fix: pass custom_instructions to session summaries

src/utils/summarizer.py

@@ -14,6 +14,7 @@
 from src.config import ConfiguredModelSettings, settings
 from src.crud.session import session_cache_key
 from src.dependencies import tracked_db
+from src.deriver.prompts import _custom_instructions_section
 from src.exceptions import ResourceNotFoundException
 from src.llm import HonchoLLMCallResponse, honcho_llm_call
 from src.llm.types import LLMTelemetryContext
@@ -57,6 +58,7 @@ class Summary(TypedDict):
 
 
 def to_schema_summary(s: Summary) -> schemas.Summary:
+    """Convert a Summary TypedDict to a Pydantic Summary schema object."""
     return schemas.Summary(
         content=s["content"],
         message_id=s["message_id"],
@@ -81,6 +83,7 @@ def to_schema_summary(s: Summary) -> schemas.Summary:
 
 
 def _get_summary_model_config() -> ConfiguredModelSettings:
+    """Return the configured model settings for summary generation."""
     return settings.SUMMARY.MODEL_CONFIG
 
 
@@ -101,8 +104,10 @@ def short_summary_prompt(
     formatted_messages: str,
     output_words: int,
     previous_summary_text: str,
+    custom_instructions: str | None = None,
 ) -> str:
     """Generate the short summary prompt."""
+    custom_instructions_section = _custom_instructions_section(custom_instructions)
     return c(f"""
 You are a system that summarizes parts of a conversation to create a concise and accurate summary. Focus on capturing:
 
@@ -117,6 +122,7 @@ def short_summary_prompt(
 
 Return only the summary without any explanation or meta-commentary.
 
+{custom_instructions_section}
 <previous_summary>
 {previous_summary_text}
 </previous_summary>
@@ -133,8 +139,10 @@ def long_summary_prompt(
     formatted_messages: str,
     output_words: int,
     previous_summary_text: str,
+    custom_instructions: str | None = None,
 ) -> str:
     """Generate the long summary prompt."""
+    custom_instructions_section = _custom_instructions_section(custom_instructions)
     return c(f"""
 You are a system that creates thorough, comprehensive summaries of conversations. Focus on capturing:
 
@@ -151,6 +159,7 @@ def long_summary_prompt(
 
 Return only the summary without any explanation or meta-commentary.
 
+{custom_instructions_section}
 <previous_summary>
 {previous_summary_text}
 </previous_summary>
@@ -172,6 +181,7 @@ def estimate_short_summary_prompt_tokens() -> int:
                 formatted_messages="",
                 output_words=0,
                 previous_summary_text="",
+                custom_instructions=None,
             )
         )
     except Exception:
@@ -188,6 +198,7 @@ def estimate_long_summary_prompt_tokens() -> int:
                 formatted_messages="",
                 output_words=0,
                 previous_summary_text="",
+                custom_instructions=None,
             )
         )
     except Exception:
@@ -200,9 +211,23 @@ async def create_short_summary(
     formatted_messages: str,
     input_tokens: int,
     previous_summary: str | None = None,
+    custom_instructions: str | None = None,
     *,
     workspace_name: str | None = None,
 ) -> HonchoLLMCallResponse[str]:
+    """
+    Generate a short summary via an LLM call.
+
+    Args:
+        formatted_messages: Pre-formatted conversation messages.
+        input_tokens: Token count of the input (messages + previous summary).
+        previous_summary: Previous summary text for continuity, if any.
+        custom_instructions: Optional custom instructions from configuration.
+        workspace_name: Workspace name for telemetry attribution.
+
+    Returns:
+        The LLM response containing the short summary text and token counts.
+    """
     # input_tokens indicates how many tokens the message list + previous summary take up
     # we want to optimize short summaries to be smaller than the actual content being summarized
     # so we ask the agent to produce a word count roughly equal to either the input, or the max
@@ -216,7 +241,10 @@ async def create_short_summary(
         previous_summary_text = "There is no previous summary -- the messages are the beginning of the conversation."
 
     prompt = short_summary_prompt(
-        formatted_messages, output_words, previous_summary_text
+        formatted_messages,
+        output_words,
+        previous_summary_text,
+        custom_instructions=custom_instructions,
     )
 
     return await honcho_llm_call(
@@ -235,9 +263,22 @@ async def create_short_summary(
 async def create_long_summary(
     formatted_messages: str,
     previous_summary: str | None = None,
+    custom_instructions: str | None = None,
     *,
     workspace_name: str | None = None,
 ) -> HonchoLLMCallResponse[str]:
+    """
+    Generate a comprehensive long summary via an LLM call.
+
+    Args:
+        formatted_messages: Pre-formatted conversation messages.
+        previous_summary: Previous summary text for continuity, if any.
+        custom_instructions: Optional custom instructions from configuration.
+        workspace_name: Workspace name for telemetry attribution.
+
+    Returns:
+        The LLM response containing the long summary text and token counts.
+    """
     # the word/token ratio is roughly 4:3 so we multiply by 0.75.
     # LLMs *seem* to respond better to getting asked for a word count but should workshop this.
     output_words = int(settings.SUMMARY.MAX_TOKENS_LONG * 0.75)
@@ -248,7 +289,10 @@ async def create_long_summary(
         previous_summary_text = "There is no previous summary -- the messages are the beginning of the conversation."
 
     prompt = long_summary_prompt(
-        formatted_messages, output_words, previous_summary_text
+        formatted_messages,
+        output_words,
+        previous_summary_text,
+        custom_instructions=custom_instructions,
     )
 
     return await honcho_llm_call(
@@ -439,6 +483,12 @@ async def _create_and_save_summary(
         previous_summary_tokens = latest_summary["token_count"] if latest_summary else 0
         input_tokens = messages_tokens + previous_summary_tokens
 
+    # Extract custom_instructions from configuration for the summarizer prompt.
+    # This mirrors the deriver pattern in src/deriver/prompts.py.
+    custom_instructions: str | None = None
+    if configuration.reasoning and configuration.reasoning.custom_instructions:
+        custom_instructions = configuration.reasoning.custom_instructions
+
     (
         new_summary,
         is_fallback,
@@ -453,6 +503,7 @@ async def _create_and_save_summary(
         last_message_id=last_message_id,
         last_message_content_preview=last_message_content_preview,
         message_count=message_count,
+        custom_instructions=custom_instructions,
         workspace_name=workspace_name,
     )
 
@@ -552,6 +603,7 @@ async def _create_summary(
     last_message_id: int,
     last_message_content_preview: str,
     message_count: int,
+    custom_instructions: str | None = None,
     *,
     workspace_name: str | None = None,
 ) -> tuple[Summary, bool, int, int]:
@@ -585,12 +637,14 @@ async def _create_summary(
                 formatted_messages,
                 input_tokens,
                 previous_summary_text,
+                custom_instructions=custom_instructions,
                 workspace_name=workspace_name,
             )
         else:
             response = await create_long_summary(
                 formatted_messages,
                 previous_summary_text,
+                custom_instructions=custom_instructions,
                 workspace_name=workspace_name,
             )