feat(rag): lite-model document summaries — non-markdown docs summarized at import, summary chunk retrieves and resolves to the full source doc

app/rag/llm.py

@@ -1,10 +1,12 @@
 """Async OpenAI-compatible client for the self-hosted aipi endpoint (PLAN A5).
 
-Provides the embeddings surface (importer, retrieval) and chat streaming
-(PLAN A15) for the RAG pipeline. Chat streaming yields typed
-:class:`StreamPiece` values (phase 17): aipi's ``turbo`` model streams
-its reasoning as ``delta.reasoning_content`` chunks (deepseek/litellm
-wire convention, verified live 2026-08-23) **before** the answer's
+Provides the embeddings surface (importer, retrieval), one-shot chat
+completions (phase 30: the ``lite`` model summarizes non-markdown
+documents at import time), and chat streaming (PLAN A15) for the RAG
+pipeline. Chat streaming yields typed :class:`StreamPiece` values
+(phase 17): aipi's ``turbo`` model streams its reasoning as
+``delta.reasoning_content`` chunks (deepseek/litellm wire convention,
+verified live 2026-08-23) **before** the answer's
 ``delta.content`` chunks, and reasoning counts against ``max_tokens``
 (an answer can in principle be empty).
 
@@ -187,6 +189,48 @@ class LLMClient:
         (vec,) = await self.embed([text])
         return vec
 
+    async def chat(
+        self, messages: list[dict[str, str]], model: str | None = None
+    ) -> str:
+        """One-shot (non-streaming) completion (A5 extended, phase 30).
+
+        Short, low-temperature request (``temperature=0.2``, 2048-token
+        cap — summaries and outlines are small, so a fixed budget is
+        enough) against ``BOR_LLM_SUMMARY_MODEL`` (default ``lite``)
+        unless *model* names another. Used by the document summarizer
+        (phase 30) and the KB overview generator (phase 31).
+
+        Any transport/HTTP/malformed failure, a choiceless reply, or an
+        empty/missing ``content`` field raises :class:`LLMError` — a
+        silent empty summary must never be stored.
+        """
+        try:
+            resp = await self._client.chat.completions.create(
+                model=model or self.settings.llm_summary_model,
+                messages=cast("list[ChatCompletionMessageParam]", messages),
+                temperature=0.2,
+                max_tokens=2048,
+                stream=False,
+            )
+        except LLMError:
+            raise
+        except Exception as e:  # noqa: BLE001 — wrap transport-level failures
+            raise LLMError(
+                f"chat completion from {self.settings.llm_base_url} failed: {e}"
+            ) from e
+        if not resp.choices:
+            raise LLMError(
+                f"chat completion from {self.settings.llm_base_url} "
+                "returned no choices"
+            )
+        content = resp.choices[0].message.content
+        if content is None or not content.strip():
+            raise LLMError(
+                f"chat completion from {self.settings.llm_base_url} returned "
+                "empty content — refusing to store a silent summary"
+            )
+        return content.strip()
+
     async def chat_stream(
         self, messages: list[dict[str, str]]
     ) -> AsyncIterator[StreamPiece]: