Add Gemini API and tests; update routes & docs

README.md

@@ -1,10 +1,16 @@
 ## Python FastAPI/Postgres App
 
-**Production-ready, open-source FastAPI application with PostgreSQL and blazing-fast full-text search.**
+> Production-ready, open-source FastAPI application with PostgreSQL and blazing-fast full-text search.
 
----
+#### Project Overview
 
-### 🚀 Features
+This project provides a scalable API backend using FastAPI and PostgreSQL, featuring:
+
+- Automatic full-text search on all text fields (via tsvector)
+- Endpoints for health checks, product management, prompt handling, and prospect management
+- Efficient ingestion and processing of large CSV files
+
+#### 🚀 Features
 
 - **Python 3.11+**
 - **FastAPI** — Modern, high-performance REST API
@@ -13,19 +19,7 @@
 - **Uvicorn** — Lightning-fast ASGI server
 - **Pytest** — Comprehensive testing
 
----
-
-## Project Overview
-
-This project provides a scalable API backend using FastAPI and PostgreSQL, featuring:
-
-- Automatic full-text search on all text fields (via tsvector)
-- Endpoints for health checks, product management, prompt handling, and prospect management
-- Efficient ingestion and processing of large CSV files
-
----
-
-## Getting Started
+#### Getting Started
 
 ### 1. Clone & Setup Environment
 
@@ -38,24 +32,21 @@ source venv/bin/activate
 pip install -r requirements.txt
 ```
 
-### 2. Run the App
+#### 2. Run the App
 
 ```bash
 uvicorn app.main:app --reload
 ```
 
 Visit [localhost:8000](http://localhost:8000) or [onrender](https://nx-ai.onrender.com)
 
----
-
-## API Documentation
+#### API Documentation
 
 FastAPI auto-generates interactive docs:
 
 - [Swagger UI](https://nx-ai.onrender.com/docs)
 - [ReDoc](https://nx-ai.onrender.com/redoc)
 
----
 
 ## Full-Text Search (tsvector)
 
@@ -69,13 +60,11 @@ SELECT * FROM prospects WHERE search_vector @@ plainto_tsquery('english', 'searc
 - On every insert/update, `search_vector` is computed using PostgreSQL's `to_tsvector('english', ...)`.
 - The GIN index (`idx_prospects_search_vector`) enables efficient search across large datasets.
 
----
 
 ## Processing Large CSV Files
 
 The `/prospects/process` endpoint supports robust ingestion of large CSVs (e.g., 1300+ rows, 300KB+), following the same normalization and insertion pattern as `/prospects/seed` but optimized for scale.
 
----
 
 ## Directory Structure
 
@@ -98,13 +87,11 @@ requirements.txt    # Python dependencies
 render.yaml         # Deployment config (Render.com)
 ```
 
----
 
 ## Contributing
 
 Contributions welcome. Please open issues or submit pull requests.
 
----
 
 ## License
 

app/api/gemini/__init__.py

@@ -0,0 +1,3 @@
+"""Gemini Routes"""
+
+from .gemini import router as gemini_router

app/api/gemini/gemini.py

@@ -0,0 +1,9 @@
+from fastapi import APIRouter
+
+router = APIRouter()
+
+@router.get("/gemini")
+def root() -> dict:
+    """GET /gemini endpoint."""
+    return {"message": "Welcome to the Gemini API!"}
+

app/api/root.py

@@ -13,13 +13,14 @@ def root() -> dict:
     base_url = os.getenv("BASE_URL", "http://localhost:8000")
     epoch = int(time.time() * 1000)
     meta = {
-        "title": "NX-AI",
+        "title": "Python",
         "severity": "success",
         "version": __version__,
         "base_url": base_url,
         "time": epoch,
     }
     endpoints = [
+        {"name": "gemini", "url": f"{base_url}/gemini"},
         {"name": "docs", "url": f"{base_url}/docs"},
         {"name": "resend", "url": f"{base_url}/resend"},
         {"name": "health", "url": f"{base_url}/health"},

app/api/routes.py

@@ -1,10 +1,7 @@
-from app import __version__
 """API routes"""
-
+from app import __version__
 from dotenv import load_dotenv
-
 from fastapi import APIRouter, Depends
-
 from app.utils.db import get_db_connection
 from app.api.schemas import EchoRequest, EchoResponse
 
@@ -16,18 +13,13 @@
 from app.api.prompts.prompts import router as prompts_router
 from app.api.prospects.prospects import router as prospects_router
 from app.api.prospects.search import router as prospects_search_router
-from app.utils.prospects.database.alter import router as prospects_alter_router
-from app.utils.prospects.database.seed import router as prospects_seed_router
-from app.utils.prospects.database.empty import router as prospects_empty_router
-from app.utils.prospects.database.process import router as prospects_process_router
+from app.api.gemini.gemini import router as gemini_router
 
 router.include_router(root_router)
 router.include_router(resend_router)
 router.include_router(health_router)
 router.include_router(prompts_router)
 router.include_router(prospects_search_router)
 router.include_router(prospects_router)
-router.include_router(prospects_alter_router)
-router.include_router(prospects_seed_router)
-router.include_router(prospects_empty_router)
-router.include_router(prospects_process_router)
+router.include_router(gemini_router)
+

render.yaml

@@ -8,7 +8,7 @@ projects:
     - type: web
       name: nx-ai
       runtime: python
-      repo: https://github.com/goldlabelapps/nx-ai
+      repo: https://github.com/goldlabelapps/python
       plan: free
       region: oregon
       buildCommand: pip install -r requirements.txt

tests/test_gemini.py

@@ -0,0 +1,14 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
+import pytest
+from fastapi.testclient import TestClient
+from app.main import app
+
+client = TestClient(app)
+
+
+def test_gemini_endpoint():
+    response = client.get("/gemini")
+    assert response.status_code == 200
+    assert response.json() == {"message": "Welcome to the Gemini API!"}

tests/test_prospects.py

@@ -11,13 +11,16 @@ def test_get_prospects_root():
     assert "meta" in data
     assert "data" in data
     assert isinstance(data["data"], list)
-    # Check that the expected keys are present in the data list
-    assert any("init" in item for item in data["data"])
-    assert any("search" in item for item in data["data"])
+    # Check that the expected keys are present in the data list (if not empty)
+    if data["data"]:
+        first_item = data["data"][0]
+        # Adjust these keys to match your actual prospect schema
+        assert "id" in first_item
+        assert "first_name" in first_item or "last_name" in first_item
     # Meta checks
     meta = data["meta"]
     assert meta["severity"] == "success"
-    assert meta["title"] == "Prospects endpoint"
+    assert meta["title"] == "Read paginated prospects"
 
 def test_prospects_returns_list():
     response = client.get("/prospects")