Refactor to remove skip_system from LLMModel.run_prompt

paperqa/agents/helpers.py

@@ -61,7 +61,7 @@ async def litellm_get_search_query(
     result = await model.run_prompt(
         prompt=search_prompt,
         data={"question": question, "count": count},
-        skip_system=True,
+        system_prompt=None,
     )
     search_query = result.text
     queries = [s for s in search_query.split("\n") if len(s) > 3]  # noqa: PLR2004

paperqa/docs.py

@@ -276,7 +276,7 @@ async def aadd(  # noqa: PLR0912
             result = await llm_model.run_prompt(
                 prompt=parse_config.citation_prompt,
                 data={"text": texts[0].text},
-                skip_system=True,  # skip system because it's too hesitant to answer
+                system_prompt=None,  # skip system because it's too hesitant to answer
             )
             citation = result.text
             if (
@@ -313,7 +313,7 @@ async def aadd(  # noqa: PLR0912
             result = await llm_model.run_prompt(
                 prompt=parse_config.structured_citation_prompt,
                 data={"citation": citation},
-                skip_system=True,
+                system_prompt=None,
             )
             # This code below tries to isolate the JSON
             # based on observed messages from LLMs

paperqa/llms.py

@@ -328,18 +328,15 @@ async def run_prompt(
         data: dict,
         callbacks: list[Callable] | None = None,
         name: str | None = None,
-        skip_system: bool = False,
-        system_prompt: str = default_system_prompt,
+        system_prompt: str | None = default_system_prompt,
     ) -> LLMResult:
         if self.llm_type is None:
             self.llm_type = self.infer_llm_type()
         if self.llm_type == "chat":
-            return await self._run_chat(
-                prompt, data, callbacks, name, skip_system, system_prompt
-            )
+            return await self._run_chat(prompt, data, callbacks, name, system_prompt)
         if self.llm_type == "completion":
             return await self._run_completion(
-                prompt, data, callbacks, name, skip_system, system_prompt
+                prompt, data, callbacks, name, system_prompt
             )
         raise ValueError(f"Unknown llm_type {self.llm_type!r}.")
 
@@ -349,8 +346,7 @@ async def _run_chat(
         data: dict,
         callbacks: list[Callable] | None = None,
         name: str | None = None,
-        skip_system: bool = False,
-        system_prompt: str = default_system_prompt,
+        system_prompt: str | None = default_system_prompt,
     ) -> LLMResult:
         """Run a chat prompt.
 
@@ -359,20 +355,18 @@ async def _run_chat(
             data: Keys for the input variables that will be formatted into prompt.
             callbacks: Optional functions to call with each chunk of the completion.
             name: Optional name for the result.
-            skip_system: Set True to skip the system prompt.
-            system_prompt: System prompt to use.
+            system_prompt: System prompt to use, or None/empty string to not use one.
 
         Returns:
             Result of the chat.
         """
-        system_message_prompt = {"role": "system", "content": system_prompt}
         human_message_prompt = {"role": "user", "content": prompt}
         messages = [
             {"role": m["role"], "content": m["content"].format(**data)}
             for m in (
-                [human_message_prompt]
-                if skip_system
-                else [system_message_prompt, human_message_prompt]
+                [{"role": "system", "content": system_prompt}, human_message_prompt]
+                if system_prompt
+                else [human_message_prompt]
             )
         ]
         result = LLMResult(
@@ -425,8 +419,7 @@ async def _run_completion(
         data: dict,
         callbacks: Iterable[Callable] | None = None,
         name: str | None = None,
-        skip_system: bool = False,
-        system_prompt: str = default_system_prompt,
+        system_prompt: str | None = default_system_prompt,
     ) -> LLMResult:
         """Run a completion prompt.
 
@@ -435,14 +428,13 @@ async def _run_completion(
             data: Keys for the input variables that will be formatted into prompt.
             callbacks: Optional functions to call with each chunk of the completion.
             name: Optional name for the result.
-            skip_system: Set True to skip the system prompt.
-            system_prompt: System prompt to use.
+            system_prompt: System prompt to use, or None/empty string to not use one.
 
         Returns:
             Result of the completion.
         """
         formatted_prompt: str = (
-            prompt if skip_system else system_prompt + "\n\n" + prompt
+            system_prompt + "\n\n" + prompt if system_prompt else prompt
         ).format(**data)
         result = LLMResult(
             model=self.name,

tests/test_llms.py

@@ -59,7 +59,7 @@ def accum(x) -> None:
         completion = await llm.run_prompt(
             prompt="The {animal} says",
             data={"animal": "duck"},
-            skip_system=True,
+            system_prompt=None,
             callbacks=[accum],
         )
         assert completion.model == "gpt-4o-mini"
@@ -72,7 +72,7 @@ def accum(x) -> None:
         completion = await llm.run_prompt(
             prompt="The {animal} says",
             data={"animal": "duck"},
-            skip_system=True,
+            system_prompt=None,
         )
         assert completion.seconds_to_first_token == 0
         assert completion.seconds_to_last_token > 0
@@ -85,7 +85,7 @@ async def ac(x) -> None:
         completion = await llm.run_prompt(
             prompt="The {animal} says",
             data={"animal": "duck"},
-            skip_system=True,
+            system_prompt=None,
             callbacks=[accum, ac],
         )
         assert completion.cost > 0

tests/test_paperqa.py

@@ -423,7 +423,7 @@ def accum(x) -> None:
     completion = await llm.run_prompt(
         prompt="The {animal} says",
         data={"animal": "duck"},
-        skip_system=True,
+        system_prompt=None,
         callbacks=[accum],
     )
     assert completion.seconds_to_first_token > 0
@@ -432,7 +432,7 @@ def accum(x) -> None:
     assert str(completion) == "".join(outputs)
 
     completion = await llm.run_prompt(
-        prompt="The {animal} says", data={"animal": "duck"}, skip_system=True
+        prompt="The {animal} says", data={"animal": "duck"}, system_prompt=None
     )
     assert completion.seconds_to_first_token == 0
     assert completion.seconds_to_last_token > 0
@@ -453,7 +453,7 @@ def accum(x) -> None:
     completion = await llm.run_prompt(
         prompt="The {animal} says",
         data={"animal": "duck"},
-        skip_system=True,
+        system_prompt=None,
         callbacks=[accum],
     )
     assert completion.seconds_to_first_token > 0
@@ -464,7 +464,7 @@ def accum(x) -> None:
     assert completion.cost > 0
 
     completion = await llm.run_prompt(
-        prompt="The {animal} says", data={"animal": "duck"}, skip_system=True
+        prompt="The {animal} says", data={"animal": "duck"}, system_prompt=None
     )
     assert completion.seconds_to_first_token == 0
     assert completion.seconds_to_last_token > 0

tests/test_rate_limiter.py

@@ -165,7 +165,7 @@ def accum(x) -> None:
         3,
         prompt="The {animal} says",
         data={"animal": "duck"},
-        skip_system=True,
+        system_prompt=None,
         callbacks=[accum],
     )
 
@@ -192,7 +192,7 @@ def accum2(x) -> None:
         use_gather=True,
         prompt="The {animal} says",
         data={"animal": "duck"},
-        skip_system=True,
+        system_prompt=None,
         callbacks=[accum2],
     )