diff --git a/fastapi_startkit/pyproject.toml b/fastapi_startkit/pyproject.toml index 84f9d113..6fdb9338 100644 --- a/fastapi_startkit/pyproject.toml +++ b/fastapi_startkit/pyproject.toml @@ -53,6 +53,11 @@ ai = [ "google-generativeai>=0.8.0", ] +langchain = [ + "langchain>=1.0.0", + "langchain-core>=1.0.0", +] + [dependency-groups] dev = [ "dumpdie>=1.5.0", @@ -68,6 +73,8 @@ dev = [ "sqlalchemy[asyncio]>=2.0.38", "fastapi[standard]>=0.124.4", "faker>=40.13.0", + "langchain>=1.0.0", + "langchain-core>=1.0.0", ] diff --git a/fastapi_startkit/src/fastapi_startkit/ai/__init__.py b/fastapi_startkit/src/fastapi_startkit/ai/__init__.py index 76c04f99..2e0f52dc 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/__init__.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/__init__.py @@ -22,6 +22,7 @@ from .config import AIConfig, AnthropicConfig, GoogleConfig, OpenAIConfig from .decorators import max_steps, max_tokens, memory, model, provider, timeout, top_p from .document import Document +from .fakes import fake_chat_model from .image import Image, ImageResponse from .image_factory import ImageFactory from .providers.ai_provider import AIProvider @@ -38,6 +39,7 @@ "AudioResponse", "AudioFactory", "Document", + "fake_chat_model", "GoogleConfig", "Image", "ImageFactory", diff --git a/fastapi_startkit/src/fastapi_startkit/ai/agent.py b/fastapi_startkit/src/fastapi_startkit/ai/agent.py index 05a7c664..f95e8ebe 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/agent.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/agent.py @@ -3,11 +3,14 @@ from __future__ import annotations import fnmatch -from typing import Any, Callable, Iterator, Optional, Type +from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Optional, Type from .document import Document from .response import AgentResponse, AgentSnapshot +if TYPE_CHECKING: + from langchain_core.messages import AIMessage + class Agent: """ @@ -39,7 +42,7 @@ class Agent: } def __init__(self): - self._fakes: dict[str, AgentResponse | AgentSnapshot] = {} + self._fakes: dict[str, AgentResponse | AgentSnapshot | "AIMessage"] = {} self._call_log: list[dict] = [] # ── Lifecycle — override in subclasses ────────────────────────────────── @@ -97,10 +100,7 @@ def prompt( match = self._match_fake(message) if match is not None: - if isinstance(match, AgentSnapshot): - response = match.resolve(self, message, **_run_kwargs) - else: - response = match + response = self._resolve_fake(match, message, _run_kwargs) self._log_call("prompt", message) return self.after(response) @@ -124,20 +124,45 @@ def stream( self._log_call("stream", message) fake = self._match_fake(message) if fake is not None: - if isinstance(fake, AgentSnapshot): - response = fake.resolve(self, message) - else: - response = fake + response = self._resolve_fake(fake, message) yield response.content return yield from self._stream(message, system=system, model=model, provider_options=provider_options) - def fake(self, patterns: dict[str, AgentResponse | AgentSnapshot]) -> "Agent": - """Register fake responses for testing. Keys are glob patterns.""" + def fake(self, patterns: dict[str, "AgentResponse | AgentSnapshot | AIMessage"]) -> "Agent": + """Register fake responses for testing. Keys are glob patterns. + + A value may be an :class:`AgentResponse`, an :class:`AgentSnapshot` + (record/replay), or a LangChain ``AIMessage`` (converted on match, + preserving ``content`` and ``tool_calls``). When a prompt matches a + pattern the agent returns the fake without ever calling the provider. + """ for pattern, value in patterns.items(): self._fakes[pattern] = value return self + def record(self, path: str, *, pattern: str = "*") -> "Agent": + """Record-and-replay (VCR style) for prompts matching ``pattern``. + + On the first run the real provider is called and the response is saved to + ``path``; every later run replays that recording without hitting the API. + Returns ``self`` for chaining. + """ + self._fakes[pattern] = AgentSnapshot(path=path) + return self + + @staticmethod + def fake_model(turns: Iterable[Any]): + """Return a LangChain fake chat model that replays ``turns`` in order. + + Pass it to ``langchain.agents.create_agent(model=..., tools=[...])`` to run + a full agent loop — tool calls included — offline. Requires the + ``langchain`` extra. See :func:`fastapi_startkit.ai.fakes.fake_chat_model`. + """ + from .fakes import fake_chat_model + + return fake_chat_model(turns) + def assert_prompted(self, times: int | None = None) -> None: """Assert that prompt() or stream() was called.""" calls = [c for c in self._call_log if c["method"] in ("prompt", "stream")] @@ -158,12 +183,25 @@ def reset(self) -> "Agent": # ── Internal helpers ──────────────────────────────────────────────────── - def _match_fake(self, message: str) -> Optional[AgentResponse | AgentSnapshot]: + def _match_fake(self, message: str) -> Optional[Any]: for pattern, value in self._fakes.items(): if fnmatch.fnmatch(message.lower(), pattern.lower()): return value return None + def _resolve_fake(self, match: Any, message: str, run_kwargs: dict | None = None) -> AgentResponse: + """Turn a registered fake into an AgentResponse without calling the provider.""" + if isinstance(match, AgentSnapshot): + return match.resolve(self, message, **(run_kwargs or {})) + if isinstance(match, AgentResponse): + return match + + from .fakes import is_ai_message, to_agent_response + + if is_ai_message(match): + return to_agent_response(match) + raise TypeError(f"Unsupported fake value: {type(match)!r}") + def _log_call(self, method: str, message: str) -> None: self._call_log.append({"method": method, "message": message}) diff --git a/fastapi_startkit/src/fastapi_startkit/ai/fakes.py b/fastapi_startkit/src/fastapi_startkit/ai/fakes.py new file mode 100644 index 00000000..a8959f7c --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/fakes.py @@ -0,0 +1,94 @@ +"""LangChain test-harness helpers — run agents offline, without a real provider. + +These utilities let tests exercise a *real* LangChain agent (``create_agent``) +without calling a model API, by replaying a scripted sequence of assistant turns +through a fake chat model. They require the ``langchain`` extra:: + + pip install "fastapi-startkit[langchain]" + +Example — drive a full tool-calling loop with no network:: + + from langchain_core.messages import AIMessage, ToolCall + from langchain.agents import create_agent + from fastapi_startkit.ai import fake_chat_model + + model = fake_chat_model([ + AIMessage(content="", tool_calls=[ + ToolCall(name="search_jobs", args={"query": "python"}, id="c1", type="tool_call"), + ]), + AIMessage(content="Here is a Python Developer role at Shopify."), + ]) + agent = create_agent(model=model, tools=[search_jobs]) + result = agent.invoke({"messages": [{"role": "user", "content": "Find me a python job"}]}) +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Iterable + +from .response import AgentResponse + +if TYPE_CHECKING: + from langchain_core.messages import AIMessage + + +def _require_langchain(): + try: + from langchain_core.language_models.fake_chat_models import GenericFakeChatModel + from langchain_core.messages import AIMessage + except ImportError as exc: # pragma: no cover - exercised only without the extra + raise ImportError( + "LangChain is required for the agent test harness. " + "Install it with: pip install \"fastapi-startkit[langchain]\"" + ) from exc + return GenericFakeChatModel, AIMessage + + +def fake_chat_model(turns: Iterable[Any]): + """Return a fake chat model that replays ``turns`` in order. + + Each turn is an ``AIMessage`` (which may carry ``tool_calls``) or a ``str`` + (shorthand for ``AIMessage(content=...)``). Pass the result straight to + ``langchain.agents.create_agent(model=..., tools=[...])`` to run a complete + agent loop — tool calls included — without hitting a real provider. + """ + generic_model, ai_message = _require_langchain() + + class _FakeChatModel(generic_model): + # create_agent calls bind_tools() when tools are present; GenericFakeChatModel + # leaves it unimplemented. The scripted turns already encode the model's + # decisions, so the bound tool schemas are irrelevant — binding is a no-op. + def bind_tools(self, tools, **kwargs): + return self + + normalized = [t if isinstance(t, ai_message) else ai_message(content=str(t)) for t in turns] + return _FakeChatModel(messages=iter(normalized)) + + +def is_ai_message(value: Any) -> bool: + """Return True if ``value`` is a LangChain ``AIMessage`` (False if langchain is absent).""" + try: + from langchain_core.messages import AIMessage + except ImportError: + return False + return isinstance(value, AIMessage) + + +def to_agent_response(value: "AIMessage") -> AgentResponse: + """Convert a LangChain ``AIMessage`` into an :class:`AgentResponse`.""" + _, ai_message = _require_langchain() + if not isinstance(value, ai_message): + raise TypeError(f"Expected an AIMessage, got {type(value)!r}") + + content = value.content if isinstance(value.content, str) else str(value.content) + tool_calls = list(getattr(value, "tool_calls", None) or []) + + usage: dict = {} + meta = getattr(value, "usage_metadata", None) + if meta: + usage = { + "input": meta.get("input_tokens", 0), + "output": meta.get("output_tokens", 0), + } + + return AgentResponse(content=content, tool_calls=tool_calls, usage=usage, raw=value) diff --git a/fastapi_startkit/tests/ai/test_agent_langchain.py b/fastapi_startkit/tests/ai/test_agent_langchain.py new file mode 100644 index 00000000..6652cc61 --- /dev/null +++ b/fastapi_startkit/tests/ai/test_agent_langchain.py @@ -0,0 +1,97 @@ +"""Tests for the LangChain test harness — fake AIMessage fakes and fake_chat_model. + +These exercise a real LangChain agent loop entirely offline. They are skipped +automatically when the optional ``langchain`` extra is not installed. +""" + +import pytest + +pytest.importorskip("langchain") +pytest.importorskip("langchain_core") + +from langchain.agents import create_agent # noqa: E402 +from langchain_core.messages import AIMessage, ToolCall # noqa: E402 +from langchain_core.tools import tool # noqa: E402 + +from fastapi_startkit.ai import fake_chat_model # noqa: E402 +from fastapi_startkit.ai.agent import Agent # noqa: E402 + + +class SimpleAgent(Agent): + pass + + +@tool +def search_jobs(query: str): + """Search for jobs matching the query.""" + mock_db = [ + {"title": "Python Developer", "company": "Shopify"}, + {"title": "Data Engineer", "company": "Google"}, + ] + return [job for job in mock_db if query.lower() in job["title"].lower()] or mock_db + + +# ─── fake() accepts AIMessage values ───────────────────────────────────────── + + +def test_fake_with_aimessage_value_returns_content(): + agent = SimpleAgent() + agent.fake({"*hi*": AIMessage(content="hello there")}) + + result = agent.prompt("hi friend") + assert result.content == "hello there" + agent.assert_prompted(times=1) + + +def test_fake_with_aimessage_preserves_tool_calls(): + agent = SimpleAgent() + call = ToolCall(name="search_jobs", args={"query": "python"}, id="c1", type="tool_call") + agent.fake({"*job*": AIMessage(content="", tool_calls=[call])}) + + result = agent.prompt("find me a job") + assert result.content == "" + assert result.tool_calls and result.tool_calls[0]["name"] == "search_jobs" + + +def test_fake_aimessage_does_not_call_run(): + agent = SimpleAgent() + agent.fake({"*": AIMessage(content="faked")}) + + called = [] + agent._run = lambda *a, **kw: called.append(True) # type: ignore[method-assign] + + agent.prompt("anything") + assert called == [] + + +# ─── fake_chat_model drives a full create_agent loop offline ────────────────── + + +def test_fake_chat_model_runs_tool_calling_agent_offline(): + model = fake_chat_model( + [ + AIMessage( + content="", + tool_calls=[ToolCall(name="search_jobs", args={"query": "python"}, id="c1", type="tool_call")], + ), + AIMessage(content="Here is a Python Developer role at Shopify."), + ] + ) + agent = create_agent(model=model, tools=[search_jobs]) + + result = agent.invoke({"messages": [{"role": "user", "content": "Find me a python job"}]}) + messages = result["messages"] + + assert messages[-1].content == "Here is a Python Developer role at Shopify." + assert any(getattr(m, "type", None) == "tool" for m in messages), "the tool should have run" + + +def test_fake_chat_model_accepts_plain_strings(): + model = fake_chat_model(["just a string answer"]) + result = model.invoke("hello") + assert result.content == "just a string answer" + + +def test_agent_fake_model_staticmethod_builds_usable_model(): + model = Agent.fake_model([AIMessage(content="from staticmethod")]) + assert model.invoke("hi").content == "from staticmethod" diff --git a/fastapi_startkit/tests/ai/test_agent_record.py b/fastapi_startkit/tests/ai/test_agent_record.py new file mode 100644 index 00000000..c17250a1 --- /dev/null +++ b/fastapi_startkit/tests/ai/test_agent_record.py @@ -0,0 +1,96 @@ +"""Tests for Agent.record() — VCR-style record-then-replay.""" + +import json +import os +import tempfile + +from fastapi_startkit.ai.agent import Agent +from fastapi_startkit.ai.response import AgentResponse + + +class SimpleAgent(Agent): + """Bare-minimum agent for testing — never touches a real API.""" + + pass + + +def test_record_replays_from_existing_file_without_calling_run(): + fixture = {"content": "recorded reply", "tool_calls": [], "usage": {"input": 1, "output": 2}} + + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json.dump(fixture, f) + path = f.name + + try: + agent = SimpleAgent() + agent.record(path) + + called = [] + agent._run = lambda *a, **kw: called.append(True) # type: ignore[method-assign] + + result = agent.prompt("anything") + + assert result.content == "recorded reply" + assert result.usage == {"input": 1, "output": 2} + assert called == [], "_run() must not run when a recording already exists" + finally: + os.unlink(path) + + +def test_record_calls_run_and_saves_when_file_missing(monkeypatch): + with tempfile.TemporaryDirectory() as tmpdir: + path = os.path.join(tmpdir, "nested", "cassette.json") + + agent = SimpleAgent() + agent.record(path) + monkeypatch.setattr(agent, "_run", lambda *a, **kw: AgentResponse(content="live")) + + result = agent.prompt("hello") + + assert result.content == "live" + assert os.path.exists(path), "recording should be written on first run" + with open(path) as f: + assert json.load(f)["content"] == "live" + + +def test_record_returns_agent_for_chaining(): + agent = SimpleAgent() + assert agent.record("ignored.json") is agent + + +def test_record_respects_custom_pattern(): + agent = SimpleAgent() + agent.record("never-written.json", pattern="*weather*") + + # Non-matching prompt falls through to _run (no provider configured) → raises. + import pytest + + with pytest.raises(Exception): + agent.prompt("tell me a joke") + + +def test_record_then_replay_roundtrip(monkeypatch): + with tempfile.TemporaryDirectory() as tmpdir: + path = os.path.join(tmpdir, "roundtrip.json") + calls = [] + + agent = SimpleAgent() + agent.record(path) + monkeypatch.setattr( + agent, + "_run", + lambda *a, **kw: calls.append(True) or AgentResponse(content="first call"), + ) + + first = agent.prompt("question") + assert first.content == "first call" + assert len(calls) == 1 + + # A fresh agent pointed at the same cassette replays without calling _run again. + replay = SimpleAgent() + replay.record(path) + replay._run = lambda *a, **kw: calls.append(True) # type: ignore[method-assign] + + second = replay.prompt("question") + assert second.content == "first call" + assert len(calls) == 1, "replay must not call the provider again" diff --git 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