", strict=True))
-class _GuardResult(TypedDict, total=False):
-    allowed: ReadOnly[object]
-    violated_rules: ReadOnly[NotRequired[list[str]]]
-
-
-@with_config(ConfigDict(extra="allow", strict=True))
-class _ModelResponseJson(TypedDict, total=False):
-    response: ReadOnly[object]
-
-
-_GUARD_RESULT: Final = TypeAdapter(_GuardResult)
-_MODEL_RESPONSE_JSON: Final = TypeAdapter(_ModelResponseJson)
-
-
 class OnyxGuardrail(CustomGuardrail):
     @classmethod
     def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]:
@@ -83,7 +66,7 @@ class OnyxGuardrail(CustomGuardrail):
         payload: object,
         input_type: Literal["request", "response"],
         conversation_id: str,
-    ) -> _GuardResult:
+    ) -> dict:
         """
         Call external Onyx Guard server for validation
         """
@@ -99,7 +82,7 @@ class OnyxGuardrail(CustomGuardrail):
             },
         )
         response.raise_for_status()
-        result: Final[_GuardResult] = _GUARD_RESULT.validate_python(response.json())
+        result: Final = response.json()
         if not result.get("allowed", True):
             detection_message = "Unknown violation"
             if "violated_rules" in result:
@@ -131,7 +114,7 @@ class OnyxGuardrail(CustomGuardrail):
         else:
             try:
                 response: Final = ModelResponse(**request_data)
-                parsed: Final[_ModelResponseJson] = _MODEL_RESPONSE_JSON.validate_python(response.json())
+                parsed: Final = response.json()
                 payload = parsed.get("response", {})
             except Exception as e:
                 verbose_proxy_logger.error(