test(backend): add E2E harness foundation (entrypoints, middleware, LLM/embedding fakes)

surfsense_backend/tests/e2e/fakes/llm.py

@@ -0,0 +1,48 @@
+"""Deterministic LLM fake for the E2E indexing pipeline.
+
+The production indexing pipeline summarizes documents with:
+
+    summary_chain = SUMMARY_PROMPT_TEMPLATE | llm
+    summary_result = await summary_chain.ainvoke({"document": ...})
+    summary_content = summary_result.content
+
+The `llm` parameter is supplied per-document by
+`app.services.llm_service.get_user_long_context_llm`. We patch THAT
+function to return a langchain-native FakeListChatModel so the rest of
+the chain works unchanged. No real LLM provider package is touched.
+
+Run-backend / run-celery use unittest.mock.patch.start() to install
+this at every binding site (the source module + every consumer that
+did `from app.services.llm_service import get_user_long_context_llm`
+at module load time).
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from langchain_core.language_models.fake_chat_models import FakeListChatModel
+
+logger = logging.getLogger(__name__)
+
+
+def _make_fake_llm() -> FakeListChatModel:
+    """Build a fresh FakeListChatModel that returns a deterministic summary."""
+    # FakeListChatModel cycles through `responses` for each invocation. We
+    # supply a single deterministic string. The summary content is tagged
+    # with a marker that specs CAN assert on if they want, but the
+    # primary indexing assertion is on the file content (chunked + stored
+    # separately by the pipeline).
+    fake = FakeListChatModel(
+        responses=[
+            "E2E_FAKE_SUMMARY: Indexed by Playwright E2E run with deterministic LLM stub."
+        ]
+    )
+    return fake
+
+
+async def fake_get_user_long_context_llm(*args: Any, **kwargs: Any) -> Any:
+    """Drop-in replacement for app.services.llm_service.get_user_long_context_llm."""
+    logger.info("[fake-llm] returning FakeListChatModel for E2E indexing")
+    return _make_fake_llm()