Add lint and typecheck jobs to AI agent CI

.github/workflows/ai-agent-ci.yml

@@ -11,6 +11,57 @@ on:
       - '.github/workflows/ai-agent-ci.yml'
 
 jobs:
+  lint:
+    name: Lint · Format
+    runs-on: ubuntu-latest
+
+    defaults:
+      run:
+        working-directory: apps/ai_agent
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+          cache-dependency-glob: 'apps/ai_agent/uv.lock'
+
+      - name: Install dependencies
+        run: uv sync --group dev
+
+      - name: Ruff check
+        run: uv run ruff check .
+
+      - name: Ruff format check
+        run: uv run ruff format --check .
+
+  typecheck:
+    name: Typecheck
+    runs-on: ubuntu-latest
+
+    defaults:
+      run:
+        working-directory: apps/ai_agent
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+          cache-dependency-glob: 'apps/ai_agent/uv.lock'
+
+      - name: Install dependencies
+        run: uv sync --group dev
+
+      - name: Mypy
+        run: uv run mypy main.py
+
   test:
     name: Test · Coverage
     runs-on: ubuntu-latest

apps/ai_agent/main.py

@@ -3,10 +3,9 @@
 from typing import Literal
 
 import uvicorn
+import weaviate
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-
-import weaviate
 from weaviate.classes.query import Filter
 
 app = FastAPI(title="AI Agent API")
@@ -24,6 +23,7 @@
 
 # ── Request / response models ─────────────────────────────────────────────────
 
+
 class ChatRequest(BaseModel):
     message: str
     conversation_id: str
@@ -69,16 +69,19 @@ class ProposalSummariseResponse(BaseModel):
 
 # ── Helpers ───────────────────────────────────────────────────────────────────
 
+
 def _openai_client():
     api_key = os.environ.get("OPENAI_API_KEY")
     if not api_key:
         raise HTTPException(status_code=500, detail="OPENAI_API_KEY is not configured")
     from openai import OpenAI  # imported lazily so missing package gives a clear error
+
     return OpenAI(api_key=api_key)
 
 
 # ── Endpoints ─────────────────────────────────────────────────────────────────
 
+
 @app.get("/health")
 def health_check():
     return {"status": "ok"}
@@ -142,9 +145,9 @@ def summarise_proposal(request: ProposalSummariseRequest):
         f"Description: {request.description}\n"
         f"Amount: {request.amount} XLM\n\n"
         "Reply with JSON only using keys: summary (a plain-English summary of "
-        "exactly 2 sentences), risk (one of \"low\", \"medium\", \"high\"). "
-        "Use \"high\" for large amounts, unclear intent, or obvious red flags; "
-        "\"low\" for small, well-scoped, low-impact proposals; otherwise \"medium\"."
+        'exactly 2 sentences), risk (one of "low", "medium", "high"). '
+        'Use "high" for large amounts, unclear intent, or obvious red flags; '
+        '"low" for small, well-scoped, low-impact proposals; otherwise "medium".'
     )
     response = client.chat.completions.create(
         model="gpt-4o-mini",
@@ -172,20 +175,20 @@ def index_message(request: IndexMessageRequest):
     try:
         # Attempt connection to Weaviate
         client = weaviate.connect_to_local()
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=503, detail="Weaviate connection failed")
-    
+
     try:
         if not client.collections.exists("Message"):
             client.collections.create(name="Message")
-        
+
         collection = client.collections.get("Message")
-        
+
         # Get embedding via OpenAI
         openai_client = _openai_client()
         res = openai_client.embeddings.create(input=request.content, model="text-embedding-3-small")
         vector = res.data[0].embedding
-        
+
         # Upsert
         if collection.data.exists(request.messageId):
             collection.data.replace(
@@ -196,7 +199,7 @@ def index_message(request: IndexMessageRequest):
                     "senderId": request.senderId,
                     "content": request.content,
                 },
-                vector=vector
+                vector=vector,
             )
         else:
             collection.data.insert(
@@ -207,49 +210,51 @@ def index_message(request: IndexMessageRequest):
                     "senderId": request.senderId,
                     "content": request.content,
                 },
-                vector=vector
+                vector=vector,
             )
     except Exception as e:
         raise HTTPException(status_code=503, detail=str(e))
     finally:
         client.close()
-        
+
     return {"status": "ok"}
 
 
 @app.get("/search")
 def search_messages(q: str, conversationId: str):
     try:
         client = weaviate.connect_to_local()
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=503, detail="Weaviate connection failed")
-        
+
     try:
         if not client.collections.exists("Message"):
             return {"results": []}
-            
+
         collection = client.collections.get("Message")
-        
+
         # Get embedding for query
         openai_client = _openai_client()
         res = openai_client.embeddings.create(input=q, model="text-embedding-3-small")
         vector = res.data[0].embedding
-        
+
         results = collection.query.near_vector(
             near_vector=vector,
             limit=5,
-            filters=Filter.by_property("conversationId").equal(conversationId)
+            filters=Filter.by_property("conversationId").equal(conversationId),
         )
-        
+
         hits = []
         for obj in results.objects:
-            hits.append({
-                "messageId": obj.properties.get("messageId"),
-                "conversationId": obj.properties.get("conversationId"),
-                "senderId": obj.properties.get("senderId"),
-                "content": obj.properties.get("content"),
-            })
-
+            hits.append(
+                {
+                    "messageId": obj.properties.get("messageId"),
+                    "conversationId": obj.properties.get("conversationId"),
+                    "senderId": obj.properties.get("senderId"),
+                    "content": obj.properties.get("content"),
+                }
+            )
+
         return {"results": hits}
     except Exception as e:
         raise HTTPException(status_code=503, detail=str(e))

apps/ai_agent/pyproject.toml

@@ -18,6 +18,9 @@ dev = [
     "pytest-mock>=3.14.0",
     "pytest-cov>=5.0.0",
     "pip-audit>=2.7",
+    "ruff>=0.4",
+    "mypy>=1.10",
+    "types-requests",
 ]
 
 [dependency-groups]
@@ -27,8 +30,24 @@ dev = [
     "httpx>=0.27.0",
     "pytest-mock>=3.14.0",
     "pip-audit>=2.7",
+    "ruff>=0.4",
+    "mypy>=1.10",
+    "types-requests",
 ]
 
+[tool.ruff]
+target-version = "py312"
+line-length = 100
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "W"]
+
+[tool.mypy]
+python_version = "3.12"
+warn_return_any = true
+warn_unused_configs = true
+ignore_missing_imports = true
+
 [tool.pytest.ini_options]
 pythonpath = ["."]
 testpaths = ["tests"]

apps/ai_agent/tests/conftest.py

@@ -1,6 +1,5 @@
 """Shared pytest fixtures for the ai_agent test suite."""
 
-import os
 import pytest
 from fastapi.testclient import TestClient
 
@@ -15,6 +14,7 @@ def set_openai_key(monkeypatch: pytest.MonkeyPatch) -> None:
 def client() -> TestClient:
     """FastAPI TestClient for the main app."""
     from main import app
+
     return TestClient(app)
 
 

apps/ai_agent/tests/test_proposals.py

@@ -1,10 +1,10 @@
 """Unit tests for POST /proposals/summarise (issue #147)."""
 
 import json
-import pytest
 from unittest.mock import MagicMock, patch
 
 from fastapi.testclient import TestClient
+
 from main import app
 
 client = TestClient(app)

apps/ai_agent/tests/test_search.py

@@ -1,9 +1,9 @@
 """Unit tests for GET /search (issue #149)."""
 
-import pytest
 from unittest.mock import MagicMock, patch
 
 from fastapi.testclient import TestClient
+
 from main import app
 
 client = TestClient(app)
@@ -62,8 +62,10 @@ def test_returns_results_with_correct_shape():
     }
     mock_wv = _make_weaviate_client(exists=True, objects=[obj])
 
-    with patch("main.weaviate.connect_to_local", return_value=mock_wv), \
-         patch("main._openai_client", return_value=_make_openai_embedding()):
+    with (
+        patch("main.weaviate.connect_to_local", return_value=mock_wv),
+        patch("main._openai_client", return_value=_make_openai_embedding()),
+    ):
         response = client.get("/search", params=_BASE_PARAMS)
 
     assert response.status_code == 200
@@ -87,8 +89,10 @@ def test_filters_by_conversation_id():
     }
     mock_wv = _make_weaviate_client(exists=True, objects=[obj])
 
-    with patch("main.weaviate.connect_to_local", return_value=mock_wv), \
-         patch("main._openai_client", return_value=_make_openai_embedding()):
+    with (
+        patch("main.weaviate.connect_to_local", return_value=mock_wv),
+        patch("main._openai_client", return_value=_make_openai_embedding()),
+    ):
         response = client.get("/search", params={"q": "transfer", "conversationId": "conv-xyz"})
 
     assert response.status_code == 200
@@ -103,8 +107,10 @@ def test_close_called_on_success():
     mock_wv = _make_weaviate_client(exists=True, objects=[])
     mock_wv.collections.get.return_value.query.near_vector.return_value.objects = []
 
-    with patch("main.weaviate.connect_to_local", return_value=mock_wv), \
-         patch("main._openai_client", return_value=_make_openai_embedding()):
+    with (
+        patch("main.weaviate.connect_to_local", return_value=mock_wv),
+        patch("main._openai_client", return_value=_make_openai_embedding()),
+    ):
         response = client.get("/search", params=_BASE_PARAMS)
 
     assert response.status_code == 200