diff --git a/cookbook/agents/README.md b/cookbook/agents/README.md
deleted file mode 100644
index 3d00f48565..0000000000
--- a/cookbook/agents/README.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# Building Agents with gpt-4o
-
-This cookbook shows how to build agents with gpt-4o
-
-> Note: Fork and clone this repository if needed
-
-### 1. Create a virtual environment
-
-```shell
-python3 -m venv ~/.venvs/aienv
-source ~/.venvs/aienv/bin/activate
-```
-
-### 2. Install libraries
-
-```shell
-pip install -r cookbook/agents/requirements.txt
-```
-
-### 3. Export credentials
-
-- We use gpt-4o as the llm, so export your OpenAI API Key
-
-```shell
-export OPENAI_API_KEY=***
-```
-
-- To use Exa for research, export your EXA_API_KEY (get it from [here](https://dashboard.exa.ai/api-keys))
-
-```shell
-export EXA_API_KEY=xxx
-```
-
-### 4. Run PgVector
-
-We use PgVector to provide long-term memory and knowledge to the LLM OS.
-Please install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run PgVector using either the helper script or the `docker run` command.
-
-- Run using a helper script
-
-```shell
-./cookbook/run_pgvector.sh
-```
-
-- OR run using the docker run command
-
-```shell
-docker run -d \
- -e POSTGRES_DB=ai \
- -e POSTGRES_USER=ai \
- -e POSTGRES_PASSWORD=ai \
- -e PGDATA=/var/lib/postgresql/data/pgdata \
- -v pgvolume:/var/lib/postgresql/data \
- -p 5532:5432 \
- --name pgvector \
- phidata/pgvector:16
-```
-
-### 5. Run the App
-
-```shell
-streamlit run cookbook/agents/app.py
-```
-
-- Open [localhost:8501](http://localhost:8501) to view your LLM OS.
-
-### 6. Message on [discord](https://discord.gg/4MtYHHrgA8) if you have any questions
-
-### 7. Star ⭐️ the project if you like it.
-
-### Share with your friends: https://phidata.link/agents
diff --git a/cookbook/agents/agent.py b/cookbook/agents/agent.py
deleted file mode 100644
index 5b8a91e304..0000000000
--- a/cookbook/agents/agent.py
+++ /dev/null
@@ -1,294 +0,0 @@
-import json
-from pathlib import Path
-from typing import Optional
-from textwrap import dedent
-from typing import List
-
-from phi.assistant import Assistant
-from phi.tools import Toolkit
-from phi.tools.exa import ExaTools
-from phi.tools.calculator import Calculator
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.tools.yfinance import YFinanceTools
-from phi.tools.file import FileTools
-from phi.llm.openai import OpenAIChat
-from phi.knowledge import AssistantKnowledge
-from phi.embedder.openai import OpenAIEmbedder
-from phi.assistant.duckdb import DuckDbAssistant
-from phi.assistant.python import PythonAssistant
-from phi.storage.assistant.postgres import PgAssistantStorage
-from phi.utils.log import logger
-from phi.vectordb.pgvector import PgVector2
-
-db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
-cwd = Path(__file__).parent.resolve()
-scratch_dir = cwd.joinpath("scratch")
-if not scratch_dir.exists():
- scratch_dir.mkdir(exist_ok=True, parents=True)
-
-
-def get_agent(
- llm_id: str = "gpt-4o",
- calculator: bool = False,
- ddg_search: bool = False,
- file_tools: bool = False,
- finance_tools: bool = False,
- data_analyst: bool = False,
- python_assistant: bool = False,
- research_assistant: bool = False,
- investment_assistant: bool = False,
- user_id: Optional[str] = None,
- run_id: Optional[str] = None,
- debug_mode: bool = True,
-) -> Assistant:
- logger.info(f"-*- Creating {llm_id} Agent -*-")
-
- # Add tools available to the Agent
- tools: List[Toolkit] = []
- extra_instructions: List[str] = []
- if calculator:
- tools.append(
- Calculator(
- add=True,
- subtract=True,
- multiply=True,
- divide=True,
- exponentiate=True,
- factorial=True,
- is_prime=True,
- square_root=True,
- )
- )
- if ddg_search:
- tools.append(DuckDuckGo(fixed_max_results=3))
- if finance_tools:
- tools.append(
- YFinanceTools(stock_price=True, company_info=True, analyst_recommendations=True, company_news=True)
- )
- if file_tools:
- tools.append(FileTools(base_dir=cwd))
- extra_instructions.append(
- "You can use the `read_file` tool to read a file, `save_file` to save a file, and `list_files` to list files in the working directory."
- )
-
- # Add team members available to the Agent
- team: List[Assistant] = []
- if data_analyst:
- _data_analyst = DuckDbAssistant(
- name="Data Analyst",
- llm=OpenAIChat(model=llm_id),
- role="Analyze movie data and provide insights",
- semantic_model=json.dumps(
- {
- "tables": [
- {
- "name": "movies",
- "description": "CSV of my favorite movies.",
- "path": "https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
- }
- ]
- }
- ),
- base_dir=scratch_dir,
- )
- team.append(_data_analyst)
- extra_instructions.append(
- "To answer questions about my favorite movies, delegate the task to the `Data Analyst`."
- )
- if python_assistant:
- _python_assistant = PythonAssistant(
- name="Python Assistant",
- llm=OpenAIChat(model=llm_id),
- role="Write and run python code",
- pip_install=True,
- charting_libraries=["streamlit"],
- base_dir=scratch_dir,
- )
- team.append(_python_assistant)
- extra_instructions.append("To write and run python code, delegate the task to the `Python Assistant`.")
- if research_assistant:
- _research_assistant = Assistant(
- name="Research Assistant",
- role="Write a research report on a given topic",
- llm=OpenAIChat(model=llm_id),
- description="You are a Senior New York Times researcher tasked with writing a cover story research report.",
- instructions=[
- "For a given topic, use the `search_exa` to get the top 10 search results.",
- "Carefully read the results and generate a final - NYT cover story worthy report in the <report_format> provided below.",
- "Make your report engaging, informative, and well-structured.",
- "Remember: you are writing for the New York Times, so the quality of the report is important.",
- ],
- expected_output=dedent(
- """\
- An engaging, informative, and well-structured report in the following format:
- <report_format>
- ## Title
-
- - **Overview** Brief introduction of the topic.
- - **Importance** Why is this topic significant now?
-
- ### Section 1
- - **Detail 1**
- - **Detail 2**
-
- ### Section 2
- - **Detail 1**
- - **Detail 2**
-
- ## Conclusion
- - **Summary of report:** Recap of the key findings from the report.
- - **Implications:** What these findings mean for the future.
-
- ## References
- - [Reference 1](Link to Source)
- - [Reference 2](Link to Source)
- </report_format>
- """
- ),
- tools=[ExaTools(num_results=5, text_length_limit=1000)],
- # This setting tells the LLM to format messages in markdown
- markdown=True,
- add_datetime_to_instructions=True,
- debug_mode=debug_mode,
- )
- team.append(_research_assistant)
- extra_instructions.append(
- "To write a research report, delegate the task to the `Research Assistant`. "
- "Return the report in the <report_format> to the user as is, without any additional text like 'here is the report'."
- )
- if investment_assistant:
- _investment_assistant = Assistant(
- name="Investment Assistant",
- role="Write a investment report on a given company (stock) symbol",
- llm=OpenAIChat(model=llm_id),
- description="You are a Senior Investment Analyst for Goldman Sachs tasked with writing an investment report for a very important client.",
- instructions=[
- "For a given stock symbol, get the stock price, company information, analyst recommendations, and company news",
- "Carefully read the research and generate a final - Goldman Sachs worthy investment report in the <report_format> provided below.",
- "Provide thoughtful insights and recommendations based on the research.",
- "When you share numbers, make sure to include the units (e.g., millions/billions) and currency.",
- "REMEMBER: This report is for a very important client, so the quality of the report is important.",
- ],
- expected_output=dedent(
- """\
- <report_format>
- ## [Company Name]: Investment Report
-
- ### **Overview**
- {give a brief introduction of the company and why the user should read this report}
- {make this section engaging and create a hook for the reader}
-
- ### Core Metrics
- {provide a summary of core metrics and show the latest data}
- - Current price: {current price}
- - 52-week high: {52-week high}
- - 52-week low: {52-week low}
- - Market Cap: {Market Cap} in billions
- - P/E Ratio: {P/E Ratio}
- - Earnings per Share: {EPS}
- - 50-day average: {50-day average}
- - 200-day average: {200-day average}
- - Analyst Recommendations: {buy, hold, sell} (number of analysts)
-
- ### Financial Performance
- {analyze the company's financial performance}
-
- ### Growth Prospects
- {analyze the company's growth prospects and future potential}
-
- ### News and Updates
- {summarize relevant news that can impact the stock price}
-
- ### [Summary]
- {give a summary of the report and what are the key takeaways}
-
- ### [Recommendation]
- {provide a recommendation on the stock along with a thorough reasoning}
-
- </report_format>
- """
- ),
- tools=[YFinanceTools(stock_price=True, company_info=True, analyst_recommendations=True, company_news=True)],
- # This setting tells the LLM to format messages in markdown
- markdown=True,
- add_datetime_to_instructions=True,
- debug_mode=debug_mode,
- )
- team.append(_investment_assistant)
- extra_instructions.extend(
- [
- "To get an investment report on a stock, delegate the task to the `Investment Assistant`. "
- "Return the report in the <report_format> to the user without any additional text like 'here is the report'.",
- "Answer any questions they may have using the information in the report.",
- "Never provide investment advise without the investment report.",
- ]
- )
-
- # Create the Agent
- agent = Assistant(
- name="agent",
- run_id=run_id,
- user_id=user_id,
- llm=OpenAIChat(model=llm_id),
- description=dedent(
- """\
- You are a powerful AI Agent called `Optimus Prime v7`.
- You have access to a set of tools and a team of AI Assistants at your disposal.
- Your goal is to assist the user in the best way possible.\
- """
- ),
- instructions=[
- "When the user sends a message, first **think** and determine if:\n"
- " - You can answer by using a tool available to you\n"
- " - You need to search the knowledge base\n"
- " - You need to search the internet\n"
- " - You need to delegate the task to a team member\n"
- " - You need to ask a clarifying question",
- "If the user asks about a topic, first ALWAYS search your knowledge base using the `search_knowledge_base` tool.",
- "If you dont find relevant information in your knowledge base, use the `duckduckgo_search` tool to search the internet.",
- "If the user asks to summarize the conversation or if you need to reference your chat history with the user, use the `get_chat_history` tool.",
- "If the users message is unclear, ask clarifying questions to get more information.",
- "Carefully read the information you have gathered and provide a clear and concise answer to the user.",
- "Do not use phrases like 'based on my knowledge' or 'depending on the information'.",
- "You can delegate tasks to an AI Assistant in your team depending of their role and the tools available to them.",
- ],
- extra_instructions=extra_instructions,
- # Add long-term memory to the Agent backed by a PostgreSQL database
- storage=PgAssistantStorage(table_name="agent_runs", db_url=db_url),
- # Add a knowledge base to the Agent
- knowledge_base=AssistantKnowledge(
- vector_db=PgVector2(
- db_url=db_url,
- collection="agent_documents",
- embedder=OpenAIEmbedder(model="text-embedding-3-small", dimensions=1536),
- ),
- # 3 references are added to the prompt when searching the knowledge base
- num_documents=3,
- ),
- # Add selected tools to the Agent
- tools=tools,
- # Add selected team members to the Agent
- team=team,
- # Show tool calls in the chat
- show_tool_calls=True,
- # This setting gives the LLM a tool to search the knowledge base for information
- search_knowledge=True,
- # This setting gives the LLM a tool to get chat history
- read_chat_history=True,
- # This setting adds chat history to the messages
- add_chat_history_to_messages=True,
- # This setting adds 4 previous messages from chat history to the messages sent to the LLM
- num_history_messages=4,
- # This setting tells the LLM to format messages in markdown
- markdown=True,
- # This setting adds the current datetime to the instructions
- add_datetime_to_instructions=True,
- # Add an introductory Assistant message
- introduction=dedent(
- """\
- Hi, I'm Optimus Prime v7, your powerful AI Assistant. Send me on my mission boss :statue_of_liberty:\
- """
- ),
- debug_mode=debug_mode,
- )
- return agent
diff --git a/cookbook/agents/app.py b/cookbook/agents/app.py
deleted file mode 100644
index 7dbc59cd67..0000000000
--- a/cookbook/agents/app.py
+++ /dev/null
@@ -1,301 +0,0 @@
-from typing import List
-
-import nest_asyncio
-import streamlit as st
-from phi.assistant import Assistant
-from phi.document import Document
-from phi.document.reader.pdf import PDFReader
-from phi.document.reader.website import WebsiteReader
-from phi.utils.log import logger
-
-from agent import get_agent # type: ignore
-
-nest_asyncio.apply()
-
-st.set_page_config(
- page_title="AI Agents",
- page_icon=":orange_heart:",
-)
-st.title("AI Agents")
-st.markdown("##### :orange_heart: built using [phidata](https://github.com/phidatahq/phidata)")
-
-
-def main() -> None:
- # Get LLM Model
- llm_id = st.sidebar.selectbox("Select LLM", options=["gpt-4o", "gpt-4-turbo"]) or "gpt-4o"
- # Set llm_id in session state
- if "llm_id" not in st.session_state:
- st.session_state["llm_id"] = llm_id
- # Restart the assistant if llm_id changes
- elif st.session_state["llm_id"] != llm_id:
- st.session_state["llm_id"] = llm_id
- restart_assistant()
-
- # Sidebar checkboxes for selecting tools
- st.sidebar.markdown("### Select Tools")
-
- # Enable Calculator
- if "calculator_enabled" not in st.session_state:
- st.session_state["calculator_enabled"] = True
- # Get calculator_enabled from session state if set
- calculator_enabled = st.session_state["calculator_enabled"]
- # Checkbox for enabling calculator
- calculator = st.sidebar.checkbox("Calculator", value=calculator_enabled, help="Enable calculator.")
- if calculator_enabled != calculator:
- st.session_state["calculator_enabled"] = calculator
- calculator_enabled = calculator
- restart_assistant()
-
- # Enable file tools
- if "file_tools_enabled" not in st.session_state:
- st.session_state["file_tools_enabled"] = True
- # Get file_tools_enabled from session state if set
- file_tools_enabled = st.session_state["file_tools_enabled"]
- # Checkbox for enabling shell tools
- file_tools = st.sidebar.checkbox("File Tools", value=file_tools_enabled, help="Enable file tools.")
- if file_tools_enabled != file_tools:
- st.session_state["file_tools_enabled"] = file_tools
- file_tools_enabled = file_tools
- restart_assistant()
-
- # Enable Web Search via DuckDuckGo
- if "ddg_search_enabled" not in st.session_state:
- st.session_state["ddg_search_enabled"] = True
- # Get ddg_search_enabled from session state if set
- ddg_search_enabled = st.session_state["ddg_search_enabled"]
- # Checkbox for enabling web search
- ddg_search = st.sidebar.checkbox("Web Search", value=ddg_search_enabled, help="Enable web search using DuckDuckGo.")
- if ddg_search_enabled != ddg_search:
- st.session_state["ddg_search_enabled"] = ddg_search
- ddg_search_enabled = ddg_search
- restart_assistant()
-
- # Enable finance tools
- if "finance_tools_enabled" not in st.session_state:
- st.session_state["finance_tools_enabled"] = True
- # Get finance_tools_enabled from session state if set
- finance_tools_enabled = st.session_state["finance_tools_enabled"]
- # Checkbox for enabling shell tools
- finance_tools = st.sidebar.checkbox("Yahoo Finance", value=finance_tools_enabled, help="Enable finance tools.")
- if finance_tools_enabled != finance_tools:
- st.session_state["finance_tools_enabled"] = finance_tools
- finance_tools_enabled = finance_tools
- restart_assistant()
-
- # Sidebar checkboxes for selecting team members
- st.sidebar.markdown("### Select Agent Team")
-
- # Enable Data Analyst
- if "data_analyst_enabled" not in st.session_state:
- st.session_state["data_analyst_enabled"] = True
- # Get data_analyst_enabled from session state if set
- data_analyst_enabled = st.session_state["data_analyst_enabled"]
- # Checkbox for enabling web search
- data_analyst = st.sidebar.checkbox(
- "Data Analyst",
- value=data_analyst_enabled,
- help="Enable the Data Analyst assistant for data related queries.",
- )
- if data_analyst_enabled != data_analyst:
- st.session_state["data_analyst_enabled"] = data_analyst
- data_analyst_enabled = data_analyst
- restart_assistant()
-
- # Enable Python Assistant
- if "python_assistant_enabled" not in st.session_state:
- st.session_state["python_assistant_enabled"] = True
- # Get python_assistant_enabled from session state if set
- python_assistant_enabled = st.session_state["python_assistant_enabled"]
- # Checkbox for enabling web search
- python_assistant = st.sidebar.checkbox(
- "Python Assistant",
- value=python_assistant_enabled,
- help="Enable the Python Assistant for writing and running python code.",
- )
- if python_assistant_enabled != python_assistant:
- st.session_state["python_assistant_enabled"] = python_assistant
- python_assistant_enabled = python_assistant
- restart_assistant()
-
- # Enable Research Assistant
- if "research_assistant_enabled" not in st.session_state:
- st.session_state["research_assistant_enabled"] = True
- # Get research_assistant_enabled from session state if set
- research_assistant_enabled = st.session_state["research_assistant_enabled"]
- # Checkbox for enabling web search
- research_assistant = st.sidebar.checkbox(
- "Research Assistant",
- value=research_assistant_enabled,
- help="Enable the research assistant (uses Exa).",
- )
- if research_assistant_enabled != research_assistant:
- st.session_state["research_assistant_enabled"] = research_assistant
- research_assistant_enabled = research_assistant
- restart_assistant()
-
- # Enable Investment Assistant
- if "investment_assistant_enabled" not in st.session_state:
- st.session_state["investment_assistant_enabled"] = True
- # Get investment_assistant_enabled from session state if set
- investment_assistant_enabled = st.session_state["investment_assistant_enabled"]
- # Checkbox for enabling web search
- investment_assistant = st.sidebar.checkbox(
- "Investment Assistant",
- value=investment_assistant_enabled,
- help="Enable the investment assistant. NOTE: This is not financial advice.",
- )
- if investment_assistant_enabled != investment_assistant:
- st.session_state["investment_assistant_enabled"] = investment_assistant
- investment_assistant_enabled = investment_assistant
- restart_assistant()
-
- # Get the agent
- agent: Assistant
- if "agent" not in st.session_state or st.session_state["agent"] is None:
- logger.info(f"---*--- Creating {llm_id} Agent ---*---")
- agent = get_agent(
- llm_id=llm_id,
- calculator=calculator_enabled,
- ddg_search=ddg_search_enabled,
- file_tools=file_tools_enabled,
- finance_tools=finance_tools_enabled,
- data_analyst=data_analyst_enabled,
- python_assistant=python_assistant_enabled,
- research_assistant=research_assistant_enabled,
- investment_assistant=investment_assistant_enabled,
- )
- st.session_state["agent"] = agent
- else:
- agent = st.session_state["agent"]
-
- # Create assistant run (i.e. log to database) and save run_id in session state
- try:
- st.session_state["agent_run_id"] = agent.create_run()
- except Exception:
- st.warning("Could not create Agent run, is the database running?")
- return
-
- # Load existing messages
- assistant_chat_history = agent.memory.get_chat_history()
- if len(assistant_chat_history) > 0:
- logger.debug("Loading chat history")
- st.session_state["messages"] = assistant_chat_history
- else:
- logger.debug("No chat history found")
- st.session_state["messages"] = [{"role": "assistant", "content": "Ask me questions..."}]
-
- # Prompt for user input
- if prompt := st.chat_input():
- st.session_state["messages"].append({"role": "user", "content": prompt})
-
- # Display existing chat messages
- for message in st.session_state["messages"]:
- if message["role"] == "system":
- continue
- with st.chat_message(message["role"]):
- st.write(message["content"])
-
- # If last message is from a user, generate a new response
- last_message = st.session_state["messages"][-1]
- if last_message.get("role") == "user":
- question = last_message["content"]
- with st.chat_message("assistant"):
- response = ""
- resp_container = st.empty()
- for delta in agent.run(question):
- response += delta # type: ignore
- resp_container.markdown(response)
- st.session_state["messages"].append({"role": "assistant", "content": response})
-
- # Load Agent knowledge base
- if agent.knowledge_base:
- # -*- Add websites to knowledge base
- if "url_scrape_key" not in st.session_state:
- st.session_state["url_scrape_key"] = 0
-
- input_url = st.sidebar.text_input(
- "Add URL to Knowledge Base", type="default", key=st.session_state["url_scrape_key"]
- )
- add_url_button = st.sidebar.button("Add URL")
- if add_url_button:
- if input_url is not None:
- alert = st.sidebar.info("Processing URLs...", icon="ℹ️")
- if f"{input_url}_scraped" not in st.session_state:
- scraper = WebsiteReader(max_links=2, max_depth=1)
- web_documents: List[Document] = scraper.read(input_url)
- if web_documents:
- agent.knowledge_base.load_documents(web_documents, upsert=True)
- else:
- st.sidebar.error("Could not read website")
- st.session_state[f"{input_url}_uploaded"] = True
- alert.empty()
-
- # Add PDFs to knowledge base
- if "file_uploader_key" not in st.session_state:
- st.session_state["file_uploader_key"] = 100
-
- uploaded_file = st.sidebar.file_uploader(
- "Add a PDF :page_facing_up:", type="pdf", key=st.session_state["file_uploader_key"]
- )
- if uploaded_file is not None:
- alert = st.sidebar.info("Processing PDF...", icon="🧠")
- auto_rag_name = uploaded_file.name.split(".")[0]
- if f"{auto_rag_name}_uploaded" not in st.session_state:
- reader = PDFReader()
- auto_rag_documents: List[Document] = reader.read(uploaded_file)
- if auto_rag_documents:
- agent.knowledge_base.load_documents(auto_rag_documents, upsert=True)
- else:
- st.sidebar.error("Could not read PDF")
- st.session_state[f"{auto_rag_name}_uploaded"] = True
- alert.empty()
-
- if agent.knowledge_base and agent.knowledge_base.vector_db:
- if st.sidebar.button("Clear Knowledge Base"):
- agent.knowledge_base.vector_db.clear()
- st.sidebar.success("Knowledge base cleared")
-
- # Show team member memory
- if agent.team and len(agent.team) > 0:
- for team_member in agent.team:
- if len(team_member.memory.chat_history) > 0:
- with st.status(f"{team_member.name} Memory", expanded=False, state="complete"):
- with st.container():
- _team_member_memory_container = st.empty()
- _team_member_memory_container.json(team_member.memory.get_llm_messages())
-
- if agent.storage:
- agent_run_ids: List[str] = agent.storage.get_all_run_ids()
- new_agent_run_id = st.sidebar.selectbox("Run ID", options=agent_run_ids)
- if st.session_state["agent_run_id"] != new_agent_run_id:
- logger.info(f"---*--- Loading {llm_id} run: {new_agent_run_id} ---*---")
- st.session_state["agent"] = get_agent(
- llm_id=llm_id,
- calculator=calculator_enabled,
- ddg_search=ddg_search_enabled,
- file_tools=file_tools_enabled,
- finance_tools=finance_tools_enabled,
- data_analyst=data_analyst_enabled,
- python_assistant=python_assistant_enabled,
- research_assistant=research_assistant_enabled,
- investment_assistant=investment_assistant_enabled,
- run_id=new_agent_run_id,
- )
- st.rerun()
-
- if st.sidebar.button("New Run"):
- restart_assistant()
-
-
-def restart_assistant():
- logger.debug("---*--- Restarting Assistant ---*---")
- st.session_state["agent"] = None
- st.session_state["agent_run_id"] = None
- if "url_scrape_key" in st.session_state:
- st.session_state["url_scrape_key"] += 1
- if "file_uploader_key" in st.session_state:
- st.session_state["file_uploader_key"] += 1
- st.rerun()
-
-
-main()
diff --git a/cookbook/agents/autonomous_rag.py b/cookbook/agents/autonomous_rag.py
new file mode 100644
index 0000000000..5661afc5eb
--- /dev/null
+++ b/cookbook/agents/autonomous_rag.py
@@ -0,0 +1,27 @@
+from rich.pretty import pprint # noqa
+from phi.agent import Agent, RunResponse # noqa
+from phi.model.openai import OpenAIChat
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.pgvector import PgVector, SearchType
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+vector_db = PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid)
+knowledge_base = PDFUrlKnowledgeBase(
+ urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+ vector_db=vector_db,
+)
+# Comment out after first run
+# knowledge_base.load(upsert=True)
+
+agent = Agent(
+ model=OpenAIChat(id="gpt-4o"),
+ knowledge=PDFUrlKnowledgeBase(
+ urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+ vector_db=vector_db,
+ ),
+ # Add a tool to search the knowledge base
+ search_knowledge=True,
+ show_tool_calls=True,
+ markdown=True,
+)
+agent.print_response("How do I make chicken and galangal in Coconut Milk Soup")
diff --git a/cookbook/agents/basic.py b/cookbook/agents/basic.py
index 1f4439a7a4..01d1ace843 100644
--- a/cookbook/agents/basic.py
+++ b/cookbook/agents/basic.py
@@ -1,52 +1,11 @@
-import asyncio # noqa
-from typing import Iterator # noqa
-from rich.pretty import pprint # noqa
from phi.agent import Agent, RunResponse # noqa
from phi.model.openai import OpenAIChat
-from phi.tools.yfinance import YFinanceTools
-from phi.storage.agent.postgres import PgAgentStorage
-agent = Agent(
- model=OpenAIChat(id="gpt-4o"),
- tools=[YFinanceTools(stock_price=True)],
- show_tool_calls=True,
- markdown=True,
- # debug_mode=True,
- # monitoring=False,
- storage=PgAgentStorage(table_name="agent_sessions", db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
-)
+agent = Agent(model=OpenAIChat(id="gpt-4o"), instructions=["Respond in a southern tone"], markdown=True)
-# run1: RunResponse = agent.run("What is the stock price of NVDA") # type: ignore
-# pprint(run1)
-# print("------------*******************------------")
-# print(run)
-# print("------------*******************------------")
-# print("------------*******************------------")
-# for m in run.messages:
-# print("---")
-# print(m)
-# print("---")
+# Get the response in a variable
+# run: RunResponse = agent.run("Explain simulation theory")
+# print(run.content)
-# run: RunResponse = agent.run("What is the stock price of NVDA")
-# pprint(run.content)
-
-run_stream: Iterator[RunResponse] = agent.run(
- "What is the stock price of NVDA", stream=True, stream_intermediate_steps=True
-)
-for chunk in run_stream:
- print("---")
- pprint(chunk.model_dump(exclude={"messages"}))
- print("---")
-
-
-# async def main():
-# run: RunResponse = await agent.arun("What is the stock price of NVDA and TSLA")
-# pprint(run)
-# # async for chunk in await agent.arun("What is the stock price of NVDA and TSLA", stream=True):
-# # print(chunk.content)
-#
-#
-# asyncio.run(main())
-
-# agent.print_response("What is the stock price of NVDA and TSLA?")
-# agent.print_response("What is the stock price of NVDA and TSLA?", stream=True)
+# Print the response in the terminal
+agent.print_response("Explain simulation theory")
diff --git a/cookbook/agents/finance.py b/cookbook/agents/finance.py
deleted file mode 100644
index 90c66bffc7..0000000000
--- a/cookbook/agents/finance.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from phi.assistant import Assistant
-from phi.llm.openai import OpenAIChat
-from phi.tools.yfinance import YFinanceTools
-
-assistant = Assistant(
- llm=OpenAIChat(model="gpt-4o"),
- tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
- show_tool_calls=True,
-)
-assistant.print_response("Compare NVDA to TSLA. Use every tool you have", markdown=True)
diff --git a/cookbook/agents/finance_agent.py b/cookbook/agents/finance_agent.py
new file mode 100644
index 0000000000..e38fff3263
--- /dev/null
+++ b/cookbook/agents/finance_agent.py
@@ -0,0 +1,13 @@
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.tools.yfinance import YFinanceTools
+
+finance_agent = Agent(
+ model=OpenAIChat(id="gpt-4o"),
+ tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+ show_tool_calls=True,
+)
+
+finance_agent.print_response(
+ "Write a report comparing NVDA to TSLA from an investment perspective. Use every tool you have", markdown=True
+)
diff --git a/cookbook/agents/image.py b/cookbook/agents/image.py
deleted file mode 100644
index 78ccc36d09..0000000000
--- a/cookbook/agents/image.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from rich.pretty import pprint # noqa
-from phi.agent import Agent, RunResponse
-from phi.model.openai import OpenAIChat
-
-agent = Agent(
- model=OpenAIChat(model="gpt-4o"),
- markdown=True,
- debug_mode=True,
-)
-
-# run: RunResponse = agent.run(
-# "What’s in this image?",
-# images=[
-# "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
-# ],
-# ) # type: ignore
-# run: RunResponse = agent.run(
-# "What’s in this image?",
-# images=[
-# {
-# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
-# "detail": "high",
-# }
-# ],
-# ) # type: ignore
-run: RunResponse = agent.run(
- "What are in these images? Is there any difference between them?",
- images=[
- "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
- "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
- ],
-) # type: ignore
-print(run.content)
-# pprint(run)
diff --git a/cookbook/agents/images.py b/cookbook/agents/images.py
new file mode 100644
index 0000000000..5e9d138060
--- /dev/null
+++ b/cookbook/agents/images.py
@@ -0,0 +1,15 @@
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+
+agent = Agent(
+ model=OpenAIChat(id="gpt-4o"),
+ markdown=True,
+)
+
+agent.print_response(
+ "What are in these images? Is there any difference between them?",
+ images=[
+ "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
+ "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
+ ],
+)
diff --git a/cookbook/agents/is_9_11_bigger_than_9_9.py b/cookbook/agents/is_9_11_bigger_than_9_9.py
new file mode 100644
index 0000000000..4d12c58c7e
--- /dev/null
+++ b/cookbook/agents/is_9_11_bigger_than_9_9.py
@@ -0,0 +1,13 @@
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.tools.calculator import Calculator
+
+agent = Agent(
+ model=OpenAIChat(id="gpt-4o"),
+ tools=[Calculator(add=True, subtract=True, multiply=True, divide=True)],
+ instructions=["Use the calculator tool for comparisons."],
+ show_tool_calls=True,
+ markdown=True,
+)
+agent.print_response("Is 9.11 bigger than 9.9?")
+agent.print_response("9.11 and 9.9 -- which is bigger?")
diff --git a/cookbook/agents/knowledge.py b/cookbook/agents/knowledge.py
deleted file mode 100644
index d18bdaf750..0000000000
--- a/cookbook/agents/knowledge.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from rich.pretty import pprint # noqa
-from phi.agent import Agent, RunResponse # noqa
-from phi.model.openai import OpenAIChat
-from phi.knowledge.pdf import PDFUrlKnowledgeBase
-from phi.vectordb.pgvector import PgVector, SearchType
-
-db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
-vector_db = PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid)
-
-agent = Agent(
- model=OpenAIChat(model="gpt-4o"),
- knowledge=PDFUrlKnowledgeBase(
- urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
- vector_db=vector_db,
- ),
- enable_rag=True,
- # search_knowledge=True,
- show_tool_calls=True,
- markdown=True,
- debug_mode=True,
-)
-# knowledge_base.load(recreate=True) # Comment out after first run
-# knowledge_base.load(recreate=True, upsert=True) # Comment out after first run
-# knowledge_base.load(upsert=True) # Comment out after first run
-
-# results = vector_db.vector_search("Gluai Buat Chi")
-# print("Vector search results:")
-# pprint([r.id for r in results])
-#
-# results = vector_db.keyword_search("Gluai Buat Chi")
-# print("Keyword search results:")
-# pprint([r.id for r in results])
-#
-# results = vector_db.hybrid_search("Gluai Buat Chi")
-# print("Hybrid search results:")
-# pprint([r.id for r in results])
-
-agent.print_response("How do i make Chicken and Galangal in Coconut Milk Soup")
-
-# run1: RunResponse = agent.run("How to make Gluai Buat Chi?") # type: ignore
-# pprint(run1)
diff --git a/cookbook/agents/rag.py b/cookbook/agents/rag.py
new file mode 100644
index 0000000000..732857c52e
--- /dev/null
+++ b/cookbook/agents/rag.py
@@ -0,0 +1,28 @@
+from rich.pretty import pprint # noqa
+from phi.agent import Agent, RunResponse # noqa
+from phi.model.openai import OpenAIChat
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.pgvector import PgVector, SearchType
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+vector_db = PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid)
+knowledge_base = PDFUrlKnowledgeBase(
+ urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+ vector_db=vector_db,
+)
+# Comment out after first run
+# knowledge_base.load(upsert=True)
+
+agent = Agent(
+ model=OpenAIChat(id="gpt-4o"),
+ knowledge=PDFUrlKnowledgeBase(
+ urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+ vector_db=vector_db,
+ ),
+ enable_rag=True,
+ # search_knowledge=True,
+ show_tool_calls=True,
+ markdown=True,
+ # debug_mode=True,
+)
+agent.print_response("How do I make chicken and galangal in Coconut Milk Soup")
diff --git a/cookbook/agents/requirements.in b/cookbook/agents/requirements.in
deleted file mode 100644
index f36a78fe1a..0000000000
--- a/cookbook/agents/requirements.in
+++ /dev/null
@@ -1,15 +0,0 @@
-bs4
-duckduckgo-search
-exa_py
-nest_asyncio
-openai
-pgvector
-phidata
-psycopg[binary]
-pypdf
-sqlalchemy
-streamlit
-yfinance
-duckdb
-pandas
-matplotlib
diff --git a/cookbook/agents/requirements.txt b/cookbook/agents/requirements.txt
deleted file mode 100644
index 2beea44642..0000000000
--- a/cookbook/agents/requirements.txt
+++ /dev/null
@@ -1,255 +0,0 @@
-#
-# This file is autogenerated by pip-compile with Python 3.11
-# by the following command:
-#
-# pip-compile cookbook/llm_os/requirements.in
-#
-altair==5.3.0
- # via streamlit
-annotated-types==0.6.0
- # via pydantic
-anyio==4.3.0
- # via
- # httpx
- # openai
-appdirs==1.4.4
- # via yfinance
-attrs==23.2.0
- # via
- # jsonschema
- # referencing
-beautifulsoup4==4.12.3
- # via
- # bs4
- # yfinance
-blinker==1.8.2
- # via streamlit
-bs4==0.0.2
- # via -r cookbook/llm_os/requirements.in
-cachetools==5.3.3
- # via streamlit
-certifi==2024.2.2
- # via
- # curl-cffi
- # httpcore
- # httpx
- # requests
-cffi==1.16.0
- # via curl-cffi
-charset-normalizer==3.3.2
- # via requests
-click==8.1.7
- # via
- # duckduckgo-search
- # streamlit
- # typer
-contourpy==1.2.1
- # via matplotlib
-curl-cffi==0.7.0b4
- # via duckduckgo-search
-cycler==0.12.1
- # via matplotlib
-distro==1.9.0
- # via openai
-duckdb==0.10.2
- # via -r cookbook/llm_os/requirements.in
-duckduckgo-search==5.3.1
- # via -r cookbook/llm_os/requirements.in
-exa-py==1.0.9
- # via -r cookbook/llm_os/requirements.in
-fonttools==4.51.0
- # via matplotlib
-frozendict==2.4.4
- # via yfinance
-gitdb==4.0.11
- # via gitpython
-gitpython==3.1.43
- # via
- # phidata
- # streamlit
-h11==0.14.0
- # via httpcore
-html5lib==1.1
- # via yfinance
-httpcore==1.0.5
- # via httpx
-httpx==0.27.0
- # via
- # openai
- # phidata
-idna==3.7
- # via
- # anyio
- # httpx
- # requests
-jinja2==3.1.4
- # via
- # altair
- # pydeck
-jsonschema==4.22.0
- # via altair
-jsonschema-specifications==2023.12.1
- # via jsonschema
-kiwisolver==1.4.5
- # via matplotlib
-lxml==5.2.1
- # via yfinance
-markdown-it-py==3.0.0
- # via rich
-markupsafe==2.1.5
- # via jinja2
-matplotlib==3.8.4
- # via -r cookbook/llm_os/requirements.in
-mdurl==0.1.2
- # via markdown-it-py
-multitasking==0.0.11
- # via yfinance
-nest-asyncio==1.6.0
- # via -r cookbook/llm_os/requirements.in
-numpy==1.26.4
- # via
- # altair
- # contourpy
- # matplotlib
- # pandas
- # pgvector
- # pyarrow
- # pydeck
- # streamlit
- # yfinance
-openai==1.28.1
- # via -r cookbook/llm_os/requirements.in
-orjson==3.10.3
- # via duckduckgo-search
-packaging==24.0
- # via
- # altair
- # matplotlib
- # streamlit
-pandas==2.2.2
- # via
- # -r cookbook/llm_os/requirements.in
- # altair
- # streamlit
- # yfinance
-peewee==3.17.5
- # via yfinance
-pgvector==0.2.5
- # via -r cookbook/llm_os/requirements.in
-phidata==2.4.20
- # via -r cookbook/llm_os/requirements.in
-pillow==10.3.0
- # via
- # matplotlib
- # streamlit
-protobuf==4.25.3
- # via streamlit
-psycopg[binary]==3.1.18
- # via -r cookbook/llm_os/requirements.in
-psycopg-binary==3.1.18
- # via psycopg
-pyarrow==16.0.0
- # via streamlit
-pycparser==2.22
- # via cffi
-pydantic==2.7.1
- # via
- # openai
- # phidata
- # pydantic-settings
-pydantic-core==2.18.2
- # via pydantic
-pydantic-settings==2.2.1
- # via phidata
-pydeck==0.9.1
- # via streamlit
-pygments==2.18.0
- # via rich
-pyparsing==3.1.2
- # via matplotlib
-pypdf==4.2.0
- # via -r cookbook/llm_os/requirements.in
-python-dateutil==2.9.0.post0
- # via
- # matplotlib
- # pandas
-python-dotenv==1.0.1
- # via
- # phidata
- # pydantic-settings
-pytz==2024.1
- # via
- # pandas
- # yfinance
-pyyaml==6.0.1
- # via phidata
-referencing==0.35.1
- # via
- # jsonschema
- # jsonschema-specifications
-requests==2.31.0
- # via
- # exa-py
- # streamlit
- # yfinance
-rich==13.7.1
- # via
- # phidata
- # streamlit
- # typer
-rpds-py==0.18.1
- # via
- # jsonschema
- # referencing
-shellingham==1.5.4
- # via typer
-six==1.16.0
- # via
- # html5lib
- # python-dateutil
-smmap==5.0.1
- # via gitdb
-sniffio==1.3.1
- # via
- # anyio
- # httpx
- # openai
-soupsieve==2.5
- # via beautifulsoup4
-sqlalchemy==2.0.30
- # via -r cookbook/llm_os/requirements.in
-streamlit==1.34.0
- # via -r cookbook/llm_os/requirements.in
-tenacity==8.3.0
- # via streamlit
-toml==0.10.2
- # via streamlit
-tomli==2.0.1
- # via phidata
-toolz==0.12.1
- # via altair
-tornado==6.4
- # via streamlit
-tqdm==4.66.4
- # via openai
-typer==0.12.3
- # via phidata
-typing-extensions==4.11.0
- # via
- # exa-py
- # openai
- # phidata
- # psycopg
- # pydantic
- # pydantic-core
- # sqlalchemy
- # streamlit
- # typer
-tzdata==2024.1
- # via pandas
-urllib3==1.26.18
- # via requests
-webencodings==0.5.1
- # via html5lib
-yfinance==0.2.38
- # via -r cookbook/llm_os/requirements.in
diff --git a/cookbook/agents/web_search.py b/cookbook/agents/web_search.py
index c7712892b2..82f3adcc7f 100644
--- a/cookbook/agents/web_search.py
+++ b/cookbook/agents/web_search.py
@@ -1,6 +1,6 @@
-from phi.assistant import Assistant
-from phi.llm.openai import OpenAIChat
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
from phi.tools.duckduckgo import DuckDuckGo
-assistant = Assistant(llm=OpenAIChat(model="gpt-4o"), tools=[DuckDuckGo()], show_tool_calls=True)
-assistant.print_response("Share 3 news stories from France", markdown=True)
+web_search_agent = Agent(model=OpenAIChat(id="gpt-4o"), tools=[DuckDuckGo()], show_tool_calls=True, markdown=True)
+web_search_agent.print_response("Share 3 news stories from France")
diff --git a/cookbook/providers/openai/agent.py b/cookbook/providers/openai/agent.py
index 94ab2c2375..63822d1c92 100644
--- a/cookbook/providers/openai/agent.py
+++ b/cookbook/providers/openai/agent.py
@@ -13,5 +13,5 @@
# run: RunResponse = agent.run("What is the stock price of NVDA and TSLA")
# print(run.content)
-# Print the response on the terminal
+# Print the response in the terminal
agent.print_response("What is the stock price of NVDA and TSLA")
diff --git a/cookbook/providers/openai/agent_stream.py b/cookbook/providers/openai/agent_stream.py
index 95fec0e9b5..6e0154ce35 100644
--- a/cookbook/providers/openai/agent_stream.py
+++ b/cookbook/providers/openai/agent_stream.py
@@ -15,5 +15,5 @@
# for chunk in run_response:
# print(chunk.content)
-# Print the response on the terminal
+# Print the response in the terminal
agent.print_response("What is the stock price of NVDA and TSLA", stream=True)
diff --git a/cookbook/providers/openai/basic.py b/cookbook/providers/openai/basic.py
index 79e7c20dd2..01d1ace843 100644
--- a/cookbook/providers/openai/basic.py
+++ b/cookbook/providers/openai/basic.py
@@ -7,5 +7,5 @@
# run: RunResponse = agent.run("Explain simulation theory")
# print(run.content)
-# Print the response on the terminal
+# Print the response in the terminal
agent.print_response("Explain simulation theory")
diff --git a/cookbook/providers/openai/basic_stream.py b/cookbook/providers/openai/basic_stream.py
index 821eedd45d..d865e98ea1 100644
--- a/cookbook/providers/openai/basic_stream.py
+++ b/cookbook/providers/openai/basic_stream.py
@@ -9,5 +9,5 @@
# for chunk in run_response:
# print(chunk.content)
-# Print the response on the terminal
+# Print the response in the terminal
agent.print_response("Explain simulation theory", stream=True)
diff --git a/phi/tools/yfinance.py b/phi/tools/yfinance.py
index 118b6bdb90..b46cd3e986 100644
--- a/phi/tools/yfinance.py
+++ b/phi/tools/yfinance.py
@@ -20,26 +20,27 @@ def __init__(
company_news: bool = False,
technical_indicators: bool = False,
historical_prices: bool = False,
+ enable_all_tools: bool = False,
):
super().__init__(name="yfinance_tools")
- if stock_price:
+ if stock_price or enable_all_tools:
self.register(self.get_current_stock_price)
- if company_info:
+ if company_info or enable_all_tools:
self.register(self.get_company_info)
- if stock_fundamentals:
+ if stock_fundamentals or enable_all_tools:
self.register(self.get_stock_fundamentals)
- if income_statements:
+ if income_statements or enable_all_tools:
self.register(self.get_income_statements)
- if key_financial_ratios:
+ if key_financial_ratios or enable_all_tools:
self.register(self.get_key_financial_ratios)
- if analyst_recommendations:
+ if analyst_recommendations or enable_all_tools:
self.register(self.get_analyst_recommendations)
- if company_news:
+ if company_news or enable_all_tools:
self.register(self.get_company_news)
- if technical_indicators:
+ if technical_indicators or enable_all_tools:
self.register(self.get_technical_indicators)
- if historical_prices:
+ if historical_prices or enable_all_tools:
self.register(self.get_historical_stock_prices)
def get_current_stock_price(self, symbol: str) -> str: