From e7e11d0b3ddae0ecf467d3a6f28c6d1c40e22bb3 Mon Sep 17 00:00:00 2001 From: S'Bussiso Dube <80188685+Sbussiso@users.noreply.github.com> Date: Wed, 9 Sep 2026 13:17:25 -0700 Subject: [PATCH] Make the model a config string: LiteLLM replaces the Ollama SDK MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The agent hardcoded an Ollama client, so "which model runs Sentinel AI" was a code change. It is now LLM_MODEL — `ollama_chat/qwen3.5:cloud` today, `anthropic/claude-sonnet-5` for a bring-your-own-key self-hoster, and eventually a SourceBox model behind an OpenAI-compatible endpoint. That last one is the point: this is the seam a custom model plugs into. LiteLLM replaces the ollama SDK outright rather than sitting beside it, because it speaks Ollama natively via `ollama_chat/`. One provider path, not two. Existing deployments need NO env changes. LLM_* wins if set; otherwise the OLLAMA_* trio is mapped onto it. Verified both directions, including that an override does not leak the Ollama host to a hosted provider. OLLAMA_API_KEY stops being a required field, so a new validator refuses to boot with no credential at all — that check used to be a side effect of the field being mandatory, and losing it would have turned a config mistake into a run that fetches work, calls the model and errors. The message translation is the real work, and I under-sold it earlier as "42 lines". Ollama and OpenAI-shaped APIs disagree structurally: - tool results key off tool_call_id, not tool_name - tool arguments arrive as a JSON string, not a dict - images cannot ride on a `tool` message at all — they need a `user` message whose content is a parts list with a `data:` URI So llm.py now owns every provider-shaped detail and the loop builds messages only through its helpers. Swapping providers again touches one file. 16 tests cover that translation because every failure mode in it is QUIET: a wrongly-shaped message does not raise, it just means the model never saw the camera frame or cannot match a result to the call it made. The run completes, reports no_action, and looks fine. Cost, accepted deliberately: +24 packages and ~160 MB, boto3 and tokenizers included as hard requirements of litellm. The web process group carries that too, since Fly gives one image per app. The alternative is two images, which is the separate-app arrangement this repo just finished removing. `import litellm` also costs ~2s, paid on every cold start — measured against the wakeup path after deploy. Co-Authored-By: Claude Opus 5 --- backend/app/sentinel_agent/agent.py | 49 +- backend/app/sentinel_agent/config.py | 76 +- backend/app/sentinel_agent/llm.py | 182 +++- backend/app/sentinel_agent/mcp_client.py | 6 +- backend/pyproject.toml | 19 +- backend/tests/test_agent_llm_provider.py | 159 ++++ backend/uv.lock | 1000 +++++++++++++++++++++- 7 files changed, 1436 insertions(+), 55 deletions(-) create mode 100644 backend/tests/test_agent_llm_provider.py diff --git a/backend/app/sentinel_agent/agent.py b/backend/app/sentinel_agent/agent.py index f74eca2..23f1739 100644 --- a/backend/app/sentinel_agent/agent.py +++ b/backend/app/sentinel_agent/agent.py @@ -31,7 +31,15 @@ import logging from typing import Any -from app.sentinel_agent.llm import LLMProvider +from app.sentinel_agent.llm import ( + LLMProvider, + assistant_message, + has_images, + image_message, + prune_images, + tool_call_arguments, + tool_result_message, +) from app.sentinel_agent.mcp_client import MCPClientManager from app.sentinel_agent.prompts import initial_user_message, system_prompt_for_trigger @@ -110,7 +118,7 @@ async def run(self, run: dict) -> dict[str, Any]: incident_id, severity, f"LLM call failed: {exc}", tool_trace, ) - messages.append(response_msg) + messages.append(assistant_message(response_msg)) # Terminal: model responded without calling a tool. if not response_msg.tool_calls: @@ -144,11 +152,12 @@ async def run(self, run: dict) -> dict[str, Any]: # any of it yet). batch_start = len(messages) for tc in response_msg.tool_calls: - args = ( - tc.function.arguments - if isinstance(tc.function.arguments, dict) - else {} - ) + # OpenAI-shaped providers hand arguments back as a JSON + # string, Ollama as a dict; the helper normalises both and + # degrades to {} on malformed JSON rather than killing the + # run — the tool then fails its own validation with a + # message the model can react to. + args = tool_call_arguments(tc) logger.info("agent: tool %s(%s)", tc.function.name, args) result = await self.mcp.call_tool(tc.function.name, args) @@ -184,17 +193,15 @@ async def run(self, run: dict) -> dict[str, Any]: incident_id = parsed_id # Feed the tool result back to the LLM. - messages.append({ - "role": "tool", - "tool_name": tc.function.name, - "content": result.get("text", ""), - }) + messages.append( + tool_result_message(tc, result.get("text", "")) + ) if result.get("images"): - messages.append({ - "role": "user", - "content": f"[Visual output from {tc.function.name}]", - "images": result["images"], - }) + # Separate user message: an OpenAI-shaped `tool` + # message cannot carry an image at all. + messages.append( + image_message(tc.function.name, result["images"]) + ) # Image pruning: every base64 frame appended above is # otherwise RE-SENT on every subsequent LLM call — O(N²) @@ -208,13 +215,9 @@ async def run(self, run: dict) -> dict[str, Any]: # and pruning to "newest only" here would make the model # assess cameras whose frames it never saw. for stale in messages[:batch_start]: - if not (isinstance(stale, dict) and stale.get("images")): + if not has_images(stale): continue - stale.pop("images", None) - stale["content"] = ( - f"{stale.get('content', '')} [frames pruned — superseded " - "by newer visual output]" - ) + prune_images(stale) # Iteration budget exhausted. return _truncated_result( diff --git a/backend/app/sentinel_agent/config.py b/backend/app/sentinel_agent/config.py index 1f47e40..5ce3121 100644 --- a/backend/app/sentinel_agent/config.py +++ b/backend/app/sentinel_agent/config.py @@ -17,8 +17,32 @@ class Settings(BaseSettings): - # ── LLM (Ollama Cloud) ─────────────────────────────────────────── - ollama_api_key: str + # ── LLM (via LiteLLM) ──────────────────────────────────────────── + # LLM_MODEL is a LiteLLM model string and is the ONLY setting needed + # to change provider: + # + # ollama_chat/qwen3.5:cloud Ollama Cloud (today's default) + # anthropic/claude-sonnet-5 bring-your-own-key + # openai/gpt-5.1 + # openai/sentinel-1 a future SourceBox model behind + # an OpenAI-compatible endpoint + # + # Leave it unset and the OLLAMA_* settings below are used instead — + # which is what keeps every existing deployment working untouched. + # + # Whatever you point this at MUST support tool calling and image + # input. The agent loop is built on both: it fans out tool calls and + # feeds camera frames back in. A text-only model does not degrade + # here, it fails every run. + llm_model: str = "" + llm_api_key: str = "" + llm_api_base: str = "" + + # ── LLM (Ollama Cloud) — the default provider ──────────────────── + # No longer required: a deployment can configure LLM_* instead. The + # validator below still refuses to boot with no credential at all, + # which is what this field's missing-by-default used to enforce. + ollama_api_key: str = "" ollama_host: str = "https://ollama.com" # Vision-capable + tool-calling. qwen3.5:cloud has 256K context, # which matters for the multi-turn tool-use loop where the @@ -147,6 +171,54 @@ class Settings(BaseSettings): # someone left an old setting around. model_config = {"env_file": ".env", "extra": "ignore"} + # ── Resolved LLM settings ──────────────────────────────────────── + # Explicit LLM_* wins; otherwise fall back to the OLLAMA_* trio so an + # existing deployment keeps working with no env changes at all. This + # is the whole backward-compatibility story for the LiteLLM move. + + @property + def resolved_llm_model(self) -> str: + if self.llm_model: + return self.llm_model + # `ollama_chat/` (not `ollama/`) — the chat endpoint is the one + # that supports tool calling, which the agent loop requires. + return f"ollama_chat/{self.ollama_model}" + + @property + def resolved_llm_api_key(self) -> str: + return self.llm_api_key or self.ollama_api_key + + @property + def resolved_llm_api_base(self) -> str: + if self.llm_api_base: + return self.llm_api_base + # Only meaningful for the Ollama fallback; a hosted provider + # resolves its own endpoint from the model string. + return self.ollama_host if not self.llm_model else "" + + @model_validator(mode="after") + def _require_an_llm_credential(self): + """Fail at boot, not mid-run, when no credential is configured. + + `ollama_api_key` used to be a required field, so a missing key + was a startup ValidationError. Making it optional (so LLM_* can + be used instead) would otherwise have turned that into a run + that fetches work, calls the model, and errors — burning a + wakeup and stranding the run. + + Skipped when api_base points somewhere local: a self-hosted + Ollama on the same box legitimately needs no key. + """ + if self.resolved_llm_api_key: + return self + base = self.resolved_llm_api_base + if any(h in base for h in ("localhost", "127.0.0.1", "host.docker.internal")): + return self + raise ValueError( + "No LLM credential configured — set LLM_API_KEY (or " + "OLLAMA_API_KEY). The agent cannot run without one." + ) + @model_validator(mode="after") def _default_mcp_key_to_agent_key(self): """Let one key configure a self-hosted agent. diff --git a/backend/app/sentinel_agent/llm.py b/backend/app/sentinel_agent/llm.py index ed832c2..39f6967 100644 --- a/backend/app/sentinel_agent/llm.py +++ b/backend/app/sentinel_agent/llm.py @@ -1,20 +1,53 @@ +"""LLM access for the agent, via LiteLLM. + +One provider path for every model. LiteLLM speaks Ollama natively +(``ollama_chat/``), so today's hosted model, a self-hoster's +bring-your-own-key provider, and eventually SourceBox's own model are all +selected by a single ``LLM_MODEL`` string rather than by swapping client +libraries. + +This module owns ALL provider-shaped knowledge. The agent loop builds +messages exclusively through the helpers below, so if the message format +ever changes again it changes here and nowhere else. That mattered when +this moved off the Ollama SDK: Ollama and OpenAI-shaped APIs disagree +about more than field names. + + - Tool results: Ollama keys them by ``tool_name``, OpenAI by + ``tool_call_id`` matched to the id it issued on the call. + - Tool arguments: Ollama hands back a dict, OpenAI a JSON *string*. + - Images: Ollama takes raw base64 in an ``images`` list on any message. + OpenAI-shaped APIs cannot carry an image on a ``tool`` message at + all — it has to be a ``user`` message whose content is a list of + parts, with the image as a ``data:`` URI. That is a structural + difference, not a formatting one, and it is why the pruning helper + below has to rewrite a content list rather than pop a key. +""" + import asyncio +import json import logging +from typing import Any -from ollama import AsyncClient +import litellm from app.sentinel_agent.config import Settings logger = logging.getLogger(__name__) +# LiteLLM chats to stdout about provider quirks on import and on first +# call; the agent's logs are read during incidents and this is noise. +litellm.suppress_debug_info = True + +# Marker left on a message whose frames were pruned, so the pruning pass +# is idempotent and a re-prune doesn't stack notices. +_PRUNED_NOTE = "[frames pruned — superseded by newer visual output]" + class LLMProvider: def __init__(self, settings: Settings) -> None: - self.client = AsyncClient( - host=settings.ollama_host, - headers={"Authorization": f"Bearer {settings.ollama_api_key}"}, - ) - self.model = settings.ollama_model + self.model = settings.resolved_llm_model + self.api_key = settings.resolved_llm_api_key + self.api_base = settings.resolved_llm_api_base self.max_tokens = settings.max_tokens self.timeout_seconds = settings.llm_call_timeout_seconds @@ -23,20 +56,133 @@ async def chat( messages: list[dict], tools: list[dict] | None = None, ): - """Send a chat request and return the response message. + """Send a chat request and return the assistant message. - Wrapped in asyncio.wait_for because the Ollama AsyncClient does - not honour a per-call timeout and a hung connection would - otherwise block until the wakeup's 270 s wall clock fires — - wasting Fly minutes and stranding the in-flight run. + Still wrapped in ``asyncio.wait_for`` despite LiteLLM taking its + own ``timeout``: that one bounds the HTTP request, not the whole + call, so a provider that accepts the connection and then stalls + mid-stream would otherwise hold the machine alive until the + 270 s wall clock in process_with_timeout fires — burning Fly + minutes and stranding the run in `running` on Command Center. """ + kwargs: dict[str, Any] = { + "model": self.model, + "messages": messages, + "max_tokens": self.max_tokens, + "timeout": self.timeout_seconds, + } + if tools: + kwargs["tools"] = tools + if self.api_key: + kwargs["api_key"] = self.api_key + if self.api_base: + kwargs["api_base"] = self.api_base + response = await asyncio.wait_for( - self.client.chat( - model=self.model, - messages=messages, - tools=tools or None, - options={"num_predict": self.max_tokens}, - ), + litellm.acompletion(**kwargs), timeout=self.timeout_seconds, ) - return response.message + return response.choices[0].message + + +# ── Message construction (the only place the wire format is known) ──── + + +def assistant_message(response_msg) -> dict: + """Convert the provider's assistant message into a plain dict. + + Appended verbatim to the running `messages` list, so it has to be + JSON-serialisable — a provider object round-trips inconsistently + once it goes back over the wire. + """ + if hasattr(response_msg, "model_dump"): + msg = response_msg.model_dump() + else: # pragma: no cover - defensive + msg = dict(response_msg) + # Drop null-valued keys some providers include; a `tool_calls: null` + # is rejected by others on the next request. + return {k: v for k, v in msg.items() if v is not None} + + +def tool_call_arguments(tool_call) -> dict: + """Arguments for a tool call, always as a dict. + + OpenAI-shaped APIs return a JSON *string* here. A model can emit + malformed JSON, and that must not take the whole run down — an empty + dict lets the tool fail on its own validation with a message the + model can actually react to. + """ + raw = tool_call.function.arguments + if isinstance(raw, dict): + return raw + if not raw: + return {} + try: + parsed = json.loads(raw) + except (TypeError, ValueError): + logger.warning( + "llm: tool %s had unparseable arguments: %.200r", + tool_call.function.name, raw, + ) + return {} + return parsed if isinstance(parsed, dict) else {} + + +def tool_result_message(tool_call, text: str) -> dict: + """The tool's textual result, keyed back to the call that asked.""" + return { + "role": "tool", + "tool_call_id": tool_call.id, + "name": tool_call.function.name, + "content": text or "", + } + + +def image_message(tool_name: str, images: list[str]) -> dict: + """Frames from a tool, as a user message with image parts. + + A separate message because an OpenAI-shaped ``tool`` message cannot + carry an image. ``images`` are raw base64 (no prefix) as returned by + the MCP layer; the ``data:`` URI is applied here so the MCP client + stays provider-neutral. + """ + parts: list[dict] = [ + {"type": "text", "text": f"[Visual output from {tool_name}]"} + ] + for b64 in images: + parts.append({ + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{b64}"}, + }) + return {"role": "user", "content": parts} + + +def has_images(message: Any) -> bool: + if not isinstance(message, dict): + return False + content = message.get("content") + if not isinstance(content, list): + return False + return any( + isinstance(p, dict) and p.get("type") == "image_url" for p in content + ) + + +def prune_images(message: dict) -> None: + """Strip image parts, collapsing the message back to its text. + + Mutates in place, matching how the caller walks the message list. + Idempotent — a message already pruned is left alone. + """ + content = message.get("content") + if not isinstance(content, list): + return + texts = [ + p.get("text", "") for p in content + if isinstance(p, dict) and p.get("type") == "text" + ] + joined = " ".join(t for t in texts if t).strip() + if _PRUNED_NOTE in joined: + message["content"] = joined + return + message["content"] = f"{joined} {_PRUNED_NOTE}".strip() diff --git a/backend/app/sentinel_agent/mcp_client.py b/backend/app/sentinel_agent/mcp_client.py index 367e46b..129bd4a 100644 --- a/backend/app/sentinel_agent/mcp_client.py +++ b/backend/app/sentinel_agent/mcp_client.py @@ -128,7 +128,11 @@ def get_tools_for_llm(self) -> list[dict]: async def call_tool(self, tool_name: str, arguments: dict) -> dict: """Execute a tool and return {text: str, images: list[str]}. - images are raw base64 strings (no data URI prefix) for Ollama. + images are raw base64 strings (no data URI prefix). Kept + provider-neutral on purpose: llm.image_message() applies whatever + wrapping the configured model wants (currently a `data:` URI in + an OpenAI-style image_url part), so switching providers never + reaches back into the MCP layer. """ if tool_name not in self._tools: return { diff --git a/backend/pyproject.toml b/backend/pyproject.toml index b51294d..6ab012f 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -39,17 +39,25 @@ dependencies = [ # (see [processes] in fly.toml), so it resolves against this one # dependency set rather than its own. # - # Only `ollama` is genuinely new. Everything else the agent needs — - # mcp, starlette, pydantic-settings, httpx, uvicorn, sentry-sdk — is - # already here, directly or via fastapi/fastmcp, and the agent's - # declared ranges all admit the versions this project resolves. + # Everything else the agent needs — mcp, starlette, pydantic-settings, + # httpx, uvicorn, sentry-sdk — is already here, directly or via + # fastapi/fastmcp, and the agent's declared ranges all admit the + # versions this project resolves. # # Notably `mcp`: the agent used to lock 2.2.0 while this project # resolves 1.28.1 via fastmcp. That is not a conflict — the agent # declares >=1.6.0,<3, and app/sentinel_agent/mcp_client.py imports # the streamable-http client under BOTH the 1.x and 2.x symbol names # (they were renamed), so it works either way. - "ollama>=0.5.0", + # + # LiteLLM is what makes the model a config string rather than a + # hardcoded client — it replaced the `ollama` SDK outright, since it + # speaks Ollama natively via `ollama_chat/`. It is heavy + # (+24 packages, boto3 and tokenizers included, both hard + # requirements) and the web process group carries that weight too, + # because Fly gives one image per app. Accepted deliberately: the + # alternative is two images, which is the separate-app arrangement + # this repo just finished removing. # IANA timezone database for Python's stdlib `zoneinfo`. # Required on Windows (where the OS doesn't ship the database) and # on slim Linux containers (alpine + python:slim) where /usr/share/ @@ -70,6 +78,7 @@ dependencies = [ # path doesn't break if FastAPI ever drops the optional dep. "jinja2>=3.1.0", "argon2-cffi>=25.1.0", + "litellm>=1.60.0", ] [project.optional-dependencies] diff --git a/backend/tests/test_agent_llm_provider.py b/backend/tests/test_agent_llm_provider.py new file mode 100644 index 0000000..f01c746 --- /dev/null +++ b/backend/tests/test_agent_llm_provider.py @@ -0,0 +1,159 @@ +"""Tests for the agent's provider layer (app/sentinel_agent/llm.py). + +These pin the message translation that moving off the Ollama SDK +introduced. It is worth pinning because every failure mode here is +quiet: a wrongly-shaped message does not raise, it just makes the model +answer as if it never saw the camera frame, or never learns what a tool +returned. The run completes, reports `no_action`, and looks fine. + +The three differences that actually bite: + + - Tool results key off ``tool_call_id`` (OpenAI) rather than + ``tool_name`` (Ollama). Get this wrong and the model cannot match a + result to the call it made. + - Tool arguments arrive as a JSON *string*, not a dict. + - Images cannot ride on a ``tool`` message and must be ``data:`` URIs + inside a content-parts list — so pruning has to rewrite that list, + where it used to just pop an ``images`` key. +""" + +from types import SimpleNamespace + +import pytest + +from app.sentinel_agent.llm import ( + assistant_message, + has_images, + image_message, + prune_images, + tool_call_arguments, + tool_result_message, +) + + +def _tool_call(name="view_camera", args='{"camera_id": 3}', call_id="call_abc"): + return SimpleNamespace( + id=call_id, + function=SimpleNamespace(name=name, arguments=args), + ) + + +# ── arguments ──────────────────────────────────────────────────────── + + +def test_json_string_arguments_are_parsed(): + """OpenAI-shaped providers send a JSON string, not a dict.""" + assert tool_call_arguments(_tool_call()) == {"camera_id": 3} + + +def test_dict_arguments_pass_through(): + """Ollama-style dicts still work, so the helper is provider-agnostic.""" + assert tool_call_arguments(_tool_call(args={"camera_id": 7})) == {"camera_id": 7} + + +@pytest.mark.parametrize("bad", ['{"camera_id": ', "", None, "[1,2,3]", '"str"']) +def test_malformed_arguments_degrade_to_empty_dict(bad): + """A model emitting bad JSON must not kill the run. + + An empty dict lets the tool fail its own validation and hand the + model an error it can react to; raising here would abort a run that + had already been paid for. + """ + assert tool_call_arguments(_tool_call(args=bad)) == {} + + +# ── tool results ───────────────────────────────────────────────────── + + +def test_tool_result_is_keyed_by_call_id(): + msg = tool_result_message(_tool_call(call_id="call_xyz"), "2 cameras online") + assert msg["role"] == "tool" + assert msg["tool_call_id"] == "call_xyz" + assert msg["content"] == "2 cameras online" + # The Ollama-era key would be silently ignored by an OpenAI-shaped + # provider, leaving the result unattributable to its call. + assert "tool_name" not in msg + + +def test_tool_result_handles_empty_output(): + assert tool_result_message(_tool_call(), "")["content"] == "" + + +# ── images ─────────────────────────────────────────────────────────── + + +def test_images_become_data_uri_parts_on_a_user_message(): + msg = image_message("view_camera", ["QUJD", "REVG"]) + # Must be `user`: an OpenAI-shaped `tool` message cannot carry an image. + assert msg["role"] == "user" + parts = msg["content"] + assert parts[0]["type"] == "text" + assert "view_camera" in parts[0]["text"] + urls = [p["image_url"]["url"] for p in parts if p["type"] == "image_url"] + assert urls == [ + "data:image/jpeg;base64,QUJD", + "data:image/jpeg;base64,REVG", + ] + + +def test_has_images_only_matches_real_image_parts(): + assert has_images(image_message("view_camera", ["QUJD"])) + assert not has_images({"role": "user", "content": "plain text"}) + assert not has_images({"role": "tool", "content": [{"type": "text", "text": "x"}]}) + assert not has_images("not a dict") + + +# ── pruning ────────────────────────────────────────────────────────── + + +def test_pruning_strips_frames_but_keeps_the_text(): + """Frames are re-sent on every subsequent call otherwise — O(N²) + retransmission that blows the context window mid-run.""" + msg = image_message("view_camera", ["QUJD", "REVG"]) + prune_images(msg) + assert not has_images(msg) + assert isinstance(msg["content"], str) + assert "view_camera" in msg["content"] + assert "pruned" in msg["content"] + + +def test_pruning_is_idempotent(): + """The pass walks every prior message each iteration, so a message + can be visited repeatedly; notices must not stack.""" + msg = image_message("view_camera", ["QUJD"]) + prune_images(msg) + once = msg["content"] + prune_images(msg) + assert msg["content"] == once + + +def test_pruning_leaves_plain_messages_alone(): + msg = {"role": "user", "content": "no images here"} + prune_images(msg) + assert msg["content"] == "no images here" + + +# ── assistant message ──────────────────────────────────────────────── + + +def test_assistant_message_drops_none_values(): + """Some providers reject `tool_calls: null` on the NEXT request, so a + null-valued key round-tripping back over the wire is a real failure.""" + class _Msg: + def model_dump(self): + return {"role": "assistant", "content": "hi", "tool_calls": None} + + out = assistant_message(_Msg()) + assert out == {"role": "assistant", "content": "hi"} + + +def test_assistant_message_is_json_serialisable(): + import json + + class _Msg: + def model_dump(self): + return {"role": "assistant", "content": "x", "tool_calls": [ + {"id": "c1", "function": {"name": "t", "arguments": "{}"}} + ]} + + json.dumps(assistant_message(_Msg())) # must not raise diff --git a/backend/uv.lock b/backend/uv.lock index 1465a10..cc3182d 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -34,6 +34,128 @@ wheels = [ { url 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