From 894637108c20680ef3268aecf74c6f30818f6f68 Mon Sep 17 00:00:00 2001
From: ducoterra <git@ducoterra.net>
Date: Mon, 7 Sep 2026 22:01:53 -0400
Subject: [PATCH] add configurable llm timeout

---
 .env.example   | 1 +
 app/config.py  | 5 +++++
 app/rag/llm.py | 2 +-
 3 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/.env.example b/.env.example
index 318c794..a495d94 100644
--- a/.env.example
+++ b/.env.example
@@ -19,6 +19,7 @@ BOR_LLM_BASE_URL=https://aipi.reeseapps.com/v1
 BOR_LLM_API_KEY=                  # falls back to $AIPI_KEY, then "not-needed"
 BOR_LLM_CHAT_MODEL=turbo
 # BOR_LLM_RETRIES=3             # retry a dead LLM request before the first token lands (phase 67); 0 = off
+# BOR_LLM_TIMEOUT=300           # HTTP timeout for LLM API calls, seconds (default 120)
 # BOR_LLM_RETRY_DELAY=5        # seconds between LLM retries (phase 67)
 BOR_LLM_EMBED_MODEL=embed
 BOR_LLM_SUMMARY_MODEL=lite          # one-shot completions: document summaries (phase 30), KB overview (phase 31)
diff --git a/app/config.py b/app/config.py
index ce933c4..b6f4f92 100644
--- a/app/config.py
+++ b/app/config.py
@@ -80,6 +80,11 @@ class Settings(BaseSettings):
     #: Flat seconds to wait between attempts (phase 67,
     #: ``BOR_LLM_RETRY_DELAY``); the TODO-locked 5 s, no backoff.
     llm_retry_delay: float = 5.0
+    #: HTTP timeout in seconds for LLM API calls (chat + embeddings).
+    #: Increase when long prompt processing or slow models exceed the
+    #: default 120 s (``BOR_LLM_TIMEOUT``; ``0`` = use the OpenAI SDK
+    #: default, which is platform-dependent).
+    llm_timeout: float = 120.0
     # --- Chat history (phase 74, TODO L4: prior turns + prior thinking) ---
     #: Newest client-provided history turns kept per ``POST /api/chat``
     #: (phase 74, ``BOR_HISTORY_MAX_TURNS``): the request's ``history``
diff --git a/app/rag/llm.py b/app/rag/llm.py
index 91ab3f0..b8edcf1 100644
--- a/app/rag/llm.py
+++ b/app/rag/llm.py
@@ -186,7 +186,7 @@ class LLMClient:
         self._client = AsyncOpenAI(
             base_url=self.settings.llm_base_url,
             api_key=self.settings.effective_api_key,
-            timeout=120.0,
+            timeout=self.settings.llm_timeout,
         )
 
     async def _post_embeddings(self, texts: list[str]) -> list[list[float]]: