fix issue with passing msg history on reasking (#346)

fix issue when msg_history passed as kwargs instead of prompt params

Author: ShreyaR

guardrails/guard.py

@@ -466,9 +466,9 @@ def _sync_parse(
         """
         with start_action(action_type="guard_parse"):
             runner = Runner(
-                instructions=kwargs.get("instructions", None),
-                prompt=kwargs.get("prompt", None),
-                msg_history=kwargs.get("msg_history", None),
+                instructions=kwargs.pop("instructions", None),
+                prompt=kwargs.pop("prompt", None),
+                msg_history=kwargs.pop("msg_history", None),
                 api=get_llm_ask(llm_api, *args, **kwargs) if llm_api else None,
                 input_schema=None,
                 output_schema=self.output_schema,
@@ -507,9 +507,9 @@ async def _async_parse(
         """
         with start_action(action_type="guard_parse"):
             runner = AsyncRunner(
-                instructions=None,
-                prompt=None,
-                msg_history=None,
+                instructions=kwargs.pop("instructions", None),
+                prompt=kwargs.pop("prompt", None),
+                msg_history=kwargs.pop("msg_history", None),
                 api=get_async_llm_ask(llm_api, *args, **kwargs) if llm_api else None,
                 input_schema=None,
                 output_schema=self.output_schema,

tests/integration_tests/mock_llm_outputs.py

@@ -88,6 +88,10 @@ def _invoke_llm(
                 pydantic.MSG_COMPILED_PROMPT_REASK,
                 pydantic.MSG_COMPILED_INSTRUCTIONS_REASK,
             ): pydantic.MSG_HISTORY_LLM_OUTPUT_CORRECT,
+            (
+                string.PARSE_COMPILED_PROMPT_REASK,
+                string.MSG_COMPILED_INSTRUCTIONS_REASK,
+            ): string.MSG_LLM_OUTPUT_CORRECT,
         }
 
         try:

tests/integration_tests/test_assets/string/__init__.py

@@ -30,6 +30,7 @@
 ]
 MSG_COMPILED_PROMPT_REASK = reader("msg_compiled_prompt_reask.txt")
 MSG_COMPILED_INSTRUCTIONS_REASK = reader("msg_compiled_instructions_reask.txt")
+PARSE_COMPILED_PROMPT_REASK = reader("parse_compiled_prompt_reask.txt")
 
 __all__ = [
     "COMPILED_PROMPT",

tests/integration_tests/test_assets/string/parse_compiled_prompt_reask.txt

@@ -0,0 +1,15 @@
+This was a previous response you generated:
+
+======
+Tomato Cheese Pizza
+======
+
+Generate a new response that corrects your old response such that the following issues are fixed
+- Value Tomato Cheese Pizza should fail.
+
+Here's a description of what I want you to generate: Some description
+
+Your generated response should satisfy the following properties:
+- always_fail
+
+Don't talk; just go.

tests/integration_tests/test_parsing.py

@@ -1,8 +1,17 @@
+from typing import Dict
+
+import openai
 import pytest
 
 import guardrails as gd
-
-from .mock_llm_outputs import MockArbitraryCallable, MockAsyncArbitraryCallable
+from guardrails import register_validator
+from guardrails.validators import FailResult, ValidationResult
+
+from .mock_llm_outputs import (
+    MockArbitraryCallable,
+    MockAsyncArbitraryCallable,
+    MockOpenAIChatCallable,
+)
 from .test_assets import pydantic
 
 
@@ -80,3 +89,34 @@ async def mock_async_callable(prompt: str):
     assert guard_history[1].prompt == gd.Prompt(pydantic.PARSING_COMPILED_REASK)
     assert guard_history[1].output == pydantic.PARSING_EXPECTED_LLM_OUTPUT
     assert guard_history[1].validated_output == pydantic.PARSING_EXPECTED_OUTPUT
+
+
+def test_reask_prompt_instructions(mocker):
+    """Test that the re-ask prompt and instructions are correct.
+
+    This is done implicitly, since if the incorrect prompt or instructions
+    are used, the mock LLM will raise a KeyError.
+    """
+
+    mocker.patch(
+        "guardrails.llm_providers.OpenAIChatCallable",
+        new=MockOpenAIChatCallable,
+    )
+
+    @register_validator(name="always_fail", data_type="string")
+    def always_fail(value: str, metadata: Dict) -> ValidationResult:
+        return FailResult(error_message=f"Value {value} should fail.")
+
+    guard = gd.Guard.from_string(
+        validators=[(always_fail, "reask")],
+        description="Some description",
+    )
+
+    guard.parse(
+        llm_output="Tomato Cheese Pizza",
+        llm_api=openai.ChatCompletion.create,
+        msg_history=[
+            {"role": "system", "content": "Some content"},
+            {"role": "user", "content": "Some prompt"},
+        ],
+    )