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<h1 align="center"> phidata </h1> <h3 align="center"> Build AI Assistants with memory, knowledge and tools </h3>

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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:

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 & knowledge make LLMs smarter while tools make them autonomous.

How it works

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

Examples

Assistant that can write and run python code

<details> <summary>Show code</summary>

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

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)

pip install pandas

python python_assistant.py
</details>

Assistant that can analyze data using SQL

<details> <summary>Show code</summary>

The DuckDbAssistant can perform data analysis using SQL.

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)

pip install duckdb

python data_assistant.py
</details>

Assistant that can generate pydantic models

<details> <summary>Show code</summary>

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.

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"))

python movie_assistant.py

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.'
)
</details>

PDF Assistant with Knowledge & Storage

<details> <summary>Show code</summary>

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
  1. Create PDF Assistant
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)
  1. Install libraries
pip install -U pgvector pypdf "psycopg[binary]" sqlalchemy
  1. Run PDF Assistant
python pdf_assistant.py

How do I make pad thai?

What was my last message?

See how the assistant now maintains storage across sessions.

python pdf_assistant.py --new
</details>

Checkout the cookbook for more examples.

Next Steps

  1. Read the <a href="https://docs.phidata.com/basics" target="_blank" rel="noopener noreferrer">basics</a> to learn more about phidata.
  2. Read about <a href="https://docs.phidata.com/assistants/introduction" target="_blank" rel="noopener noreferrer">Assistants</a> and how to customize them.
  3. Checkout the <a href="https://docs.phidata.com/examples/cookbook" target="_blank" rel="noopener noreferrer">cookbook</a> for in-depth examples and code.

Demos

Checkout the following AI Applications built using phidata:

Tutorials

LLM OS with gpt-4o

Building the LLM OS with gpt-4o

Autonomous RAG

Autonomous RAG

Local RAG with Llama3

Local RAG with Llama3

Llama3 Research Assistant powered by Groq

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

Roadmap

Our roadmap is available <a href="https://github.com/orgs/phidatahq/projects/2/views/1" target="_blank" rel="noopener noreferrer">here</a>. If you have a feature request, please open an issue/discussion.