Example / Game Generator (PHI-2194)

cookbook/run_qdrant.sh

@@ -0,0 +1,5 @@
+docker run -p 6333:6333 \
+  -d \
+  -v qdrant-volume:/qdrant/storage \
+  --name qdrant \
+  qdrant/qdrant

cookbook/agents/45_generate_fal_video.py → cookbook/tools/fal_tools.py

@@ -1,20 +1,20 @@
-from phi.agent import Agent
-from phi.model.openai import OpenAIChat
-from phi.tools.fal_tools import FalTools
-
-fal_agent = Agent(
-    name="Fal Video Generator Agent",
-    model=OpenAIChat(id="gpt-4o"),
-    tools=[FalTools("fal-ai/hunyuan-video")],
-    description="You are an AI agent that can generate videos using the Fal API.",
-    instructions=[
-        "When the user asks you to create a video, use the `generate_media` tool to create the video.",
-        "Return the URL as raw to the user.",
-        "Don't convert video URL to markdown or anything else.",
-    ],
-    markdown=True,
-    debug_mode=True,
-    show_tool_calls=True,
-)
-
-fal_agent.print_response("Generate video of balloon in the ocean")
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.tools.fal_tools import FalTools
+
+fal_agent = Agent(
+    name="Fal Video Generator Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[FalTools("fal-ai/hunyuan-video")],
+    description="You are an AI agent that can generate videos using the Fal API.",
+    instructions=[
+        "When the user asks you to create a video, use the `generate_media` tool to create the video.",
+        "Return the URL as raw to the user.",
+        "Don't convert video URL to markdown or anything else.",
+    ],
+    markdown=True,
+    debug_mode=True,
+    show_tool_calls=True,
+)
+
+fal_agent.print_response("Generate video of balloon in the ocean")

cookbook/workflows/.gitignore

@@ -1 +1,2 @@
 reports
+games

cookbook/workflows/05_playground.py

@@ -3,6 +3,7 @@
 2. Run the script using: `python cookbook/workflows/05_playground.py`
 """
 
+from cookbook.workflows.game_generator import GameGenerator
 from phi.playground import Playground, serve_playground_app
 from phi.storage.workflow.sqlite import SqlWorkflowStorage
 
@@ -35,6 +36,14 @@
     ),
 )
 
+game_generator = GameGenerator(
+    workflow_id="game-generator",
+    storage=SqlWorkflowStorage(
+        table_name="game_generator_workflows",
+        db_file="tmp/workflows.db",
+    ),
+)
+
 # Initialize the Playground with the workflows
 app = Playground(workflows=[blog_post_generator, news_report_generator, investment_report_generator]).get_app()
 

cookbook/workflows/game_generator.py

@@ -0,0 +1,136 @@
+"""
+1. Install dependencies using: `pip install openai yfinance phidata`
+2. Run the script using: `python cookbook/workflows/investment_report_generator.py`
+"""
+
+import json
+from pathlib import Path
+from typing import Iterator
+
+from pydantic import BaseModel, Field
+
+from phi.agent import Agent, RunResponse
+from phi.model.openai import OpenAIChat
+from phi.run.response import RunEvent
+from phi.storage.workflow.sqlite import SqlWorkflowStorage
+from phi.utils.log import logger
+from phi.utils.pprint import pprint_run_response
+from phi.utils.string import hash_string_sha256
+from phi.utils.web import open_html_file
+from phi.workflow import Workflow
+
+
+games_dir = Path(__file__).parent.joinpath("games")
+games_dir.mkdir(parents=True, exist_ok=True)
+game_output_path = games_dir / "game_output_file.html"
+game_output_path.unlink(missing_ok=True)
+
+
+class GameOutput(BaseModel):
+    reasoning: str = Field(..., description="Explain your reasoning")
+    code: str = Field(..., description="The html5 code for the game")
+    instructions: str = Field(..., description="Instructions how to play the game")
+
+
+class QAOutput(BaseModel):
+    reasoning: str = Field(..., description="Explain your reasoning")
+    correct: bool = Field(False, description="Does the game pass your criteria?")
+
+
+class GameGenerator(Workflow):
+    # This description is only used in the workflow UI
+    description: str = "Generator for single-page HTML5 games"
+
+    game_developer: Agent = Agent(
+        name="Game Developer Agent",
+        description="You are a game developer that produces working HTML5 code.",
+        model=OpenAIChat(id="gpt-4o"),
+        instructions=[
+            "Create a game based on the user's prompt. "
+            "The game should be HTML5, completely self-contained and must be runnable simply by opening on a browser",
+            "Ensure the game has a alert that pops up if the user dies and then allows the user to restart or exit the game.",
+            "Ensure instructions for the game are displayed on the HTML page."
+            "Use user-friendly colours and make the game canvas large enough for the game to be playable on a larger screen.",
+        ],
+        response_model=GameOutput,
+    )
+
+    qa_agent: Agent = Agent(
+        name="QA Agent",
+        model=OpenAIChat(id="gpt-4o"),
+        description="You are a game QA and you evaluate html5 code for correctness.",
+        instructions=[
+            "You will be given some HTML5 code."
+            "Your task is to read the code and evaluate it for correctness, but also that it matches the original task description.",
+        ],
+        response_model=QAOutput,
+    )
+
+    def run(self, game_description: str) -> Iterator[RunResponse]:
+        logger.info(f"Game description: {game_description}")
+
+        game_output = self.game_developer.run(game_description)
+
+        if game_output and game_output.content and isinstance(game_output.content, GameOutput):
+            game_code = game_output.content.code
+            logger.info(f"Game code: {game_code}")
+        else:
+            yield RunResponse(
+                run_id=self.run_id, event=RunEvent.workflow_completed, content="Sorry, could not generate a game."
+            )
+            return
+
+        logger.info("QA'ing the game code")
+        qa_input = {
+            "game_description": game_description,
+            "game_code": game_code,
+        }
+        qa_output = self.qa_agent.run(json.dumps(qa_input, indent=2))
+
+        if qa_output and qa_output.content and isinstance(qa_output.content, QAOutput):
+            logger.info(qa_output.content)
+            if not qa_output.content.correct:
+                raise Exception(f"QA failed for code: {game_code}")
+
+            # Store the resulting code
+            game_output_path.write_text(game_code)
+
+            yield RunResponse(
+                run_id=self.run_id, event=RunEvent.workflow_completed, content=game_output.content.instructions
+            )
+        else:
+            yield RunResponse(
+                run_id=self.run_id, event=RunEvent.workflow_completed, content="Sorry, could not QA the game."
+            )
+            return
+
+
+# Run the workflow if the script is executed directly
+if __name__ == "__main__":
+    from rich.prompt import Prompt
+
+    game_description = Prompt.ask(
+        "[bold]Describe the game you want to make (keep it simple)[/bold]\n✨",
+        # default="An asteroids game."
+        default="An asteroids game. Make sure the asteroids move randomly and are random sizes. They should continually spawn more and become more difficult over time. Keep score. Make my spaceship's movement realistic.",
+    )
+
+    hash_of_description = hash_string_sha256(game_description)
+
+    # Initialize the investment analyst workflow
+    game_generator = GameGenerator(
+        session_id=f"game-gen-{hash_of_description}",
+        storage=SqlWorkflowStorage(
+            table_name="game_generator_workflows",
+            db_file="tmp/workflows.db",
+        ),
+    )
+
+    # Execute the workflow
+    result: Iterator[RunResponse] = game_generator.run(game_description=game_description)
+
+    # Print the report
+    pprint_run_response(result)
+
+    if game_output_path.exists():
+        open_html_file(game_output_path)

phi/agent/agent.py

@@ -497,7 +497,7 @@ def update_model(self) -> None:
                     "Please provide a `model` or install `openai`."
                 )
                 exit(1)
-            self.model = OpenAIChat()
+            self.model = OpenAIChat()  # We default to OpenAIChat as a base model
 
         # Set response_format if it is not set on the Model
         if self.response_model is not None and self.model.response_format is None:

phi/embedder/ollama.py

@@ -5,9 +5,8 @@
 
 try:
     from ollama import Client as OllamaClient
-except ImportError:
-    logger.error("`ollama` not installed")
-    raise
+except (ModuleNotFoundError, ImportError):
+    raise ImportError("`ollama` not installed. Please install using `pip install ollama`")
 
 
 class OllamaEmbedder(Embedder):

phi/tools/baidusearch.py

@@ -22,7 +22,7 @@ class BaiduSearch(Toolkit):
     Args:
         fixed_max_results (Optional[int]): A fixed number of maximum results.
         fixed_language (Optional[str]): A fixed language for the search results.
-        headers (Optional[Any]): Headers to be used in the search request.
+        headers (Optional[Any]):
         proxy (Optional[str]): Proxy to be used in the search request.
         debug (Optional[bool]): Enable debug output.
     """

phi/tools/duckduckgo.py

@@ -11,6 +11,18 @@
 
 
 class DuckDuckGo(Toolkit):
+    """
+    DuckDuckGo is a toolkit for searching DuckDuckGo easily.
+
+    Args:
+        search (bool): Enable DuckDuckGo search function.
+        news (bool): Enable DuckDuckGo news function.
+        fixed_max_results (Optional[int]): A fixed number of maximum results.
+        headers (Optional[Any]):
+        proxy (Optional[str]): Proxy to be used in the search request.
+        proxies (Optional[Any]): A list of proxies to be used in the search request.
+        timeout (Optional[int]): The maximum number of seconds to wait for a response.
+    """
     def __init__(
         self,
         search: bool = True,

phi/tools/linear_tools.py

@@ -22,7 +22,6 @@ def __init__(
         if not self.api_token:
             api_error_message = "API token 'LINEAR_API_KEY' is missing. Please set it as an environment variable."
             logger.error(api_error_message)
-            raise ValueError(api_error_message)
 
         self.endpoint = "https://api.linear.app/graphql"
         self.headers = {"Authorization": f"{self.api_token}"}
@@ -359,7 +358,7 @@ def get_high_priority_issues(self) -> Optional[str]:
 
         query = """
         query HighPriorityIssues {
-        issues(filter: { 
+        issues(filter: {
           priority: { lte: 2 }
         }) {
           nodes {

phi/tools/replicate.py

@@ -1,5 +1,6 @@
 import os
 from os import getenv
+from typing import Optional
 from urllib.parse import urlparse
 from uuid import uuid4
 
@@ -18,10 +19,11 @@
 class ReplicateTools(Toolkit):
     def __init__(
         self,
+        api_key: Optional[str] = None,
         model: str = "minimax/video-01",
     ):
         super().__init__(name="replicate_toolkit")
-        self.api_key = getenv("REPLICATE_API_TOKEN")
+        self.api_key = api_key or getenv("REPLICATE_API_TOKEN")
         if not self.api_key:
             logger.error("REPLICATE_API_TOKEN not set. Please set the REPLICATE_API_TOKEN environment variable.")
         self.model = model

phi/utils/string.py

@@ -0,0 +1,17 @@
+import hashlib
+
+
+def hash_string_sha256(input_string):
+    # Encode the input string to bytes
+    encoded_string = input_string.encode("utf-8")
+
+    # Create a SHA-256 hash object
+    sha256_hash = hashlib.sha256()
+
+    # Update the hash object with the encoded string
+    sha256_hash.update(encoded_string)
+
+    # Get the hexadecimal digest of the hash
+    hex_digest = sha256_hash.hexdigest()
+
+    return hex_digest

phi/utils/web.py

@@ -0,0 +1,24 @@
+import webbrowser
+from pathlib import Path
+
+from phi.utils.log import logger
+
+
+def open_html_file(file_path: Path):
+    """
+    Opens the specified HTML file in the default web browser.
+
+    :param file_path: Path to the HTML file.
+    """
+    # Resolve the absolute path
+    absolute_path = file_path.resolve()
+
+    if not absolute_path.is_file():
+        logger.error(f"The file '{absolute_path}' does not exist.")
+        raise FileNotFoundError(f"The file '{absolute_path}' does not exist.")
+
+    # Convert the file path to a file URI
+    file_url = absolute_path.as_uri()
+
+    # Open the file in the default web browser
+    webbrowser.open(file_url)

phi/vectordb/qdrant/qdrant.py

@@ -83,7 +83,7 @@ def client(self) -> QdrantClient:
                 https=self.https,
                 api_key=self.api_key,
                 prefix=self.prefix,
-                timeout=self.timeout,
+                timeout=int(self.timeout) if self.timeout is not None else None,
                 host=self.host,
                 path=self.path,
                 **self.kwargs,