diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index a884c6b7..5b26fc1c 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -28,6 +28,11 @@ jobs: with: python-version: "3.13" + - name: Set up Node + uses: actions/setup-node@v4 + with: + node-version: "20" + - name: Start MySQL run: docker compose -f docker-compose.yml up -d --wait @@ -62,6 +67,25 @@ jobs: working-directory: example/database-app run: uv run pytest tests/ -v + # ── example/agents ──────────────────────────────────────────────────── + - name: Install dependencies (agents) + working-directory: example/agents + run: uv sync + + - name: Build frontend assets (agents) + working-directory: example/agents + run: | + npm ci + npm run build + + - name: Copy .env.example to .env (agents) + working-directory: example/agents + run: cp .env.example .env + + - name: Run tests (agents) + working-directory: example/agents + run: uv run pytest tests/ -v + - name: Stop MySQL if: always() run: docker compose -f docker-compose.yml down diff --git a/.gitignore b/.gitignore index 5a93aab3..2cd7994e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ .idea .DS_Store **/.venv +**/node_modules/ **/__pycache__/ **/*.db **/*.sqlite3 diff --git a/bin/test.sh b/bin/test.sh index b04d9f4c..380fc008 100755 --- a/bin/test.sh +++ b/bin/test.sh @@ -43,3 +43,11 @@ echo "============================================================" echo " Running: example/database-app tests" echo "============================================================" (cd "$ROOT/example/database-app" && uv run pytest) + +echo "" +echo "============================================================" +echo " Running: example/agents tests" +echo "============================================================" +# Build frontend assets — the index page resolves them via the Vite manifest. +(cd "$ROOT/example/agents" && npm ci && npm run build) +(cd "$ROOT/example/agents" && uv run pytest) diff --git a/example/agents/.ai/fastapi-startkit/fastapi/SKILL.md b/example/agents/.ai/fastapi-startkit/fastapi/SKILL.md new file mode 100644 index 00000000..ace0a63b --- /dev/null +++ b/example/agents/.ai/fastapi-startkit/fastapi/SKILL.md @@ -0,0 +1,109 @@ +--- +name: fastapi-startkit +description: Routing, controllers, ORM, requests, resources, and action pattern for fastapi-startkit applications. +--- + +# Fastapi's Routing + +### Fastapi Startkit's Router +```python +# routes/web.py +from fastapi_startkit.fastapi import Router + +router = Router() +``` + +and use the crud resources, for example +```python +router.post("/users", users_controller.store) +router.put("/users/{user_id}", users_controller.update) +router.patch("/users/{user_id}", users_controller.patch) +router.delete("/users", users_controller.destroy) +``` + +the controller will look like +```python +# app/http/controllers/users_controller.py +async def index(request: Request): + pass + +async def show(user_id: int): + pass + +async def store(data: UserSchema): + pass + +async def update(user_id: int, data: UserSchema): + pass + +async def destroy(user_id: int): + pass +``` + +or use the resource function as: +```python +router.resource("users", users_controller, excepts=['create', 'edit']) +``` + +## ORM +```python +# app/models/user.py +from fastapi_startkit.masoniteorm import Model + +class User(Model): + id: int + name: str + email: str + metadata: dict +``` + +and use the orm as: +```python +# app/http/controllers/users_controller.py +from app.models import User + +async def store(request: UserStoreRequest): + user = User.create(request.model_dump()) + ... +``` + +the `UserStoreRequest` will look like: +```python +# app/http/requests/user_store_request.py +from pydantic import BaseModel + +class UserStoreRequest(BaseModel): + name: str +``` + +and use JsonApiResource to return JSON response from the controller: +```python +from fastapi_startkit.jsonapi import JsonResource + +# app/http/controllers/users_controller.py +from app.models import User + +async def store(request: UserStoreRequest): + user = User.create(request.model_dump()) + return JsonResource(user) +``` + +## Architecture + +use the action pattern to write complex logic. +```python +# app/actions/user_actions.py +from app.models import User + +class UserStoreAction: + def __init__(self, request: UserStoreRequest): + self.request = request + + @staticmethod + def prepare(request: UserStoreRequest) -> 'UserStoreAction': + return UserStoreAction(request) + + def handle(self) -> JsonResource[User]: + user = User.create(self.request.model_dump()) + return JsonResource(user) +``` diff --git a/example/agents/.env.example b/example/agents/.env.example new file mode 100644 index 00000000..10581e95 --- /dev/null +++ b/example/agents/.env.example @@ -0,0 +1,5 @@ +APP_ENV=local +APP_URL=http://127.0.0.1:7654 + +AI_PROVIDER=google +GEMINI_API_KEY= diff --git a/example/agents/.gitignore b/example/agents/.gitignore new file mode 100644 index 00000000..b2db17eb --- /dev/null +++ b/example/agents/.gitignore @@ -0,0 +1,9 @@ +.idea +.DS_Store +.venv +**__pycache__ +storage +.env +node_modules +/public/build +/public/hot diff --git a/example/agents/app/agents/chat.py b/example/agents/app/agents/chat.py new file mode 100644 index 00000000..6d134452 --- /dev/null +++ b/example/agents/app/agents/chat.py @@ -0,0 +1,17 @@ +from typing import Callable + +from fastapi_startkit.ai import Agent, Middleware + +from app.middleware.agent_logger import AgentLogger +from app.tools.job_search_tool import job_search_tool + + +class RouterAgent(Agent): + def middleware(self) -> list[Middleware]: + return [AgentLogger()] + + def tools(self) -> list[Callable]: + return [job_search_tool] + + def instructions(self) -> str: + return "You are a friendly customer support assistant." diff --git a/example/agents/app/middleware/agent_logger.py b/example/agents/app/middleware/agent_logger.py new file mode 100644 index 00000000..8fd8d7b9 --- /dev/null +++ b/example/agents/app/middleware/agent_logger.py @@ -0,0 +1,25 @@ +import time +from collections.abc import Callable +from typing import Any + +from langchain_core.language_models.chat_models import BaseChatModel + +from fastapi_startkit.logging import Logger + + +def _model_name(model: BaseChatModel) -> str: + return getattr(model, "model", None) or getattr(model, "model_name", None) or type(model).__name__ + + +class AgentLogger: + def handle(self, model: BaseChatModel, handler: Callable) -> Any: + Logger.info(f"request | model={_model_name(model)}") + started_at = time.monotonic() + + def log_response(final: Any) -> None: + elapsed = time.monotonic() - started_at + meta = getattr(final, "usage_metadata", None) or {} + preview = str(getattr(final, "content", final) or "")[:200].replace("\n", " ") + Logger.info(f"response | {elapsed:.2f}s | in={meta.get('input_tokens', '?')} out={meta.get('output_tokens', '?')} tokens | {preview}") + + return handler(model).then(log_response) diff --git a/example/agents/app/providers/fastapi_provider.py b/example/agents/app/providers/fastapi_provider.py new file mode 100644 index 00000000..f015a34c --- /dev/null +++ b/example/agents/app/providers/fastapi_provider.py @@ -0,0 +1,13 @@ +from fastapi_startkit.fastapi import FastAPIProvider as BaseFastAPIProvider +from fastapi.templating import Jinja2Templates + + +class FastapiProvider(BaseFastAPIProvider): + def boot(self): + templates_dir = self.app.use_base_path("resources/templates") + self.app.bind("templates", Jinja2Templates(directory=str(templates_dir))) + + super().boot() + from routes.api import api + + self.app.fastapi.include_router(api) diff --git a/example/agents/app/requests/chat.py b/example/agents/app/requests/chat.py new file mode 100644 index 00000000..163422ac --- /dev/null +++ b/example/agents/app/requests/chat.py @@ -0,0 +1,5 @@ +from pydantic import BaseModel, Field + + +class ChatRequest(BaseModel): + message: str = Field(...) diff --git a/example/agents/app/tools/job_search_tool.py b/example/agents/app/tools/job_search_tool.py new file mode 100644 index 00000000..4d76fd83 --- /dev/null +++ b/example/agents/app/tools/job_search_tool.py @@ -0,0 +1,25 @@ +from langchain_core.tools import tool + +jobs = [ + {"id": 1, "title": "Software Engineer", "location": "San Francisco", "company": "Acme Corp", "type": "Full-time"}, + {"id": 2, "title": "Frontend Developer", "location": "Remote", "company": "Startup Inc", "type": "Full-time"}, + {"id": 3, "title": "Data Scientist", "location": "New York", "company": "DataCo", "type": "Full-time"}, + {"id": 4, "title": "DevOps Engineer", "location": "Austin", "company": "CloudBase", "type": "Contract"}, + {"id": 5, "title": "Product Manager", "location": "Remote", "company": "ProductHQ", "type": "Full-time"}, +] + + +@tool +def job_search_tool(query: str) -> list: + """Searches for jobs based on the given query. Supports wildcards (* and ?) in each term.""" + import fnmatch + + patterns = [f"*{term}*" for term in query.lower().split()] + + return [ + job for job in jobs + if any( + fnmatch.fnmatch(" ".join(str(v) for v in job.values()).lower(), pattern) + for pattern in patterns + ) + ] diff --git a/example/agents/artisan b/example/agents/artisan new file mode 100644 index 00000000..2a546fc4 --- /dev/null +++ b/example/agents/artisan @@ -0,0 +1,9 @@ +#!/usr/bin/env python3 + +import sys + +from bootstrap.application import app + +if __name__ == "__main__": + status = app.handle_command() + sys.exit(status if isinstance(status, int) else 0) diff --git a/example/agents/bootstrap/application.py b/example/agents/bootstrap/application.py new file mode 100644 index 00000000..f198c0a0 --- /dev/null +++ b/example/agents/bootstrap/application.py @@ -0,0 +1,25 @@ +from pathlib import Path + +from fastapi_startkit import Application +from fastapi_startkit.inertia import InertiaProvider +from fastapi_startkit.logging import LogProvider +from fastapi_startkit.skills import AISkillProvider +from fastapi_startkit.vite import ViteProvider +from fastapi_startkit.ai import AIProvider + +from config.fastapi import FastAPIConfig +from config.logging import LoggingConfig +from config.vite import ViteConfig +from app.providers.fastapi_provider import FastapiProvider + +app: Application = Application( + base_path=Path(__file__).resolve().parent.parent, + providers=[ + AISkillProvider, + (LogProvider,LoggingConfig), + (FastapiProvider, FastAPIConfig), + AIProvider, + (ViteProvider, ViteConfig), + InertiaProvider, + ], +) diff --git a/example/agents/config/fastapi.py b/example/agents/config/fastapi.py new file mode 100644 index 00000000..e6cd55ab --- /dev/null +++ b/example/agents/config/fastapi.py @@ -0,0 +1,18 @@ +import dataclasses + +from fastapi_startkit.environment import env + + +@dataclasses.dataclass +class FastAPIConfig: + app_url: str = dataclasses.field(default_factory=lambda: env("APP_URL", "http://127.0.0.1:8000")) + reload: bool = dataclasses.field(default_factory=lambda: env("APP_RELOAD", True)) + reload_dirs: list | None = None + reload_excludes: list = dataclasses.field( + default_factory=lambda: [ + "*.log", + "tests/*", + "node_modules/*", + "storage/*", + ] + ) diff --git a/example/agents/config/logging.py b/example/agents/config/logging.py new file mode 100644 index 00000000..553a7426 --- /dev/null +++ b/example/agents/config/logging.py @@ -0,0 +1,22 @@ +import dataclasses + +from fastapi_startkit.environment import env +from fastapi_startkit.logging.config import StackChannel, DailyChannel, TerminalChannel + + +@dataclasses.dataclass +class LoggingConfig: + default: str = dataclasses.field(default_factory=lambda: env("LOG_CHANNEL", "stack")) + + channels: dict = dataclasses.field( + default_factory=lambda: { + "stack": StackChannel(driver="stack", channels=["daily", "terminal"]), + "daily": DailyChannel( + level=env("LOG_DAILY_LEVEL", "debug"), + path=env("LOG_DAILY_PATH", "storage/logs"), + ), + "terminal": TerminalChannel( + level=env("LOG_TERMINAL_LEVEL", "info"), + ), + } + ) diff --git a/example/agents/config/vite.py b/example/agents/config/vite.py new file mode 100644 index 00000000..71431700 --- /dev/null +++ b/example/agents/config/vite.py @@ -0,0 +1,12 @@ +from pydantic.dataclasses import dataclass + + +@dataclass +class ViteConfig: + public_path: str = "public" + build_directory: str = "build" + hot_file: str = "hot" + manifest_filename: str = "manifest.json" + asset_url: str = "" + static_url: str = "/build" + mount_static: bool = True diff --git a/example/agents/package-lock.json b/example/agents/package-lock.json new file mode 100644 index 00000000..64a422b0 --- /dev/null +++ b/example/agents/package-lock.json @@ -0,0 +1,1454 @@ +{ + "name": "agents", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "@inertiajs/react": "^3.4.0", + 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a/example/agents/package.json b/example/agents/package.json new file mode 100644 index 00000000..4296bade --- /dev/null +++ b/example/agents/package.json @@ -0,0 +1,24 @@ +{ + "private": true, + "type": "module", + "scripts": { + "dev": "concurrently -c \"#fb7185,#fdba74\" \"uv run python artisan serve\" \"vite\"", + "build": "vite build", + "types:check": "tsc --noEmit" + }, + "devDependencies": { + "@tailwindcss/vite": "^4.0.0", + "@types/react": "^19.2.17", + "@types/react-dom": "^19.2.3", + "fastapi-vite-plugin": "^0.0.3", + "tailwindcss": "^4.0.0", + "typescript": "^5.0.0", + "vite": "^8.0.0" + }, + "dependencies": { + "@inertiajs/react": "^3.4.0", + "@vitejs/plugin-react": "^6.0.2", + "react": "^19.2.7", + "react-dom": "^19.2.7" + } +} diff --git a/example/agents/pyproject.toml b/example/agents/pyproject.toml new file mode 100644 index 00000000..aa074982 --- /dev/null +++ b/example/agents/pyproject.toml @@ -0,0 +1,44 @@ +[project] +name = "fastapi-startkit-application" +version = "0.1.0" +description = "" +authors = [ + { name = "Bedram Tamang", email = "tmgbedu@gmail.com" } +] +readme = "README.md" +requires-python = ">=3.12,<4.0" +dependencies = [ + "cleo>=2.1.0", + "fastapi-startkit[ai,database,fastapi,sqlite]==0.44.0", + "langchain>=1.3.10", + "langchain-google-genai>=4.2.5", +] + +[tool.uv] +package = false + +[dependency-groups] +dev = [ + "dumpdie>=1.5.0", + "pytest>=9.0.2", + "pytest-asyncio>=0.24.0", + "ruff>=0.9.0", +] + +[tool.ruff] +line-length = 120 + +[tool.ruff.lint] +select = ["F401"] +fixable = ["F401"] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +pythonpath = ["."] +testpaths = ["tests"] + +[tool.uv.sources] +fastapi-startkit = { path = "../../fastapi_startkit", editable = true } diff --git a/example/agents/readme.md b/example/agents/readme.md new file mode 100644 index 00000000..92f80498 --- /dev/null +++ b/example/agents/readme.md @@ -0,0 +1,42 @@ +# FastAPI StartKit + +FastAPI StartKit is a modular, provider-driven framework for building robust FastAPI applications with minimal boilerplate. That said, it doesn't lock you into FastAPI at all — you can build entirely headless CLI utilities, cron scripts, or background task workers and still get the full suite of infrastructure components: logging, database, configuration, and dependency injection. + +[Full Documentation](https://fastapi-startkit.github.io/docs/fastapi.html) + +## Quick Start + +### 1. Clone the repository +```shell +git clone https://github.com/fastapi-startkit/fastapi_startkit example-app +cd example-app +cp .env.example .env +``` + +### 2. Install dependencies +```shell +uv sync +npm install +``` + +### 3. Run the application +```shell +npm run dev +``` +This boots the backend (`artisan serve`) and the Vite frontend together. + +## AI Skills + +Skills teach AI agents (Claude Code, Gemini) this project's conventions. + +List providers that declare skills: + +```shell +uv run python artisan ai:skills +``` + +Publish stubs and sync them to AI agents: + +```shell +uv run python artisan ai:skills --sync +``` diff --git a/example/agents/resources/css/app.css b/example/agents/resources/css/app.css new file mode 100644 index 00000000..f1d8c73c --- /dev/null +++ b/example/agents/resources/css/app.css @@ -0,0 +1 @@ +@import "tailwindcss"; diff --git a/example/agents/resources/js/app.tsx b/example/agents/resources/js/app.tsx new file mode 100644 index 00000000..b5d849d6 --- /dev/null +++ b/example/agents/resources/js/app.tsx @@ -0,0 +1,21 @@ +import '../css/app.css' +import { createInertiaApp } from "@inertiajs/react" +import { createRoot } from "react-dom/client" + +// @ts-ignore +const appName = import.meta.env.VITE_APP_NAME || "My App" + +await createInertiaApp({ + title: title => `${title} - ${appName}`, + resolve: name => { + // @ts-ignore + const pages = import.meta.glob('./pages/**/*.tsx', { eager: true }) + return pages[`./pages/${name}.tsx`] + }, + setup({ el, App, props }) { + createRoot(el).render() + }, + progress: { + color: "#F87415", + }, +}) diff --git a/example/agents/resources/js/pages/chat/Index.tsx b/example/agents/resources/js/pages/chat/Index.tsx new file mode 100644 index 00000000..d3a112a4 --- /dev/null +++ b/example/agents/resources/js/pages/chat/Index.tsx @@ -0,0 +1,98 @@ +import { useState, useRef, useEffect } from "react" + +type Message = { + role: "user" | "assistant" + content: string +} + +export default function Chat() { + const [messages, setMessages] = useState([]) + const [input, setInput] = useState("") + const [loading, setLoading] = useState(false) + const bottomRef = useRef(null) + + useEffect(() => { + bottomRef.current?.scrollIntoView({ behavior: "smooth" }) + }, [messages]) + + const handleSubmit = async (e: { preventDefault(): void }) => { + e.preventDefault() + if (!input.trim() || loading) return + + const userMessage = input.trim() + setInput("") + setMessages(prev => [...prev, { role: "user", content: userMessage }]) + setLoading(true) + setMessages(prev => [...prev, { role: "assistant", content: "" }]) + + try { + const response = await fetch("/chat/stream", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ message: userMessage }), + }) + + const reader = response.body?.getReader() + const decoder = new TextDecoder() + if (!reader) return + + while (true) { + const { done, value } = await reader.read() + if (done) break + const chunk = decoder.decode(value, { stream: true }) + setMessages(prev => { + const updated = [...prev] + updated[updated.length - 1] = { + role: "assistant", + content: updated[updated.length - 1].content + chunk, + } + return updated + }) + } + } finally { + setLoading(false) + } + } + + return ( +
+

Chat

+ +
+ {messages.length === 0 && ( +

Send a message to start chatting.

+ )} + {messages.map((msg, i) => ( +
+
+ {msg.content || (loading && i === messages.length - 1 ? "▋" : "")} +
+
+ ))} +
+
+ +
+ setInput(e.target.value)} + placeholder="Type a message..." + disabled={loading} + /> + +
+
+ ) +} diff --git a/example/agents/resources/js/pages/dashboard/Index.tsx b/example/agents/resources/js/pages/dashboard/Index.tsx new file mode 100644 index 00000000..b0ec20e2 --- /dev/null +++ b/example/agents/resources/js/pages/dashboard/Index.tsx @@ -0,0 +1,3 @@ +export default function Dashboard({user}) { + return (

Welcome

) +} diff --git a/example/agents/resources/templates/index.html b/example/agents/resources/templates/index.html new file mode 100644 index 00000000..a19fa916 --- /dev/null +++ b/example/agents/resources/templates/index.html @@ -0,0 +1,12 @@ + + + + + + {{ vite_react_refresh() }} + {{ vite('resources/js/app.tsx') }} + + + {{ inertia(page) }} + + diff --git a/example/agents/routes/api.py b/example/agents/routes/api.py new file mode 100644 index 00000000..e1804813 --- /dev/null +++ b/example/agents/routes/api.py @@ -0,0 +1,33 @@ +from fastapi import APIRouter, Request +from fastapi.responses import StreamingResponse +from fastapi_startkit.inertia import Inertia + +from app.agents.chat import RouterAgent +from app.requests.chat import ChatRequest + +api = APIRouter() + + +@api.get("/") +async def index(request: Request): + return Inertia.render( + "chat/Index", + { + "user": {"name": "Alice"}, + }, + ) + + +@api.post("/chat") +async def chat(request: ChatRequest): + response = await RouterAgent().prompt(request.message) + return {"content": response.content} + + +@api.post("/chat/stream") +async def chat_stream(request: ChatRequest): + async def generate(): + async for chunk in RouterAgent().stream(request.message): + yield f"data: {chunk}\n\n" + + return StreamingResponse(generate(), media_type="text/event-stream") diff --git a/example/agents/tests/features/record_no_stream.json b/example/agents/tests/features/record_no_stream.json new file mode 100644 index 00000000..37cca398 --- /dev/null +++ b/example/agents/tests/features/record_no_stream.json @@ -0,0 +1,3 @@ +{ + "1c27b2b038cae60eab7297ec0a808bfc67246c77a56a9e7c5ce1d9e5fdee0ba3": "Hi Alex, it's a pleasure to meet you! How can I help you today?" +} \ No newline at end of file diff --git a/example/agents/tests/features/record_stream.json b/example/agents/tests/features/record_stream.json new file mode 100644 index 00000000..764667ec --- /dev/null +++ b/example/agents/tests/features/record_stream.json @@ -0,0 +1,6 @@ +{ + "32e9c85d324f4cadef79b130717a28d0e23a85a2ac349ad2b1b78a233b31dbc4": [ + "Hi", + " Bedram, it's a pleasure to assist you today! How may I help you?" + ] +} \ No newline at end of file diff --git a/example/agents/tests/features/test_chat_controller.py b/example/agents/tests/features/test_chat_controller.py new file mode 100644 index 00000000..c0228d72 --- /dev/null +++ b/example/agents/tests/features/test_chat_controller.py @@ -0,0 +1,44 @@ +from app.agents.chat import RouterAgent + +from tests.test_case import TestCase + + +class TestChatController(TestCase): + @RouterAgent.fake({"*hello*": "Hello there, This is no stream chat, Hope you are doing well."}) + async def test_it_responds_without_stream(self): + response = await self.post("/chat", json={"message": "hello"}) + + response.assert_ok() + response.assert_contents("Hello there, This is no stream chat, Hope you are doing well.") + + @RouterAgent.fake({"*hello*": "Hello there, This is no stream chat, Hope you are doing well."}) + async def test_stream_assertions_are_rejected_on_a_buffered_response(self): + response = await self.post("/chat", json={"message": "hello"}) + + # A buffered prompt() response is not a stream — stream assertions must fail. + with self.assertRaises(AssertionError): + response.assert_stream_contains("Hope you are doing well") + with self.assertRaises(AssertionError): + response.assert_stream("Hello there, This is no stream chat, Hope you are doing well.") + + @RouterAgent.fake({"*hello*": "Hello there, This is stream chat, Hope you are doing well."}) + async def test_it_responds_with_stream(self): + response = await self.post("/chat/stream", json={"message": "hello"}) + + response.assert_ok() + response.assert_stream_contains('Hello there, This is stream chat, Hope you are doing well.') + + @RouterAgent.record("record_no_stream.json") + async def test_it_records_without_stream(self): + response = await self.post("/chat", json={"message": "Hi, I am Alex, This is unittest, Please respond by calling my name."}) + response.assert_ok() + response.assert_contents("Alex") + + @RouterAgent.record("record_stream.json") + async def test_chat_responds_for_other_greetings(self): + response = await self.post("/chat/stream", json={ + "message": "Hi, I am Bedram, This is unittest, Please respond by calling my name." + }) + + response.assert_ok() + response.assert_stream_contains("Bedram") diff --git a/example/agents/tests/features/test_home_controller.py b/example/agents/tests/features/test_home_controller.py new file mode 100644 index 00000000..43c66662 --- /dev/null +++ b/example/agents/tests/features/test_home_controller.py @@ -0,0 +1,8 @@ +from tests.test_case import TestCase + + +class TestHomeController(TestCase): + async def test_home(self) -> None: + response = await self.get("/") + + self.assertEqual(response.status_code, 200) diff --git a/example/agents/tests/test_case.py b/example/agents/tests/test_case.py new file mode 100644 index 00000000..b1a97db6 --- /dev/null +++ b/example/agents/tests/test_case.py @@ -0,0 +1,8 @@ +from fastapi_startkit.fastapi.testing import HttpTestCase + + +class TestCase(HttpTestCase): + def get_application(self): + from bootstrap.application import app + + return app diff --git a/example/agents/tsconfig.json b/example/agents/tsconfig.json new file mode 100644 index 00000000..ebe4d31a --- /dev/null +++ b/example/agents/tsconfig.json @@ -0,0 +1,115 @@ +{ + "compilerOptions": { + /* Visit https://aka.ms/tsconfig to read more about this file */ + /* Projects */ + // "incremental": true, /* Save .tsbuildinfo files to allow for incremental compilation of projects. */ + // "composite": true, /* Enable constraints that allow a TypeScript project to be used with project references. */ + // "tsBuildInfoFile": "./.tsbuildinfo", /* Specify the path to .tsbuildinfo incremental compilation file. */ + // "disableSourceOfProjectReferenceRedirect": true, /* Disable preferring source files instead of declaration files when referencing composite projects. */ + // "disableSolutionSearching": true, /* Opt a project out of multi-project reference checking when editing. */ + // "disableReferencedProjectLoad": true, /* Reduce the number of projects loaded automatically by TypeScript. */ + /* Language and Environment */ + "target": "ESNext" /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */, + // "lib": [], /* Specify a set of bundled library declaration files that describe the target runtime environment. */ + // "jsx": "preserve", /* Specify what JSX code is generated. */ + // "experimentalDecorators": true, /* Enable experimental support for legacy experimental decorators. */ + // "emitDecoratorMetadata": true, /* Emit design-type metadata for decorated declarations in source files. */ + // "jsxFactory": "", /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */ + // "jsxFragmentFactory": "", /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */ + // "jsxImportSource": "", /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */ + // "reactNamespace": "", /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */ + // "noLib": true, /* Disable including any library files, including the default lib.d.ts. */ + // "useDefineForClassFields": true, /* Emit ECMAScript-standard-compliant class fields. */ + // "moduleDetection": "auto", /* Control what method is used to detect module-format JS files. */ + /* Modules */ + "module": "ESNext" /* Specify what module code is generated. */, + // "rootDir": "./", /* Specify the root folder within your source files. */ + "moduleResolution": "bundler" /* Specify how TypeScript looks up a file from a given module specifier. */, + // "baseUrl": "./", /* Specify the base directory to resolve non-relative module names. */ + // "paths": {}, /* Specify a set of entries that re-map imports to additional lookup locations. */ + // "rootDirs": [], /* Allow multiple folders to be treated as one when resolving modules. */ + // "typeRoots": [], /* Specify multiple folders that act like './node_modules/@types'. */ + // "types": [], /* Specify type package names to be included without being referenced in a source file. */ + // "allowUmdGlobalAccess": true, /* Allow accessing UMD globals from modules. */ + // "moduleSuffixes": [], /* List of file name suffixes to search when resolving a module. */ + // "allowImportingTsExtensions": true, /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */ + // "rewriteRelativeImportExtensions": true, /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */ + // "resolvePackageJsonExports": true, /* Use the package.json 'exports' field when resolving package imports. */ + // "resolvePackageJsonImports": true, /* Use the package.json 'imports' field when resolving imports. */ + // "customConditions": [], /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */ + // "noUncheckedSideEffectImports": true, /* Check side effect imports. */ + // "resolveJsonModule": true, /* Enable importing .json files. */ + // "allowArbitraryExtensions": true, /* Enable importing files with any extension, provided a declaration file is present. */ + // "noResolve": true, /* Disallow 'import's, 'require's or ''s from expanding the number of files TypeScript should add to a project. */ + /* JavaScript Support */ + "allowJs": true /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */, + // "checkJs": true, /* Enable error reporting in type-checked JavaScript files. */ + // "maxNodeModuleJsDepth": 1, /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */ + /* Emit */ + // "declaration": true, /* Generate .d.ts files from TypeScript and JavaScript files in your project. */ + // "declarationMap": true, /* Create sourcemaps for d.ts files. */ + // "emitDeclarationOnly": true, /* Only output d.ts files and not JavaScript files. */ + // "sourceMap": true, /* Create source map files for emitted JavaScript files. */ + // "inlineSourceMap": true, /* Include sourcemap files inside the emitted JavaScript. */ + "noEmit": true /* Disable emitting files from a compilation. */, + // "outFile": "./", /* Specify a file that bundles all outputs into one JavaScript file. 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upload-time = "2025-09-14T22:18:19.088Z" }, +] diff --git a/example/agents/vite.config.ts b/example/agents/vite.config.ts new file mode 100644 index 00000000..07341428 --- /dev/null +++ b/example/agents/vite.config.ts @@ -0,0 +1,15 @@ +import fastapi from "fastapi-vite-plugin" +import { defineConfig } from "vite" +import react from "@vitejs/plugin-react" +import tailwindcss from '@tailwindcss/vite'; + +export default defineConfig({ + plugins: [ + fastapi({ + input: "resources/js/app.tsx", + refresh: true, + }), + react(), + tailwindcss() + ], +}) diff --git a/fastapi_startkit/pyproject.toml b/fastapi_startkit/pyproject.toml index 84f9d113..2fa260a5 100644 --- a/fastapi_startkit/pyproject.toml +++ b/fastapi_startkit/pyproject.toml @@ -48,9 +48,8 @@ inertia = [ ] ai = [ - "anthropic>=0.49.0", - "openai>=1.0.0", - "google-generativeai>=0.8.0", + "langchain>=1.0.0", + "langchain-core>=1.0.0", ] [dependency-groups] @@ -68,6 +67,8 @@ 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..dbf4c85e 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/__init__.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/__init__.py @@ -1,43 +1,36 @@ -"""FastAPI Startkit AI module. - -Provides a LangGraph-powered declarative API for building AI agents backed -by Anthropic, OpenAI, or Google provider SDKs. - -Also exposes a Laravel-style fluent API for image generation and text-to-speech:: - - from fastapi_startkit.ai import Image, Audio, Document - - image = await Image.of("A donut on a counter").generate() - - # With a photo attachment - doc = await Document.from_url("https://example.com/photo.jpg") - image = await Image.of("Make impressionist").attachments([doc]).generate() - - audio = await Audio.of("Hello world").female().generate() -""" - from .agent import Agent +from .pipeline import Middleware from .audio import Audio, AudioResponse from .audio_factory import AudioFactory -from .config import AIConfig, AnthropicConfig, GoogleConfig, OpenAIConfig -from .decorators import max_steps, max_tokens, memory, model, provider, timeout, top_p +from .config.config import AnthropicConfig, ElevenLabsConfig, GoogleConfig, OpenAIConfig +from .config.ai import AIConfig +from .decorators import max_steps, max_tokens, 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 from .response import AgentResponse, AgentSnapshot +from .testing import AgentBinding, FakeAgent, NoFakeResponse, RecordingAgent __all__ = [ "Agent", + "Middleware", + "AgentBinding", "AgentResponse", "AgentSnapshot", "AIConfig", "AIProvider", "AnthropicConfig", + "FakeAgent", + "NoFakeResponse", + "RecordingAgent", "Audio", "AudioResponse", "AudioFactory", "Document", + "ElevenLabsConfig", + "fake_chat_model", "GoogleConfig", "Image", "ImageFactory", @@ -45,7 +38,6 @@ "OpenAIConfig", "max_steps", "max_tokens", - "memory", "model", "provider", "timeout", diff --git a/fastapi_startkit/src/fastapi_startkit/ai/agent.py b/fastapi_startkit/src/fastapi_startkit/ai/agent.py index 05a7c664..17c947f3 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/agent.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/agent.py @@ -1,96 +1,62 @@ -"""Agent base class — subclass this and apply decorators to build an AI agent.""" - from __future__ import annotations import fnmatch -from typing import Any, Callable, Iterator, Optional, Type +from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Optional, Type from .document import Document from .response import AgentResponse, AgentSnapshot +from .testing import AgentBinding + +if TYPE_CHECKING: + from langchain_core.tools import BaseTool class Agent: - """ - Base class for all agents. Subclass this and override lifecycle methods. - - Class-level configuration (set via decorators or subclass attributes):: - - _provider = "anthropic" # LLM provider - _model = "" # model ID (empty = provider default) - _max_steps = 10 # max agentic loop iterations - _max_tokens = 4096 # max output tokens - _timeout = 30.0 # request timeout in seconds - _top_p = 1.0 # top-p nucleus sampling - _memory_backend = "" # memory backend name (reserved) - """ - - _provider: str = "anthropic" - _model: str = "" - _max_steps: int = 10 - _max_tokens: int = 4096 - _timeout: float = 30.0 - _top_p: float = 1.0 - _memory_backend: str = "" - - _DEFAULT_MODELS: dict[str, str] = { - "anthropic": "claude-sonnet-4-6", - "openai": "gpt-4o", - "google": "gemini-2.0-flash", - } + provider: str | None = None + model: str | None = None + max_steps: int = 10 + max_tokens: int = 4096 + timeout: float = 30.0 + top_p: float = 1.0 def __init__(self): self._fakes: dict[str, AgentResponse | AgentSnapshot] = {} self._call_log: list[dict] = [] - # ── Lifecycle — override in subclasses ────────────────────────────────── - def messages(self) -> list[dict]: - """Return initial messages / few-shot examples.""" return [] + def instructions(self) -> str | None: + return None + def schema(self) -> Optional[Type]: - """Return a Pydantic model class for structured output, or None for plain text.""" return None - def tools(self) -> list[Callable]: - """Return a list of callable tools the agent may invoke.""" + def tools(self) -> list[BaseTool]: return [] def middleware(self) -> list[Callable]: - """Return middleware callables that wrap each LLM request.""" return [] def provider_options(self) -> dict: - """Return provider-specific options keyed by provider name.""" return {} - def before(self, message: str) -> str: - """Called before the message is sent. Return the (possibly modified) message.""" - return message - - def after(self, response: AgentResponse) -> AgentResponse: - """Called after the LLM responds. Return the (possibly modified) response.""" - return response - - # ── Public API ────────────────────────────────────────────────────────── - - def prompt( + async def prompt( self, message: str, *, - system: str | None = None, model: str | None = None, - messages: list[dict] | None = None, attachments: list[Document] | None = None, provider_options: dict | None = None, ) -> AgentResponse: - """Send a prompt and return an AgentResponse.""" - message = self.before(message) + stand_in = self._faked() + if stand_in is not None: + response = await stand_in.prompt(message, attachments=attachments) + self._log_call("prompt", message) + return self._apply_schema(response) _run_kwargs = dict( - system=system, model=model, - extra_messages=messages, attachments=attachments, provider_options=provider_options, ) @@ -98,48 +64,78 @@ def prompt( match = self._match_fake(message) if match is not None: if isinstance(match, AgentSnapshot): - response = match.resolve(self, message, **_run_kwargs) + response = await match.resolve(self, message, **_run_kwargs) else: response = match self._log_call("prompt", message) - return self.after(response) + return self._apply_schema(response) - def _call(msg: str) -> AgentResponse: - return self._run(msg, **_run_kwargs) + messages = self._build_messages(message, attachments) + chat_model = self._build_model(model, provider_options) - response = self._apply_middleware(message, _call) + response = await self._run_pipeline(chat_model, messages) self._log_call("prompt", message) - return self.after(response) + return self._apply_schema(response) - def stream( + async def stream( self, message: str, *, - system: str | None = None, model: str | None = None, provider_options: dict | None = None, - ) -> Iterator[str]: - """Stream a response token by token.""" - message = self.before(message) + ) -> AsyncIterator[str]: self._log_call("stream", message) + + swapped = self._faked() + if swapped is not None: + if hasattr(swapped, "stream"): + async for chunk in swapped.stream(message): + yield chunk + else: + response = await swapped.prompt(message) + yield response.content + return + fake = self._match_fake(message) if fake is not None: if isinstance(fake, AgentSnapshot): - response = fake.resolve(self, message) + response = await fake.resolve(self, message) else: response = fake yield response.content return - yield from self._stream(message, system=system, model=model, provider_options=provider_options) + async for chunk in self._stream(message, model=model, provider_options=provider_options): + yield chunk - def fake(self, patterns: dict[str, AgentResponse | AgentSnapshot]) -> "Agent": - """Register fake responses for testing. Keys are glob patterns.""" - for pattern, value in patterns.items(): - self._fakes[pattern] = value - return self + @classmethod + def fake(cls, responses: dict | None = None) -> "AgentBinding": + from .testing import AgentBinding, FakeAgent + + return AgentBinding(cls, FakeAgent(responses)) + + @classmethod + def record(cls, cassette: str | None = None) -> "AgentBinding": + from .testing import AgentBinding, RecordingAgent + + return AgentBinding(cls, RecordingAgent(cls(), cassette)) + + @classmethod + def _binding(cls) -> Any: + from fastapi_startkit.application import app + + container = app() + return container.make(cls.__name__) if container.has(cls.__name__) else None + + @classmethod + def make(cls) -> "Agent": + binding = cls._binding() + return binding if binding is not None else cls() + + def _faked(self) -> Any: + binding = type(self)._binding() + return binding if binding is not self else None 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")] if times is not None: assert len(calls) == times, f"Expected {times} prompt call(s), got {len(calls)}" @@ -147,17 +143,13 @@ def assert_prompted(self, times: int | None = None) -> None: assert len(calls) > 0, "Expected at least one prompt() or stream() call, but none were made" def assert_not_prompted(self) -> None: - """Assert that prompt() and stream() were never called.""" self.assert_prompted(times=0) def reset(self) -> "Agent": - """Clear fakes and call log. Useful between test cases.""" self._fakes.clear() self._call_log.clear() return self - # ── Internal helpers ──────────────────────────────────────────────────── - def _match_fake(self, message: str) -> Optional[AgentResponse | AgentSnapshot]: for pattern, value in self._fakes.items(): if fnmatch.fnmatch(message.lower(), pattern.lower()): @@ -167,8 +159,30 @@ def _match_fake(self, message: str) -> Optional[AgentResponse | AgentSnapshot]: def _log_call(self, method: str, message: str) -> None: self._call_log.append({"method": method, "message": message}) - def _apply_middleware(self, message: str, final: Callable[[str], AgentResponse]) -> AgentResponse: - """Build a left-to-right middleware chain and invoke it.""" + async def _run_pipeline(self, chat_model: Any, messages: list) -> AgentResponse: + from .pipeline import Response, build_pipeline # noqa: PLC0415 + from .runner import Runner # noqa: PLC0415 + + chain = list(self.middleware()) + if not chain: + return await self._invoke(chat_model, messages) + + def core(model: Any) -> Response: + async def _run(): + result = await Runner(self, model).run(messages) + yield result + + return Response(_run) + + pipeline = build_pipeline(chain, core) + raw = await pipeline(chat_model) + return self._to_agent_response(raw) + + async def _apply_middleware( + self, + chat_model: Any, + final: Callable[[Any], Any], + ) -> AgentResponse: chain = list(self.middleware()) def build(mw_list: list, fn: Callable) -> Callable: @@ -176,323 +190,110 @@ def build(mw_list: list, fn: Callable) -> Callable: return fn head, *tail = mw_list next_fn = build(tail, fn) - return lambda msg: head(msg, next_fn) - - return build(chain, final)(message) - - def _execute_tool(self, name: str, inputs: dict) -> Any: - """Find a tool by function name and call it with the given inputs.""" - for tool in self.tools(): - if callable(tool) and tool.__name__ == name: - return tool(**inputs) - raise ValueError(f"Tool {name!r} not found") - - def _resolve_model(self, override: str | None = None) -> str: - if override: - return override - if self._model: - return self._model - return self._DEFAULT_MODELS.get(self._provider, "") - - def _get_provider_options(self, override: dict | None = None) -> dict: - options = dict(self.provider_options().get(self._provider, {})) - if override: - provider_specific = override.get(self._provider, override) - if isinstance(provider_specific, dict): - options.update(provider_specific) - return options + mw = head() if isinstance(head, type) else head + return lambda model: mw(model, next_fn) + + return await build(chain, final)(chat_model) + + def _build_instruction(self) -> str | None: + return self.instructions() def _build_messages( self, message: str, - system: str | None = None, - extra_messages: list[dict] | None = None, attachments: list[Document] | None = None, - ) -> tuple[str | None, list[dict]]: - base = self.messages() + ) -> list[dict]: + messages: list[dict] = [] - resolved_system = system - if resolved_system is None: - sys_entries = [m for m in base if m.get("role") == "system"] - if sys_entries: - resolved_system = sys_entries[0]["content"] + instruction = self.instructions() + if instruction: + messages.append({"role": "system", "content": instruction}) - history = [m for m in base if m.get("role") != "system"] - if extra_messages: - history.extend(extra_messages) + messages.extend(self.messages() or []) + + if message: + messages.append({"role": "user", "content": message}) if attachments: content: Any = [{"type": "text", "text": message}] for doc in attachments: - if self._provider == "anthropic": - content.append(doc.to_anthropic_block()) - else: - content.append(doc.to_openai_block()) - history.append({"role": "user", "content": content}) - else: - history.append({"role": "user", "content": message}) + content.append(doc.to_langchain_block()) + messages.append({"role": "user", "content": content}) - return resolved_system, history + return messages - def _run( + def _build_model(self, model: str | None = None, provider_options: dict | None = None) -> Any: + from .model_builder import ModelBuilder # noqa: PLC0415 + + return ModelBuilder(agent=self).build(model, provider_options) + + def _to_agent_response(self, result: Any) -> AgentResponse: + messages = result.get("messages", []) if isinstance(result, dict) else [] + final = messages[-1] if messages else result + + content = getattr(final, "content", "") + if not isinstance(content, str): + content = str(content) + + tool_calls = list(getattr(final, "tool_calls", None) or []) + + usage: dict[str, Any] = {} + meta = getattr(final, "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=result) + + def _apply_schema(self, response: AgentResponse) -> AgentResponse: + schema = self.schema() + if schema is not None and response.parsed is None and response.content: + response.parsed = self._build_schema(schema, response.content) + return response + + @staticmethod + def _build_schema(schema: Any, content: str) -> Any: + import json # noqa: PLC0415 + + if hasattr(schema, "model_validate_json"): + return schema.model_validate_json(content) + if hasattr(schema, "model_validate"): + return schema.model_validate(json.loads(content)) + return schema(**json.loads(content)) + + async def _invoke(self, chat_model: Any, messages: list[dict]) -> AgentResponse: + from .runner import Runner # noqa: PLC0415 + + result = await Runner(self, chat_model).run(messages) + return self._to_agent_response(result) + + async def _run( self, message: str, - system: str | None = None, model: str | None = None, - extra_messages: list[dict] | None = None, attachments: list[Document] | None = None, provider_options: dict | None = None, ) -> AgentResponse: - resolved_system, messages = self._build_messages(message, system, extra_messages, attachments) - resolved_model = self._resolve_model(model) - options = self._get_provider_options(provider_options) - - if self._provider == "anthropic": - return self._run_anthropic(resolved_system, messages, resolved_model, options) - if self._provider == "openai": - return self._run_openai(resolved_system, messages, resolved_model, options) - if self._provider == "google": - return self._run_google(resolved_system, messages, resolved_model, options) - raise ValueError(f"Unsupported provider: {self._provider!r}. Use 'anthropic', 'openai', or 'google'.") - - def _stream( + messages = self._build_messages(message, attachments) + chat_model = self._build_model(model, provider_options) + return await self._invoke(chat_model, messages) + + async def _stream( self, message: str, - system: str | None = None, model: str | None = None, provider_options: dict | None = None, - ) -> Iterator[str]: - resolved_system, messages = self._build_messages(message, system) - resolved_model = self._resolve_model(model) - options = self._get_provider_options(provider_options) - - if self._provider == "anthropic": - yield from self._stream_anthropic(resolved_system, messages, resolved_model, options) - elif self._provider == "openai": - yield from self._stream_openai(resolved_system, messages, resolved_model, options) - elif self._provider == "google": - yield from self._stream_google(resolved_system, messages, resolved_model, options) - else: - raise ValueError(f"Unsupported provider: {self._provider!r}. Use 'anthropic', 'openai', or 'google'.") - - # ── Anthropic ────────────────────────────────────────────────────────── + ) -> AsyncIterator[str]: + from .pipeline import Response, build_pipeline # noqa: PLC0415 + from .runner import StreamRunner # noqa: PLC0415 - def _run_anthropic( - self, - system: str | None, - messages: list[dict], - model: str, - options: dict, - ) -> AgentResponse: - from anthropic import Anthropic # noqa: PLC0415 - - api_key = self._resolve_api_key("anthropic") - client = Anthropic(api_key=api_key) - params: dict[str, Any] = { - "model": model, - "max_tokens": self._max_tokens, - "messages": messages, - **options, - } - if system: - params["system"] = system - - resp = client.messages.create(**params) - content = "".join(b.text for b in resp.content if hasattr(b, "text")) - return AgentResponse( - content=content, - usage={"input": resp.usage.input_tokens, "output": resp.usage.output_tokens}, - raw=resp, - ) + messages = self._build_messages(message) + chat_model = self._build_model(model, provider_options) + chain = list(self.middleware()) - def _stream_anthropic( - self, - system: str | None, - messages: list[dict], - model: str, - options: dict, - ) -> Iterator[str]: - from anthropic import Anthropic # noqa: PLC0415 - - api_key = self._resolve_api_key("anthropic") - client = Anthropic(api_key=api_key) - params: dict[str, Any] = { - "model": model, - "max_tokens": self._max_tokens, - "messages": messages, - **options, - } - if system: - params["system"] = system - - with client.messages.stream(**params) as stream: - for text in stream.text_stream: - yield text - - # ── OpenAI ───────────────────────────────────────────────────────────── - - def _run_openai( - self, - system: str | None, - messages: list[dict], - model: str, - options: dict, - ) -> AgentResponse: - from openai import OpenAI # noqa: PLC0415 - - api_key = self._resolve_api_key("openai") - client = OpenAI(api_key=api_key) - all_messages: list[dict] = [] - if system: - all_messages.append({"role": "system", "content": system}) - all_messages.extend(messages) - - params: dict[str, Any] = { - "model": model, - "max_tokens": self._max_tokens, - "messages": all_messages, - **options, - } - resp = client.chat.completions.create(**params) - content = resp.choices[0].message.content or "" - return AgentResponse( - content=content, - usage={ - "input": resp.usage.prompt_tokens if resp.usage else 0, - "output": resp.usage.completion_tokens if resp.usage else 0, - }, - raw=resp, - ) + def core(m: Any) -> Response: + return Response(lambda: StreamRunner(self, m).run(messages)) - def _stream_openai( - self, - system: str | None, - messages: list[dict], - model: str, - options: dict, - ) -> Iterator[str]: - from openai import OpenAI # noqa: PLC0415 - - api_key = self._resolve_api_key("openai") - client = OpenAI(api_key=api_key) - all_messages: list[dict] = [] - if system: - all_messages.append({"role": "system", "content": system}) - all_messages.extend(messages) - - params: dict[str, Any] = { - "model": model, - "max_tokens": self._max_tokens, - "messages": all_messages, - "stream": True, - **options, - } - for chunk in client.chat.completions.create(**params): - delta = chunk.choices[0].delta.content - if delta: - yield delta - - def _resolve_api_key(self, provider_name: str) -> str | None: - """Try Config.get("ai") first, fallback to None (SDK reads env var).""" - try: - from fastapi_startkit.facades.Config import Config # noqa: PLC0415 - - ai_config = Config.get("ai") - return ai_config.providers[provider_name].key or None - except Exception: - return None - - # ── Google ───────────────────────────────────────────────────────────── - - def _run_google( - self, - system: str | None, - messages: list[dict], - model: str, - options: dict, - ) -> AgentResponse: - import google.generativeai as genai # noqa: PLC0415 - - api_key = self._resolve_api_key("google") - if api_key: - genai.configure(api_key=api_key) - - generation_config: dict[str, Any] = {} - if self._max_tokens: - generation_config["max_output_tokens"] = self._max_tokens - if self._top_p != 1.0: - generation_config["top_p"] = self._top_p - generation_config.update(options) - - google_model = genai.GenerativeModel( - model_name=model, - system_instruction=system, - generation_config=generation_config if generation_config else None, - ) - - google_messages = _to_google_messages(messages) - response = google_model.generate_content(google_messages) - content = response.text if hasattr(response, "text") else "" - usage: dict[str, Any] = {} - if hasattr(response, "usage_metadata"): - meta = response.usage_metadata - usage = { - "input": getattr(meta, "prompt_token_count", 0), - "output": getattr(meta, "candidates_token_count", 0), - } - return AgentResponse(content=content, usage=usage, raw=response) - - def _stream_google( - self, - system: str | None, - messages: list[dict], - model: str, - options: dict, - ) -> Iterator[str]: - import google.generativeai as genai # noqa: PLC0415 - - api_key = self._resolve_api_key("google") - if api_key: - genai.configure(api_key=api_key) - - generation_config: dict[str, Any] = {} - if self._max_tokens: - generation_config["max_output_tokens"] = self._max_tokens - if self._top_p != 1.0: - generation_config["top_p"] = self._top_p - generation_config.update(options) - - google_model = genai.GenerativeModel( - model_name=model, - system_instruction=system, - generation_config=generation_config if generation_config else None, - ) + pipeline = build_pipeline(chain, core) if chain else core - google_messages = _to_google_messages(messages) - for chunk in google_model.generate_content(google_messages, stream=True): - if chunk.text: - yield chunk.text - - -# ─── Utilities ───────────────────────────────────────────────────────────────── - - -def _to_google_messages(messages: list[dict]) -> list[dict]: - """ - Convert OpenAI-style messages to Google GenerativeAI content format. - Maps 'assistant' role → 'model'; omits 'system' (handled via system_instruction). - """ - result = [] - for msg in messages: - role = msg.get("role", "user") - content = msg.get("content", "") - if role == "system": - continue # system_instruction is set at model-construction level - google_role = "model" if role == "assistant" else "user" - if isinstance(content, list): - # Multi-part content — extract text parts only - text = " ".join(p.get("text", "") for p in content if isinstance(p, dict) and "text" in p) - result.append({"role": google_role, "parts": [{"text": text}]}) - else: - result.append({"role": google_role, "parts": [{"text": str(content)}]}) - return result + async for chunk in pipeline(chat_model): + yield chunk diff --git a/fastapi_startkit/src/fastapi_startkit/ai/audio.py b/fastapi_startkit/src/fastapi_startkit/ai/audio.py index b709daff..138cbd15 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/audio.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/audio.py @@ -46,8 +46,6 @@ def data(self) -> bytes: def _auto_filename(self) -> str: return f"{uuid.uuid4()}.{self._fmt}" - # ── Storage helpers ──────────────────────────────────────────────────────── - async def store(self) -> str: """Save to the default private disk with an auto-generated filename.""" return await self._save(self._auto_filename(), disk="local") @@ -64,8 +62,6 @@ async def storePubliclyAs(self, name: str) -> str: """Save to the public disk with a custom filename.""" return await self._save(name, disk="public") - # ── Internal ─────────────────────────────────────────────────────────────── - async def _save(self, name: str, disk: str = "local") -> str: return await asyncio.to_thread(self._save_sync, name, disk) @@ -89,7 +85,7 @@ def _save_sync(self, name: str, disk: str) -> str: class Audio: """Fluent builder for text-to-speech generation. - The active backend is selected from :attr:`~fastapi_startkit.ai.AIConfig.audio_provider` + The active backend is selected from :attr:`~fastapi_startkit.ai.AIConfig.default_audio` (env: ``AI_AUDIO_PROVIDER``). Defaults to OpenAI TTS. Usage:: @@ -102,7 +98,6 @@ class Audio: Available OpenAI TTS voices: alloy, echo, fable, onyx, nova, shimmer. """ - # OpenAI TTS voice presets _DEFAULT_VOICE = "alloy" _DEFAULT_FEMALE_VOICE = "nova" _DEFAULT_MALE_VOICE = "onyx" @@ -119,8 +114,6 @@ def of(cls, text: str) -> "Audio": """Create an :class:`Audio` builder with the given input text.""" return cls(text) - # ── Modifier methods (chainable) ─────────────────────────────────────────── - def female(self) -> "Audio": """Use a female voice (``nova``).""" self._voice = self._DEFAULT_FEMALE_VOICE @@ -158,8 +151,6 @@ def format(self, fmt: str) -> "Audio": self._response_format = fmt return self - # ── Generation ───────────────────────────────────────────────────────────── - async def generate(self) -> AudioResponse: """Call the configured TTS provider and return an :class:`AudioResponse`.""" provider = self._resolve_provider() @@ -172,8 +163,6 @@ async def generate(self) -> AudioResponse: ) return AudioResponse(data=data, fmt=self._response_format) - # ── Internal ─────────────────────────────────────────────────────────────── - def _resolve_provider(self) -> "AudioFactory": from .audio_factory import ( # noqa: PLC0415 ElevenLabsAudioFactory, @@ -191,7 +180,7 @@ def _resolve_provider(self) -> "AudioFactory": ai_config = Config.get("ai") if Config is not None else None # type: ignore[union-attr] if ai_config is None: raise RuntimeError("Config not available") - provider_name = ai_config.audio_provider + provider_name = ai_config.default_audio openai_cfg = ai_config.providers.get("openai") if openai_cfg: api_key = openai_cfg.key or None diff --git a/fastapi_startkit/src/fastapi_startkit/ai/audio_factory.py b/fastapi_startkit/src/fastapi_startkit/ai/audio_factory.py index 14c817b6..cecfd54e 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/audio_factory.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/audio_factory.py @@ -3,7 +3,7 @@ Providers implement the :class:`AudioFactory` ABC so that the :class:`~fastapi_startkit.ai.Audio` builder is not hard-wired to a single vendor. Select the active provider via ``AI_AUDIO_PROVIDER`` in your -``.env`` (or ``AIConfig.audio_provider``). +``.env`` (or ``AIConfig.default_audio``). Supported providers ------------------- @@ -100,7 +100,6 @@ class GoogleAudioFactory(AudioFactory): because Gemini TTS yields raw PCM16 which is wrapped in a WAV container. """ - # Map OpenAI-style voice aliases → Google Gemini voice names _VOICE_MAP: dict[str, str] = { "nova": "Aoede", "alloy": "Kore", @@ -149,7 +148,6 @@ async def synthesize( ), ) - # Gemini TTS returns raw PCM16 samples — wrap in WAV container pcm_data = response.candidates[0].content.parts[0].inline_data.data return _pcm_to_wav(pcm_data) @@ -187,14 +185,13 @@ class ElevenLabsAudioFactory(AudioFactory): not currently supported by ElevenLabs. """ - # Map OpenAI-style aliases → ElevenLabs voice IDs _VOICE_MAP: dict[str, str] = { - "nova": "21m00Tcm4TlvDq8ikWAM", # Rachel — female - "alloy": "EXAVITQu4vr4xnSDxMaL", # Bella — female - "shimmer": "MF3mGyEYCl7XYWbV9V6O", # Elli — female - "onyx": "pNInz6obpgDQGcFmaJgB", # Adam — male - "echo": "ErXwobaYiN019PkySvjV", # Antoni — male - "fable": "VR6AewLTigWG4xSOukaG", # Arnold — male + "nova": "21m00Tcm4TlvDq8ikWAM", + "alloy": "EXAVITQu4vr4xnSDxMaL", + "shimmer": "MF3mGyEYCl7XYWbV9V6O", + "onyx": "pNInz6obpgDQGcFmaJgB", + "echo": "ErXwobaYiN019PkySvjV", + "fable": "VR6AewLTigWG4xSOukaG", } def __init__(self, api_key: str | None = None): @@ -223,9 +220,6 @@ async def synthesize( return b"".join(audio_chunks) -# ─── PCM → WAV helper ───────────────────────────────────────────────────────── - - def _pcm_to_wav( pcm_data: bytes, sample_rate: int = 24000, @@ -248,7 +242,7 @@ def _pcm_to_wav( b"WAVE", b"fmt ", 16, - 1, # PCM + 1, channels, sample_rate, byte_rate, diff --git a/fastapi_startkit/src/fastapi_startkit/ai/config/ai.py b/fastapi_startkit/src/fastapi_startkit/ai/config/ai.py new file mode 100644 index 00000000..1dda6a48 --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/config/ai.py @@ -0,0 +1,24 @@ +from dataclasses import dataclass, field + +from fastapi_startkit.environment import env + +from fastapi_startkit.ai import AnthropicConfig, ElevenLabsConfig, GoogleConfig, OpenAIConfig + + +@dataclass +class AIConfig: + """Top-level AI configuration — selects the default provider per modality and holds per-provider configs.""" + + default: str = field(default_factory=lambda: env("AI_PROVIDER", "google")) + default_image: str = field(default_factory=lambda: env("AI_DEFAULT_IMAGE_PROVIDER", "openai")) + default_audio: str = field(default_factory=lambda: env("AI_DEFAULT_AUDIO_PROVIDER", "openai")) + default_transcribe: str = field(default_factory=lambda: env("AI_DEFAULT_TRANSCRIBE_PROVIDER", "openai")) + + providers: dict = field( + default_factory=lambda: { + "google": GoogleConfig(), + "openai": OpenAIConfig(), + "anthropic": AnthropicConfig(), + "elevenlabs": ElevenLabsConfig(), + } + ) diff --git a/fastapi_startkit/src/fastapi_startkit/ai/config.py b/fastapi_startkit/src/fastapi_startkit/ai/config/config.py similarity index 62% rename from fastapi_startkit/src/fastapi_startkit/ai/config.py rename to fastapi_startkit/src/fastapi_startkit/ai/config/config.py index 16a74206..39e2d702 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/config.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/config/config.py @@ -1,5 +1,3 @@ -"""AI configuration dataclasses for the FastAPI Startkit AI module.""" - from __future__ import annotations from dataclasses import dataclass, field @@ -15,6 +13,12 @@ class AnthropicConfig: key: str = field(default_factory=lambda: env("ANTHROPIC_API_KEY", "")) url: str = field(default_factory=lambda: env("ANTHROPIC_BASE_URL", "https://api.anthropic.com")) + models: dict = field( + default_factory=lambda: { + "default": "claude-sonnet-4-6", + } + ) + @dataclass class OpenAIConfig: @@ -24,6 +28,13 @@ class OpenAIConfig: key: str = field(default_factory=lambda: env("OPENAI_API_KEY", "")) url: str = field(default_factory=lambda: env("OPENAI_BASE_URL", "https://api.openai.com/v1")) + models: dict = field( + default_factory=lambda: { + "default": "gpt-4o", + "default_image": "dall-e-3", + } + ) + @dataclass class GoogleConfig: @@ -32,6 +43,13 @@ class GoogleConfig: driver: str = "google" key: str = field(default_factory=lambda: env("GEMINI_API_KEY", "") or env("GOOGLE_API_KEY", "")) + models: dict = field( + default_factory=lambda: { + "default": "gemini-2.5-flash-lite", + "default_image": "imagen-3.0-generate-002", + } + ) + @dataclass class ElevenLabsConfig: @@ -40,22 +58,9 @@ class ElevenLabsConfig: driver: str = "elevenlabs" key: str = field(default_factory=lambda: env("ELEVENLABS_API_KEY", "")) - -@dataclass -class AIConfig: - """Top-level AI configuration — selects the default provider and holds per-provider configs.""" - - default: str = field(default_factory=lambda: env("AI_PROVIDER", "google")) - - providers: dict = field( + models: dict = field( default_factory=lambda: { - "openai": OpenAIConfig(), - "anthropic": AnthropicConfig(), - "google": GoogleConfig(), - "elevenlabs": ElevenLabsConfig(), + "default_audio": "eleven_multilingual_v2", + "default_transcribe": "scribe_v1", } ) - - # Media-generation provider selection - image_provider: str = field(default_factory=lambda: env("AI_IMAGE_PROVIDER", "openai")) - audio_provider: str = field(default_factory=lambda: env("AI_AUDIO_PROVIDER", "openai")) diff --git a/fastapi_startkit/src/fastapi_startkit/ai/decorators.py b/fastapi_startkit/src/fastapi_startkit/ai/decorators.py index bd427bb6..3c28476b 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/decorators.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/decorators.py @@ -7,7 +7,7 @@ def provider(name: str): """Set the LLM provider: 'anthropic', 'openai', 'google', etc.""" def decorator(cls): - cls._provider = name + cls.provider = name return cls return decorator @@ -17,7 +17,7 @@ def model(name: str = ""): """Set the model identifier (e.g. 'claude-sonnet-4-6', 'gpt-4o').""" def decorator(cls): - cls._model = name + cls.model = name return cls return decorator @@ -27,7 +27,7 @@ def max_steps(n: int = 10): """Maximum agentic loop iterations before stopping.""" def decorator(cls): - cls._max_steps = n + cls.max_steps = n return cls return decorator @@ -37,7 +37,7 @@ def max_tokens(n: int = 4096): """Maximum output tokens per response.""" def decorator(cls): - cls._max_tokens = n + cls.max_tokens = n return cls return decorator @@ -47,7 +47,7 @@ def timeout(seconds: float = 30.0): """Request timeout in seconds.""" def decorator(cls): - cls._timeout = seconds + cls.timeout = seconds return cls return decorator @@ -57,17 +57,7 @@ def top_p(value: float = 1.0): """Top-p nucleus sampling parameter.""" def decorator(cls): - cls._top_p = value - return cls - - return decorator - - -def memory(backend: str = ""): - """Attach a named memory backend to this agent.""" - - def decorator(cls): - cls._memory_backend = backend + cls.top_p = value return cls return decorator diff --git a/fastapi_startkit/src/fastapi_startkit/ai/document.py b/fastapi_startkit/src/fastapi_startkit/ai/document.py index 9ac7fdbb..ce71ad06 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/document.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/document.py @@ -5,7 +5,6 @@ import asyncio import base64 -# Optional runtime dependency — imported at module level so tests can patch it. try: from fastapi_startkit.storage.storage import Storage except Exception: # pragma: no cover @@ -21,7 +20,7 @@ class Document: Text:: doc = Document.from_path("report.txt") - agent.prompt("Summarise this", attachments=[doc]) + await agent.prompt("Summarise this", attachments=[doc]) Binary image:: @@ -34,8 +33,6 @@ def __init__(self, content: str | bytes, name: str = "", media_type: str = "text self.name = name self.media_type = media_type - # ── Sync constructors (text) ─────────────────────────────────────────────── - @classmethod def from_path(cls, path: str) -> "Document": """Load a document from a local file path. @@ -51,8 +48,6 @@ def from_path(cls, path: str) -> "Document": content = f.read() return cls(content=content, name=path) - # ── Async constructors (binary) ──────────────────────────────────────────── - @classmethod async def from_storage(cls, key: str) -> "Document": """Load a binary file from application storage (``storage/``) asynchronously. @@ -65,7 +60,6 @@ def _read() -> bytes: if Storage is not None: try: disk = Storage.disk("local") - # Resolve the full path and read as binary resolved_path = disk.get_path(key) with open(resolved_path, "rb") as f: return f.read() @@ -95,8 +89,6 @@ async def from_url(cls, url: str) -> "Document": name = url.rstrip("/").split("/")[-1] return cls(content=response.content, name=name) - # ── Binary accessor ──────────────────────────────────────────────────────── - def to_bytes(self) -> bytes: """Return the document content as raw bytes. @@ -117,8 +109,6 @@ def to_base64(self) -> str: """ return base64.b64encode(self.to_bytes()).decode("utf-8") - # ── LLM content blocks ───────────────────────────────────────────────────── - def to_anthropic_block(self) -> dict: """Return an Anthropic-compatible content block for this document.""" return { @@ -137,3 +127,16 @@ def to_openai_block(self) -> dict: "type": "text", "text": f"[Document: {self.name}]\n{self.content}", } + + def to_langchain_block(self) -> dict: + """Return a LangChain content block for this document. + + Text (or ``text/*`` media) is inlined as a labelled text part; everything + else becomes a base64 ``image``/``file`` block the model reads natively. + """ + is_text = isinstance(self.content, str) or self.media_type.startswith("text/") + if is_text: + text = self.content if isinstance(self.content, str) else self.content.decode("utf-8", "replace") + return {"type": "text", "text": f"[Document: {self.name}]\n{text}"} + block_type = "image" if self.media_type.startswith("image/") else "file" + return {"type": block_type, "base64": self.to_base64(), "mime_type": self.media_type} 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..870e624a --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/fakes.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +import json +import re +from typing import Any, Iterable + + +def _require_langchain(): + try: + from langchain_core.language_models.fake_chat_models import GenericFakeChatModel + from langchain_core.messages import AIMessage + except ImportError as exc: + raise ImportError( + "The agent test harness requires the 'ai' extra. Install it with: pip install \"fastapi-startkit[ai]\"" + ) from exc + return GenericFakeChatModel, AIMessage + + +def fake_chat_model(turns: Iterable[Any]): + generic_model, ai_message = _require_langchain() + + from langchain_core.messages import AIMessageChunk + from langchain_core.outputs import ChatGenerationChunk + + class _FakeChatModel(generic_model): + def bind_tools(self, tools, **kwargs): + return self + + def _stream(self, messages, stop=None, run_manager=None, **kwargs): + message = next(self.messages) + if not isinstance(message, ai_message): + message = ai_message(content=str(message)) + + content = message.content if isinstance(message.content, str) else str(message.content) + for token in re.split(r"(\s)", content): + if token: + yield ChatGenerationChunk(message=AIMessageChunk(content=token, id=message.id)) + + tool_calls = list(message.tool_calls or []) + if tool_calls: + chunks = [ + { + "name": call["name"], + "args": json.dumps(call.get("args", {})), + "id": call.get("id"), + "index": index, + } + for index, call in enumerate(tool_calls) + ] + yield ChatGenerationChunk(message=AIMessageChunk(content="", tool_call_chunks=chunks, id=message.id)) + + normalized = [t if isinstance(t, ai_message) else ai_message(content=str(t)) for t in turns] + return _FakeChatModel(messages=iter(normalized)) diff --git a/fastapi_startkit/src/fastapi_startkit/ai/image.py b/fastapi_startkit/src/fastapi_startkit/ai/image.py index c6794293..0009cb21 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/image.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/image.py @@ -47,8 +47,6 @@ def data(self) -> bytes: def _auto_filename(self) -> str: return f"{uuid.uuid4()}.{self._fmt}" - # ── Storage helpers ──────────────────────────────────────────────────────── - async def store(self) -> str: """Save to the default private disk with an auto-generated filename.""" return await self._save(self._auto_filename(), disk="local") @@ -65,8 +63,6 @@ async def storePubliclyAs(self, name: str) -> str: """Save to the public disk with a custom filename.""" return await self._save(name, disk="public") - # ── Internal ─────────────────────────────────────────────────────────────── - async def _save(self, name: str, disk: str = "local") -> str: return await asyncio.to_thread(self._save_sync, name, disk) @@ -90,7 +86,7 @@ def _save_sync(self, name: str, disk: str) -> str: class Image: """Fluent builder for image generation and editing. - The active backend is selected from :attr:`~fastapi_startkit.ai.AIConfig.image_provider` + The active backend is selected from :attr:`~fastapi_startkit.ai.AIConfig.default_image` (env: ``AI_IMAGE_PROVIDER``). Defaults to OpenAI DALL-E. Usage — text to image:: @@ -109,7 +105,6 @@ class Image: ) """ - # DALL-E 3 size presets _LANDSCAPE_SIZE = "1792x1024" _PORTRAIT_SIZE = "1024x1792" _SQUARE_SIZE = "1024x1024" @@ -126,8 +121,6 @@ def of(cls, prompt: str) -> "Image": """Create an :class:`Image` builder with the given prompt.""" return cls(prompt) - # ── Modifier methods (chainable) ─────────────────────────────────────────── - def attachments(self, docs: list) -> "Image": """Attach :class:`~fastapi_startkit.ai.Document` objects for an editing request.""" self._attachments = list(docs) @@ -158,8 +151,6 @@ def quality(self, q: str) -> "Image": self._quality = q return self - # ── Generation ───────────────────────────────────────────────────────────── - async def generate(self) -> ImageResponse: """Call the configured image provider and return an :class:`ImageResponse`.""" provider = self._resolve_provider() @@ -180,8 +171,6 @@ async def generate(self) -> ImageResponse: return ImageResponse(data=image_bytes, fmt="png") - # ── Internal ─────────────────────────────────────────────────────────────── - def _resolve_provider(self) -> "ImageFactory": from .image_factory import ( # noqa: PLC0415 GoogleImageFactory, @@ -198,7 +187,7 @@ def _resolve_provider(self) -> "ImageFactory": ai_config = Config.get("ai") if Config is not None else None # type: ignore[union-attr] if ai_config is None: raise RuntimeError("Config not available") - provider_name = ai_config.image_provider + provider_name = ai_config.default_image openai_cfg = ai_config.providers.get("openai") if openai_cfg: api_key = openai_cfg.key or None diff --git a/fastapi_startkit/src/fastapi_startkit/ai/image_factory.py b/fastapi_startkit/src/fastapi_startkit/ai/image_factory.py index b699dafa..eb92ddb6 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/image_factory.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/image_factory.py @@ -3,7 +3,7 @@ Providers implement the :class:`ImageFactory` ABC so that the :class:`~fastapi_startkit.ai.Image` builder is not hard-wired to a single vendor. Select the active provider via ``AI_IMAGE_PROVIDER`` in your -``.env`` (or ``AIConfig.image_provider``). +``.env`` (or ``AIConfig.default_image``). Supported providers ------------------- @@ -105,7 +105,6 @@ class GoogleImageFactory(ImageFactory): +--------------+-----------+ """ - # Map DALL-E-style pixel sizes to Imagen aspect ratios _ASPECT_MAP: dict[str, str] = { "1024x1024": "1:1", "1792x1024": "16:9", diff --git a/fastapi_startkit/src/fastapi_startkit/ai/lab.py b/fastapi_startkit/src/fastapi_startkit/ai/lab.py new file mode 100644 index 00000000..d8cc81e6 --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/lab.py @@ -0,0 +1,54 @@ +from enum import StrEnum + +from fastapi_startkit import Config + + +class ModelType(StrEnum): + TEXT = "text" + IMAGE = "image" + AUDIO = "audio" + TRANSCRIBE = "transcribe" + + @property + def key(self) -> str: + return { + ModelType.TEXT: "default", + ModelType.IMAGE: "default_image", + ModelType.AUDIO: "default_audio", + ModelType.TRANSCRIBE: "default_transcribe", + }[self] + + +class Lab(StrEnum): + GOOGLE = "google" + OPENAI = "openai" + ANTHROPIC = "anthropic" + ELEVENLABS = "elevenlabs" + + def get_api_key(self) -> str: + return Config.get(f"ai.providers.{self.value}.key") + + def get_model(self, model: str | None = None, model_type: ModelType = ModelType.TEXT) -> str: + return model or Config.get(f"ai.providers.{self.value}.models.{model_type.key}") + + def get_provider_key(self) -> str: + return { + "anthropic": "anthropic", + "openai": "openai", + "google": "google_genai", + "elevenlabs": "elevenlabs", + }[self.value] + + @staticmethod + def get_provider(provider: str | None = None, model_type: ModelType = ModelType.TEXT) -> "Lab": + provider = provider or Config.get(f"ai.{model_type.key}") + return Lab(provider) + + @staticmethod + def get_model_url( + provider: str | None = None, + model: str | None = None, + model_type: ModelType = ModelType.TEXT, + ) -> str: + lab = Lab.get_provider(provider, model_type) + return f"{lab.get_provider_key()}:{lab.get_model(model, model_type)}" diff --git a/fastapi_startkit/src/fastapi_startkit/ai/model_builder.py b/fastapi_startkit/src/fastapi_startkit/ai/model_builder.py new file mode 100644 index 00000000..7f82254f --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/model_builder.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from .lab import Lab + +if TYPE_CHECKING: + from .agent import Agent + + +class ModelBuilder: + def __init__(self, agent: "Agent") -> None: + self._agent = agent + + def build(self, model: str | None = None, provider_options: dict | None = None) -> Any: + from langchain.chat_models import init_chat_model # noqa: PLC0415 + + lab = Lab.get_provider(self._agent.provider) + kwargs: dict[str, Any] = {"model_provider": lab.get_provider_key()} + + api_key = lab.get_api_key() + if api_key: + kwargs["api_key"] = api_key + if self._agent.max_tokens: + kwargs["max_tokens"] = self._agent.max_tokens + if self._agent.top_p != 1.0: + kwargs["top_p"] = self._agent.top_p + if self._agent.timeout: + kwargs["timeout"] = self._agent.timeout + + kwargs.update(self._resolve_provider_options(provider_options)) + + chat_model = init_chat_model(self._resolve_model(model), **kwargs) + + tools = list(self._agent.tools()) + return chat_model.bind_tools(tools) if tools else chat_model + + def _resolve_model(self, override: str | None = None) -> str: + return Lab.get_provider(self._agent.provider).get_model(override or self._agent.model or None) + + def _resolve_provider_options(self, override: dict | None = None) -> dict: + options = dict(self._agent.provider_options().get(self._agent.provider, {})) + if override: + provider_specific = override.get(self._agent.provider, override) + if isinstance(provider_specific, dict): + options.update(provider_specific) + return options diff --git a/fastapi_startkit/src/fastapi_startkit/ai/pipeline.py b/fastapi_startkit/src/fastapi_startkit/ai/pipeline.py new file mode 100644 index 00000000..85f0e107 --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/pipeline.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator, Callable +from typing import Any, Protocol, runtime_checkable + + +@runtime_checkable +class Middleware(Protocol): + """An agent middleware layer. + + ``handler(model)`` returns a :class:`Response`. Return it (optionally with + ``.then(callback)`` for an after-hook) to stay streaming-safe — do NOT + ``await`` it, as awaiting buffers the whole stream. ``handle`` may be sync or + ``async``; both are supported. + """ + + def handle(self, model: Any, handler: Callable) -> Any: ... + + +class Response: + """Deferred response — the model only runs when consumed. + + - ``await response`` → buffered; returns the final message + - ``async for chunk in response`` → streaming chunks + - ``.then(cb)`` → callback fired with the final value (sync or async) + """ + + def __init__(self, source: Callable[[], AsyncIterator]) -> None: + self._source = source + self._cbs: list[Callable] = [] + + def then(self, cb: Callable[[Any], Any]) -> "Response": + self._cbs.append(cb) + return self + + async def _finish(self, final: Any) -> Any: + for cb in self._cbs: + result = cb(final) + if hasattr(result, "__await__"): + await result + return final + + def __aiter__(self) -> AsyncIterator: + async def _it() -> AsyncIterator: + accumulated = None + finished = False + try: + async for chunk in self._source(): + accumulated = chunk if accumulated is None else accumulated + chunk + yield chunk + finished = True + await self._finish(accumulated) + finally: + # The consumer may close the stream before it is fully drained + # (client disconnect, connection reuse, early break). Run the + # after-hooks on close too, so they fire exactly once either way. + if not finished: + await self._finish(accumulated) + + return _it() + + def __await__(self): + async def _run() -> Any: + accumulated = None + async for chunk in self._source(): + accumulated = chunk if accumulated is None else accumulated + chunk + return await self._finish(accumulated) + + return _run().__await__() + + +def build_pipeline(middlewares: list[Callable], core: Callable) -> Callable: + """Compose middlewares around core — first in list = outermost (the onion). + + Every layer (and the ``handler`` handed to each middleware) returns a + :class:`Response`. A middleware may ``await`` it to buffer the full result, + or — to stay streaming-safe — attach ``.then(callback)`` and return it + without awaiting. Awaiting consumes the whole stream, so streaming after-hooks + must use ``.then`` rather than ``final = await handler(model)``. + """ + handler = core + for mw in reversed(middlewares): + nxt = handler + instance = mw() if isinstance(mw, type) else mw + call = instance.handle if hasattr(instance, "handle") else instance + + def layer(model: Any, call: Callable = call, nxt: Callable = nxt) -> Response: + async def _source() -> AsyncIterator: + produced = call(model, nxt) + if not isinstance(produced, Response) and hasattr(produced, "__await__"): + produced = await produced + if isinstance(produced, Response): + async for chunk in produced: + yield chunk + else: + yield produced + + return Response(_source) + + handler = layer + return handler diff --git a/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py b/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py index d45f2e73..2eee207f 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py @@ -6,25 +6,13 @@ class AIProvider(Provider): - """Service provider that bootstraps the AI module. - - Registers :class:`~fastapi_startkit.ai.config.AIConfig` under the ``ai`` - key in the application container so it is accessible via ``Config.get('ai')``. - - Register it in your application:: - - app = Application(providers=[AIProvider]) - """ - provider_key = "ai" def register(self) -> None: - """Bind AIConfig into the container under the 'ai' key.""" - from fastapi_startkit.ai.config import AIConfig + from fastapi_startkit.ai import AIConfig - self.app.bind("ai", AIConfig()) + config = self.resolve_config(AIConfig) + self.merge_config_from(config, self.provider_key) def boot(self) -> None: - """Merge AI config into the shared Config store.""" - ai_config = self.app.make("ai") - self.app.make("config").set("ai", ai_config) + pass diff --git a/fastapi_startkit/src/fastapi_startkit/ai/response.py b/fastapi_startkit/src/fastapi_startkit/ai/response.py index 77383bdb..46d02ebd 100644 --- a/fastapi_startkit/src/fastapi_startkit/ai/response.py +++ b/fastapi_startkit/src/fastapi_startkit/ai/response.py @@ -19,6 +19,7 @@ class AgentResponse: tool_calls: list[dict] = field(default_factory=list) usage: dict = field(default_factory=dict) raw: Any = None + parsed: Any = None def text(self) -> str: """Return the text content.""" @@ -80,13 +81,13 @@ def save(self, response: AgentResponse) -> None: indent=2, ) - def resolve(self, agent: "Agent", message: str, **run_kwargs: Any) -> AgentResponse: + async def resolve(self, agent: "Agent", message: str, **run_kwargs: Any) -> AgentResponse: """ Return the response — from disk if recorded, or from the real API (which is then saved for future runs). """ if self.exists(): return self.load() - response = agent._run(message, **run_kwargs) + response = await agent._run(message, **run_kwargs) self.save(response) return response diff --git a/fastapi_startkit/src/fastapi_startkit/ai/runner.py b/fastapi_startkit/src/fastapi_startkit/ai/runner.py new file mode 100644 index 00000000..49fe5a44 --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/runner.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator, Sequence +from typing import TYPE_CHECKING, Any + +from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, ToolCall +from langchain_core.runnables import Runnable +from langchain_core.tools import BaseTool + +if TYPE_CHECKING: + from .agent import Agent + +Message = BaseMessage | dict[str, Any] + + +def _as_text(content: Any) -> str: + return content if isinstance(content, str) else str(content) + + +class Runner: + def __init__(self, agent: Agent, model: Runnable[Any, BaseMessage]) -> None: + self._tools: dict[str, BaseTool] = {tool.name: tool for tool in agent.tools()} + self.model: Runnable[Any, BaseMessage] = model + self.max_steps = agent.max_steps + + async def run(self, messages: Sequence[Message]) -> BaseMessage: + history: list[Message] = list(messages) + response: AIMessage = await self.model.ainvoke(history) # type: ignore[assignment] + + if not response.tool_calls: + return response + + return (await self._run_tools(response.tool_calls))[-1] + + async def _run_tools(self, tool_calls: list[ToolCall]) -> list[BaseMessage]: + return [await self._resolve_tool(call["name"]).ainvoke(call) for call in tool_calls] + + def _resolve_tool(self, name: str) -> BaseTool: + try: + return self._tools[name] + except KeyError: + raise ValueError(f"Agent has no tool named {name!r}") from None + + +class StreamRunner(Runner): + async def run(self, messages: Sequence[Message]) -> AsyncIterator[str]: # type: ignore[override] + history: list[Message] = list(messages) + + gathered: AIMessageChunk | None = None + async for chunk in self.model.astream(history): + if chunk.content: + yield _as_text(chunk.content) + gathered = chunk if gathered is None else gathered + chunk # type: ignore[operator] + + if gathered is None or not gathered.tool_calls: + return + + for message in await self._run_tools(gathered.tool_calls): + yield _as_text(message.content) diff --git a/fastapi_startkit/src/fastapi_startkit/ai/testing.py b/fastapi_startkit/src/fastapi_startkit/ai/testing.py new file mode 100644 index 00000000..e3e8533b --- /dev/null +++ b/fastapi_startkit/src/fastapi_startkit/ai/testing.py @@ -0,0 +1,190 @@ +from __future__ import annotations + +import fnmatch +import functools +import hashlib +import inspect +import json +import re +import sys +from collections.abc import AsyncIterator +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable + +from .response import AgentResponse + +if TYPE_CHECKING: + from .agent import Agent + from .document import Document + + +class NoFakeResponse(LookupError): + pass + + +def _matches(pattern: str, message: str) -> bool: + pattern, message = pattern.lower(), message.lower() + if any(ch in pattern for ch in "*?["): + return fnmatch.fnmatch(message, pattern) + return pattern in message + + +def _reply_text(reply: Any) -> str: + if isinstance(reply, AgentResponse): + return reply.content + return getattr(reply, "content", None) or str(reply) + + +class _Recorder: + def __init__(self) -> None: + self.calls: list[str] = [] + self.attachments: list[list[Document]] = [] + + def _record_call(self, message: str, attachments: list[Document] | None) -> None: + self.calls.append(message) + self.attachments.append(list(attachments or [])) + + @property + def prompt_count(self) -> int: + return len(self.calls) + + def assert_prompted(self, pattern: str | None = None) -> None: + if pattern is None: + assert self.calls, "Expected the agent to be prompted, but it never was." + return + assert any(_matches(pattern, message) for message in self.calls), ( + f"Expected a prompt matching {pattern!r}, but none did. Got: {self.calls!r}" + ) + + def assert_not_prompted(self) -> None: + assert not self.calls, f"Expected no prompts, but got: {self.calls!r}" + + +def _joined(value: Any) -> str: + """A cassette value is a buffered string or a list of stream chunks.""" + return "".join(value) if isinstance(value, list) else value + + +def _word_chunks(text: str) -> list[str]: + """Split text into word chunks (word + trailing whitespace) so a fake can + mimic a token stream. Loss-less: ``"".join(_word_chunks(t)) == t``.""" + return re.findall(r"\S+\s*", text) or [text] + + +class FakeAgent(_Recorder): + def __init__(self, responses: dict[str, Any] | None = None) -> None: + super().__init__() + self.responses = responses or {} + + def _resolve(self, message: str) -> str: + if not self.responses: + return "" + for pattern, reply in self.responses.items(): + if _matches(pattern, message): + return _reply_text(reply) + raise NoFakeResponse(f"No fake response matched message: {message!r}") + + async def prompt(self, message: str, attachments: list[Document] | None = None) -> AgentResponse: + self._record_call(message, attachments) + return AgentResponse(content=self._resolve(message)) + + async def stream(self, message: str) -> AsyncIterator[str]: + self._record_call(message, None) + for chunk in _word_chunks(self._resolve(message)): + yield chunk + + +class RecordingAgent(_Recorder): + def __init__(self, real: Agent, cassette: str | None = None) -> None: + super().__init__() + self._real = real + self.cassette: Path | None = Path(cassette) if cassette else None + + @staticmethod + def _key(message: str, attachments: list[Document] | None) -> str: + names = [getattr(doc, "name", "") for doc in (attachments or [])] + payload = json.dumps({"message": message, "attachments": names}, sort_keys=True) + return hashlib.sha256(payload.encode()).hexdigest() + + def _load(self) -> tuple[Path, dict]: + cassette = self.cassette + assert cassette is not None, "RecordingAgent has no cassette resolved" + return cassette, (json.loads(cassette.read_text()) if cassette.exists() else {}) + + def _save(self, cassette: Path, store: dict, key: str, value: Any) -> None: + store[key] = value + cassette.parent.mkdir(parents=True, exist_ok=True) + cassette.write_text(json.dumps(store, indent=2, sort_keys=True)) + + async def prompt(self, message: str, attachments: list[Document] | None = None) -> AgentResponse: + self._record_call(message, attachments) + cassette, store = self._load() + key = self._key(message, attachments) + if key in store: + return AgentResponse(content=_joined(store[key])) + response = await self._real._run(message, attachments=attachments) + self._save(cassette, store, key, response.content) + return response + + async def stream(self, message: str) -> AsyncIterator[str]: + self._record_call(message, None) + cassette, store = self._load() + key = self._key(message, None) + if key in store: + value = store[key] + for chunk in value if isinstance(value, list) else [value]: + yield chunk + return + chunks = [chunk async for chunk in self._real._stream(message)] + self._save(cassette, store, key, chunks) + for chunk in chunks: + yield chunk + + +class AgentBinding: + def __init__(self, agent_cls: type[Agent], stand_in: Any) -> None: + self._agent_cls = agent_cls + self._stand_in = stand_in + + def _resolve_cassette(self, filename: str, qualname: str) -> None: + stand_in = self._stand_in + if not isinstance(stand_in, RecordingAgent): + return + here = Path(filename).parent + if stand_in.cassette is None: + stand_in.cassette = here / "cassettes" / f"{qualname.replace('.', '_')}.json" + elif not stand_in.cassette.is_absolute(): + stand_in.cassette = here / stand_in.cassette + + def __enter__(self) -> Any: + from fastapi_startkit.application import app + + caller = sys._getframe(1).f_code + self._resolve_cassette(caller.co_filename, caller.co_qualname) + app().bind(self._agent_cls.__name__, self._stand_in) + return self._stand_in + + def __exit__(self, *_exc: Any) -> bool: + from fastapi_startkit.application import app + + app().unbind(self._agent_cls.__name__) + return False + + def __call__(self, func: Callable) -> Callable: + self._resolve_cassette(inspect.getfile(func), func.__qualname__) + + if inspect.iscoroutinefunction(func): + + @functools.wraps(func) + async def async_wrapper(*args: Any, **kwargs: Any) -> Any: + with self: + return await func(*args, **kwargs) + + return async_wrapper + + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + with self: + return func(*args, **kwargs) + + return wrapper diff --git a/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi b/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi index b6b8ab22..019df3f1 100644 --- a/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi +++ b/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi @@ -1,4 +1,4 @@ -from fastapi_startkit.ai.config import AIConfig +from fastapi_startkit.ai import AIConfig class AI: """Facade for accessing the AI configuration registered under the 'ai' key.""" diff --git a/fastapi_startkit/src/fastapi_startkit/fastapi/testing/test_response.py b/fastapi_startkit/src/fastapi_startkit/fastapi/testing/test_response.py index f37e7bd4..fd609b27 100644 --- a/fastapi_startkit/src/fastapi_startkit/fastapi/testing/test_response.py +++ b/fastapi_startkit/src/fastapi_startkit/fastapi/testing/test_response.py @@ -100,3 +100,81 @@ def assert_json_structure(self, structure: Any) -> "TestResponse": """ assert_json_structure(structure, self._response.json()) return self + + # ------------------------------------------------------------------ # + # Body assertions (buffered) + # ------------------------------------------------------------------ # + def assert_contents(self, *expected: str) -> "TestResponse": + """Assert the response body contains each of the given substrings. + + Works on any (buffered) response by checking the raw text body. For + streamed responses, prefer ``assert_stream`` / ``assert_stream_contains``. + """ + text = self._response.text + for sub in expected: + assert sub in text, f"Expected response body to contain {sub!r}, but it did not. Body: {text!r}" + return self + + # ------------------------------------------------------------------ # + # Streaming (Server-Sent Events) assertions + # ------------------------------------------------------------------ # + def is_stream(self) -> bool: + """Whether the response is a Server-Sent Events stream.""" + return "text/event-stream" in self._response.headers.get("content-type", "") + + def _require_stream(self, method: str) -> None: + assert self.is_stream(), ( + f"{method}() can only be used on a streaming (text/event-stream) response, " + f"but the content-type was {self._response.headers.get('content-type', '')!r}. " + f"Use assert_contents() for a buffered response." + ) + + def stream_chunks(self) -> list[str]: + """Return the ``data:`` payloads of a Server-Sent Events body. + + Each ``data:`` line becomes one chunk, with the single optional space + after the colon removed per the SSE spec. + """ + chunks: list[str] = [] + for line in self._response.text.splitlines(): + if not line.startswith("data:"): + continue + value = line[len("data:") :] + chunks.append(value[1:] if value.startswith(" ") else value) + return chunks + + def stream_content(self) -> str: + """Concatenate the SSE ``data:`` payloads into the full streamed text.""" + return "".join(self.stream_chunks()) + + def assert_stream(self, *expected: str) -> "TestResponse": + """Assert against a streamed (``text/event-stream``) body. + + Fails if the response is not a stream — use ``assert_contents`` for + buffered responses. + + * ``assert_stream("Hello there!")`` — assert the concatenated stream + content equals the single expected string. + * ``assert_stream("Hello", " there!")`` — assert the exact sequence of + streamed chunks. + """ + self._require_stream("assert_stream") + chunks = self.stream_chunks() + if len(expected) == 1: + content = "".join(chunks) + assert content == expected[0], f"Expected streamed content [{expected[0]!r}] but received [{content!r}]." + else: + assert chunks == list(expected), f"Expected streamed chunks {list(expected)!r} but received {chunks!r}." + return self + + def assert_stream_contains(self, *expected: str) -> "TestResponse": + """Assert the streamed content contains each of the given substrings. + + Fails if the response is not a stream — use ``assert_contents`` for + buffered responses. + """ + self._require_stream("assert_stream_contains") + content = self.stream_content() + for sub in expected: + assert sub in content, f"Expected streamed content to contain {sub!r}, but it did not. Content: {content!r}" + return self diff --git a/fastapi_startkit/tests/ai/test_agent.py b/fastapi_startkit/tests/ai/test_agent.py new file mode 100644 index 00000000..91a2203a --- /dev/null +++ b/fastapi_startkit/tests/ai/test_agent.py @@ -0,0 +1,207 @@ +import unittest +from unittest import mock + +import langchain.chat_models as chat_models +from langchain_core.messages import AIMessage, ToolCall +from langchain_core.tools import tool + +from fastapi_startkit.ai import AIConfig, Document, fake_chat_model +from fastapi_startkit.ai.agent import Agent +from fastapi_startkit.ai.model_builder import ModelBuilder +from fastapi_startkit.ai.response import AgentResponse +from fastapi_startkit.application import app + + +@tool +def search_jobs(query: str) -> str: + """Search the job board for roles matching the query.""" + return "Python Developer at Shopify" + + +class JobAssistant(Agent): + def instructions(self): + return "You help users find jobs." + + def tools(self): + return [search_jobs] + + +class TestAgent(unittest.IsolatedAsyncioTestCase): + def setUp(self): + container = app() + container.bind("ai", AIConfig()) + container.make("config").set("ai", AIConfig()) + + def setup_agent(self, turns: list[AIMessage]): + model = fake_chat_model(turns) + patcher = mock.patch.object(chat_models, "init_chat_model", lambda *a, **k: model) + patcher.start() + self.addCleanup(patcher.stop) + return model + + async def test_prompt_returns_agent_response(self): + self.setup_agent([AIMessage(content="hello back")]) + + agent = Agent() + result = await agent.prompt("hi there") + + self.assertIsInstance(result, AgentResponse) + self.assertEqual(result.content, "hello back") + agent.assert_prompted() + + async def test_search_jobs_tool_returns_listing(self): + self.setup_agent( + [ + AIMessage( + content="", + tool_calls=[ToolCall(name="search_jobs", args={"query": "python"}, id="c1", type="tool_call")], + ), + ] + ) + + result = await JobAssistant().prompt("find me a python job") + + self.assertEqual(result.content, "Python Developer at Shopify") + self.assertEqual(result.tool_calls, []) + + async def test_prompt_maps_usage_metadata(self): + self.setup_agent( + [AIMessage(content="done", usage_metadata={"input_tokens": 11, "output_tokens": 7, "total_tokens": 18})] + ) + + result = await Agent().prompt("anything") + + self.assertEqual(result.usage, {"input": 11, "output": 7}) + + async def test_build_model_passes_langchain_provider_key(self): + captured = {} + + def fake_init(model, **kwargs): + captured["model"] = model + captured["provider"] = kwargs.get("model_provider") + return fake_chat_model([AIMessage(content="ok")]) + + patcher = mock.patch.object(chat_models, "init_chat_model", fake_init) + patcher.start() + self.addCleanup(patcher.stop) + + class GoogleAgent(Agent): + provider = "google" + + await GoogleAgent().prompt("hi") + + self.assertEqual(captured["provider"], "google_genai") + self.assertEqual(captured["model"], "gemini-2.5-flash-lite") + + async def test_stream_yields_tokens_from_the_model(self): + self.setup_agent([AIMessage(content="streamed reply")]) + + chunks = [chunk async for chunk in Agent().stream("hello")] + + self.assertEqual("".join(chunks), "streamed reply") + + async def test_middleware_streams_token_by_token_and_runs_after_hook(self): + self.setup_agent([AIMessage(content="one two three")]) + + events: list = [] + + class Logger: + def handle(self, model, handler): + events.append("before") + return handler(model).then(lambda final: events.append("after")) + + class LoggedAgent(Agent): + def middleware(self): + return [Logger()] + + chunks = [chunk async for chunk in LoggedAgent().stream("hi")] + + # Middleware must not buffer: the model's tokens arrive as separate chunks... + self.assertEqual("".join(chunks), "one two three") + self.assertGreater(len(chunks), 1) + # ...and the after-hook fires exactly once, after the stream is drained. + self.assertEqual(events, ["before", "after"]) + + async def test_middleware_after_hook_runs_on_prompt(self): + self.setup_agent([AIMessage(content="done")]) + + events: list = [] + + class Logger: + async def handle(self, model, handler): + events.append("before") + return handler(model).then(lambda final: events.append("after")) + + class LoggedAgent(Agent): + def middleware(self): + return [Logger()] + + result = await LoggedAgent().prompt("hi") + + self.assertEqual(result.content, "done") + self.assertEqual(events, ["before", "after"]) + + async def test_stream_yields_tool_result_without_calling_model_again(self): + self.setup_agent( + [ + AIMessage( + content="", + tool_calls=[ToolCall(name="search_jobs", args={"query": "python"}, id="c1", type="tool_call")], + ), + ] + ) + + chunks = [chunk async for chunk in JobAssistant().stream("find me a python job")] + + self.assertEqual(chunks, ["Python Developer at Shopify"]) + + def test_resolve_model_falls_back_to_lab_default(self): + self.assertEqual(ModelBuilder(Agent())._resolve_model(), "gemini-2.5-flash-lite") + + class AnthropicAgent(Agent): + provider = "anthropic" + + self.assertEqual(ModelBuilder(AnthropicAgent())._resolve_model(), "claude-sonnet-4-6") + + def test_resolve_model_prefers_explicit_override(self): + self.assertEqual(ModelBuilder(Agent())._resolve_model("my-model"), "my-model") + + def test_instructions_lead_the_message_list(self): + messages = JobAssistant()._build_messages("find me a job") + + self.assertEqual(messages[0], {"role": "system", "content": "You help users find jobs."}) + self.assertEqual(sum(m.get("role") == "system" for m in messages), 1) + + def test_instructions_can_be_a_method_override(self): + class DynamicAgent(Agent): + def instructions(self) -> str: + return "Computed identity." + + messages = DynamicAgent()._build_messages("hi") + + self.assertEqual(messages[0], {"role": "system", "content": "Computed identity."}) + + def test_no_instructions_prepends_no_system_message(self): + messages = Agent()._build_messages("hi") + + self.assertTrue(all(m.get("role") != "system" for m in messages)) + + def test_build_messages_inlines_text_attachment(self): + doc = Document(content="Q3 revenue was $1.2M.", name="q3-report.txt") + + messages = Agent()._build_messages("Summarise this report.", attachments=[doc]) + + user_content = messages[-1]["content"] + self.assertEqual(user_content[0], {"type": "text", "text": "Summarise this report."}) + self.assertEqual(user_content[1]["type"], "text") + self.assertIn("q3-report.txt", user_content[1]["text"]) + + def test_build_messages_encodes_binary_attachment_as_file_block(self): + doc = Document(content=b"%PDF-1.7 ...", name="q3.pdf", media_type="application/pdf") + + messages = Agent()._build_messages("Summarise", attachments=[doc]) + + block = messages[-1]["content"][1] + self.assertEqual(block["type"], "file") + self.assertEqual(block["mime_type"], "application/pdf") + self.assertEqual(block["base64"], doc.to_base64()) diff --git a/fastapi_startkit/tests/ai/test_agent_decorators.py b/fastapi_startkit/tests/ai/test_agent_decorators.py index 0ce11bc4..2beffd14 100644 --- a/fastapi_startkit/tests/ai/test_agent_decorators.py +++ b/fastapi_startkit/tests/ai/test_agent_decorators.py @@ -1,10 +1,11 @@ """Tests for Agent class decorators.""" +import unittest + from fastapi_startkit.ai.agent import Agent from fastapi_startkit.ai.decorators import ( max_steps, max_tokens, - memory, model, provider, timeout, @@ -12,195 +13,137 @@ ) -# ─── Decorator: @provider ────────────────────────────────────────────────────── - - -def test_provider_decorator_sets_provider(): - @provider("openai") - class MyAgent(Agent): - pass - - assert MyAgent._provider == "openai" - - -def test_provider_decorator_sets_anthropic(): - @provider("anthropic") - class MyAgent(Agent): - pass - - assert MyAgent._provider == "anthropic" - - -def test_provider_decorator_sets_google(): - @provider("google") - class MyAgent(Agent): - pass - - assert MyAgent._provider == "google" - - -# ─── Decorator: @model ──────────────────────────────────────────────────────── - - -def test_model_decorator_sets_model(): - @model("gpt-4o") - class MyAgent(Agent): - pass - - assert MyAgent._model == "gpt-4o" - - -def test_model_decorator_sets_claude_model(): - @model("claude-sonnet-4-6") - class MyAgent(Agent): - pass - - assert MyAgent._model == "claude-sonnet-4-6" - - -# ─── Decorator: @max_tokens ─────────────────────────────────────────────────── - - -def test_max_tokens_decorator_sets_value(): - @max_tokens(2048) - class MyAgent(Agent): - pass - - assert MyAgent._max_tokens == 2048 - - -def test_max_tokens_decorator_overrides_default(): - @max_tokens(512) - class MyAgent(Agent): - pass - - assert MyAgent._max_tokens == 512 - - -# ─── Decorator: @max_steps ──────────────────────────────────────────────────── - - -def test_max_steps_decorator_sets_value(): - @max_steps(5) - class MyAgent(Agent): - pass - - assert MyAgent._max_steps == 5 - - -def test_max_steps_decorator_sets_one(): - @max_steps(1) - class MyAgent(Agent): - pass - - assert MyAgent._max_steps == 1 - - -# ─── Decorator: @timeout ────────────────────────────────────────────────────── - - -def test_timeout_decorator_sets_seconds(): - @timeout(60.0) - class MyAgent(Agent): - pass +class TestAgentDecorators(unittest.TestCase): + def test_provider_decorator_sets_provider(self): + @provider("openai") + class MyAgent(Agent): + pass - assert MyAgent._timeout == 60.0 + self.assertEqual(MyAgent.provider, "openai") + def test_provider_decorator_sets_anthropic(self): + @provider("anthropic") + class MyAgent(Agent): + pass -def test_timeout_decorator_sets_fractional(): - @timeout(2.5) - class MyAgent(Agent): - pass + self.assertEqual(MyAgent.provider, "anthropic") - assert MyAgent._timeout == 2.5 + def test_provider_decorator_sets_google(self): + @provider("google") + class MyAgent(Agent): + pass + self.assertEqual(MyAgent.provider, "google") -# ─── Decorator: @top_p ──────────────────────────────────────────────────────── + def test_model_decorator_sets_model(self): + @model("gpt-4o") + class MyAgent(Agent): + pass + self.assertEqual(MyAgent.model, "gpt-4o") -def test_top_p_decorator_sets_value(): - @top_p(0.9) - class MyAgent(Agent): - pass + def test_model_decorator_sets_claude_model(self): + @model("claude-sonnet-4-6") + class MyAgent(Agent): + pass - assert MyAgent._top_p == 0.9 + self.assertEqual(MyAgent.model, "claude-sonnet-4-6") + def test_max_tokens_decorator_sets_value(self): + @max_tokens(2048) + class MyAgent(Agent): + pass -def test_top_p_decorator_sets_zero(): - @top_p(0.0) - class MyAgent(Agent): - pass + self.assertEqual(MyAgent.max_tokens, 2048) - assert MyAgent._top_p == 0.0 + def test_max_tokens_decorator_overrides_default(self): + @max_tokens(512) + class MyAgent(Agent): + pass + self.assertEqual(MyAgent.max_tokens, 512) -# ─── Decorator: @memory ─────────────────────────────────────────────────────── + def test_max_steps_decorator_sets_value(self): + @max_steps(5) + class MyAgent(Agent): + pass + self.assertEqual(MyAgent.max_steps, 5) -def test_memory_decorator_sets_backend(): - @memory("redis") - class MyAgent(Agent): - pass + def test_max_steps_decorator_sets_one(self): + @max_steps(1) + class MyAgent(Agent): + pass - assert MyAgent._memory_backend == "redis" + self.assertEqual(MyAgent.max_steps, 1) + def test_timeout_decorator_sets_seconds(self): + @timeout(60.0) + class MyAgent(Agent): + pass -def test_memory_decorator_sets_custom_backend(): - @memory("postgres") - class MyAgent(Agent): - pass + self.assertEqual(MyAgent.timeout, 60.0) - assert MyAgent._memory_backend == "postgres" + def test_timeout_decorator_sets_fractional(self): + @timeout(2.5) + class MyAgent(Agent): + pass + self.assertEqual(MyAgent.timeout, 2.5) -# ─── Stacking multiple decorators ───────────────────────────────────────────── + def test_top_p_decorator_sets_value(self): + @top_p(0.9) + class MyAgent(Agent): + pass + self.assertEqual(MyAgent.top_p, 0.9) -def test_multiple_decorators_can_be_stacked(): - @provider("openai") - @model("gpt-4o") - @max_tokens(1024) - @max_steps(3) - @timeout(15.0) - @top_p(0.95) - @memory("redis") - class FullyConfiguredAgent(Agent): - pass + def test_top_p_decorator_sets_zero(self): + @top_p(0.0) + class MyAgent(Agent): + pass - assert FullyConfiguredAgent._provider == "openai" - assert FullyConfiguredAgent._model == "gpt-4o" - assert FullyConfiguredAgent._max_tokens == 1024 - assert FullyConfiguredAgent._max_steps == 3 - assert FullyConfiguredAgent._timeout == 15.0 - assert FullyConfiguredAgent._top_p == 0.95 - assert FullyConfiguredAgent._memory_backend == "redis" + self.assertEqual(MyAgent.top_p, 0.0) + def test_multiple_decorators_can_be_stacked(self): + @provider("openai") + @model("gpt-4o") + @max_tokens(1024) + @max_steps(3) + @timeout(15.0) + @top_p(0.95) + class FullyConfiguredAgent(Agent): + pass -def test_stacking_does_not_affect_base_class(): - """Decorator-applied values must not leak into the Agent base class.""" + self.assertEqual(FullyConfiguredAgent.provider, "openai") + self.assertEqual(FullyConfiguredAgent.model, "gpt-4o") + self.assertEqual(FullyConfiguredAgent.max_tokens, 1024) + self.assertEqual(FullyConfiguredAgent.max_steps, 3) + self.assertEqual(FullyConfiguredAgent.timeout, 15.0) + self.assertEqual(FullyConfiguredAgent.top_p, 0.95) - @provider("openai") - @model("gpt-4o") - class SubAgent(Agent): - pass + def test_stacking_does_not_affect_base_class(self): + """Decorator-applied values must not leak into the Agent base class.""" - # Base Agent must retain its own defaults - assert Agent._provider == "anthropic" - assert Agent._model == "" + @provider("openai") + @model("gpt-4o") + class SubAgent(Agent): + pass - # Subclass has decorated values - assert SubAgent._provider == "openai" - assert SubAgent._model == "gpt-4o" + self.assertIsNone(Agent.provider) + self.assertIsNone(Agent.model) + self.assertEqual(SubAgent.provider, "openai") + self.assertEqual(SubAgent.model, "gpt-4o") -def test_instance_inherits_class_config(): - """Instantiating a decorated class reads the right config values.""" + def test_instance_inherits_class_config(self): + """Instantiating a decorated class reads the right config values.""" - @provider("openai") - @max_tokens(256) - class TinyAgent(Agent): - pass + @provider("openai") + @max_tokens(256) + class TinyAgent(Agent): + pass - agent = TinyAgent() - assert agent._provider == "openai" - assert agent._max_tokens == 256 + agent = TinyAgent() + self.assertEqual(agent.provider, "openai") + self.assertEqual(agent.max_tokens, 256) diff --git a/fastapi_startkit/tests/ai/test_agent_document.py b/fastapi_startkit/tests/ai/test_agent_document.py index 997ed0d9..264e5e55 100644 --- a/fastapi_startkit/tests/ai/test_agent_document.py +++ b/fastapi_startkit/tests/ai/test_agent_document.py @@ -2,150 +2,126 @@ import os import tempfile +import unittest -import pytest from fastapi_startkit.ai.document import Document -# ─── Document.from_path() ───────────────────────────────────────────────────── - - -def test_from_path_reads_file_content(): - content = "Hello from file!" - with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: - f.write(content) - path = f.name - - try: - doc = Document.from_path(path) - assert doc.content == content - finally: - os.unlink(path) - - -def test_from_path_sets_name_to_path(): - with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: - f.write("content") - path = f.name - - try: - doc = Document.from_path(path) - assert doc.name == path - finally: - os.unlink(path) - - -def test_from_path_reads_multiline_content(): - content = "line one\nline two\nline three\n" - with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: - f.write(content) - path = f.name - - try: - doc = Document.from_path(path) - assert doc.content == content - finally: - os.unlink(path) - - -def test_from_path_raises_on_missing_file(): - with pytest.raises(FileNotFoundError): - Document.from_path("/nonexistent/path/file.txt") - - -def test_from_path_reads_empty_file(): - with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: - f.write("") - path = f.name - - try: - doc = Document.from_path(path) - assert doc.content == "" - finally: - os.unlink(path) - - -# ─── Document.to_anthropic_block() ──────────────────────────────────────────── - - -def test_to_anthropic_block_returns_dict(): - doc = Document(content="some text", name="report.txt") - block = doc.to_anthropic_block() - assert isinstance(block, dict) - - -def test_to_anthropic_block_has_type_document(): - doc = Document(content="some text", name="report.txt") - block = doc.to_anthropic_block() - assert block["type"] == "document" - - -def test_to_anthropic_block_has_source_key(): - doc = Document(content="some text") - block = doc.to_anthropic_block() - assert "source" in block - - -def test_to_anthropic_block_source_type_is_text(): - doc = Document(content="some text") - block = doc.to_anthropic_block() - assert block["source"]["type"] == "text" - - -def test_to_anthropic_block_source_media_type_default(): - doc = Document(content="some text") - block = doc.to_anthropic_block() - assert block["source"]["media_type"] == "text/plain" - - -def test_to_anthropic_block_source_data_contains_content(): - doc = Document(content="the actual text content") - block = doc.to_anthropic_block() - assert block["source"]["data"] == "the actual text content" - - -def test_to_anthropic_block_title_is_name(): - doc = Document(content="text", name="my_document.txt") - block = doc.to_anthropic_block() - assert block["title"] == "my_document.txt" - - -def test_to_anthropic_block_custom_media_type(): - doc = Document(content="", name="page.html", media_type="text/html") - block = doc.to_anthropic_block() - assert block["source"]["media_type"] == "text/html" - - -def test_to_anthropic_block_full_structure(): - """Assert the complete expected block structure matches exactly.""" - doc = Document(content="contract text", name="contract.txt", media_type="text/plain") - block = doc.to_anthropic_block() - - expected = { - "type": "document", - "source": { - "type": "text", - "media_type": "text/plain", - "data": "contract text", - }, - "title": "contract.txt", - } - assert block == expected - - -# ─── Document constructor ────────────────────────────────────────────────────── - - -def test_document_name_defaults_to_empty_string(): - doc = Document(content="text") - assert doc.name == "" - - -def test_document_media_type_defaults_to_text_plain(): - doc = Document(content="text") - assert doc.media_type == "text/plain" - - -def test_document_stores_content(): - doc = Document(content="stored content") - assert doc.content == "stored content" +class TestDocument(unittest.TestCase): + def test_from_path_reads_file_content(self): + content = "Hello from file!" + with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: + f.write(content) + path = f.name + + try: + doc = Document.from_path(path) + self.assertEqual(doc.content, content) + finally: + os.unlink(path) + + def test_from_path_sets_name_to_path(self): + with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: + f.write("content") + path = f.name + + try: + doc = Document.from_path(path) + self.assertEqual(doc.name, path) + finally: + os.unlink(path) + + def test_from_path_reads_multiline_content(self): + content = "line one\nline two\nline three\n" + with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: + f.write(content) + path = f.name + + try: + doc = Document.from_path(path) + self.assertEqual(doc.content, content) + finally: + os.unlink(path) + + def test_from_path_raises_on_missing_file(self): + with self.assertRaises(FileNotFoundError): + Document.from_path("/nonexistent/path/file.txt") + + def test_from_path_reads_empty_file(self): + with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f: + f.write("") + path = f.name + + try: + doc = Document.from_path(path) + self.assertEqual(doc.content, "") + finally: + os.unlink(path) + + def test_to_anthropic_block_returns_dict(self): + doc = Document(content="some text", name="report.txt") + block = doc.to_anthropic_block() + self.assertIsInstance(block, dict) + + def test_to_anthropic_block_has_type_document(self): + doc = Document(content="some text", name="report.txt") + block = doc.to_anthropic_block() + self.assertEqual(block["type"], "document") + + def test_to_anthropic_block_has_source_key(self): + doc = Document(content="some text") + block = doc.to_anthropic_block() + self.assertIn("source", block) + + def test_to_anthropic_block_source_type_is_text(self): + doc = Document(content="some text") + block = doc.to_anthropic_block() + self.assertEqual(block["source"]["type"], "text") + + def test_to_anthropic_block_source_media_type_default(self): + doc = Document(content="some text") + block = doc.to_anthropic_block() + self.assertEqual(block["source"]["media_type"], "text/plain") + + def test_to_anthropic_block_source_data_contains_content(self): + doc = Document(content="the actual text content") + block = doc.to_anthropic_block() + self.assertEqual(block["source"]["data"], "the actual text content") + + def test_to_anthropic_block_title_is_name(self): + doc = Document(content="text", name="my_document.txt") + block = doc.to_anthropic_block() + self.assertEqual(block["title"], "my_document.txt") + + def test_to_anthropic_block_custom_media_type(self): + doc = Document(content="", name="page.html", media_type="text/html") + block = doc.to_anthropic_block() + self.assertEqual(block["source"]["media_type"], "text/html") + + def test_to_anthropic_block_full_structure(self): + """Assert the complete expected block structure matches exactly.""" + doc = Document(content="contract text", name="contract.txt", media_type="text/plain") + block = doc.to_anthropic_block() + + expected = { + "type": "document", + "source": { + "type": "text", + "media_type": "text/plain", + "data": "contract text", + }, + "title": "contract.txt", + } + self.assertEqual(block, expected) + + def test_document_name_defaults_to_empty_string(self): + doc = Document(content="text") + self.assertEqual(doc.name, "") + + def test_document_media_type_defaults_to_text_plain(self): + doc = Document(content="text") + self.assertEqual(doc.media_type, "text/plain") + + def test_document_stores_content(self): + doc = Document(content="stored content") + self.assertEqual(doc.content, "stored content") diff --git a/fastapi_startkit/tests/ai/test_agent_fake.py b/fastapi_startkit/tests/ai/test_agent_fake.py index ff52563c..3191a8b3 100644 --- a/fastapi_startkit/tests/ai/test_agent_fake.py +++ b/fastapi_startkit/tests/ai/test_agent_fake.py @@ -1,276 +1,309 @@ -"""Tests for Agent.fake(), assert_prompted(), assert_not_prompted(), and reset().""" +"""Tests for Agent.fake() / Agent.record() and the assert_prompted/reset helpers. + +``Agent.fake()`` binds a canned stand-in into the container for the duration of a +``with`` block. ``Agent.record()`` binds a record-and-replay stand-in: on a cassette +miss it calls the real agent once and caches the response to disk; on a hit it +replays from the cassette without calling the agent again. +""" import json import os import tempfile +import unittest +from unittest import mock -import pytest from fastapi_startkit.ai.agent import Agent -from fastapi_startkit.ai.response import AgentResponse, AgentSnapshot - - -# ─── Helpers ────────────────────────────────────────────────────────────────── +from fastapi_startkit.ai.response import AgentResponse class SimpleAgent(Agent): - """Bare-minimum agent for testing — never touches a real API.""" - pass -# ─── fake() with AgentResponse returns it without hitting the API ────────────── - - -def test_fake_with_agent_response_returns_it(): - agent = SimpleAgent() - expected = AgentResponse(content="Hello world!") - agent.fake({"*": expected}) - - result = agent.prompt("anything") - - assert result.content == "Hello world!" - - -def test_fake_does_not_call_provider_run(): - """fake() must short-circuit before _run() is ever invoked.""" - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="faked")}) - - called = [] - - original_run = agent._run - - def patched_run(*args, **kwargs): - called.append(True) - return original_run(*args, **kwargs) - - agent._run = patched_run # type: ignore[method-assign] - agent.prompt("hello") - - assert called == [], "_run() must not be called when a fake matches" - - -def test_fake_with_exact_pattern(): - agent = SimpleAgent() - agent.fake({"hello": AgentResponse(content="matched hello")}) - - result = agent.prompt("hello") - assert result.content == "matched hello" - - -# ─── fake() with glob pattern matching ──────────────────────────────────────── - - -def test_fake_glob_hello_wildcard(): - agent = SimpleAgent() - agent.fake({"*hello*": AgentResponse(content="hi there")}) - - result = agent.prompt("say hello to me") - assert result.content == "hi there" - - -def test_fake_glob_analyze_wildcard(): - agent = SimpleAgent() - agent.fake({"*analyze*": AgentResponse(content="analysis done")}) - - result = agent.prompt("please analyze this report") - assert result.content == "analysis done" - - -def test_fake_glob_no_match_raises_on_missing_run(): - """When a pattern does not match and no real provider is configured, _run raises.""" - agent = SimpleAgent() - agent.fake({"*hello*": AgentResponse(content="hi")}) - - with pytest.raises(Exception): - agent.prompt("goodbye") # pattern does not match → falls through to _run - - -def test_fake_glob_case_insensitive(): - agent = SimpleAgent() - agent.fake({"*HELLO*": AgentResponse(content="case insensitive")}) - - result = agent.prompt("say hello please") - assert result.content == "case insensitive" - - -def test_fake_first_matching_pattern_wins(): - agent = SimpleAgent() - agent.fake( - { - "*hello*": AgentResponse(content="first match"), - "*hello world*": AgentResponse(content="second match"), - } - ) - - result = agent.prompt("hello world") - assert result.content == "first match" - - -# ─── fake() with AgentSnapshot loads from fixture if file exists ─────────────── - - -def test_fake_with_snapshot_loads_from_file_if_exists(): - fixture_data = {"content": "snapshot reply", "tool_calls": [], "usage": {"input": 5, "output": 10}} - - with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: - json.dump(fixture_data, f) - fixture_path = f.name - - try: +class TestAgentFake(unittest.IsolatedAsyncioTestCase): + async def test_fake_with_agent_response_returns_it(self): agent = SimpleAgent() - snapshot = AgentSnapshot(path=fixture_path) - agent.fake({"*": snapshot}) - - result = agent.prompt("any prompt") - assert result.content == "snapshot reply" - assert result.usage == {"input": 5, "output": 10} - finally: - os.unlink(fixture_path) - - -def test_fake_with_snapshot_missing_file_calls_run(monkeypatch): - """When the snapshot file does not exist, _run() is called and the result is saved.""" - expected_response = AgentResponse(content="live result") + with SimpleAgent.fake({"*": AgentResponse(content="Hello world!")}): + result = await agent.prompt("anything") - with tempfile.TemporaryDirectory() as tmpdir: - snapshot_path = os.path.join(tmpdir, "snap.json") + self.assertEqual(result.content, "Hello world!") + async def test_fake_does_not_call_provider_run(self): agent = SimpleAgent() - snapshot = AgentSnapshot(path=snapshot_path) - agent.fake({"*": snapshot}) + called = [] - # Patch _run to avoid real API call - monkeypatch.setattr(agent, "_run", lambda *a, **kw: expected_response) + original_run = agent._run - result = agent.prompt("test message") - assert result.content == "live result" + async def patched_run(*args, **kwargs): + called.append(True) + return await original_run(*args, **kwargs) - # Snapshot should now be saved to disk - assert os.path.exists(snapshot_path) - with open(snapshot_path) as f: - saved = json.load(f) - assert saved["content"] == "live result" + agent._run = patched_run + with SimpleAgent.fake({"*": AgentResponse(content="faked")}): + await agent.prompt("hello") -# ─── assert_prompted() ──────────────────────────────────────────────────────── + self.assertEqual(called, [], "_run() must not be called when a fake matches") + async def test_fake_with_exact_pattern(self): + agent = SimpleAgent() + with SimpleAgent.fake({"hello": AgentResponse(content="matched hello")}): + result = await agent.prompt("hello") -def test_assert_prompted_passes_after_one_call(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) - - agent.prompt("first") - agent.assert_prompted() # must not raise - - -def test_assert_prompted_times_2_passes_after_exactly_2_calls(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) - - agent.prompt("first") - agent.prompt("second") - agent.assert_prompted(times=2) # must not raise + self.assertEqual(result.content, "matched hello") + async def test_fake_glob_hello_wildcard(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*hello*": AgentResponse(content="hi there")}): + result = await agent.prompt("say hello to me") -def test_assert_prompted_times_fails_when_count_mismatch(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) + self.assertEqual(result.content, "hi there") - agent.prompt("only once") + async def test_fake_glob_analyze_wildcard(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*analyze*": AgentResponse(content="analysis done")}): + result = await agent.prompt("please analyze this report") - with pytest.raises(AssertionError): - agent.assert_prompted(times=2) + self.assertEqual(result.content, "analysis done") + async def test_fake_no_match_raises(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*hello*": AgentResponse(content="hi")}): + with self.assertRaises(Exception): + await agent.prompt("goodbye") -def test_assert_prompted_fails_when_never_called(): - agent = SimpleAgent() + async def test_fake_glob_case_insensitive(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*HELLO*": AgentResponse(content="case insensitive")}): + result = await agent.prompt("say hello please") - with pytest.raises(AssertionError): - agent.assert_prompted() + self.assertEqual(result.content, "case insensitive") + async def test_fake_first_matching_pattern_wins(self): + agent = SimpleAgent() + with SimpleAgent.fake( + { + "*hello*": AgentResponse(content="first match"), + "*hello world*": AgentResponse(content="second match"), + } + ): + result = await agent.prompt("hello world") -def test_assert_prompted_times_zero_passes_when_never_called(): - agent = SimpleAgent() - agent.assert_prompted(times=0) # must not raise + self.assertEqual(result.content, "first match") + async def test_assert_prompted_passes_after_one_call(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="ok")}): + await agent.prompt("first") + agent.assert_prompted() -# ─── assert_not_prompted() ──────────────────────────────────────────────────── + async def test_assert_prompted_times_2_passes_after_exactly_2_calls(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="ok")}): + await agent.prompt("first") + await agent.prompt("second") + agent.assert_prompted(times=2) + async def test_assert_prompted_times_fails_when_count_mismatch(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="ok")}): + await agent.prompt("only once") -def test_assert_not_prompted_passes_when_no_calls_made(): - agent = SimpleAgent() - agent.assert_not_prompted() # must not raise + with self.assertRaises(AssertionError): + agent.assert_prompted(times=2) + def test_assert_prompted_fails_when_never_called(self): + agent = SimpleAgent() -def test_assert_not_prompted_fails_after_one_call(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) + with self.assertRaises(AssertionError): + agent.assert_prompted() - agent.prompt("a prompt") + def test_assert_prompted_times_zero_passes_when_never_called(self): + agent = SimpleAgent() + agent.assert_prompted(times=0) - with pytest.raises(AssertionError): + def test_assert_not_prompted_passes_when_no_calls_made(self): + agent = SimpleAgent() agent.assert_not_prompted() + async def test_assert_not_prompted_fails_after_one_call(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="ok")}): + await agent.prompt("a prompt") -# ─── reset() ────────────────────────────────────────────────────────────────── - - -def test_reset_clears_call_log(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) - - agent.prompt("first") - assert len(agent._call_log) == 1 - - agent.reset() - assert agent._call_log == [] - - -def test_reset_clears_fakes(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) - assert len(agent._fakes) == 1 - - agent.reset() - assert agent._fakes == {} - - -def test_reset_returns_agent_for_chaining(): - agent = SimpleAgent() - result = agent.reset() - assert result is agent + with self.assertRaises(AssertionError): + agent.assert_not_prompted() + async def test_reset_clears_call_log(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="ok")}): + await agent.prompt("first") + self.assertEqual(len(agent._call_log), 1) -def test_assert_not_prompted_passes_after_reset(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="ok")}) + agent.reset() + self.assertEqual(agent._call_log, []) - agent.prompt("call before reset") - agent.reset() + def test_reset_returns_agent_for_chaining(self): + agent = SimpleAgent() + result = agent.reset() + self.assertIs(result, agent) - agent.assert_not_prompted() # call log was cleared + async def test_assert_not_prompted_passes_after_reset(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="ok")}): + await agent.prompt("call before reset") + agent.reset() + agent.assert_not_prompted() -def test_fake_after_reset_works_normally(): - agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="first fake")}) - agent.prompt("call") - agent.reset() + async def test_fake_rebinding_overrides_previous(self): + agent = SimpleAgent() - agent.fake({"*": AgentResponse(content="second fake")}) - result = agent.prompt("call again") - assert result.content == "second fake" + with SimpleAgent.fake({"*": AgentResponse(content="first fake")}): + self.assertEqual((await agent.prompt("call")).content, "first fake") + with SimpleAgent.fake({"*": AgentResponse(content="second fake")}): + self.assertEqual((await agent.prompt("call again")).content, "second fake") -# ─── stream() respects fake() ───────────────────────────────────────────────── + async def test_stream_returns_fake_response(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*hello*": AgentResponse(content="Faked stream!")}): + chunks = [chunk async for chunk in agent.stream("hello world")] + # A faked stream is split into word chunks but rejoins to the value. + self.assertEqual("".join(chunks), "Faked stream!") + self.assertGreater(len(chunks), 1) + agent.assert_prompted(times=1) -def test_stream_returns_fake_response(): - agent = SimpleAgent() - agent.fake({"*hello*": AgentResponse(content="Faked stream!")}) + async def test_fake_stream_splits_value_into_word_chunks(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": "Hello there, friend"}): + chunks = [chunk async for chunk in agent.stream("hi")] - chunks = list(agent.stream("hello world")) + self.assertEqual(chunks, ["Hello ", "there, ", "friend"]) + self.assertEqual("".join(chunks), "Hello there, friend") - assert chunks == ["Faked stream!"] - agent.assert_prompted(times=1) + async def test_stream_records_one_call_not_two(self): + agent = SimpleAgent() + with SimpleAgent.fake({"*": AgentResponse(content="x")}): + [chunk async for chunk in agent.stream("once")] + + # Streaming must log exactly one prompt — not one for stream + one for prompt. + agent.assert_prompted(times=1) + + +class TestAgentRecord(unittest.IsolatedAsyncioTestCase): + def setup_agent(self, content): + calls = [] + + async def fake_run(agent_self, message, **kwargs): + calls.append(message) + return AgentResponse(content=content) + + patcher = mock.patch.object(SimpleAgent, "_run", fake_run) + patcher.start() + self.addCleanup(patcher.stop) + return calls + + async def test_first_run_records_response_to_cassette(self): + calls = self.setup_agent("recorded reply") + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "c.json") + with SimpleAgent.record(cassette): + result = await SimpleAgent().prompt("hello") + + self.assertEqual(result.content, "recorded reply") + self.assertEqual(calls, ["hello"]) + self.assertTrue(os.path.exists(cassette)) + with open(cassette) as f: + self.assertIn("recorded reply", json.load(f).values()) + + async def test_second_run_replays_without_calling_run(self): + calls = self.setup_agent("recorded reply") + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "c.json") + with SimpleAgent.record(cassette): + await SimpleAgent().prompt("hello") + with SimpleAgent.record(cassette): + replayed = await SimpleAgent().prompt("hello") + + self.assertEqual(replayed.content, "recorded reply") + self.assertEqual(calls, ["hello"]) + + async def test_replay_prefers_cassette_over_live_response(self): + async def first_run(s, m, **k): + return AgentResponse(content="from first record") + + async def changed_run(s, m, **k): + return AgentResponse(content="changed live value") + + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "c.json") + with mock.patch.object(SimpleAgent, "_run", first_run): + with SimpleAgent.record(cassette): + await SimpleAgent().prompt("hello") + with mock.patch.object(SimpleAgent, "_run", changed_run): + with SimpleAgent.record(cassette): + result = await SimpleAgent().prompt("hello") + + self.assertEqual(result.content, "from first record") + + async def test_distinct_messages_are_recorded_separately(self): + calls = self.setup_agent("reply") + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "c.json") + with SimpleAgent.record(cassette): + await SimpleAgent().prompt("hello") + await SimpleAgent().prompt("goodbye") + + self.assertEqual(calls, ["hello", "goodbye"]) + with open(cassette) as f: + self.assertEqual(len(json.load(f)), 2) + + def setup_stream(self, chunks): + calls = [] + + async def fake_stream(agent_self, message, **kwargs): + calls.append(message) + for chunk in chunks: + yield chunk + + patcher = mock.patch.object(SimpleAgent, "_stream", fake_stream) + patcher.start() + self.addCleanup(patcher.stop) + return calls + + async def test_stream_first_run_records_chunk_list_to_cassette(self): + calls = self.setup_stream(["Hel", "lo!"]) + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "s.json") + with SimpleAgent.record(cassette): + chunks = [c async for c in SimpleAgent().stream("hi")] + + self.assertEqual(chunks, ["Hel", "lo!"]) + self.assertEqual(calls, ["hi"]) + with open(cassette) as f: + self.assertEqual(list(json.load(f).values()), [["Hel", "lo!"]]) + + async def test_stream_second_run_replays_chunks_without_calling_stream(self): + calls = self.setup_stream(["Hel", "lo!"]) + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "s.json") + with SimpleAgent.record(cassette): + [c async for c in SimpleAgent().stream("hi")] + with SimpleAgent.record(cassette): + replayed = [c async for c in SimpleAgent().stream("hi")] + + self.assertEqual(replayed, ["Hel", "lo!"]) + self.assertEqual(calls, ["hi"]) # real stream invoked only on the first run + + async def test_prompt_reads_a_stream_recorded_cassette_as_joined_content(self): + self.setup_stream(["Hel", "lo!"]) + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "s.json") + with SimpleAgent.record(cassette): + [c async for c in SimpleAgent().stream("hi")] + with SimpleAgent.record(cassette): + response = await SimpleAgent().prompt("hi") + + self.assertEqual(response.content, "Hello!") diff --git a/fastapi_startkit/tests/ai/test_agent_response.py b/fastapi_startkit/tests/ai/test_agent_response.py index d57f7b5b..0d203076 100644 --- a/fastapi_startkit/tests/ai/test_agent_response.py +++ b/fastapi_startkit/tests/ai/test_agent_response.py @@ -1,136 +1,104 @@ """Tests for the AgentResponse dataclass.""" -import pytest -from fastapi_startkit.ai.response import AgentResponse - - -# ─── AgentResponse.text() ───────────────────────────────────────────────────── - - -def test_text_returns_content(): - response = AgentResponse(content="Hello, world!") - assert response.text() == "Hello, world!" - - -def test_text_returns_empty_string_when_no_content(): - response = AgentResponse() - assert response.text() == "" - - -def test_text_returns_multiline_content(): - content = "Line 1\nLine 2\nLine 3" - response = AgentResponse(content=content) - assert response.text() == content - - -# ─── AgentResponse.json() ───────────────────────────────────────────────────── - - -def test_json_parses_content_as_json(): - response = AgentResponse(content='{"key": "value", "number": 42}') - parsed = response.json() - assert parsed == {"key": "value", "number": 42} - - -def test_json_parses_list_content(): - response = AgentResponse(content="[1, 2, 3]") - assert response.json() == [1, 2, 3] - - -def test_json_parses_nested_object(): - response = AgentResponse(content='{"nested": {"a": 1}}') - assert response.json()["nested"]["a"] == 1 - - -def test_json_raises_on_invalid_content(): - response = AgentResponse(content="not valid json") - with pytest.raises(Exception): # json.JSONDecodeError - response.json() - - -def test_json_raises_on_empty_content(): - response = AgentResponse(content="") - with pytest.raises(Exception): - response.json() - - -# ─── AgentResponse.__str__() ────────────────────────────────────────────────── - - -def test_str_returns_content(): - response = AgentResponse(content="My response text") - assert str(response) == "My response text" +import unittest - -def test_str_returns_empty_string_when_no_content(): - response = AgentResponse() - assert str(response) == "" - - -def test_str_works_in_f_string(): - response = AgentResponse(content="hello") - assert f"Result: {response}" == "Result: hello" - - -# ─── AgentResponse.__bool__() ───────────────────────────────────────────────── - - -def test_bool_is_true_when_content_non_empty(): - response = AgentResponse(content="some text") - assert bool(response) is True - - -def test_bool_is_false_when_content_empty(): - response = AgentResponse(content="") - assert bool(response) is False - - -def test_bool_is_false_when_content_not_set(): - response = AgentResponse() - assert bool(response) is False - - -def test_bool_is_true_with_whitespace_content(): - """A response with only whitespace still evaluates as truthy (non-empty string).""" - response = AgentResponse(content=" ") - assert bool(response) is True - - -def test_bool_usable_in_conditional(): - response = AgentResponse(content="text") - assert response # truthy - - empty = AgentResponse(content="") - assert not empty # falsy - - -# ─── AgentResponse dataclass fields ─────────────────────────────────────────── - - -def test_tool_calls_default_to_empty_list(): - response = AgentResponse() - assert response.tool_calls == [] - - -def test_usage_defaults_to_empty_dict(): - response = AgentResponse() - assert response.usage == {} - - -def test_raw_defaults_to_none(): - response = AgentResponse() - assert response.raw is None +from fastapi_startkit.ai.response import AgentResponse -def test_all_fields_can_be_set(): - raw_obj = object() - response = AgentResponse( - content="text", - tool_calls=[{"name": "search", "input": {"q": "test"}}], - usage={"input": 10, "output": 20}, - raw=raw_obj, - ) - assert response.content == "text" - assert response.tool_calls == [{"name": "search", "input": {"q": "test"}}] - assert response.usage == {"input": 10, "output": 20} - assert response.raw is raw_obj +class TestAgentResponse(unittest.TestCase): + def test_text_returns_content(self): + response = AgentResponse(content="Hello, world!") + self.assertEqual(response.text(), "Hello, world!") + + def test_text_returns_empty_string_when_no_content(self): + response = AgentResponse() + self.assertEqual(response.text(), "") + + def test_text_returns_multiline_content(self): + content = "Line 1\nLine 2\nLine 3" + response = AgentResponse(content=content) + self.assertEqual(response.text(), content) + + def test_json_parses_content_as_json(self): + response = AgentResponse(content='{"key": "value", "number": 42}') + parsed = response.json() + self.assertEqual(parsed, {"key": "value", "number": 42}) + + def test_json_parses_list_content(self): + response = AgentResponse(content="[1, 2, 3]") + self.assertEqual(response.json(), [1, 2, 3]) + + def test_json_parses_nested_object(self): + response = AgentResponse(content='{"nested": {"a": 1}}') + self.assertEqual(response.json()["nested"]["a"], 1) + + def test_json_raises_on_invalid_content(self): + response = AgentResponse(content="not valid json") + with self.assertRaises(Exception): # json.JSONDecodeError + response.json() + + def test_json_raises_on_empty_content(self): + response = AgentResponse(content="") + with self.assertRaises(Exception): + response.json() + + def test_str_returns_content(self): + response = AgentResponse(content="My response text") + self.assertEqual(str(response), "My response text") + + def test_str_returns_empty_string_when_no_content(self): + response = AgentResponse() + self.assertEqual(str(response), "") + + def test_str_works_in_f_string(self): + response = AgentResponse(content="hello") + self.assertEqual(f"Result: {response}", "Result: hello") + + def test_bool_is_true_when_content_non_empty(self): + response = AgentResponse(content="some text") + self.assertIs(bool(response), True) + + def test_bool_is_false_when_content_empty(self): + response = AgentResponse(content="") + self.assertIs(bool(response), False) + + def test_bool_is_false_when_content_not_set(self): + response = AgentResponse() + self.assertIs(bool(response), False) + + def test_bool_is_true_with_whitespace_content(self): + """A response with only whitespace still evaluates as truthy (non-empty string).""" + response = AgentResponse(content=" ") + self.assertIs(bool(response), True) + + def test_bool_usable_in_conditional(self): + response = AgentResponse(content="text") + self.assertTrue(response) + + empty = AgentResponse(content="") + self.assertFalse(empty) + + def test_tool_calls_default_to_empty_list(self): + response = AgentResponse() + self.assertEqual(response.tool_calls, []) + + def test_usage_defaults_to_empty_dict(self): + response = AgentResponse() + self.assertEqual(response.usage, {}) + + def test_raw_defaults_to_none(self): + response = AgentResponse() + self.assertIsNone(response.raw) + + def test_all_fields_can_be_set(self): + raw_obj = object() + response = AgentResponse( + content="text", + tool_calls=[{"name": "search", "input": {"q": "test"}}], + usage={"input": 10, "output": 20}, + raw=raw_obj, + ) + self.assertEqual(response.content, "text") + self.assertEqual(response.tool_calls, [{"name": "search", "input": {"q": "test"}}]) + self.assertEqual(response.usage, {"input": 10, "output": 20}) + self.assertIs(response.raw, raw_obj) diff --git a/fastapi_startkit/tests/ai/test_agent_schema.py b/fastapi_startkit/tests/ai/test_agent_schema.py new file mode 100644 index 00000000..0e7d705b --- /dev/null +++ b/fastapi_startkit/tests/ai/test_agent_schema.py @@ -0,0 +1,59 @@ +import os +import tempfile +import unittest +from unittest import mock + +from pydantic import BaseModel + +from fastapi_startkit.ai.agent import Agent +from fastapi_startkit.ai.response import AgentResponse + + +class User(BaseModel): + id: str + name: str = "" + + +class UserAgent(Agent): + def schema(self): + return User + + +class TestAgentSchema(unittest.IsolatedAsyncioTestCase): + async def test_fake_json_is_built_into_the_schema(self): + agent = UserAgent() + with UserAgent.fake({"*": '{"id": "u-1", "name": "Alex"}'}): + response = await agent.prompt("get the user") + + self.assertIsInstance(response.parsed, User) + self.assertEqual(response.parsed.id, "u-1") + self.assertEqual(response.parsed.name, "Alex") + self.assertEqual(response.content, '{"id": "u-1", "name": "Alex"}') + + async def test_no_schema_leaves_parsed_none(self): + agent = Agent() + with Agent.fake({"*": '{"id": "u-1"}'}): + response = await agent.prompt("anything") + + self.assertIsNone(response.parsed) + + async def test_invalid_json_for_schema_raises(self): + agent = UserAgent() + with UserAgent.fake({"*": '{"name": "no id here"}'}): + with self.assertRaises(Exception): + await agent.prompt("get the user") + + async def test_record_stores_json_and_rebuilds_schema_on_replay(self): + async def fake_run(self, message, **kwargs): + return AgentResponse(content='{"id": "u-9", "name": "Sam"}') + + with tempfile.TemporaryDirectory() as tmp: + cassette = os.path.join(tmp, "user.json") + with mock.patch.object(UserAgent, "_run", fake_run): + with UserAgent.record(cassette): + recorded = await UserAgent().prompt("get the user") + with UserAgent.record(cassette): + replayed = await UserAgent().prompt("get the user") + + self.assertEqual(recorded.parsed, User(id="u-9", name="Sam")) + self.assertEqual(replayed.parsed, User(id="u-9", name="Sam")) diff --git a/fastapi_startkit/tests/ai/test_audio.py b/fastapi_startkit/tests/ai/test_audio.py index 0087de9f..4f14915b 100644 --- a/fastapi_startkit/tests/ai/test_audio.py +++ b/fastapi_startkit/tests/ai/test_audio.py @@ -248,7 +248,7 @@ def test_unknown_voice_passed_through(self): async def test_audio_builder_resolves_google_factory(self): mock_ai_config = MagicMock() - mock_ai_config.audio_provider = "google" + mock_ai_config.default_audio = "google" mock_ai_config.providers = { "google": MagicMock(key="gkey"), "openai": MagicMock(key=""), @@ -327,7 +327,7 @@ def test_direct_voice_id_passed_through(self): async def test_audio_builder_resolves_elevenlabs_factory(self): mock_ai_config = MagicMock() - mock_ai_config.audio_provider = "elevenlabs" + mock_ai_config.default_audio = "elevenlabs" mock_ai_config.providers = { "google": MagicMock(key=""), "openai": MagicMock(key=""), diff --git a/fastapi_startkit/tests/ai/test_config.py b/fastapi_startkit/tests/ai/test_config.py index a5c91390..46dd58b9 100644 --- a/fastapi_startkit/tests/ai/test_config.py +++ b/fastapi_startkit/tests/ai/test_config.py @@ -1,149 +1,97 @@ """Tests for AI configuration dataclasses.""" -from fastapi_startkit.ai.config import AIConfig, AnthropicConfig, GoogleConfig, OpenAIConfig +import os +import unittest +from unittest import mock +from fastapi_startkit.ai import AIConfig, AnthropicConfig, GoogleConfig, OpenAIConfig -# ─── AIConfig defaults ──────────────────────────────────────────────────────── +class TestAIConfiguration(unittest.TestCase): + def _patch_env(self, set_=None, unset=()): + patcher = mock.patch.dict(os.environ, set_ or {}) + patcher.start() + self.addCleanup(patcher.stop) + for key in unset: + os.environ.pop(key, None) -def test_aiconfig_default_provider_is_google(monkeypatch): - """Default provider is 'google' when AI_PROVIDER env var is not set.""" - monkeypatch.delenv("AI_PROVIDER", raising=False) - config = AIConfig() - assert config.default == "google" + def test_aiconfig_default_provider_is_google(self): + self._patch_env(unset=["AI_PROVIDER"]) + self.assertEqual(AIConfig().default, "google") + def test_aiconfig_default_reads_ai_provider_env(self): + self._patch_env({"AI_PROVIDER": "anthropic"}) + self.assertEqual(AIConfig().default, "anthropic") -def test_aiconfig_default_reads_ai_provider_env(monkeypatch): - monkeypatch.setenv("AI_PROVIDER", "anthropic") - config = AIConfig() - assert config.default == "anthropic" + def test_aiconfig_providers_has_anthropic_key(self): + self.assertIn("anthropic", AIConfig().providers) + def test_aiconfig_providers_has_openai_key(self): + self.assertIn("openai", AIConfig().providers) -def test_aiconfig_providers_has_anthropic_key(): - config = AIConfig() - assert "anthropic" in config.providers + def test_aiconfig_providers_has_google_key(self): + self.assertIn("google", AIConfig().providers) + def test_aiconfig_providers_anthropic_is_instance(self): + self.assertIsInstance(AIConfig().providers["anthropic"], AnthropicConfig) -def test_aiconfig_providers_has_openai_key(): - config = AIConfig() - assert "openai" in config.providers + def test_aiconfig_providers_openai_is_instance(self): + self.assertIsInstance(AIConfig().providers["openai"], OpenAIConfig) + def test_aiconfig_providers_google_is_instance(self): + self.assertIsInstance(AIConfig().providers["google"], GoogleConfig) -def test_aiconfig_providers_has_google_key(): - config = AIConfig() - assert "google" in config.providers + def test_anthropic_config_driver_is_anthropic(self): + self.assertEqual(AnthropicConfig().driver, "anthropic") + def test_anthropic_config_picks_up_api_key_from_env(self): + self._patch_env({"ANTHROPIC_API_KEY": "test-anthropic-key-123"}) + self.assertEqual(AnthropicConfig().key, "test-anthropic-key-123") -def test_aiconfig_providers_anthropic_is_instance(): - config = AIConfig() - assert isinstance(config.providers["anthropic"], AnthropicConfig) + def test_anthropic_config_key_defaults_to_empty_when_env_not_set(self): + self._patch_env(unset=["ANTHROPIC_API_KEY"]) + self.assertEqual(AnthropicConfig().key, "") + def test_anthropic_config_url_default(self): + self.assertEqual(AnthropicConfig().url, "https://api.anthropic.com") -def test_aiconfig_providers_openai_is_instance(): - config = AIConfig() - assert isinstance(config.providers["openai"], OpenAIConfig) + def test_anthropic_config_url_can_be_overridden(self): + self._patch_env({"ANTHROPIC_BASE_URL": "https://my-proxy.example.com"}) + self.assertEqual(AnthropicConfig().url, "https://my-proxy.example.com") + def test_openai_config_driver_is_openai(self): + self.assertEqual(OpenAIConfig().driver, "openai") -def test_aiconfig_providers_google_is_instance(): - config = AIConfig() - assert isinstance(config.providers["google"], GoogleConfig) + def test_openai_config_picks_up_api_key_from_env(self): + self._patch_env({"OPENAI_API_KEY": "sk-openai-test-key"}) + self.assertEqual(OpenAIConfig().key, "sk-openai-test-key") + def test_openai_config_key_defaults_to_empty_when_env_not_set(self): + self._patch_env(unset=["OPENAI_API_KEY"]) + self.assertEqual(OpenAIConfig().key, "") -# ─── AnthropicConfig ────────────────────────────────────────────────────────── + def test_openai_config_url_default(self): + self.assertEqual(OpenAIConfig().url, "https://api.openai.com/v1") + def test_openai_config_url_can_be_overridden(self): + self._patch_env({"OPENAI_BASE_URL": "https://openai-proxy.example.com/v1"}) + self.assertEqual(OpenAIConfig().url, "https://openai-proxy.example.com/v1") -def test_anthropic_config_driver_is_anthropic(): - config = AnthropicConfig() - assert config.driver == "anthropic" + def test_google_config_driver_is_google(self): + self.assertEqual(GoogleConfig().driver, "google") + def test_google_config_picks_up_gemini_api_key(self): + self._patch_env({"GEMINI_API_KEY": "gemini-key-abc"}, unset=["GOOGLE_API_KEY"]) + self.assertEqual(GoogleConfig().key, "gemini-key-abc") -def test_anthropic_config_picks_up_api_key_from_env(monkeypatch): - monkeypatch.setenv("ANTHROPIC_API_KEY", "test-anthropic-key-123") - config = AnthropicConfig() - assert config.key == "test-anthropic-key-123" + def test_google_config_falls_back_to_google_api_key(self): + self._patch_env({"GOOGLE_API_KEY": "google-api-fallback"}, unset=["GEMINI_API_KEY"]) + self.assertEqual(GoogleConfig().key, "google-api-fallback") + def test_google_config_gemini_key_takes_precedence(self): + self._patch_env({"GEMINI_API_KEY": "gemini-wins", "GOOGLE_API_KEY": "google-loses"}) + self.assertEqual(GoogleConfig().key, "gemini-wins") -def test_anthropic_config_key_defaults_to_empty_when_env_not_set(monkeypatch): - monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False) - config = AnthropicConfig() - assert config.key == "" - - -def test_anthropic_config_url_default(): - config = AnthropicConfig() - assert config.url == "https://api.anthropic.com" - - -def test_anthropic_config_url_can_be_overridden(monkeypatch): - monkeypatch.setenv("ANTHROPIC_BASE_URL", "https://my-proxy.example.com") - config = AnthropicConfig() - assert config.url == "https://my-proxy.example.com" - - -# ─── OpenAIConfig ───────────────────────────────────────────────────────────── - - -def test_openai_config_driver_is_openai(): - config = OpenAIConfig() - assert config.driver == "openai" - - -def test_openai_config_picks_up_api_key_from_env(monkeypatch): - monkeypatch.setenv("OPENAI_API_KEY", "sk-openai-test-key") - config = OpenAIConfig() - assert config.key == "sk-openai-test-key" - - -def test_openai_config_key_defaults_to_empty_when_env_not_set(monkeypatch): - monkeypatch.delenv("OPENAI_API_KEY", raising=False) - config = OpenAIConfig() - assert config.key == "" - - -def test_openai_config_url_default(): - config = OpenAIConfig() - assert config.url == "https://api.openai.com/v1" - - -def test_openai_config_url_can_be_overridden(monkeypatch): - monkeypatch.setenv("OPENAI_BASE_URL", "https://openai-proxy.example.com/v1") - config = OpenAIConfig() - assert config.url == "https://openai-proxy.example.com/v1" - - -# ─── GoogleConfig ───────────────────────────────────────────────────────────── - - -def test_google_config_driver_is_google(): - config = GoogleConfig() - assert config.driver == "google" - - -def test_google_config_picks_up_gemini_api_key(monkeypatch): - monkeypatch.setenv("GEMINI_API_KEY", "gemini-key-abc") - monkeypatch.delenv("GOOGLE_API_KEY", raising=False) - config = GoogleConfig() - assert config.key == "gemini-key-abc" - - -def test_google_config_falls_back_to_google_api_key(monkeypatch): - """When GEMINI_API_KEY is not set, fall back to GOOGLE_API_KEY.""" - monkeypatch.delenv("GEMINI_API_KEY", raising=False) - monkeypatch.setenv("GOOGLE_API_KEY", "google-api-fallback") - config = GoogleConfig() - assert config.key == "google-api-fallback" - - -def test_google_config_gemini_key_takes_precedence(monkeypatch): - """GEMINI_API_KEY wins over GOOGLE_API_KEY when both are set.""" - monkeypatch.setenv("GEMINI_API_KEY", "gemini-wins") - monkeypatch.setenv("GOOGLE_API_KEY", "google-loses") - config = GoogleConfig() - assert config.key == "gemini-wins" - - -def test_google_config_key_defaults_to_empty_when_neither_set(monkeypatch): - monkeypatch.delenv("GEMINI_API_KEY", raising=False) - monkeypatch.delenv("GOOGLE_API_KEY", raising=False) - config = GoogleConfig() - assert config.key == "" + def test_google_config_key_defaults_to_empty_when_neither_set(self): + self._patch_env(unset=["GEMINI_API_KEY", "GOOGLE_API_KEY"]) + self.assertEqual(GoogleConfig().key, "") diff --git a/fastapi_startkit/tests/ai/test_image.py b/fastapi_startkit/tests/ai/test_image.py index 4c98c3df..c7ca7a01 100644 --- a/fastapi_startkit/tests/ai/test_image.py +++ b/fastapi_startkit/tests/ai/test_image.py @@ -283,7 +283,7 @@ async def test_edit_raises_not_implemented(self): async def test_image_builder_resolves_google_factory(self): mock_ai_config = MagicMock() - mock_ai_config.image_provider = "google" + mock_ai_config.default_image = "google" mock_ai_config.providers = {"google": MagicMock(key="gkey"), "openai": MagicMock(key="")} with patch("fastapi_startkit.ai.image.Config") as mock_config: diff --git a/fastapi_startkit/tests/ai/test_lab.py b/fastapi_startkit/tests/ai/test_lab.py new file mode 100644 index 00000000..644c0bac --- /dev/null +++ b/fastapi_startkit/tests/ai/test_lab.py @@ -0,0 +1,42 @@ +"""Lab resolves the active provider, model, and LangChain model URL from config.""" + +import unittest + +from fastapi_startkit.ai import AIConfig +from fastapi_startkit.ai.lab import Lab, ModelType +from fastapi_startkit.application import app + + +class TestLab(unittest.TestCase): + def setUp(self): + container = app() + container.bind("ai", AIConfig()) + container.make("config").set("ai", AIConfig()) + + def test_provider_key_maps_google_to_genai(self): + self.assertEqual(Lab.GOOGLE.get_provider_key(), "google_genai") + self.assertEqual(Lab.OPENAI.get_provider_key(), "openai") + self.assertEqual(Lab.ANTHROPIC.get_provider_key(), "anthropic") + self.assertEqual(Lab.ELEVENLABS.get_provider_key(), "elevenlabs") + + def test_get_model_returns_explicit_override(self): + self.assertEqual(Lab.GOOGLE.get_model("custom-model"), "custom-model") + + def test_get_model_text_default(self): + self.assertEqual(Lab.GOOGLE.get_model(), "gemini-2.5-flash-lite") + self.assertEqual(Lab.ANTHROPIC.get_model(), "claude-sonnet-4-6") + self.assertEqual(Lab.OPENAI.get_model(), "gpt-4o") + + def test_get_model_image_default(self): + self.assertEqual(Lab.OPENAI.get_model(model_type=ModelType.IMAGE), "dall-e-3") + self.assertEqual(Lab.GOOGLE.get_model(model_type=ModelType.IMAGE), "imagen-3.0-generate-002") + + def test_get_provider_uses_config_default(self): + self.assertEqual(Lab.get_provider().value, "google") + + def test_get_provider_explicit_wins(self): + self.assertIs(Lab.get_provider("anthropic"), Lab.ANTHROPIC) + + def test_get_model_url_text(self): + self.assertEqual(Lab.get_model_url(), "google_genai:gemini-2.5-flash-lite") + self.assertEqual(Lab.get_model_url("anthropic"), "anthropic:claude-sonnet-4-6") diff --git a/fastapi_startkit/tests/ai/test_pipeline.py b/fastapi_startkit/tests/ai/test_pipeline.py new file mode 100644 index 00000000..abd654e2 --- /dev/null +++ b/fastapi_startkit/tests/ai/test_pipeline.py @@ -0,0 +1,258 @@ +import unittest + +from fastapi_startkit.ai.pipeline import Middleware, Response, build_pipeline + + +def _stream(*chunks): + async def _source(): + for chunk in chunks: + yield chunk + + return _source + + +class TestResponse(unittest.IsolatedAsyncioTestCase): + async def test_await_returns_accumulated_value(self): + result = await Response(_stream("a", "b", "c")) + + self.assertEqual(result, "abc") + + async def test_async_for_yields_each_chunk(self): + chunks = [chunk async for chunk in Response(_stream("a", "b", "c"))] + + self.assertEqual(chunks, ["a", "b", "c"]) + + async def test_then_callback_receives_accumulated_final_on_await(self): + seen = [] + await Response(_stream("a", "b", "c")).then(seen.append) + + self.assertEqual(seen, ["abc"]) + + async def test_then_callback_fires_after_streaming_is_drained(self): + events = [] + + response = Response(_stream("a", "b")).then(lambda final: events.append(("after", final))) + async for chunk in response: + events.append(("chunk", chunk)) + + self.assertEqual(events, [("chunk", "a"), ("chunk", "b"), ("after", "ab")]) + + async def test_then_fires_exactly_once_per_consumption(self): + calls = [] + + await Response(_stream("x", "y")).then(lambda final: calls.append(final)) + + self.assertEqual(calls, ["xy"]) + + async def test_then_fires_once_on_full_streaming_drain(self): + calls = [] + + async for _ in Response(_stream("x", "y")).then(lambda final: calls.append(final)): + pass + + self.assertEqual(calls, ["xy"]) + + async def test_then_fires_on_early_close_not_only_full_drain(self): + calls = [] + + response = Response(_stream("a", "b", "c")).then(lambda final: calls.append(final)) + agen = response.__aiter__() + self.assertEqual(await agen.__anext__(), "a") + await agen.aclose() + + self.assertEqual(calls, ["a"]) + + async def test_multiple_callbacks_fire_in_registration_order(self): + order = [] + + response = Response(_stream("z")).then(lambda f: order.append("first")).then(lambda f: order.append("second")) + await response + + self.assertEqual(order, ["first", "second"]) + + async def test_then_returns_self_for_chaining(self): + response = Response(_stream("a")) + + self.assertIs(response.then(lambda f: None), response) + + async def test_async_callback_is_awaited(self): + awaited = [] + + async def record(final): + awaited.append(final) + + await Response(_stream("a", "b")).then(record) + + self.assertEqual(awaited, ["ab"]) + + async def test_empty_stream_passes_none_to_callback(self): + seen = [] + + await Response(_stream()).then(seen.append) + + self.assertEqual(seen, [None]) + + +class TestBuildPipeline(unittest.IsolatedAsyncioTestCase): + def setUp(self): + async def _core_source(model): + for token in ("one", " two", " three"): + yield token + + self.core = lambda model: Response(lambda: _core_source(model)) + + async def test_no_middleware_passes_core_through(self): + pipeline = build_pipeline([], self.core) + + self.assertEqual(await pipeline("m"), "one two three") + + async def test_after_hook_fires_on_buffered_await(self): + events = [] + + class Logger: + def handle(self, model, handler): + events.append("request") + return handler(model).then(lambda final: events.append(("response", final))) + + pipeline = build_pipeline([Logger()], self.core) + result = await pipeline("m") + + self.assertEqual(result, "one two three") + self.assertEqual(events, ["request", ("response", "one two three")]) + + async def test_after_hook_fires_on_streaming_iteration(self): + events = [] + + class Logger: + def handle(self, model, handler): + events.append("request") + return handler(model).then(lambda final: events.append(("response", final))) + + pipeline = build_pipeline([Logger()], self.core) + chunks = [chunk async for chunk in pipeline("m")] + + self.assertEqual(chunks, ["one", " two", " three"]) + self.assertEqual(events, ["request", ("response", "one two three")]) + + async def test_async_handle_is_supported(self): + events = [] + + class Logger: + async def handle(self, model, handler): + events.append("request") + return handler(model).then(lambda final: events.append("response")) + + pipeline = build_pipeline([Logger()], self.core) + await pipeline("m") + + self.assertEqual(events, ["request", "response"]) + + async def test_middleware_class_is_instantiated(self): + events = [] + + class Logger: + def handle(self, model, handler): + events.append("request") + return handler(model) + + pipeline = build_pipeline([Logger], self.core) + await pipeline("m") + + self.assertEqual(events, ["request"]) + + async def test_onion_order_before_outer_to_inner_after_inner_to_outer(self): + events = [] + + def layer(name): + class Layer: + def handle(self, model, handler): + events.append(f"before:{name}") + return handler(model).then(lambda final: events.append(f"after:{name}")) + + return Layer() + + pipeline = build_pipeline([layer("outer"), layer("inner")], self.core) + await pipeline("m") + + self.assertEqual( + events, + ["before:outer", "before:inner", "after:inner", "after:outer"], + ) + + async def test_middleware_may_short_circuit_with_a_bare_value(self): + class Canned: + def handle(self, model, handler): + return "canned reply" + + pipeline = build_pipeline([Canned()], self.core) + + self.assertEqual(await pipeline("m"), "canned reply") + self.assertEqual([chunk async for chunk in pipeline("m")], ["canned reply"]) + + async def test_middleware_receives_the_model(self): + seen = [] + + class Capture: + def handle(self, model, handler): + seen.append(model) + return handler(model) + + pipeline = build_pipeline([Capture()], self.core) + await pipeline("gpt-test") + + self.assertEqual(seen, ["gpt-test"]) + + async def test_after_hook_runs_once_even_with_multiple_chunks(self): + calls = [] + + class Logger: + def handle(self, model, handler): + return handler(model).then(lambda final: calls.append(final)) + + pipeline = build_pipeline([Logger()], self.core) + [chunk async for chunk in pipeline("m")] + + self.assertEqual(calls, ["one two three"]) + + async def test_logger_pattern_request_and_response_lines(self): + log = [] + + class AgentLogger: + def handle(self, model, handler): + log.append(f"request | model={model}") + + def log_response(final): + log.append(f"response | {final}") + + return handler(model).then(log_response) + + buffered = build_pipeline([AgentLogger()], self.core) + await buffered("gemini") + + streamed = build_pipeline([AgentLogger()], self.core) + [chunk async for chunk in streamed("gemini")] + + self.assertEqual( + log, + [ + "request | model=gemini", + "response | one two three", + "request | model=gemini", + "response | one two three", + ], + ) + + +class TestMiddlewareProtocol(unittest.TestCase): + def test_object_with_handle_satisfies_protocol(self): + class Logger: + def handle(self, model, handler): + return handler(model) + + self.assertIsInstance(Logger(), Middleware) + + def test_object_without_handle_does_not_satisfy_protocol(self): + class NotMiddleware: + pass + + self.assertNotIsInstance(NotMiddleware(), Middleware) diff --git a/fastapi_startkit/tests/ai/test_provider.py b/fastapi_startkit/tests/ai/test_provider.py index ddf97379..3f050763 100644 --- a/fastapi_startkit/tests/ai/test_provider.py +++ b/fastapi_startkit/tests/ai/test_provider.py @@ -1,118 +1,59 @@ """Tests for AIProvider service provider.""" +import unittest from unittest.mock import MagicMock -from fastapi_startkit.ai.config import AIConfig +from fastapi_startkit.ai import AIConfig from fastapi_startkit.ai.providers.ai_provider import AIProvider from fastapi_startkit.providers import Provider -# ─── AIProvider class contract ──────────────────────────────────────────────── +class TestAIProvider(unittest.TestCase): + def test_ai_provider_is_a_provider(self): + self.assertTrue(issubclass(AIProvider, Provider)) + def test_ai_provider_key(self): + self.assertEqual(AIProvider.provider_key, "ai") -def test_ai_provider_is_a_provider(): - assert issubclass(AIProvider, Provider) + def test_register_merges_ai_config_into_config_store(self): + """register() merges the resolved AIConfig into the config store under 'ai'.""" + fake_config = MagicMock() + fake_app = MagicMock() + fake_app.make.return_value = fake_config + provider = AIProvider(fake_app) + provider.register() -def test_ai_provider_key(): - assert AIProvider.provider_key == "ai" + fake_app.make.assert_called_with("config") + fake_config.merge_with.assert_called_once() + path, source = fake_config.merge_with.call_args[0] + self.assertEqual(path, "ai") + self.assertEqual(source["default"], AIConfig().default) + def test_register_does_not_raise(self): + fake_app = MagicMock() + provider = AIProvider(fake_app) -# ─── AIProvider.register() ──────────────────────────────────────────────────── + provider.register() + def test_boot_does_not_raise(self): + fake_app = MagicMock() + provider = AIProvider(fake_app) -def test_register_binds_ai_config_to_container(): - """register() must call app.bind('ai', ).""" - fake_app = MagicMock() - provider = AIProvider(fake_app) + provider.boot() - provider.register() + def test_register_and_boot_expose_ai_config(self): + """Full integration: after AIProvider registers, Config.get('ai') exposes AI config data.""" + from fastapi_startkit.application import Application + from fastapi_startkit.configuration.config import Config - fake_app.bind.assert_called_once() - call_args = fake_app.bind.call_args - assert call_args[0][0] == "ai" - assert isinstance(call_args[0][1], AIConfig) + app = Application() + provider = AIProvider(app) + provider.register() + provider.boot() -def test_register_does_not_raise(): - fake_app = MagicMock() - provider = AIProvider(fake_app) - - # Should not raise any exception - provider.register() - - -# ─── AIProvider.boot() ──────────────────────────────────────────────────────── - - -def test_boot_sets_ai_in_config_store(): - """boot() must call config.set('ai', ) so Config.get('ai') works.""" - ai_config_instance = AIConfig() - - fake_config_store = MagicMock() - fake_app = MagicMock() - fake_app.make.side_effect = lambda key: ai_config_instance if key == "ai" else fake_config_store - - provider = AIProvider(fake_app) - provider.boot() - - # Verify config store received the AIConfig under the 'ai' key - fake_config_store.set.assert_called_once_with("ai", ai_config_instance) - - -def test_boot_does_not_raise(): - ai_config_instance = AIConfig() - fake_config_store = MagicMock() - fake_app = MagicMock() - fake_app.make.side_effect = lambda key: ai_config_instance if key == "ai" else fake_config_store - - provider = AIProvider(fake_app) - provider.boot() # must not raise - - -# ─── Integration: Config.get('ai') after boot ───────────────────────────────── - - -def test_config_get_ai_returns_ai_config_data_after_provider_boots(): - """Full integration: after AIProvider boots, Config.get('ai') exposes AI config data. - - The framework's Configuration.set() serialises dataclasses to a dotty-dict, so - Config.get('ai') returns a mapping rather than an AIConfig instance. The test - verifies the 'default' key is present and the raw container binding stays typed. - """ - from fastapi_startkit.application import Application - from fastapi_startkit.configuration.config import Config - - # Use the test Application singleton (initialised by the session fixture) - app = Application() - - ai_config_instance = AIConfig() - - # Simulate what AIProvider.register() does - app.bind("ai", ai_config_instance) - - # Simulate what AIProvider.boot() does — config.set() serialises the dataclass - app.make("config").set("ai", ai_config_instance) - - # Config.get('ai') returns the serialised dict structure - result = Config.get("ai") - assert result is not None - # The 'default' field must survive serialisation - assert result["default"] == ai_config_instance.default - - # The raw container binding retains the typed AIConfig instance - assert isinstance(app.make("ai"), AIConfig) - - -def test_ai_provider_register_and_boot_together(): - """register() followed by boot() produces an AIConfig in the container.""" - from fastapi_startkit.application import Application - - app = Application() - - provider = AIProvider(app) - provider.register() - provider.boot() - - ai_value = app.make("ai") - assert isinstance(ai_value, AIConfig) + ai = Config.get("ai") + self.assertIsNotNone(ai) + self.assertEqual(ai["default"], AIConfig().default) + self.assertIn("providers", ai) diff --git a/fastapi_startkit/tests/fastapi/test_test_response.py b/fastapi_startkit/tests/fastapi/test_test_response.py index 95a5f9a4..f726b211 100644 --- a/fastapi_startkit/tests/fastapi/test_test_response.py +++ b/fastapi_startkit/tests/fastapi/test_test_response.py @@ -1,9 +1,9 @@ """Unit tests for the TestResponse httpx wrapper + fluent assert_json.""" import json as jsonlib +import unittest import httpx -import pytest from fastapi_startkit.fastapi.testing.test_response import TestResponse @@ -18,62 +18,134 @@ def make_response(status_code=200, payload=None): return TestResponse(raw) -def test_passthrough_attributes(): - resp = make_response(200, {"message": "ok"}) - assert resp.status_code == 200 - assert resp.json() == {"message": "ok"} - assert resp.text == '{"message": "ok"}' - - -def test_assert_status_and_ok(): - resp = make_response(200, {"a": 1}) - assert resp.assert_status(200).assert_ok() is resp - - -def test_assert_status_failure_includes_body(): - resp = make_response(500, {"error": "boom"}) - with pytest.raises(AssertionError) as exc: - resp.assert_status(200) - assert "boom" in str(exc.value) - - -def test_assert_created_and_no_content(): - make_response(201, {}).assert_created() - make_response(204, {}).assert_no_content() - - -def test_assert_json_fluent_chain(): - resp = make_response(200, {"id": 1, "name": "Bedu"}) - resp.assert_ok().assert_json(lambda j: j.where("id", 1).where("name", "Bedu").etc()) - - -def test_assert_json_strict_failure_on_extra_key(): - resp = make_response(200, {"id": 1, "secret": "leak"}) - with pytest.raises(AssertionError) as exc: - resp.assert_json(lambda j: j.where("id", 1)) - assert "secret" in str(exc.value) - - -def test_assert_json_exact(): - resp = make_response(200, {"id": 1}) - resp.assert_json(exact={"id": 1}) - with pytest.raises(AssertionError): - resp.assert_json(exact={"id": 2}) - - -def test_assert_json_structure_wildcard(): - payload = {"teams": [{"name": "Suns", "sport": "b"}, {"name": "Cardinals", "sport": "f"}]} - resp = make_response(200, payload) - resp.assert_ok().assert_json_structure({"teams": {"*": ["name", "sport"]}}) - - -def test_assert_json_structure_failure(): - resp = make_response(200, {"teams": [{"name": "Suns"}]}) - with pytest.raises(AssertionError): - resp.assert_json_structure({"teams": {"*": ["name", "sport"]}}) +def make_sse_response(*chunks, status_code=200): + body = "".join(f"data: {chunk}\n\n" for chunk in chunks) + raw = httpx.Response( + status_code=status_code, + content=body.encode(), + headers={"content-type": "text/event-stream"}, + ) + return TestResponse(raw) -def test_assert_json_dotted_list_index_scope(): - payload = {"teams": [{"name": "Phoenix Suns", "sport": "basketball"}]} - resp = make_response(200, payload) - resp.assert_json(lambda j: j.has("teams", 1).has("teams.0", lambda team: team.where("name", "Phoenix Suns").etc())) +class TestTestResponse(unittest.TestCase): + def test_passthrough_attributes(self): + resp = make_response(200, {"message": "ok"}) + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.json(), {"message": "ok"}) + self.assertEqual(resp.text, '{"message": "ok"}') + + def test_assert_status_and_ok(self): + resp = make_response(200, {"a": 1}) + self.assertIs(resp.assert_status(200).assert_ok(), resp) + + def test_assert_status_failure_includes_body(self): + resp = make_response(500, {"error": "boom"}) + with self.assertRaises(AssertionError) as ctx: + resp.assert_status(200) + self.assertIn("boom", str(ctx.exception)) + + def test_assert_created_and_no_content(self): + make_response(201, {}).assert_created() + make_response(204, {}).assert_no_content() + + def test_assert_json_fluent_chain(self): + resp = make_response(200, {"id": 1, "name": "Bedu"}) + resp.assert_ok().assert_json(lambda j: j.where("id", 1).where("name", "Bedu").etc()) + + def test_assert_json_strict_failure_on_extra_key(self): + resp = make_response(200, {"id": 1, "secret": "leak"}) + with self.assertRaises(AssertionError) as ctx: + resp.assert_json(lambda j: j.where("id", 1)) + self.assertIn("secret", str(ctx.exception)) + + def test_assert_json_exact(self): + resp = make_response(200, {"id": 1}) + resp.assert_json(exact={"id": 1}) + with self.assertRaises(AssertionError): + resp.assert_json(exact={"id": 2}) + + def test_assert_json_structure_wildcard(self): + payload = {"teams": [{"name": "Suns", "sport": "b"}, {"name": "Cardinals", "sport": "f"}]} + resp = make_response(200, payload) + resp.assert_ok().assert_json_structure({"teams": {"*": ["name", "sport"]}}) + + def test_assert_json_structure_failure(self): + resp = make_response(200, {"teams": [{"name": "Suns"}]}) + with self.assertRaises(AssertionError): + resp.assert_json_structure({"teams": {"*": ["name", "sport"]}}) + + def test_assert_json_dotted_list_index_scope(self): + payload = {"teams": [{"name": "Phoenix Suns", "sport": "basketball"}]} + resp = make_response(200, payload) + resp.assert_json( + lambda j: j.has("teams", 1).has("teams.0", lambda team: team.where("name", "Phoenix Suns").etc()) + ) + + def test_stream_chunks_decodes_each_sse_event(self): + resp = make_sse_response("Hello", " there!") + self.assertEqual(resp.stream_chunks(), ["Hello", " there!"]) + + def test_stream_content_concatenates_payloads(self): + resp = make_sse_response("Hello", " there!") + self.assertEqual(resp.stream_content(), "Hello there!") + + def test_assert_stream_single_string_matches_joined_content(self): + resp = make_sse_response("Hello", " there!") + self.assertIs(resp.assert_ok().assert_stream("Hello there!"), resp) + + def test_assert_stream_multiple_args_match_exact_chunks(self): + resp = make_sse_response("Hello", " there!") + resp.assert_stream("Hello", " there!") + + def test_assert_stream_joined_mismatch_raises(self): + resp = make_sse_response("Hello") + with self.assertRaises(AssertionError) as ctx: + resp.assert_stream("Goodbye") + self.assertIn("Goodbye", str(ctx.exception)) + + def test_assert_stream_chunk_sequence_mismatch_raises(self): + resp = make_sse_response("Hello", " there!") + with self.assertRaises(AssertionError): + resp.assert_stream("Hello", " world!") + + def test_stream_chunks_ignores_non_data_lines(self): + body = "event: message\ndata: only this\nid: 1\n\n" + raw = httpx.Response(200, content=body.encode(), headers={"content-type": "text/event-stream"}) + self.assertEqual(TestResponse(raw).stream_chunks(), ["only this"]) + + def test_is_stream_true_for_event_stream_and_false_for_json(self): + self.assertTrue(make_sse_response("x").is_stream()) + self.assertFalse(make_response(200, {"a": 1}).is_stream()) + + def test_assert_stream_raises_on_non_stream_response(self): + resp = make_response(200, {"content": "Hello there!"}) + with self.assertRaises(AssertionError) as ctx: + resp.assert_stream("Hello there!") + self.assertIn("streaming", str(ctx.exception)) + self.assertIn("assert_contents", str(ctx.exception)) + + def test_assert_stream_contains_raises_on_non_stream_response(self): + resp = make_response(200, {"content": "Hello there!"}) + with self.assertRaises(AssertionError): + resp.assert_stream_contains("Hello") + + def test_assert_stream_contains_matches_substrings(self): + resp = make_sse_response("Hello ", "Bedram", "!") + self.assertIs(resp.assert_stream_contains("Bedram", "Hello"), resp) + + def test_assert_stream_contains_mismatch_raises(self): + resp = make_sse_response("Hello there!") + with self.assertRaises(AssertionError) as ctx: + resp.assert_stream_contains("Goodbye") + self.assertIn("Goodbye", str(ctx.exception)) + + def test_assert_contents_matches_buffered_body(self): + resp = make_response(200, {"content": "Hello Alex, nice to meet you"}) + self.assertIs(resp.assert_contents("Alex", "nice to meet"), resp) + + def test_assert_contents_mismatch_raises(self): + resp = make_response(200, {"content": "Hello there"}) + with self.assertRaises(AssertionError) as ctx: + resp.assert_contents("Goodbye") + self.assertIn("Goodbye", str(ctx.exception)) diff --git a/fastapi_startkit/uv.lock b/fastapi_startkit/uv.lock index 8b674dea..a0b4a09d 100644 --- a/fastapi_startkit/uv.lock +++ b/fastapi_startkit/uv.lock @@ -46,25 +46,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = 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