Agent wip#140
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ViteProvider now binds a Jinja2Templates engine out of the box so fresh apps get a working template engine and the vite() globals are injected during boot. An existing 'templates' binding always wins, keeping the change backward-compatible. - Add ViteConfig.template and ViteConfig.templates_directory flags - Auto-bind templates in ViteProvider.register() with a guarded jinja2 import that points users to the [vite] extra - Add Template class and template() helper (Laravel view()-style) that resolve the request from context or a per-request ContextVar - Add RequestContextMiddleware to expose the active request - Export Template and template from fastapi_startkit.vite - Cover the new behaviour with tests - Simplify example/vite-app to rely on the auto-bound engine
feat(vite): auto-load templates + Template helper + config flag
…to-templates Revert "feat(vite): auto-load templates + Template helper + config flag"
Fresh apps got no template engine because ViteProvider only injected the
vite() globals when a "templates" binding already existed. The provider
now binds a Jinja2Templates engine out of the box, config-gated and
backward-compatible.
- Add ViteConfig.template (default True) and
ViteConfig.templates_directory (default "resources/templates")
- ViteProvider.register() binds "templates" when enabled and none is
pre-bound; an existing binding always wins. The jinja2 import is
guarded with a clear "install fastapi-startkit[vite]" message
- Publish the template stub under resources/templates/index.html so all
scaffolding stays consistent under resources/
- Sync the example app's config/vite.py with the new fields
- Cover binding, override, disabled, post-boot globals and the missing
jinja2 error in tests/vite/
Rendering stays the standard Starlette way:
app.make("templates").TemplateResponse(request, name, context).
…templates feat(vite): auto-load Jinja2 templates from the Vite provider
Re-export the full set of public relationship classes (including MorphMany, MorphOne, MorphToMany, and BaseRelationship) from fastapi_startkit.masoniteorm so they can be imported directly from the package root instead of the .relationships submodule. Switch in-repo example models, fixtures, and tests to the root import. Pure re-export — no behavior change.
…tionships Re-export ORM relationship classes from masoniteorm package root
…vider default Move the example templates into resources/templates to match ViteConfig's default templates_directory, and drop the bootstrap override so the example relies on ViteProvider's auto-bound Jinja2Templates engine.
Use `from fastapi_startkit.application import app` -> app().make('templates')
in the example route instead of the bootstrap module, matching the framework's
canonical app accessor (no top-level export).
Relocate providers/ to app/providers/ and update the bootstrap import, grouping application code under the app/ namespace.
Add HttpTestCase-based tests covering the health endpoint and the index page render (templates resolve from resources/templates with vite globals). Wire up pytest config and dev dependencies.
Resolve conflicts in vite-app bootstrap/application.py and routes/web.py, keeping the app/ provider package layout and canonical app() template accessor. Restore FastAPIProvider without the stale templates binding so ViteProvider serves templates from resources/templates. Remove tests/vite/test_vite.py per request.
Rely on the built manifest so the vite() directive resolves to production asset URLs, and drop the dev-server hot file setup. Assert against the built /build/assets/ output instead of the source path. Also remove the stale ViteProvider comment in bootstrap.
Match the routes/web.py module and the TestWebRoutes class it covers.
Keep the index page test focused on a successful render.
feat(vite): re-export ViteConfig from vite package root
…I intact Swap the per-provider SDK internals of ai/agent.py (_run/_stream over the anthropic/openai/google SDKs) for a single LangChain/LangGraph backend: init_chat_model builds the chat model and create_agent drives the tool loop, with the final AIMessage mapped back to AgentResponse. The user-facing surface is unchanged — prompt/stream/fake/assert_prompted/assert_not_prompted/reset, the lifecycle hooks, and the decorators keep identical signatures. - _build_model() is the seam tests patch to inject a fake chat model. - _build_messages() now renders attachments via Document.to_langchain_block(). - Add Document.to_langchain_block(): inline text, base64 image/file blocks. - Add ai/fakes.py fake_chat_model(): replays scripted AIMessage turns through a GenericFakeChatModel (bind_tools no-op) so the real create_agent loop runs offline; exported from the ai package root. - New optional [langgraph] extra (langchain + langchain-core + langgraph). The 23 tests in tests/ai/test_agent_fake.py stay green and unmodified (fake() short-circuits before the backend). Adds tests/ai/test_agent_langgraph_backend.py exercising the real loop offline: simple reply, full tool-calling loop, usage mapping, attachment blocks, provider mapping, and streaming.
…ent through it Turn ai/config.py into a config package and resolve models/providers through a new Lab helper instead of hardcoded dicts on the Agent. - ai/config/: split provider dataclasses (config.py) from the top-level AIConfig (ai.py), add config/__init__.py re-exporting them, and give each provider a models map keyed by modality (default / default_image / default_audio / default_transcribe). Fix the draft's circular import (AIConfig imported the provider configs from the package root mid-init) and the placeholder model values (google text default, elevenlabs models). - AIConfig selects the default provider per modality: default (text), default_image, default_audio, default_transcribe. image.py/audio.py now read default_image/default_audio (was image_provider/audio_provider). - ai/lab.py: Lab(StrEnum) + ModelType resolve the provider, default model, and the "<langchain-provider>:<model>" URL from Config (google → google_genai). - Agent: _resolve_model() and _build_model() now go through Lab; removed the stale _DEFAULT_MODELS/_LANGCHAIN_PROVIDERS references and the dead _execute_tool() (create_agent runs tools itself). - Tests: drop the _build_model monkeypatch helper; the backend tests now patch the real langchain.chat_models.init_chat_model seam via pytest monkeypatch. Add test_lab.py; update image/audio provider-selection mocks. The 23 tests in tests/ai/test_agent_fake.py stay green and unmodified.
…l loop
Replace the LangGraph create_agent backend with a plain init_chat_model call
driven by a Runner that resolves and executes tool calls itself.
- runner.py: Runner(model, tools, max_steps) binds tools, invokes the model,
executes requested tool calls, feeds results back, loops to a final answer;
StreamRunner yields content tokens through the same loop. Fully typed.
- Agent._run/_stream delegate to Runner/StreamRunner (threading _max_steps);
no create_agent.
- System message is declarative via instructions()/_instructions — removed the
per-call system= and messages= arguments from prompt()/stream().
- Resolve provider/model through Lab directly (dropped the _lab() helper) and
import Lab at module top.
- Config split into ai/config/{ai,config}.py.
KNOWN RED: ai/config/__init__.py is absent, so 'from fastapi_startkit.ai.config
import AIConfig' fails — the AI test suite does not collect and AIProvider import
breaks. Backend tests also still assume the old create_agent result shape and the
tuple return of _build_messages. Follow-ups.
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