Update README for 8 tasks and final scores

README.md

@@ -27,27 +27,60 @@ making it a meaningful benchmark for AI reasoning and code generation.
 
 ---
 
+## Baseline Agent Results
+
+| Metric | Score |
+|---|---|
+| Tasks solved | 6 / 8 |
+| Average reward | 0.985 / 1.000 |
+| Model used | llama-3.1-8b-instant (Groq) |
+| Time to complete | Under 3 minutes |
+
+---
+
 ## Tasks
 
-Three tasks of increasing difficulty, each graded by an automated SQL result comparator.
+Eight tasks of increasing difficulty, each graded by an automated SQL result comparator.
 
 ### Task 1 — Easy
 **"How many completed orders were placed in 2024?"**
 - Requires: COUNT, WHERE, date filtering
-- Tests: basic aggregation and filtering
-- Expected output: single row, single column
+- Expected output: single row — total_orders
 
 ### Task 2 — Medium
 **"Find the top 5 customers by total revenue from completed orders."**
 - Requires: JOIN, GROUP BY, SUM, ORDER BY, LIMIT
-- Tests: multi-table joins and aggregation
-- Expected output: 5 rows with first_name, last_name, total_revenue
+- Expected output: 5 rows — first_name, last_name, total_revenue
 
 ### Task 3 — Hard
 **"Rank product categories by total revenue using a window function."**
-- Requires: CTE (WITH), JOIN, GROUP BY, RANK() OVER (...)
-- Tests: advanced SQL — CTEs and window functions
-- Expected output: all categories with total_revenue and revenue_rank
+- Requires: CTE, JOIN, GROUP BY, RANK() OVER (...)
+- Expected output: all categories — category, total_revenue, revenue_rank
+
+### Task 4 — Medium
+**"Find average product price per category, only for categories with more than 2 products."**
+- Requires: GROUP BY, AVG, HAVING
+- Expected output: category, avg_price
+
+### Task 5 — Hard
+**"Find customers who ordered from both Electronics and Clothing categories."**
+- Requires: INTERSECT, multiple JOINs
+- Expected output: customer_id, first_name
+
+### Task 6 — Expert
+**"Calculate month-over-month revenue growth % for 2024."**
+- Requires: LAG() window function, CTE, STRFTIME
+- Expected output: month, total_revenue, prev_revenue, growth_pct
+
+### Task 7 — Expert
+**"For each city, find the best-selling product by quantity."**
+- Requires: RANK() OVER (PARTITION BY city ...), CTE
+- Expected output: city, product_name, total_quantity
+
+### Task 8 — Expert
+**"Find customers who spent more in H2 2024 than H1 2024."**
+- Requires: CASE WHEN, conditional SUM, HAVING
+- Expected output: customer_id, first_name, last_name, h1_revenue, h2_revenue
 
 ---
 
@@ -73,12 +106,12 @@ Response:
     "schema": "Tables:\n  customers (...)\n  products (...)\n  orders (...)",
     "hint": "Use COUNT with WHERE filters on status and order_date"
   },
-  "info": {"message": "Task 1 loaded. Use POST /step with your SQL query."}
+  "info": {"message": "Task 1 loaded."}
 }
 ```
 
 ### POST /step
-Submit a SQL query. Returns reward and feedback.
+Submit a SQL query. Returns reward 0.0-1.0 and feedback.
 
 Request:
 ```json
@@ -105,6 +138,9 @@ Response:
 ### GET /state
 Get current session — task info, all attempts, best score.
 
+### GET /health
+Returns {"status": "ok", "db_exists": true} when server is ready.
+
 ---
 
 ## Observation Space
@@ -139,21 +175,25 @@ FROM orders o JOIN customers c ON o.customer_id = c.customer_id
 WHERE o.status = 'completed'
 GROUP BY o.customer_id ORDER BY total_revenue DESC LIMIT 5
 
--- Hard
-WITH rev AS (
-  SELECT p.category, SUM(o.total_amount) AS total_revenue
-  FROM orders o JOIN products p ON o.product_id = p.product_id
-  WHERE o.status = 'completed' GROUP BY p.category
+-- Expert
+WITH monthly AS (
+  SELECT STRFTIME('%Y-%m', order_date) AS month, SUM(total_amount) AS total_revenue
+  FROM orders WHERE status = 'completed' AND order_date LIKE '2024%'
+  GROUP BY month
 )
-SELECT category, total_revenue, RANK() OVER (ORDER BY total_revenue DESC) AS revenue_rank
-FROM rev ORDER BY revenue_rank ASC
+SELECT month, total_revenue,
+  LAG(total_revenue) OVER (ORDER BY month) AS prev_revenue,
+  ROUND((total_revenue - LAG(total_revenue) OVER (ORDER BY month))
+    / LAG(total_revenue) OVER (ORDER BY month) * 100, 2) AS growth_pct
+FROM monthly ORDER BY month ASC
 ```
 
 ---
 
 ## Reward Function
 
-Partial credit score from 0.0 to 1.0 with three components:
+Partial credit score from 0.0 to 1.0 with three components.
+Even an imperfect query receives meaningful feedback — not just pass/fail.
 
 | Component | Weight | How it is measured |
 |---|---|---|
@@ -161,9 +201,6 @@ Partial credit score from 0.0 to 1.0 with three components:
 | Row count | 0.30 | Ratio of returned rows vs expected rows |
 | Cell values | 0.40 | Fraction of cells matching expected values |
 
-Even an imperfect query receives meaningful feedback — not just pass/fail.
-This gives the agent a gradient signal to improve from.
-
 ---
 
 ## Database Schema
@@ -171,31 +208,34 @@ This gives the agent a gradient signal to improve from.
 A realistic e-commerce dataset with 600 orders, 100 customers, 30 products.
 
 ```
-customers  (customer_id, first_name, last_name, email, city, signup_date)
-products   (product_id, product_name, category, price, stock)
-orders     (order_id, customer_id, product_id, quantity, total_amount, order_date, status)
+customers  (customer_id, first_name, last_name, email, city, signup_date)  — 100 rows
+products   (product_id, product_name, category, price, stock)              — 30 rows
+orders     (order_id, customer_id, product_id, quantity, total_amount, order_date, status) — 600 rows
 ```
 
 All orders are dated in 2024. Status values: completed, pending, cancelled.
+10 product categories: Electronics, Clothing, Books, Home & Garden, Sports, Beauty, Toys, Food & Grocery, Automotive, Music.
 
 ---
 
 ## Running the Baseline Agent
 
 ```bash
-pip install openai requests
+pip install openai requests python-dotenv
 
-export API_BASE_URL=https://api.openai.com/v1
-export MODEL_NAME=gpt-4o-mini
-export HF_TOKEN=your_api_key_here
+export API_BASE_URL=https://api.groq.com/openai/v1
+export MODEL_NAME=llama-3.1-8b-instant
+export HF_TOKEN=your_groq_api_key_here
 
 python inference.py
 ```
 
 Expected output:
 ```
-Tasks solved   : 3 / 3
-Average reward : 1.000 / 1.000
+SQL Analyst OpenEnv — Baseline Inference Agent
+Model      : llama-3.1-8b-instant
+Tasks solved   : 6 / 8
+Average reward : 0.985 / 1.000
 Results saved to results.json
 ```
 
@@ -224,10 +264,13 @@ docker run -p 7860:7860 sql-analyst-env
 
 ## Hardware Requirements
 
-- CPU: 1-2 vCPU
-- RAM: 8GB
-- GPU: Not required
-- Inference time: Under 5 minutes for all 3 tasks
+| Resource | Requirement |
+|---|---|
+| CPU | 1-2 vCPU |
+| RAM | 8GB |
+| GPU | Not required |
+| Disk | ~50MB |
+| Inference time | Under 5 minutes for all 8 tasks |
 
 ---
 
@@ -235,12 +278,22 @@ docker run -p 7860:7860 sql-analyst-env
 
 ```
 sql-analyst-env/
-├── main.py          # FastAPI server
-├── seed.py          # Database seeder
-├── inference.py     # Baseline AI agent
-├── openenv.yaml     # OpenEnv specification
-├── Dockerfile       # Container for HF Spaces
+├── main.py          # FastAPI server — /reset, /step, /state endpoints
+├── seed.py          # Database seeder — creates ecommerce.db
+├── inference.py     # Baseline AI agent using OpenAI-compatible client
+├── openenv.yaml     # OpenEnv specification file
+├── Dockerfile       # Container for Hugging Face Spaces
 ├── requirements.txt # Python dependencies
 └── data/
-    └── ecommerce.db # SQLite database
-```
+    └── ecommerce.db # SQLite database (auto-created by seed.py)
+```
+
+---
+
+## Built With
+
+- FastAPI — REST API framework
+- SQLite — embedded database, no setup required
+- OpenAI Python client — compatible with Groq, Together, OpenAI
+- Docker — containerized deployment
+- Hugging Face Spaces — hosting