Type llm messages with ChatCompletionMessageParam instead of suppressing

aai_cli/core/llm.py

@@ -2,14 +2,14 @@
 
 import json
 from collections.abc import Sequence
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 from aai_cli.core import environments
 from aai_cli.core.errors import APIError, UsageError
 
 if TYPE_CHECKING:
     from openai import OpenAI
-    from openai.types.chat import ChatCompletion
+    from openai.types.chat import ChatCompletion, ChatCompletionMessageParam
 
 # The LLM Gateway is OpenAI-compatible, so we talk to it through the OpenAI SDK
 # pointed at the active environment's gateway base (see _client / code_gen).
@@ -76,7 +76,7 @@ def build_messages(
     system: str | None = None,
     transcript_id: str | None = None,
     transcript_text: str | None = None,
-) -> list[dict[str, str]]:
+) -> list[ChatCompletionMessageParam]:
     """Assemble the chat `messages` array for a transcript transform or plain prompt.
 
     With a `transcript_id`, the gateway injects the transcript server-side, so we
@@ -88,7 +88,7 @@ def build_messages(
         content = f"{prompt}\n\nTranscript:\n{transcript_text}"
     else:
         content = prompt
-    messages: list[dict[str, str]] = []
+    messages: list[ChatCompletionMessageParam] = []
     if system:
         messages.append({"role": "system", "content": system})
     messages.append({"role": "user", "content": content})
@@ -130,7 +130,7 @@ def complete(
     api_key: str,
     *,
     model: str,
-    messages: list[dict[str, str]],
+    messages: list[ChatCompletionMessageParam],
     max_tokens: int = DEFAULT_MAX_TOKENS,
     transcript_id: str | None = None,
     extra: dict[str, object] | None = None,
@@ -153,7 +153,7 @@ def complete(
     try:
         return client.chat.completions.create(
             model=model,
-            messages=messages,  # type: ignore[arg-type]
+            messages=messages,
             max_tokens=max_tokens,
             extra_body=extra_body or None,
         )
@@ -182,7 +182,7 @@ def content_of(response: ChatCompletion) -> str:
     return content or ""
 
 
-def usage_of(response: ChatCompletion) -> dict[str, Any] | None:
+def usage_of(response: ChatCompletion) -> dict[str, object] | None:
     """Return the token-usage block as a plain dict, if present."""
     usage = response.usage
     if usage is None:

tests/test_llm.py

@@ -164,7 +164,7 @@ def test_build_messages_transcript_id_uses_tag():
 
 def test_build_messages_inline_text():
     msgs = llm.build_messages("summarize", transcript_text="hello world")
-    assert msgs[0]["content"] == "summarize\n\nTranscript:\nhello world"
+    assert msgs == [{"role": "user", "content": "summarize\n\nTranscript:\nhello world"}]
 
 
 def test_build_messages_with_system_prompt():