From e57c3a7d0c0f4f1fbe29382a97635fb01e5db44a Mon Sep 17 00:00:00 2001 From: "DESKTOP-RTLN3BA\\$punk" Date: Fri, 1 May 2026 05:10:53 -0700 Subject: [PATCH] feat: prompt caching - Updated `litellm` dependency version from `1.83.4` to `1.83.7`. - Adjusted `aiohttp` version from `3.13.5` to `3.13.4` in the lock file. - Implemented `apply_litellm_prompt_caching` in `chat_deepagent.py` to improve prompt caching. - Added model name resolution logic in `chat_deepagent.py` to ensure correct provider-variant dispatch. - Enhanced `llm_config.py` to configure prompt caching for various LLM providers. - Updated tests to verify correct model name forwarding and prompt caching behavior. --- .../app/agents/new_chat/chat_deepagent.py | 60 ++- .../app/agents/new_chat/llm_config.py | 22 +- .../app/agents/new_chat/prompt_caching.py | 166 +++++++++ .../app/services/llm_router_service.py | 35 +- surfsense_backend/pyproject.toml | 2 +- .../agents/new_chat/prompts/test_composer.py | 25 ++ .../agents/new_chat/test_prompt_caching.py | 350 ++++++++++++++++++ .../test_resolve_prompt_model_name.py | 117 ++++++ .../unit/test_stream_new_chat_contract.py | 36 ++ surfsense_backend/uv.lock | 160 ++++---- .../components/pricing/pricing-section.tsx | 1 - .../settings/more-pages-content.tsx | 59 +-- 12 files changed, 877 insertions(+), 156 deletions(-) create mode 100644 surfsense_backend/app/agents/new_chat/prompt_caching.py create mode 100644 surfsense_backend/tests/unit/agents/new_chat/test_prompt_caching.py create mode 100644 surfsense_backend/tests/unit/agents/new_chat/test_resolve_prompt_model_name.py diff --git a/surfsense_backend/app/agents/new_chat/chat_deepagent.py b/surfsense_backend/app/agents/new_chat/chat_deepagent.py index fdd72ea92..c0e9a3b96 100644 --- a/surfsense_backend/app/agents/new_chat/chat_deepagent.py +++ b/surfsense_backend/app/agents/new_chat/chat_deepagent.py @@ -10,7 +10,9 @@ We use ``create_agent`` (from langchain) rather than ``create_deep_agent`` This lets us swap in ``SurfSenseFilesystemMiddleware`` — a customisable subclass of the default ``FilesystemMiddleware`` — while preserving every other behaviour that ``create_deep_agent`` provides (todo-list, subagents, -summarisation, prompt-caching, etc.). +summarisation, etc.). Prompt caching is configured at LLM-build time via +``apply_litellm_prompt_caching`` (LiteLLM-native, multi-provider) rather +than as a middleware. """ import asyncio @@ -33,7 +35,6 @@ from langchain.agents.middleware import ( TodoListMiddleware, ToolCallLimitMiddleware, ) -from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware from langchain_core.language_models import BaseChatModel from langchain_core.tools import BaseTool from langgraph.types import Checkpointer @@ -74,6 +75,7 @@ from app.agents.new_chat.plugin_loader import ( load_allowed_plugin_names_from_env, load_plugin_middlewares, ) +from app.agents.new_chat.prompt_caching import apply_litellm_prompt_caching from app.agents.new_chat.subagents import build_specialized_subagents from app.agents.new_chat.system_prompt import ( build_configurable_system_prompt, @@ -94,6 +96,39 @@ from app.utils.perf import get_perf_logger _perf_log = get_perf_logger() + +def _resolve_prompt_model_name( + agent_config: AgentConfig | None, + llm: BaseChatModel, +) -> str | None: + """Resolve the model id to feed to provider-variant detection. + + Preference order (matches the established idiom in + ``llm_router_service.py`` — see ``params.get("base_model") or + params.get("model", "")`` usages there): + + 1. ``agent_config.litellm_params["base_model"]`` — required for Azure + deployments where ``model_name`` is the deployment slug, not the + underlying family. Without this, a deployment named e.g. + ``"prod-chat-001"`` would silently miss every provider regex. + 2. ``agent_config.model_name`` — the user's configured model id. + 3. ``getattr(llm, "model", None)`` — fallback for direct callers that + don't supply an ``AgentConfig`` (currently a defensive path; all + production callers pass ``agent_config``). + + Returns ``None`` when nothing is available; ``compose_system_prompt`` + treats that as the ``"default"`` variant (no provider block emitted). + """ + if agent_config is not None: + params = agent_config.litellm_params or {} + base_model = params.get("base_model") + if isinstance(base_model, str) and base_model.strip(): + return base_model + if agent_config.model_name: + return agent_config.model_name + return getattr(llm, "model", None) + + # ============================================================================= # Connector Type Mapping # ============================================================================= @@ -279,6 +314,14 @@ async def create_surfsense_deep_agent( ) """ _t_agent_total = time.perf_counter() + + # Layer thread-aware prompt caching onto the LLM. Idempotent with the + # build-time call in ``llm_config.py``; this run merely adds + # ``prompt_cache_key=f"surfsense-thread-{thread_id}"`` for OpenAI-family + # configs now that ``thread_id`` is known. No-op when ``thread_id`` is + # None or the provider is non-OpenAI-family. + apply_litellm_prompt_caching(llm, agent_config=agent_config, thread_id=thread_id) + filesystem_selection = filesystem_selection or FilesystemSelection() backend_resolver = build_backend_resolver( filesystem_selection, @@ -398,6 +441,7 @@ async def create_surfsense_deep_agent( enabled_tool_names=_enabled_tool_names, disabled_tool_names=_user_disabled_tool_names, mcp_connector_tools=_mcp_connector_tools, + model_name=_resolve_prompt_model_name(agent_config, llm), ) else: system_prompt = build_surfsense_system_prompt( @@ -405,6 +449,7 @@ async def create_surfsense_deep_agent( enabled_tool_names=_enabled_tool_names, disabled_tool_names=_user_disabled_tool_names, mcp_connector_tools=_mcp_connector_tools, + model_name=_resolve_prompt_model_name(agent_config, llm), ) _perf_log.info( "[create_agent] System prompt built in %.3fs", time.perf_counter() - _t0 @@ -568,7 +613,6 @@ def _build_compiled_agent_blocking( ), create_surfsense_compaction_middleware(llm, StateBackend), PatchToolCallsMiddleware(), - AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"), ] general_purpose_spec: SubAgent = { # type: ignore[typeddict-unknown-key] @@ -1006,12 +1050,12 @@ def _build_compiled_agent_blocking( action_log_mw, PatchToolCallsMiddleware(), DedupHITLToolCallsMiddleware(agent_tools=list(tools)), - # Plugin slot — sits just before AnthropicCache so plugin-side - # transforms see the final tool result and run before any - # caching heuristics. Multiple plugins in declared order; loader - # filtered by the admin allowlist already. + # Plugin slot — sits at the tail so plugin-side transforms see the + # final tool result. Prompt caching is now applied at LLM build time + # via ``apply_litellm_prompt_caching`` (see prompt_caching.py), so no + # caching middleware is needed here. Multiple plugins run in declared + # order; loader filtered by the admin allowlist already. *plugin_middlewares, - AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"), ] deepagent_middleware = [m for m in deepagent_middleware if m is not None] diff --git a/surfsense_backend/app/agents/new_chat/llm_config.py b/surfsense_backend/app/agents/new_chat/llm_config.py index 58d8f84d0..99bb719f6 100644 --- a/surfsense_backend/app/agents/new_chat/llm_config.py +++ b/surfsense_backend/app/agents/new_chat/llm_config.py @@ -27,6 +27,7 @@ from litellm import get_model_info from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession +from app.agents.new_chat.prompt_caching import apply_litellm_prompt_caching from app.services.llm_router_service import ( AUTO_MODE_ID, ChatLiteLLMRouter, @@ -494,6 +495,11 @@ def create_chat_litellm_from_config(llm_config: dict) -> ChatLiteLLM | None: llm = SanitizedChatLiteLLM(**litellm_kwargs) _attach_model_profile(llm, model_string) + # Configure LiteLLM-native prompt caching (cache_control_injection_points + # for Anthropic/Bedrock/Vertex/Gemini/Azure-AI/OpenRouter/Databricks/etc.). + # ``agent_config=None`` here — the YAML path doesn't have provider intent + # in a structured form, so we set only the universal injection points. + apply_litellm_prompt_caching(llm) return llm @@ -518,7 +524,16 @@ def create_chat_litellm_from_agent_config( print("Error: Auto mode requested but LLM Router not initialized") return None try: - return get_auto_mode_llm() + router_llm = get_auto_mode_llm() + if router_llm is not None: + # Universal cache_control_injection_points only — auto-mode + # fans out across providers, so OpenAI-only kwargs (e.g. + # ``prompt_cache_key``) are left off here. ``drop_params`` + # would strip them at the provider boundary anyway, but + # there's no point setting them when we don't know the + # destination. + apply_litellm_prompt_caching(router_llm, agent_config=agent_config) + return router_llm except Exception as e: print(f"Error creating ChatLiteLLMRouter: {e}") return None @@ -549,4 +564,9 @@ def create_chat_litellm_from_agent_config( llm = SanitizedChatLiteLLM(**litellm_kwargs) _attach_model_profile(llm, model_string) + # Build-time prompt caching: sets ``cache_control_injection_points`` for + # all providers and (for OpenAI/DeepSeek/xAI) ``prompt_cache_retention``. + # Per-thread ``prompt_cache_key`` is layered on later in + # ``create_surfsense_deep_agent`` once ``thread_id`` is known. + apply_litellm_prompt_caching(llm, agent_config=agent_config) return llm diff --git a/surfsense_backend/app/agents/new_chat/prompt_caching.py b/surfsense_backend/app/agents/new_chat/prompt_caching.py new file mode 100644 index 000000000..86bc57725 --- /dev/null +++ b/surfsense_backend/app/agents/new_chat/prompt_caching.py @@ -0,0 +1,166 @@ +"""LiteLLM-native prompt caching configuration for SurfSense agents. + +Replaces the legacy ``AnthropicPromptCachingMiddleware`` (which never +activated for our LiteLLM-based stack — its ``isinstance(model, ChatAnthropic)`` +gate always failed) with LiteLLM's universal caching mechanism. + +Coverage: + +- Marker-based providers (need ``cache_control`` injection, which LiteLLM + performs automatically when ``cache_control_injection_points`` is set): + ``anthropic/``, ``bedrock/``, ``vertex_ai/``, ``gemini/``, ``azure_ai/``, + ``openrouter/`` (Claude/Gemini/MiniMax/GLM/z-ai routes), ``databricks/`` + (Claude), ``dashscope/`` (Qwen), ``minimax/``, ``zai/`` (GLM). +- Auto-cached (LiteLLM strips the marker silently): ``openai/``, + ``deepseek/``, ``xai/`` — these caches automatically for prompts ≥1024 + tokens and surface ``prompt_cache_key`` / ``prompt_cache_retention``. + +We inject **two** breakpoints per request: + +- ``role: system`` — pins the SurfSense system prompt (provider variant, + citation rules, tool catalog, KB tree, skills metadata) into the cache. +- ``index: -1`` — pins the latest message so multi-turn savings compound: + Anthropic-family providers use longest-matching-prefix lookup, so turn + N+1 still reads turn N's cache up to the shared prefix. + +For OpenAI-family configs we additionally pass: + +- ``prompt_cache_key=f"surfsense-thread-{thread_id}"`` — routing hint that + raises hit rate by sending requests with a shared prefix to the same + backend. +- ``prompt_cache_retention="24h"`` — extends cache TTL beyond the default + 5-10 min in-memory cache. + +Safety net: ``litellm.drop_params=True`` is set globally in +``app.services.llm_service`` at module-load time. Any kwarg the destination +provider doesn't recognise is auto-stripped at the provider transformer +layer, so an OpenAI→Bedrock auto-mode fallback can't 400 on +``prompt_cache_key`` etc. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from langchain_core.language_models import BaseChatModel + +if TYPE_CHECKING: + from app.agents.new_chat.llm_config import AgentConfig + +logger = logging.getLogger(__name__) + + +# Two-breakpoint policy: system + latest message. See module docstring for +# rationale. Anthropic limits requests to 4 ``cache_control`` blocks; we +# use 2 here, leaving headroom for Phase-2 tool caching. +_DEFAULT_INJECTION_POINTS: tuple[dict[str, Any], ...] = ( + {"location": "message", "role": "system"}, + {"location": "message", "index": -1}, +) + +# Providers (uppercase ``AgentConfig.provider`` values) that natively expose +# OpenAI-style automatic prompt caching with ``prompt_cache_key`` and +# ``prompt_cache_retention`` kwargs. Strict whitelist — many other providers +# in ``PROVIDER_MAP`` route through litellm's ``openai`` prefix without +# implementing the OpenAI prompt-cache surface (e.g. MOONSHOT, ZHIPU, +# MINIMAX), so we can't infer family from the litellm prefix alone. +_OPENAI_FAMILY_PROVIDERS: frozenset[str] = frozenset({"OPENAI", "DEEPSEEK", "XAI"}) + + +def _is_router_llm(llm: BaseChatModel) -> bool: + """Detect ``ChatLiteLLMRouter`` (auto-mode) without an eager import. + + Importing ``app.services.llm_router_service`` at module-load time would + create a cycle via ``llm_config -> prompt_caching -> llm_router_service``. + Class-name comparison is sufficient since the class is defined in a + single place. + """ + return type(llm).__name__ == "ChatLiteLLMRouter" + + +def _is_openai_family_config(agent_config: AgentConfig | None) -> bool: + """Whether the config targets an OpenAI-style prompt-cache surface. + + Strict — only returns True when the user explicitly chose OPENAI, + DEEPSEEK, or XAI as the provider in their ``NewLLMConfig`` / + ``YAMLConfig``. Auto-mode and custom providers return False because + we can't statically know the destination. + """ + if agent_config is None or not agent_config.provider: + return False + if agent_config.is_auto_mode: + return False + if agent_config.custom_provider: + return False + return agent_config.provider.upper() in _OPENAI_FAMILY_PROVIDERS + + +def _get_or_init_model_kwargs(llm: BaseChatModel) -> dict[str, Any] | None: + """Return ``llm.model_kwargs`` as a writable dict, or ``None`` to bail. + + Initialises the field to ``{}`` when present-but-None on a Pydantic v2 + model. Returns ``None`` if the LLM type doesn't expose a writable + ``model_kwargs`` attribute (caller should treat as no-op). + """ + model_kwargs = getattr(llm, "model_kwargs", None) + if isinstance(model_kwargs, dict): + return model_kwargs + try: + llm.model_kwargs = {} # type: ignore[attr-defined] + except Exception: + return None + refreshed = getattr(llm, "model_kwargs", None) + return refreshed if isinstance(refreshed, dict) else None + + +def apply_litellm_prompt_caching( + llm: BaseChatModel, + *, + agent_config: AgentConfig | None = None, + thread_id: int | None = None, +) -> None: + """Configure LiteLLM prompt caching on a ChatLiteLLM/ChatLiteLLMRouter. + + Idempotent — values already present in ``llm.model_kwargs`` (e.g. from + ``agent_config.litellm_params`` overrides) are preserved. Mutates + ``llm.model_kwargs`` in place; the kwargs flow to ``litellm.completion`` + via ``ChatLiteLLM._default_params`` and via ``self.model_kwargs`` merge + in our custom ``ChatLiteLLMRouter``. + + Args: + llm: ChatLiteLLM, SanitizedChatLiteLLM, or ChatLiteLLMRouter instance. + agent_config: Optional ``AgentConfig`` driving provider-specific + behaviour. When omitted (or auto-mode), only the universal + ``cache_control_injection_points`` are set. + thread_id: Optional thread id used to construct a per-thread + ``prompt_cache_key`` for OpenAI-family providers. Caching still + works without it (server-side automatic), but the key improves + backend routing affinity and therefore hit rate. + """ + model_kwargs = _get_or_init_model_kwargs(llm) + if model_kwargs is None: + logger.debug( + "apply_litellm_prompt_caching: %s exposes no writable model_kwargs; skipping", + type(llm).__name__, + ) + return + + if "cache_control_injection_points" not in model_kwargs: + model_kwargs["cache_control_injection_points"] = [ + dict(point) for point in _DEFAULT_INJECTION_POINTS + ] + + # OpenAI-family extras only when we statically know the destination is + # OpenAI / DeepSeek / xAI. Auto-mode router fans out across providers + # so we can't safely set OpenAI-only kwargs there (drop_params would + # strip them but it's wasteful to set them in the first place). + if _is_router_llm(llm): + return + if not _is_openai_family_config(agent_config): + return + + if thread_id is not None and "prompt_cache_key" not in model_kwargs: + model_kwargs["prompt_cache_key"] = f"surfsense-thread-{thread_id}" + if "prompt_cache_retention" not in model_kwargs: + model_kwargs["prompt_cache_retention"] = "24h" diff --git a/surfsense_backend/app/services/llm_router_service.py b/surfsense_backend/app/services/llm_router_service.py index 4bce79a43..fbd42b458 100644 --- a/surfsense_backend/app/services/llm_router_service.py +++ b/surfsense_backend/app/services/llm_router_service.py @@ -28,6 +28,7 @@ from litellm.exceptions import ( BadRequestError as LiteLLMBadRequestError, ContextWindowExceededError, ) +from pydantic import Field from app.utils.perf import get_perf_logger @@ -573,6 +574,11 @@ class ChatLiteLLMRouter(BaseChatModel): # Public attributes that Pydantic will manage model: str = "auto" streaming: bool = True + # Static kwargs that flow through to ``litellm.completion(...)`` on every + # invocation (e.g. ``cache_control_injection_points`` set by + # ``apply_litellm_prompt_caching``). Per-call ``**kwargs`` from + # ``invoke()`` still take precedence — see ``_generate``/``_astream``. + model_kwargs: dict[str, Any] = Field(default_factory=dict) # Bound tools and tool choice for tool calling _bound_tools: list[dict] | None = None @@ -898,13 +904,16 @@ class ChatLiteLLMRouter(BaseChatModel): logger.warning(f"Failed to convert tool {tool}: {e}") continue - # Create a new instance with tools bound + # Create a new instance with tools bound. Carry through ``model_kwargs`` + # so static settings (e.g. cache_control_injection_points) survive the + # bind_tools rebuild. return ChatLiteLLMRouter( router=self._router, bound_tools=formatted_tools if formatted_tools else None, tool_choice=tool_choice, model=self.model, streaming=self.streaming, + model_kwargs=dict(self.model_kwargs), **kwargs, ) @@ -929,8 +938,10 @@ class ChatLiteLLMRouter(BaseChatModel): formatted_messages = self._convert_messages(messages) formatted_messages = self._trim_messages_to_fit_context(formatted_messages) - # Add tools if bound - call_kwargs = {**kwargs} + # Merge static model_kwargs (e.g. cache_control_injection_points) under + # per-call kwargs so callers can still override per invocation. Then add + # bound tools. + call_kwargs = {**self.model_kwargs, **kwargs} if self._bound_tools: call_kwargs["tools"] = self._bound_tools if self._tool_choice is not None: @@ -997,8 +1008,10 @@ class ChatLiteLLMRouter(BaseChatModel): formatted_messages = self._convert_messages(messages) formatted_messages = self._trim_messages_to_fit_context(formatted_messages) - # Add tools if bound - call_kwargs = {**kwargs} + # Merge static model_kwargs (e.g. cache_control_injection_points) under + # per-call kwargs so callers can still override per invocation. Then add + # bound tools. + call_kwargs = {**self.model_kwargs, **kwargs} if self._bound_tools: call_kwargs["tools"] = self._bound_tools if self._tool_choice is not None: @@ -1060,8 +1073,10 @@ class ChatLiteLLMRouter(BaseChatModel): formatted_messages = self._convert_messages(messages) formatted_messages = self._trim_messages_to_fit_context(formatted_messages) - # Add tools if bound - call_kwargs = {**kwargs} + # Merge static model_kwargs (e.g. cache_control_injection_points) under + # per-call kwargs so callers can still override per invocation. Then add + # bound tools. + call_kwargs = {**self.model_kwargs, **kwargs} if self._bound_tools: call_kwargs["tools"] = self._bound_tools if self._tool_choice is not None: @@ -1110,8 +1125,10 @@ class ChatLiteLLMRouter(BaseChatModel): formatted_messages = self._convert_messages(messages) formatted_messages = self._trim_messages_to_fit_context(formatted_messages) - # Add tools if bound - call_kwargs = {**kwargs} + # Merge static model_kwargs (e.g. cache_control_injection_points) under + # per-call kwargs so callers can still override per invocation. Then add + # bound tools. + call_kwargs = {**self.model_kwargs, **kwargs} if self._bound_tools: call_kwargs["tools"] = self._bound_tools if self._tool_choice is not None: diff --git a/surfsense_backend/pyproject.toml b/surfsense_backend/pyproject.toml index 131627386..cd683e2e1 100644 --- a/surfsense_backend/pyproject.toml +++ b/surfsense_backend/pyproject.toml @@ -74,7 +74,7 @@ dependencies = [ "deepagents>=0.4.12", "stripe>=15.0.0", "azure-ai-documentintelligence>=1.0.2", - "litellm>=1.83.4", + "litellm>=1.83.7", "langchain-litellm>=0.6.4", ] diff --git a/surfsense_backend/tests/unit/agents/new_chat/prompts/test_composer.py b/surfsense_backend/tests/unit/agents/new_chat/prompts/test_composer.py index 397b1c787..36fe04aa2 100644 --- a/surfsense_backend/tests/unit/agents/new_chat/prompts/test_composer.py +++ b/surfsense_backend/tests/unit/agents/new_chat/prompts/test_composer.py @@ -226,6 +226,31 @@ class TestCompose: # Default block should NOT be present assert "" not in prompt + def test_provider_hints_render_with_custom_system_instructions( + self, fixed_today: datetime + ) -> None: + """Regression guard for the always-append decision: provider hints + append AFTER a custom system prompt. + + Provider hints are stylistic nudges (parallel tool-call rules, + formatting guidance, etc.) that help the model regardless of + what the system instructions say. Suppressing them when a + custom prompt is set would partially defeat the per-family + prompt machinery. + """ + prompt = compose_system_prompt( + today=fixed_today, + custom_system_instructions="You are a custom assistant.", + model_name="anthropic/claude-3-5-sonnet", + ) + assert "You are a custom assistant." in prompt + assert "" in prompt + # The custom prompt must come BEFORE the provider hints so the + # user's framing isn't drowned out by the stylistic nudges. + assert prompt.index("You are a custom assistant.") < prompt.index( + "" + ) + def test_use_default_false_with_no_custom_yields_no_system_block( self, fixed_today: datetime ) -> None: diff --git a/surfsense_backend/tests/unit/agents/new_chat/test_prompt_caching.py b/surfsense_backend/tests/unit/agents/new_chat/test_prompt_caching.py new file mode 100644 index 000000000..5b3a03581 --- /dev/null +++ b/surfsense_backend/tests/unit/agents/new_chat/test_prompt_caching.py @@ -0,0 +1,350 @@ +"""Tests for ``apply_litellm_prompt_caching`` in +:mod:`app.agents.new_chat.prompt_caching`. + +The helper replaces the legacy ``AnthropicPromptCachingMiddleware`` (which +never activated for our LiteLLM stack) with LiteLLM-native multi-provider +prompt caching. It mutates ``llm.model_kwargs`` so the kwargs flow to +``litellm.completion(...)``. The tests below pin its public contract: + +1. Always sets BOTH ``role: system`` and ``index: -1`` injection points so + savings compound across multi-turn conversations on Anthropic-family + providers. +2. Adds ``prompt_cache_key``/``prompt_cache_retention`` only for + single-model OPENAI/DEEPSEEK/XAI configs (where OpenAI's automatic + prompt-cache surface is available). +3. Treats ``ChatLiteLLMRouter`` (auto-mode) as universal-only — no + OpenAI-only kwargs because the router fans out across providers. +4. Idempotent: user-supplied values in ``model_kwargs`` are preserved. +5. Defensive: LLMs without a writable ``model_kwargs`` are silently + skipped rather than raising. +""" + +from __future__ import annotations + +from typing import Any + +import pytest + +from app.agents.new_chat.llm_config import AgentConfig +from app.agents.new_chat.prompt_caching import apply_litellm_prompt_caching + +pytestmark = pytest.mark.unit + + +# --------------------------------------------------------------------------- +# Test doubles +# --------------------------------------------------------------------------- + + +class _FakeLLM: + """Stand-in for ``ChatLiteLLM``/``SanitizedChatLiteLLM``. + + The helper only inspects ``getattr(llm, "model_kwargs", None)``, + ``getattr(llm, "model", None)``, and ``type(llm).__name__``. A simple + object suffices — we don't need to spin up real LangChain/LiteLLM + machinery for unit tests of the helper's logic. + """ + + def __init__( + self, + model: str = "openai/gpt-4o", + model_kwargs: dict[str, Any] | None = None, + ) -> None: + self.model = model + self.model_kwargs: dict[str, Any] = dict(model_kwargs) if model_kwargs else {} + + +class ChatLiteLLMRouter: + """Class-name-only impostor of the real router. + + The helper's router gate is ``type(llm).__name__ == "ChatLiteLLMRouter"`` + (a deliberate stringly-typed check to avoid an import cycle with + ``app.services.llm_router_service``). Reusing the same class name here + triggers the same code path without instantiating a real ``Router``. + """ + + def __init__(self) -> None: + self.model = "auto" + self.model_kwargs: dict[str, Any] = {} + + +def _make_cfg(**overrides: Any) -> AgentConfig: + """Build an ``AgentConfig`` with sensible defaults for the helper test.""" + defaults: dict[str, Any] = { + "provider": "OPENAI", + "model_name": "gpt-4o", + "api_key": "k", + } + return AgentConfig(**{**defaults, **overrides}) + + +# --------------------------------------------------------------------------- +# (a) Universal injection points +# --------------------------------------------------------------------------- + + +def test_sets_both_cache_control_injection_points_with_no_config() -> None: + """Bare call (no agent_config, no thread_id) still sets the two + universal breakpoints — these cost nothing on providers that don't + consume them and unlock caching on every supported provider.""" + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm) + + points = llm.model_kwargs["cache_control_injection_points"] + assert {"location": "message", "role": "system"} in points + assert {"location": "message", "index": -1} in points + assert len(points) == 2 + + +def test_injection_points_set_for_anthropic_config() -> None: + """Anthropic-family configs need the marker — verify it lands.""" + cfg = _make_cfg(provider="ANTHROPIC", model_name="claude-3-5-sonnet") + llm = _FakeLLM(model="anthropic/claude-3-5-sonnet") + + apply_litellm_prompt_caching(llm, agent_config=cfg) + + assert "cache_control_injection_points" in llm.model_kwargs + + +# --------------------------------------------------------------------------- +# (b) Idempotency / user override wins +# --------------------------------------------------------------------------- + + +def test_does_not_overwrite_user_supplied_cache_control_injection_points() -> None: + """Users who set their own injection points (e.g. with ``ttl: "1h"`` + via ``litellm_params``) keep them — the helper merges, never + clobbers.""" + user_points = [ + {"location": "message", "role": "system", "ttl": "1h"}, + ] + llm = _FakeLLM( + model_kwargs={"cache_control_injection_points": user_points}, + ) + + apply_litellm_prompt_caching(llm) + + assert llm.model_kwargs["cache_control_injection_points"] is user_points + + +def test_idempotent_when_called_multiple_times() -> None: + """Build-time + thread-time double-call must be a no-op the second time.""" + cfg = _make_cfg(provider="OPENAI") + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=1) + snapshot = { + "cache_control_injection_points": list( + llm.model_kwargs["cache_control_injection_points"] + ), + "prompt_cache_key": llm.model_kwargs["prompt_cache_key"], + "prompt_cache_retention": llm.model_kwargs["prompt_cache_retention"], + } + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=1) + + assert ( + llm.model_kwargs["cache_control_injection_points"] + == snapshot["cache_control_injection_points"] + ) + assert llm.model_kwargs["prompt_cache_key"] == snapshot["prompt_cache_key"] + assert ( + llm.model_kwargs["prompt_cache_retention"] == snapshot["prompt_cache_retention"] + ) + + +def test_does_not_overwrite_user_supplied_prompt_cache_key() -> None: + """A pre-set ``prompt_cache_key`` (e.g. tenant-aware override via + ``litellm_params``) wins over our default per-thread key.""" + cfg = _make_cfg(provider="OPENAI") + llm = _FakeLLM(model_kwargs={"prompt_cache_key": "tenant-abc"}) + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42) + + assert llm.model_kwargs["prompt_cache_key"] == "tenant-abc" + + +# --------------------------------------------------------------------------- +# (c) OpenAI-family extras (OPENAI / DEEPSEEK / XAI) +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("provider", ["OPENAI", "DEEPSEEK", "XAI"]) +def test_sets_openai_family_extras(provider: str) -> None: + """OpenAI-style providers gain ``prompt_cache_key`` (raises hit rate + via routing affinity) and ``prompt_cache_retention="24h"`` (extends + cache TTL beyond the default 5-10 min).""" + cfg = _make_cfg(provider=provider) + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42) + + assert llm.model_kwargs["prompt_cache_key"] == "surfsense-thread-42" + assert llm.model_kwargs["prompt_cache_retention"] == "24h" + + +def test_skips_prompt_cache_key_when_no_thread_id() -> None: + """Without a thread id we can't construct a per-thread key. Retention + is still useful so we set it (it's free).""" + cfg = _make_cfg(provider="OPENAI") + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=None) + + assert "prompt_cache_key" not in llm.model_kwargs + assert llm.model_kwargs["prompt_cache_retention"] == "24h" + + +@pytest.mark.parametrize( + "provider", + ["ANTHROPIC", "BEDROCK", "VERTEX_AI", "GOOGLE_AI_STUDIO", "GROQ", "MOONSHOT"], +) +def test_no_openai_extras_for_other_providers(provider: str) -> None: + """Non-OpenAI-family providers don't expose ``prompt_cache_key`` — + skip it. ``cache_control_injection_points`` is still set (universal).""" + cfg = _make_cfg(provider=provider) + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42) + + assert "prompt_cache_key" not in llm.model_kwargs + assert "prompt_cache_retention" not in llm.model_kwargs + assert "cache_control_injection_points" in llm.model_kwargs + + +def test_no_openai_extras_in_auto_mode() -> None: + """Auto-mode fans out across mixed providers — we can't statically + target OpenAI-only kwargs.""" + cfg = AgentConfig.from_auto_mode() + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42) + + assert "prompt_cache_key" not in llm.model_kwargs + assert "prompt_cache_retention" not in llm.model_kwargs + assert "cache_control_injection_points" in llm.model_kwargs + + +def test_no_openai_extras_for_custom_provider() -> None: + """Custom providers route through arbitrary user-supplied prefixes — + we don't try to infer OpenAI-family compatibility.""" + cfg = _make_cfg(provider="OPENAI", custom_provider="my_proxy") + llm = _FakeLLM() + + apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42) + + assert "prompt_cache_key" not in llm.model_kwargs + assert "prompt_cache_retention" not in llm.model_kwargs + + +# --------------------------------------------------------------------------- +# (d) ChatLiteLLMRouter — universal injection points only +# --------------------------------------------------------------------------- + + +def test_router_llm_gets_only_universal_injection_points() -> None: + """Even with an OpenAI-flavoured config, a ``ChatLiteLLMRouter`` must + receive only the universal injection points — its requests dispatch + across provider deployments and OpenAI-only kwargs would be wasted + (or stripped by ``drop_params``) on non-OpenAI legs.""" + router = ChatLiteLLMRouter() + cfg = _make_cfg(provider="OPENAI") + + apply_litellm_prompt_caching(router, agent_config=cfg, thread_id=42) + + assert "cache_control_injection_points" in router.model_kwargs + assert "prompt_cache_key" not in router.model_kwargs + assert "prompt_cache_retention" not in router.model_kwargs + + +# --------------------------------------------------------------------------- +# (e) Defensive paths +# --------------------------------------------------------------------------- + + +def test_handles_llm_with_no_writable_model_kwargs() -> None: + """Some LLM implementations (e.g. fakes / minimal subclasses) don't + expose a writable ``model_kwargs``. The helper must skip silently — + raising would crash the entire LLM build path on a non-critical + optimisation.""" + + class _ImmutableLLM: + # ``__slots__`` blocks attribute creation, so ``setattr`` raises. + __slots__ = ("model",) + + def __init__(self) -> None: + self.model = "openai/gpt-4o" + + llm = _ImmutableLLM() + + apply_litellm_prompt_caching(llm) + + +def test_initialises_missing_model_kwargs_dict() -> None: + """When ``model_kwargs`` is present-but-None (Pydantic v2 default + pattern when no factory is set), the helper initialises it to an + empty dict before mutating.""" + + class _LazyLLM: + def __init__(self) -> None: + self.model = "openai/gpt-4o" + self.model_kwargs: dict[str, Any] | None = None + + llm = _LazyLLM() + + apply_litellm_prompt_caching(llm) + + assert isinstance(llm.model_kwargs, dict) + assert "cache_control_injection_points" in llm.model_kwargs + + +def test_falls_back_to_llm_model_prefix_when_no_agent_config() -> None: + """Direct caller path (e.g. ``create_chat_litellm_from_config`` for + YAML configs without a structured ``AgentConfig``): without + ``agent_config`` the helper sets only the universal injection points + — no OpenAI-family extras even if the prefix says ``openai/``. + Conservative: we'd rather miss the speedup than silently misroute.""" + llm = _FakeLLM(model="openai/gpt-4o") + + apply_litellm_prompt_caching(llm, agent_config=None, thread_id=99) + + assert "cache_control_injection_points" in llm.model_kwargs + assert "prompt_cache_key" not in llm.model_kwargs + assert "prompt_cache_retention" not in llm.model_kwargs + + +# --------------------------------------------------------------------------- +# (f) drop_params safety net (regression guard for #19346) +# --------------------------------------------------------------------------- + + +def test_litellm_drop_params_is_globally_enabled() -> None: + """``litellm.drop_params=True`` is set globally in + :mod:`app.services.llm_service` so any ``prompt_cache_key`` / + ``prompt_cache_retention`` we set on an OpenAI-family config is + auto-stripped if the request later routes to a non-supporting + provider (e.g. via auto-mode router fallback). This test pins that + invariant — losing it would mean Bedrock/Vertex 400s on ``prompt_cache_key``. + """ + import litellm + + import app.services.llm_service # noqa: F401 (side-effect: sets globals) + + assert litellm.drop_params is True + + +# --------------------------------------------------------------------------- +# Regression note: LiteLLM #15696 (multi-content-block last message) +# --------------------------------------------------------------------------- +# +# Before LiteLLM 1.81 a list-form last message ``[block_a, block_b]`` +# would get ``cache_control`` applied to *every* content block instead +# of only the last one — wasting cache breakpoints and triggering 400s +# on Anthropic when it exceeded the 4-breakpoint limit. Fixed in +# https://github.com/BerriAI/litellm/pull/15699. +# +# We pin ``litellm>=1.83.7`` in ``pyproject.toml`` (well past the fix). +# An end-to-end behavioural test would need to run ``litellm.completion`` +# through the Anthropic transformer, which is integration territory and +# better covered by LiteLLM's own test suite. The unit guard here is the +# version pin plus the build-time ``model_kwargs`` shape we verify above. diff --git a/surfsense_backend/tests/unit/agents/new_chat/test_resolve_prompt_model_name.py b/surfsense_backend/tests/unit/agents/new_chat/test_resolve_prompt_model_name.py new file mode 100644 index 000000000..ffe3dbaa4 --- /dev/null +++ b/surfsense_backend/tests/unit/agents/new_chat/test_resolve_prompt_model_name.py @@ -0,0 +1,117 @@ +"""Tests for ``_resolve_prompt_model_name`` in :mod:`app.agents.new_chat.chat_deepagent`. + +The helper picks the model id fed to ``detect_provider_variant`` so the +right ```` block lands in the system prompt. The tests +below pin its preference order: + +1. ``agent_config.litellm_params["base_model"]`` (Azure-correct). +2. ``agent_config.model_name``. +3. ``getattr(llm, "model", None)``. + +Without (1) an Azure deployment named e.g. ``"prod-chat-001"`` would +silently miss every provider regex. +""" + +from __future__ import annotations + +import pytest + +from app.agents.new_chat.chat_deepagent import _resolve_prompt_model_name +from app.agents.new_chat.llm_config import AgentConfig + +pytestmark = pytest.mark.unit + + +def _make_cfg(**overrides) -> AgentConfig: + """Build an ``AgentConfig`` with sensible defaults for the helper test.""" + defaults = { + "provider": "OPENAI", + "model_name": "x", + "api_key": "k", + } + return AgentConfig(**{**defaults, **overrides}) + + +class _FakeLLM: + """Stand-in for a ``ChatLiteLLM`` / ``ChatLiteLLMRouter`` instance. + + The resolver only reads the ``.model`` attribute via ``getattr``, + matching the established idiom in ``knowledge_search.py`` / + ``stream_new_chat.py`` / ``document_summarizer.py``. + """ + + def __init__(self, model: str | None) -> None: + self.model = model + + +def test_prefers_litellm_params_base_model_over_deployment_name() -> None: + """Azure deployment slug must NOT shadow the underlying model family. + + This is the failure mode the helper exists to prevent: a deployment + named ``"azure/prod-chat-001"`` would not match any provider regex + on its own, but the family ``"gpt-4o"`` lives in + ``litellm_params["base_model"]`` and routes to ``openai_classic``. + """ + cfg = _make_cfg( + model_name="azure/prod-chat-001", + litellm_params={"base_model": "gpt-4o"}, + ) + assert _resolve_prompt_model_name(cfg, _FakeLLM("azure/prod-chat-001")) == "gpt-4o" + + +def test_falls_back_to_model_name_when_litellm_params_is_none() -> None: + cfg = _make_cfg( + model_name="anthropic/claude-3-5-sonnet", + litellm_params=None, + ) + got = _resolve_prompt_model_name(cfg, _FakeLLM("anthropic/claude-3-5-sonnet")) + assert got == "anthropic/claude-3-5-sonnet" + + +def test_handles_litellm_params_without_base_model_key() -> None: + cfg = _make_cfg( + model_name="openai/gpt-4o", + litellm_params={"temperature": 0.5}, + ) + assert _resolve_prompt_model_name(cfg, _FakeLLM("openai/gpt-4o")) == "openai/gpt-4o" + + +def test_ignores_blank_base_model() -> None: + """Whitespace-only ``base_model`` must not shadow ``model_name``.""" + cfg = _make_cfg( + model_name="openai/gpt-4o", + litellm_params={"base_model": " "}, + ) + assert _resolve_prompt_model_name(cfg, _FakeLLM("openai/gpt-4o")) == "openai/gpt-4o" + + +def test_ignores_non_string_base_model() -> None: + """Defensive: a non-string ``base_model`` should not crash the resolver.""" + cfg = _make_cfg( + model_name="openai/gpt-4o", + litellm_params={"base_model": 42}, + ) + assert _resolve_prompt_model_name(cfg, _FakeLLM("openai/gpt-4o")) == "openai/gpt-4o" + + +def test_falls_back_to_llm_model_when_no_agent_config() -> None: + """No ``agent_config`` -> use ``llm.model`` directly. Defensive path + for direct callers; production callers always supply a config.""" + assert ( + _resolve_prompt_model_name(None, _FakeLLM("openai/gpt-4o-mini")) + == "openai/gpt-4o-mini" + ) + + +def test_returns_none_when_nothing_available() -> None: + """``compose_system_prompt`` treats ``None`` as the ``"default"`` + variant and emits no provider block.""" + assert _resolve_prompt_model_name(None, _FakeLLM(None)) is None + + +def test_auto_mode_resolves_to_auto_string() -> None: + """Auto mode -> ``"auto"``. ``detect_provider_variant("auto")`` + returns ``"default"``, which is correct: the child model isn't + known until the LiteLLM Router dispatches.""" + cfg = AgentConfig.from_auto_mode() + assert _resolve_prompt_model_name(cfg, _FakeLLM("auto")) == "auto" diff --git a/surfsense_backend/tests/unit/test_stream_new_chat_contract.py b/surfsense_backend/tests/unit/test_stream_new_chat_contract.py index 5e6ad6abd..5935d73ae 100644 --- a/surfsense_backend/tests/unit/test_stream_new_chat_contract.py +++ b/surfsense_backend/tests/unit/test_stream_new_chat_contract.py @@ -372,3 +372,39 @@ def test_turn_status_sse_contract_exists(): assert 'type: "data-turn-status"' in state_source assert 'case "data-turn-status":' in pipeline_source assert "end_turn(str(chat_id))" in stream_source + + +def test_chat_deepagent_forwards_resolved_model_name_to_both_builders(): + """Regression guard: both system-prompt builders in chat_deepagent.py + must receive ``model_name=_resolve_prompt_model_name(...)`` so the + provider-variant dispatch can render the right ```` + block. Without this the prompt silently falls back to the empty + ``"default"`` variant — the original bug being fixed. + + This test mirrors :func:`test_stream_error_emission_keeps_machine_error_codes` + in style: it inspects module source text + a regex to enforce the + call-site shape, not just the wrapper layer (the wrappers already + forward ``model_name`` correctly, so testing them would not catch + the actual missed plumbing). + """ + import app.agents.new_chat.chat_deepagent as chat_deepagent_module + + source = inspect.getsource(chat_deepagent_module) + + # Helper itself must be defined. + assert "def _resolve_prompt_model_name(" in source + + # Both builder calls must forward the resolved model name. Match + # across newlines + whitespace because the kwargs are split over + # multiple lines. + pattern = re.compile( + r"build_(?:surfsense|configurable)_system_prompt\([^)]*" + r"model_name=_resolve_prompt_model_name\(", + re.DOTALL, + ) + matches = pattern.findall(source) + assert len(matches) == 2, ( + "Expected both system-prompt builder call sites to forward " + "`model_name=_resolve_prompt_model_name(...)`, found " + f"{len(matches)}" + ) diff --git a/surfsense_backend/uv.lock b/surfsense_backend/uv.lock index 209c42a9c..efe670d05 100644 --- a/surfsense_backend/uv.lock +++ b/surfsense_backend/uv.lock @@ -62,7 +62,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.13.5" +version = "3.13.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -73,76 +73,76 @@ dependencies = [ { name = "propcache" }, { name = "yarl" }, ] -sdist = { url = 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a/surfsense_web/components/pricing/pricing-section.tsx +++ b/surfsense_web/components/pricing/pricing-section.tsx @@ -17,7 +17,6 @@ const demoPlans = [ "Self Hostable", "500 pages included to start", "3 million premium tokens to start", - "Earn up to 3,000+ bonus pages for free", "Includes access to OpenAI text, audio and image models", "Realtime Collaborative Group Chats with teammates", "Community support on Discord", diff --git a/surfsense_web/components/settings/more-pages-content.tsx b/surfsense_web/components/settings/more-pages-content.tsx index 944f7418f..8de61b0c7 100644 --- a/surfsense_web/components/settings/more-pages-content.tsx +++ b/surfsense_web/components/settings/more-pages-content.tsx @@ -1,21 +1,14 @@ "use client"; import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query"; -import { Check, ExternalLink, Mail } from "lucide-react"; +import { Check, ExternalLink } from "lucide-react"; import Link from "next/link"; import { useParams } from "next/navigation"; -import { useEffect, useState } from "react"; +import { useEffect } from "react"; import { toast } from "sonner"; import { USER_QUERY_KEY } from "@/atoms/user/user-query.atoms"; import { Button } from "@/components/ui/button"; import { Card, CardContent } from "@/components/ui/card"; -import { - Dialog, - DialogContent, - DialogDescription, - DialogHeader, - DialogTitle, -} from "@/components/ui/dialog"; import { Separator } from "@/components/ui/separator"; import { Skeleton } from "@/components/ui/skeleton"; import { Spinner } from "@/components/ui/spinner"; @@ -33,7 +26,6 @@ export function MorePagesContent() { const params = useParams(); const queryClient = useQueryClient(); const searchSpaceId = params?.search_space_id ?? ""; - const [claimOpen, setClaimOpen] = useState(false); useEffect(() => { trackIncentivePageViewed(); @@ -79,35 +71,10 @@ export function MorePagesContent() {

Get Free Pages

- Claim your free page offer and earn bonus pages + Earn bonus pages by completing tasks

- {/* 3k free offer */} - - -
- 3k -
-
-

Claim 3,000 Free Pages

-

- Limited offer. Schedule a meeting or email us to claim. -

-
- - - - - - - {/* Free tasks */}

Earn Bonus Pages

{isLoading ? ( @@ -182,7 +149,6 @@ export function MorePagesContent() { - {/* Link to buy pages */}

Need more?

{pageBuyingEnabled ? ( @@ -197,25 +163,6 @@ export function MorePagesContent() {

)}
- - {/* Claim 3k dialog */} - - - - Claim 3,000 Free Pages - - Send us an email to claim your free 3,000 pages. Include your account email and - primary usecase for free pages. - - - - -
); }