Groq Research Agent

cookbook/providers/groq/agent.py

@@ -5,7 +5,7 @@
 from phi.tools.yfinance import YFinanceTools
 
 agent = Agent(
-    model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     tools=[YFinanceTools(stock_price=True)],
     show_tool_calls=True,
     markdown=True,

cookbook/providers/groq/agent_stream.py

@@ -6,7 +6,7 @@
 from phi.tools.yfinance import YFinanceTools
 
 agent = Agent(
-    model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     tools=[YFinanceTools(stock_price=True)],
     show_tool_calls=True,
     markdown=True,

cookbook/providers/groq/agent_team.py

@@ -0,0 +1,34 @@
+from phi.agent import Agent
+from phi.model.groq import Groq
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+
+web_agent = Agent(
+    name="Web Agent",
+    role="Search the web for information",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[DuckDuckGo()],
+    instructions=["Always include sources"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
+    instructions=["Use tables to display data"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+team_leader = Agent(
+    team=[web_agent, finance_agent],
+    model=Groq(id="llama-3.3-70b-versatile"),
+    instructions=["Always include sources", "Use tables to display data"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+team_leader.print_response("Summarize analyst recommendations and share the latest news for NVDA", stream=True)

cookbook/providers/groq/agent_ui.py

@@ -0,0 +1,84 @@
+"""Usage:
+1. Install libraries: `pip install groq duckduckgo-search yfinance pypdf sqlalchemy 'fastapi[standard]' youtube-transcript-api phidata`
+2. Run the script: `python cookbook/providers/groq/agent_ui.py`
+"""
+
+from phi.agent import Agent
+from phi.model.groq import Groq
+from phi.playground import Playground, serve_playground_app
+from phi.storage.agent.sqlite import SqlAgentStorage
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+from phi.tools.youtube_tools import YouTubeTools
+
+groq_agent_storage: str = "tmp/groq_agents.db"
+common_instructions = [
+    "If the user about you or your skills, tell them your name and role.",
+]
+
+web_agent = Agent(
+    name="Web Agent",
+    role="Search the web for information",
+    agent_id="web-agent",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[DuckDuckGo()],
+    instructions=[
+        "Use the `duckduckgo_search` or `duckduckgo_news` tools to search the web for information.",
+        "Always include sources you used to generate the answer.",
+    ]
+    + common_instructions,
+    storage=SqlAgentStorage(table_name="web_agent", db_file=groq_agent_storage),
+    show_tool_calls=True,
+    add_history_to_messages=True,
+    num_history_responses=2,
+    add_name_to_instructions=True,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    agent_id="finance-agent",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+    description="You are an investment analyst that researches stocks and helps users make informed decisions.",
+    instructions=["Always use tables to display data"] + common_instructions,
+    storage=SqlAgentStorage(table_name="finance_agent", db_file=groq_agent_storage),
+    show_tool_calls=True,
+    add_history_to_messages=True,
+    num_history_responses=5,
+    add_name_to_instructions=True,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+
+youtube_agent = Agent(
+    name="YouTube Agent",
+    role="Understand YouTube videos and answer questions",
+    agent_id="youtube-agent",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[YouTubeTools()],
+    description="You are a YouTube agent that has the special skill of understanding YouTube videos and answering questions about them.",
+    instructions=[
+        "Using a video URL, get the video data using the `get_youtube_video_data` tool and captions using the `get_youtube_video_data` tool.",
+        "Using the data and captions, answer the user's question in an engaging and thoughtful manner. Focus on the most important details.",
+        "If you cannot find the answer in the video, say so and ask the user to provide more details.",
+        "Keep your answers concise and engaging.",
+        "If the user just provides a URL, summarize the video and answer questions about it.",
+    ]
+    + common_instructions,
+    storage=SqlAgentStorage(table_name="youtube_agent", db_file=groq_agent_storage),
+    show_tool_calls=True,
+    add_history_to_messages=True,
+    num_history_responses=5,
+    add_name_to_instructions=True,
+    add_datetime_to_instructions=True,
+    markdown=True,
+)
+
+app = Playground(agents=[finance_agent, youtube_agent, web_agent]).get_app(use_async=False)
+
+if __name__ == "__main__":
+    serve_playground_app("agent_ui:app", reload=True)

cookbook/providers/groq/basic.py

@@ -1,7 +1,7 @@
 from phi.agent import Agent, RunResponse  # noqa
 from phi.model.groq import Groq
 
-agent = Agent(model=Groq(id="llama3-groq-70b-8192-tool-use-preview"), markdown=True)
+agent = Agent(model=Groq(id="llama-3.3-70b-versatile"), markdown=True)
 
 # Get the response in a variable
 # run: RunResponse = agent.run("Share a 2 sentence horror story")

cookbook/providers/groq/basic_stream.py

@@ -2,7 +2,7 @@
 from phi.agent import Agent, RunResponse  # noqa
 from phi.model.groq import Groq
 
-agent = Agent(model=Groq(id="llama3-groq-70b-8192-tool-use-preview"), markdown=True)
+agent = Agent(model=Groq(id="llama-3.3-70b-versatile"), markdown=True)
 
 # Get the response in a variable
 # run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)

cookbook/providers/groq/data_analyst.py

@@ -11,7 +11,7 @@
 )
 
 agent = Agent(
-    model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     tools=[duckdb_tools],
     markdown=True,
     show_tool_calls=True,

cookbook/providers/groq/knowledge.py

@@ -14,7 +14,7 @@
 knowledge_base.load(recreate=False)  # Comment out after first run
 
 agent = Agent(
-    model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     knowledge_base=knowledge_base,
     use_tools=True,
     show_tool_calls=True,

cookbook/providers/groq/research_agent_ddg.py

@@ -0,0 +1,24 @@
+"""Please install dependencies using:
+pip install openai duckduckgo-search newspaper4k lxml_html_clean phidata
+"""
+
+from phi.agent import Agent
+from phi.model.groq import Groq
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.newspaper4k import Newspaper4k
+
+agent = Agent(
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[DuckDuckGo(), Newspaper4k()],
+    description="You are a senior NYT researcher writing an article on a topic.",
+    instructions=[
+        "For a given topic, search for the top 5 links.",
+        "Then read each URL and extract the article text, if a URL isn't available, ignore it.",
+        "Analyse and prepare an NYT worthy article based on the information.",
+    ],
+    markdown=True,
+    show_tool_calls=True,
+    add_datetime_to_instructions=True,
+    # debug_mode=True,
+)
+agent.print_response("Simulation theory", stream=True)

cookbook/providers/groq/research_agent.py → cookbook/providers/groq/research_agent_exa.py

@@ -15,8 +15,8 @@
 
 today = datetime.now().strftime("%Y-%m-%d")
 
-assistant = Agent(
-    llm=Groq(model="llama-3.3-70b-versatile"),
+agent = Agent(
+    model=Groq(id="llama-3.3-70b-versatile"),
     tools=[ExaTools(start_published_date=today, type="keyword")],
     description="You are an advanced AI researcher writing a report on a topic.",
     instructions=[
@@ -58,4 +58,4 @@
     save_response_to_file=str(tmp.joinpath("{message}.md")),
     # debug_mode=True,
 )
-assistant.print_response("Llama 3.3 running on Groq", stream=True)
+agent.print_response("Llama 3.3 running on Groq", stream=True)

cookbook/providers/groq/storage.py

@@ -8,7 +8,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 agent = Agent(
-    model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     storage=PgAgentStorage(table_name="agent_sessions", db_url=db_url),
     tools=[DuckDuckGo()],
     add_history_to_messages=True,

cookbook/providers/groq/structured_output.py

@@ -17,7 +17,7 @@ class MovieScript(BaseModel):
 
 
 json_mode_agent = Agent(
-    model=Groq(id="mixtral-8x7b-32768"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     description="You help people write movie scripts.",
     response_model=MovieScript,
     # debug_mode=True,

cookbook/providers/groq/web_search.py

@@ -5,7 +5,7 @@
 from phi.tools.duckduckgo import DuckDuckGo
 
 agent = Agent(
-    model=Groq(id="llama3-groq-70b-8192-tool-use-preview"),
+    model=Groq(id="llama-3.3-70b-versatile"),
     tools=[DuckDuckGo()],
     show_tool_calls=True,
     markdown=True,

cookbook/providers/openai/finance_agent.py

@@ -14,4 +14,6 @@
 )
 
 # agent.print_response("Share the NVDA stock price and analyst recommendations", stream=True)
-agent.print_response("Summarize fundamentals for TSLA", stream=True)
+agent.print_response(
+    "Summarize and compare analyst recommendations and fundamentals for TSLA and NVDA. Show in tables.", stream=True
+)