actually don't introduce new funciton for now

ddtrace/llmobs/_constants.py

@@ -54,3 +54,4 @@
 RAGAS_ML_APP_PREFIX = "dd-ragas"
 
 ANNOTATIONS_CONTEXT_ID = "annotations_context_id"
+INTERNAL_CONTEXT_VARIABLE_KEYS = "_dd_context_variable_keys"

ddtrace/llmobs/_llmobs.py

@@ -3,7 +3,6 @@
 import time
 from typing import Any
 from typing import Dict
-from typing import List
 from typing import Optional
 from typing import Union
 
@@ -269,38 +268,6 @@ def disable(cls) -> None:
 
         log.debug("%s disabled", cls.__name__)
 
-    @classmethod
-    def using_prompt(
-        cls,
-        prompt_id: Optional[str] = None,
-        template: Optional[str] = None,
-        variables: Optional[Dict] = None,
-        context_variable_keys: Optional[List[str]] = None,
-        tags: Optional[Dict[str, object]] = None,
-    ):
-        """
-        Sets LLM-specific attributes on LLMObs spans created while the returned context is active.
-        Annotations are applied in the order in which contexts are entered.
-
-        :param str name: span names of any LLM spans started within the returned context
-        :param dict prompt: A dictionary that represents the prompt used for an LLM call in the following form:
-                        `{"template": "...", "id": "...", "version": "...", "variables": {"variable_1": "...", ...}}`.
-                        Can also be set using the `ddtrace.llmobs.utils.Prompt` constructor class.
-        :param list context_variable_keys: list of variable names in the prompt that hold ground truth
-                                            context information
-        """
-        if context_variable_keys is None:
-            context_variable_keys = ["context"]
-        return cls.annotation_context(
-            prompt={
-                "id": prompt_id,
-                "template": template,
-                "variables": variables,
-                "_dd_context_variable_keys": context_variable_keys,
-            },
-            tags=tags,
-        )
-
     @classmethod
     def annotation_context(
         cls, tags: Optional[Dict[str, Any]] = None, prompt: Optional[dict] = None, name: Optional[str] = None

ddtrace/llmobs/_utils.py

@@ -10,6 +10,7 @@
 from ddtrace.ext import SpanTypes
 from ddtrace.internal.logger import get_logger
 from ddtrace.llmobs._constants import GEMINI_APM_SPAN_NAME
+from ddtrace.llmobs._constants import INTERNAL_CONTEXT_VARIABLE_KEYS
 from ddtrace.llmobs._constants import LANGCHAIN_APM_SPAN_NAME
 from ddtrace.llmobs._constants import ML_APP
 from ddtrace.llmobs._constants import OPENAI_APM_SPAN_NAME
@@ -29,7 +30,7 @@ def validate_prompt(prompt: dict) -> Dict[str, Union[str, dict, List[str]]]:
     template = prompt.get("template")
     version = prompt.get("version")
     prompt_id = prompt.get("id")
-    ctx_variable_keys = prompt.get("_dd_context_variable_keys")
+    ctx_variable_keys = prompt.get("context_variable_keys")
     if variables is not None:
         if not isinstance(variables, dict):
             raise TypeError("Prompt variables must be a dictionary.")
@@ -53,7 +54,7 @@ def validate_prompt(prompt: dict) -> Dict[str, Union[str, dict, List[str]]]:
             raise TypeError("Prompt `context_variable_keys` must be a list.")
         if not all(isinstance(k, str) for k in ctx_variable_keys):
             raise TypeError("Prompt `context_variable_keys` must be a list of strings.")
-        validated_prompt["_dd_context_variable_keys"] = ctx_variable_keys
+        validated_prompt[INTERNAL_CONTEXT_VARIABLE_KEYS] = ctx_variable_keys
     return validated_prompt
 
 

ddtrace/llmobs/utils.py

@@ -21,7 +21,7 @@
 Message = TypedDict("Message", {"content": str, "role": str}, total=False)
 Prompt = TypedDict(
     "Prompt",
-    {"variables": Dict[str, str], "template": str, "id": str, "version": str, "_dd_context_variable_keys": List[str]},
+    {"variables": Dict[str, str], "template": str, "id": str, "version": str, "context_variable_keys": List[str]},
     total=False,
 )