v2.5.7

cookbook/agents/generate_image.py

@@ -1,10 +1,10 @@
 from phi.agent import Agent
 from phi.model.openai import OpenAIChat
-from phi.tools.dalle import DalleTools
+from phi.tools.dalle import Dalle
 
 agent = Agent(
     model=OpenAIChat(id="gpt-4o"),
-    tools=[DalleTools()],
+    tools=[Dalle()],
     markdown=True,
     debug_mode=True,
     instructions=[

cookbook/agents/generate_video.py

@@ -1,12 +1,12 @@
 from phi.agent import Agent
 from phi.model.openai import OpenAIChat
-from phi.tools.video_gen import VideoGenTools
+from phi.tools.models_labs import ModelsLabs
 
 agent = Agent(
     name="Video Generation Agent",
     agent_id="video-generation-agent",
     model=OpenAIChat(id="gpt-4o"),
-    tools=[VideoGenTools()],
+    tools=[ModelsLabs()],
     markdown=True,
     debug_mode=True,
     show_tool_calls=True,

cookbook/playground/demo.py

@@ -0,0 +1,126 @@
+"""Run `pip install openai sqlalchemy pypdf duckduckgo-search yfinance exa_py lancedb tantivy` to install dependencies."""
+
+from textwrap import dedent
+from datetime import datetime
+
+from phi.agent import Agent
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.model.openai import OpenAIChat
+from phi.playground import Playground, serve_playground_app
+from phi.storage.agent.sqlite import SqlAgentStorage
+from phi.tools.dalle import Dalle
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.exa import ExaTools
+from phi.tools.yfinance import YFinanceTools
+from phi.vectordb.lancedb import LanceDb, SearchType
+
+lance_db_uri = "tmp/lancedb"
+db_session_storage_file: str = "agents.db"
+
+web_agent = Agent(
+    name="Web Agent",
+    role="Search the web for information",
+    agent_id="web-agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[DuckDuckGo()],
+    instructions=["Break down the users request into 2-3 different searches.", "Always include sources"],
+    storage=SqlAgentStorage(table_name="web_agent", db_file=db_session_storage_file),
+    add_history_to_messages=True,
+    num_history_responses=5,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    agent_id="finance-agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+    instructions=["Always use tables to display data"],
+    storage=SqlAgentStorage(table_name="finance_agent", db_file=db_session_storage_file),
+    add_history_to_messages=True,
+    num_history_responses=5,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+image_agent = Agent(
+    name="Image Agent",
+    role="Generate images given a prompt",
+    agent_id="image-agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[Dalle(model="dall-e-3", size="1792x1024", quality="hd", style="vivid")],
+    storage=SqlAgentStorage(table_name="image_agent", db_file=db_session_storage_file),
+    add_history_to_messages=True,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+research_agent = Agent(
+    name="Research Agent",
+    role="Write research reports for the New York Times",
+    agent_id="research-agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[ExaTools(start_published_date=datetime.now().strftime("%Y-%m-%d"), type="keyword")],
+    description=(
+        "You are a Research Agent that has the special skill of writing New York Times worthy articles. "
+        "If you can directly respond to the user, do so. If the user asks for a report or provides a topic, follow the instructions below."
+    ),
+    instructions=[
+        "For the provided topic, run 3 different searches.",
+        "Read the results carefully and prepare a NYT worthy article.",
+        "Focus on facts and make sure to provide references.",
+    ],
+    expected_output=dedent("""\
+    Your articles should be engaging, informative, well-structured and in markdown format. They should follow the following structure:
+
+    ## Engaging Article Title
+
+    ### Overview
+    {give a brief introduction of the article and why the user should read this report}
+    {make this section engaging and create a hook for the reader}
+
+    ### Section 1
+    {break the article into sections}
+    {provide details/facts/processes in this section}
+
+    ... more sections as necessary...
+
+    ### Takeaways
+    {provide key takeaways from the article}
+
+    ### References
+    - [Reference 1](link)
+    - [Reference 2](link)
+    """),
+    storage=SqlAgentStorage(table_name="research_agent", db_file=db_session_storage_file),
+    add_history_to_messages=True,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+recipes_knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=LanceDb(table_name="thai_recipes", uri=lance_db_uri, search_type=SearchType.vector),
+)
+
+recipe_agent = Agent(
+    name="Thai Recipes Agent",
+    agent_id="thai-recipes-agent",
+    model=OpenAIChat(id="gpt-4o"),
+    description="You are an expert at Thai Recipes and have a knowledge base full of special Thai recipes.",
+    instructions=["Always search your knowledge base first for the recipe."],
+    knowledge=recipes_knowledge_base,
+    storage=SqlAgentStorage(table_name="thai_recipe_agent", db_file=db_session_storage_file),
+    add_history_to_messages=True,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+app = Playground(agents=[web_agent, finance_agent, image_agent, research_agent, recipe_agent]).get_app()
+
+if __name__ == "__main__":
+    # Load the knowledge base: Comment out after first run
+    recipes_knowledge_base.load(upsert=True)
+    serve_playground_app("demo:app", reload=True)

cookbook/playground/serve_ollama.py → cookbook/playground/ollama.py

@@ -70,4 +70,4 @@
 app = Playground(agents=[finance_agent, research_agent]).get_app()
 
 if __name__ == "__main__":
-    serve_playground_app("serve_ollama:app", port=8118, reload=True)
+    serve_playground_app("ollama:app", port=8118, reload=True)

cookbook/playground/serve.py → cookbook/playground/test.py

@@ -1,4 +1,4 @@
-"""Run `pip install yfinance exa_py` to install dependencies."""
+"""Run `pip install openai yfinance exa_py` to install dependencies."""
 
 from textwrap import dedent
 from datetime import datetime
@@ -11,16 +11,16 @@
 from phi.knowledge.pdf import PDFUrlKnowledgeBase
 from phi.vectordb.pgvector import PgVector, SearchType
 from phi.playground import Playground, serve_playground_app
-from phi.tools.video_gen import VideoGenTools
-from phi.tools.dalle import DalleTools
+from phi.tools.models_labs import ModelsLabs
+from phi.tools.dalle import Dalle
 
 db_url: str = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 video_gen_agent = Agent(
     name="Video Gen Agent",
     agent_id="video-gen-agent",
     model=OpenAIChat(id="gpt-4o"),
-    tools=[VideoGenTools()],
+    tools=[ModelsLabs()],
     markdown=True,
     debug_mode=True,
     show_tool_calls=True,
@@ -56,7 +56,7 @@
     name="Dalle Agent",
     agent_id="dalle-agent",
     model=OpenAIChat(id="gpt-4o"),
-    tools=[DalleTools()],
+    tools=[Dalle()],
     markdown=True,
     debug_mode=True,
 )
@@ -129,4 +129,4 @@
 if __name__ == "__main__":
     # Load the knowledge base: Comment out after first run
     # recipe_knowledge_base.load(upsert=True)
-    serve_playground_app("serve:app", reload=True)
+    serve_playground_app("test:app", reload=True)

phi/tools/dalle.py

@@ -1,6 +1,7 @@
-from phi.tools import Toolkit
-from typing import Optional
 from os import getenv
+from typing import Optional, Literal
+
+from phi.tools import Toolkit
 from phi.utils.log import logger
 
 
@@ -10,33 +11,34 @@
     raise ImportError("`openai` not installed. Please install using `pip install openai`")
 
 
-class DalleTools(Toolkit):
+class Dalle(Toolkit):
     def __init__(
         self,
         model: str = "dall-e-3",
-        size: str = "1280x720",
-        quality: str = "standard",
         n: int = 1,
+        size: Optional[Literal["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"]] = "1024x1024",
+        quality: Literal["standard", "hd"] = "standard",
+        style: Literal["vivid", "natural"] = "vivid",
         api_key: Optional[str] = None,
     ):
         super().__init__(name="dalle")
 
         self.model = model
+        self.n = n
         self.size = size
         self.quality = quality
-        self.n = n
-
+        self.style = style
         self.api_key = api_key or getenv("OPENAI_API_KEY")
         if not self.api_key:
             logger.error("OPENAI_API_KEY not set. Please set the OPENAI_API_KEY environment variable.")
 
         self.register(self.generate_image)
 
     def generate_image(self, prompt: str) -> str:
-        """Use this function to generate an image using DALL-E.
+        """Use this function to generate an image given a prompt.
 
         Args:
-            prompt (str): The prompt to generate an image from.
+            prompt (str): A text description of the desired image.
 
         Returns:
             str: The URL of the generated image, or an error message.
@@ -48,11 +50,12 @@ def generate_image(self, prompt: str) -> str:
             client = OpenAI(api_key=self.api_key)
             logger.info(f"Generating image for prompt: {prompt}")
             response = client.images.generate(
-                model=self.model,
                 prompt=prompt,
-                size=self.size,  # type: ignore
-                quality=self.quality,  # type: ignore
+                model=self.model,
                 n=self.n,
+                quality=self.quality,
+                size=self.size,
+                style=self.style,
             )
             return response.data[0].url or "Error: No image URL returned"
         except Exception as e: