feat: openai example (#9)

* feat: bump dependencies and readme

* add citation cff

* small tweaks

* small tweaks to readme

* feat: minor tweaks

* small tweaks

README.md

@@ -102,7 +102,7 @@ Coming soon...
 We've created some examples using populat agent frameworks you can use as inspiration (feel free to contribute): 
 
 - [Vercel AI SDK](https://github.com/superagent-ai/poker-eval/tree/main/examples/ai-sdk)
-- [OpeaAI]() Coming soon
+- [OpenAI](https://github.com/superagent-ai/poker-eval/tree/main/examples/openai)
 - [Mastra]() Coming soon
 - [LlamaIndex]() Coming soon
 - [Langchain]() Coming soon

examples/ai-sdk/agent.ts

@@ -2,8 +2,7 @@ import { config } from "dotenv";
 import { generateObject } from "ai";
 import { openai } from "@ai-sdk/openai";
 import { z } from "zod";
-
-import { PlayerAction, TableState } from "../../src/types";
+import { PlayerAction, TableState } from "@superagent-ai/poker-eval/dist/types";
 
 config();
 

examples/openai/.env.example

@@ -0,0 +1 @@
+OPENAI_API_KEY=

examples/openai/.gitignore

@@ -0,0 +1,2 @@
+node_modules
+.env

examples/openai/README.md

@@ -0,0 +1,15 @@
+# PokerEval and Vercel AI SDK
+
+This examples shows how to integrate an Agent built with the [Vercel AI SDK](https://github.com/vercel/ai) and PokerEval.
+
+## How to use
+1. Install dependencies
+    ```
+    npm install
+    ```
+2. Update environment variables and rename `.env.example` to `.env`
+
+3. Run evaluation
+    ```
+    ts-node index.ts
+    ```

examples/openai/agent.ts

@@ -0,0 +1,71 @@
+import { config } from "dotenv";
+import OpenAI from "openai";
+import { z } from "zod";
+import { zodResponseFormat } from "openai/helpers/zod";
+import { PlayerAction, TableState } from "@superagent-ai/poker-eval/dist/types";
+
+config();
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+const ActionEnum = z
+  .enum(["bet", "fold", "call", "check", "raise"])
+  .describe("The action to take");
+
+const responseSchema = z.object({
+  action: ActionEnum,
+  bet: z
+    .number()
+    .describe("The amount to bet, call, raise, or check")
+    .optional(),
+});
+
+export const generateAction = async (
+  state: TableState
+): Promise<PlayerAction> => {
+  const ActionEnum = z
+    .enum(["bet", "fold", "call", "check", "raise"] as const)
+    .describe("The action to take");
+
+  const prompt = `
+You are a poker assistant named ${state.playerName}. 
+You are playing at a table with ${state.tableInfo.length} players.
+Act based on the information below.
+
+Players:
+${state.tableInfo
+  .map(
+    (player) =>
+      `${player.name}${player.hasButton ? " (Button)" : ""}, Stack: $${
+        player.stack
+      }, Bet size: $${player.betSize}`
+  )
+  .join("\n")}
+
+Game stage: ${state.gameStage}
+Your cards: ${state.playerCards}
+Cards on the table: ${state.tableCards}
+Legal actions: ${state.legalActions.actions.join(", ")}
+Min bet: $${state.minRaise}
+Max bet: $${state.maxRaise}
+
+Considering the hand strength, potential opponent hands, and optimal strategy, what action should the player take? Explain your reasoning.
+
+Make sure to return the correct amount when calling, raising or bettings.
+  `;
+
+  const completion = await openai.beta.chat.completions.parse({
+    model: "gpt-4o",
+    messages: [{ role: "user", content: prompt }],
+    response_format: zodResponseFormat(responseSchema, "action"),
+  });
+
+  const { action, bet } = completion.choices[0].message.parsed;
+
+  return {
+    action,
+    bet: bet ?? 0,
+  } as PlayerAction;
+};

examples/openai/index.ts

@@ -0,0 +1,40 @@
+import { generateAction } from "./agent";
+
+import { PokerGame } from "@superagent-ai/poker-eval";
+import { Player, PlayerAction } from "@superagent-ai/poker-eval/dist/types";
+
+async function executeGameSimulation(numHands: number): Promise<void> {
+  // Setup AI players
+  const players: Player[] = [
+    {
+      name: "GPT 1",
+      action: async (state): Promise<PlayerAction> => {
+        const action = await generateAction(state);
+        return action;
+      },
+    },
+    {
+      name: "GPT 2",
+      action: async (state): Promise<PlayerAction> => {
+        const action = await generateAction(state);
+        return action;
+      },
+    },
+  ];
+
+  // Setup a game
+  const game = new PokerGame(players, {
+    defaultChipSize: 1000,
+    smallBlind: 1,
+    bigBlind: 2,
+  });
+
+  // Set the output director for stats collection
+  const results = await game.runSimulation(numHands, { outputPath: "./stats" });
+
+  console.log(`Simulation completed for ${numHands} hands.`);
+  console.log("Results:", results);
+}
+
+// Execute the function with ts-node index.ts
+executeGameSimulation(5).catch(console.error);