What is phidata?

Phidata is a framework for building Autonomous Assistants (aka Agents) that have long-term memory, contextual knowledge and the ability to take actions using function calling.

Use phidata to turn any LLM into an AI Assistant that can:

• Search the web using DuckDuckGo, Google etc.
• Analyze data using SQL, DuckDb, etc.
• Conduct research and generate reports.
• Answer questions from PDFs, APIs, etc.
• Write scripts for movies, books, etc.
• Summarize articles, videos, etc.
• Perform tasks like sending emails, querying databases, etc.
• And much more...

Why phidata?

Problem: We need to turn general-purpose LLMs into specialized assistants for our use-case.

Solution: Extend LLMs with memory, knowledge and tools:

• Memory: Stores chat history in a database and enables LLMs to have long-term conversations.
• Knowledge: Stores information in a vector database and provides LLMs with business context.
• Tools: Enable LLMs to take actions like pulling data from an API, sending emails or querying a database.

Memory & knowledge make LLMs smarter while tools make them autonomous.

How it works

• Step 1: Create an Assistant
• Step 2: Add Tools (functions), Knowledge (vectordb) and Storage (database)
• Step 3: Serve using Streamlit, FastApi or Django to build your AI application

Installation

pip install -U phidata

Quickstart

Assistant that can search the web

Create a file assistant.py

from phi.assistant import Assistant
from phi.tools.duckduckgo import DuckDuckGo

assistant = Assistant(tools=[DuckDuckGo()], show_tool_calls=True)
assistant.print_response("Whats happening in France?", markdown=True)

Install libraries, export your OPENAI_API_KEY and run the Assistant

pip install openai duckduckgo-search

export OPENAI_API_KEY=sk-xxxx

python assistant.py

Assistant that can query financial data

Create a file finance_assistant.py

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,
    markdown=True,
)
assistant.print_response("What is the stock price of NVDA")
assistant.print_response("Write a comparison between NVDA and AMD, use all tools available.")

Install libraries and run the Assistant

pip install yfinance

python finance_assistant.py

More information

• Read the docs at docs.phidata.com
• Chat with us on discord

Examples

• LLM OS: Using LLMs as the CPU for an emerging Operating System.
• Autonomous RAG: Gives LLMs tools to search its knowledge, web or chat history.
• Local RAG: Fully local RAG with Llama3 on Ollama and PgVector.
• Investment Researcher: Generate investment reports on stocks using Llama3 and Groq.
• News Articles: Write News Articles using Llama3 and Groq.
• Video Summaries: YouTube video summaries using Llama3 and Groq.
• Research Assistant: Write research reports using Llama3 and Groq.

Assistant that can write and run python code

The PythonAssistant can achieve tasks by writing and running python code.

• Create a file python_assistant.py

from phi.assistant.python import PythonAssistant
from phi.file.local.csv import CsvFile

python_assistant = PythonAssistant(
    files=[
        CsvFile(
            path="https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
            description="Contains information about movies from IMDB.",
        )
    ],
    pip_install=True,
    show_tool_calls=True,
)

python_assistant.print_response("What is the average rating of movies?", markdown=True)

• Install pandas and run the python_assistant.py

pip install pandas

python python_assistant.py

Assistant that can analyze data using SQL

The DuckDbAssistant can perform data analysis using SQL.

• Create a file data_assistant.py

import json
from phi.assistant.duckdb import DuckDbAssistant

duckdb_assistant = DuckDbAssistant(
    semantic_model=json.dumps({
        "tables": [
            {
                "name": "movies",
                "description": "Contains information about movies from IMDB.",
                "path": "https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
            }
        ]
    }),
)

duckdb_assistant.print_response("What is the average rating of movies? Show me the SQL.", markdown=True)

• Install duckdb and run the data_assistant.py file

pip install duckdb

python data_assistant.py

Assistant that can generate pydantic models

One of our favorite LLM features is generating structured data (i.e. a pydantic model) from text. Use this feature to extract features, generate movie scripts, produce fake data etc.

Let's create a Movie Assistant to write a MovieScript for us.

• Create a file movie_assistant.py

from typing import List
from pydantic import BaseModel, Field
from rich.pretty import pprint
from phi.assistant import Assistant

class MovieScript(BaseModel):
    setting: str = Field(..., description="Provide a nice setting for a blockbuster movie.")
    ending: str = Field(..., description="Ending of the movie. If not available, provide a happy ending.")
    genre: str = Field(..., description="Genre of the movie. If not available, select action, thriller or romantic comedy.")
    name: str = Field(..., description="Give a name to this movie")
    characters: List[str] = Field(..., description="Name of characters for this movie.")
    storyline: str = Field(..., description="3 sentence storyline for the movie. Make it exciting!")

movie_assistant = Assistant(
    description="You help write movie scripts.",
    output_model=MovieScript,
)

pprint(movie_assistant.run("New York"))

• Run the movie_assistant.py file

python movie_assistant.py

• The output is an object of the MovieScript class, here's how it looks:

MovieScript(
│   setting='A bustling and vibrant New York City',
│   ending='The protagonist saves the city and reconciles with their estranged family.',
│   genre='action',
│   name='City Pulse',
│   characters=['Alex Mercer', 'Nina Castillo', 'Detective Mike Johnson'],
│   storyline='In the heart of New York City, a former cop turned vigilante, Alex Mercer, teams up with a street-smart activist, Nina Castillo, to take down a corrupt political figure who threatens to destroy the city. As they navigate through the intricate web of power and deception, they uncover shocking truths that push them to the brink of their abilities. With time running out, they must race against the clock to save New York and confront their own demons.'
)

PDF Assistant with Knowledge & Storage

Lets create a PDF Assistant that can answer questions from a PDF. We'll use PgVector for knowledge and storage.

Knowledge Base: information that the Assistant can search to improve its responses (uses a vector db).

Storage: provides long term memory for Assistants (uses a database).

1. Run PgVector

Install docker desktop and run PgVector on port 5532 using:

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

2. Create PDF Assistant

• Create a file pdf_assistant.py

import typer
from typing import Optional, List
from phi.assistant import Assistant
from phi.storage.assistant.postgres import PgAssistantStorage
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector2

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector2(collection="recipes", db_url=db_url),
)
# Comment out after first run
knowledge_base.load()

storage = PgAssistantStorage(table_name="pdf_assistant", db_url=db_url)


def pdf_assistant(new: bool = False, user: str = "user"):
    run_id: Optional[str] = None

    if not new:
        existing_run_ids: List[str] = storage.get_all_run_ids(user)
        if len(existing_run_ids) > 0:
            run_id = existing_run_ids[0]

    assistant = Assistant(
        run_id=run_id,
        user_id=user,
        knowledge_base=knowledge_base,
        storage=storage,
        # Show tool calls in the response
        show_tool_calls=True,
        # Enable the assistant to search the knowledge base
        search_knowledge=True,
        # Enable the assistant to read the chat history
        read_chat_history=True,
    )
    if run_id is None:
        run_id = assistant.run_id
        print(f"Started Run: {run_id}\n")
    else:
        print(f"Continuing Run: {run_id}\n")

    # Runs the assistant as a cli app
    assistant.cli_app(markdown=True)


if __name__ == "__main__":
    typer.run(pdf_assistant)

3. Install libraries

pip install -U pgvector pypdf "psycopg[binary]" sqlalchemy

4. Run PDF Assistant

python pdf_assistant.py

• Ask a question:

How do I make pad thai?

• See how the Assistant searches the knowledge base and returns a response.

• Message bye to exit, start the assistant again using python pdf_assistant.py and ask:

What was my last message?

See how the assistant now maintains storage across sessions.

• Run the pdf_assistant.py file with the --new flag to start a new run.

python pdf_assistant.py --new

Checkout the cookbook for more examples.

Next Steps

1. Read the basics to learn more about phidata.
2. Read about Assistants and how to customize them.
3. Checkout the cookbook for in-depth examples and code.

Demos

Checkout the following AI Applications built using phidata:

• PDF AI that summarizes and answers questions from PDFs.
• ArXiv AI that answers questions about ArXiv papers using the ArXiv API.
• HackerNews AI summarize stories, users and shares what's new on HackerNews.

Tutorials

LLM OS with gpt-4o

Autonomous RAG

Local RAG with Llama3

Llama3 Research Assistant powered by Groq

Looking to build an AI product?

We've helped many companies build AI products, the general workflow is:

1. Build an Assistant with proprietary data to perform tasks specific to your product.
2. Connect your product to the Assistant via an API.
3. Monitor and Improve your AI product.

We also provide dedicated support and development, book a call to get started.

Contributions

We're an open-source project and welcome contributions, please read the contributing guide for more information.

Request a feature

• If you have a feature request, please open an issue or make a pull request.
• If you have ideas on how we can improve, please create a discussion.

Roadmap

Our roadmap is available here. If you have a feature request, please open an issue/discussion.