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https://github.com/phidatahq/phidata
Build AI Assistants with function calling and connect LLMs to external tools.
https://github.com/phidatahq/phidata
ai aws data-engineering developer-tools docker gpt-4 llm llmops python
Last synced: 7 days ago
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Build AI Assistants with function calling and connect LLMs to external tools.
- Host: GitHub
- URL: https://github.com/phidatahq/phidata
- Owner: phidatahq
- License: mpl-2.0
- Created: 2022-05-04T15:23:02.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-14T22:13:53.000Z (7 months ago)
- Last Synced: 2024-04-15T08:14:23.055Z (7 months ago)
- Topics: ai, aws, data-engineering, developer-tools, docker, gpt-4, llm, llmops, python
- Language: Python
- Homepage: https://docs.phidata.com
- Size: 42.4 MB
- Stars: 2,881
- Watchers: 19
- Forks: 303
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
phidata
Build Agents with memory, knowledge, tools and reasoning
## What is phidata?
**Phidata is a framework for building agentic systems**, use phidata to:
- **Build Agents with memory, knowledge, tools and reasoning.** [examples](#web-search-agent)
- **Build teams of Agents that can work together.** [example](#team-of-agents)
- **Chat with your Agents using a beautiful Agent UI.** [example](#agent-ui)
- **Monitor, evaluate and optimize your Agents.** [example](#monitoring)
- **Build Agentic systems with an API, database and vectordb.**
## Install
```shell
pip install -U phidata
```
## Agents
### Web Search Agent
Let's start by building a simple agent that can search the web, create a file `web_search.py`
```python
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.duckduckgo import DuckDuckGo
web_agent = Agent(
name="Web Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[DuckDuckGo()],
instructions=["Always include sources"],
show_tool_calls=True,
markdown=True,
)
web_agent.print_response("Whats happening in France?", stream=True)
```
Install libraries, export your `OPENAI_API_KEY` and run the Agent:
```shell
pip install phidata openai duckduckgo-search
export OPENAI_API_KEY=sk-xxxx
python web_search.py
```
### Finance Agent
Lets create another agent that can query financial data, create a file `finance_agent.py`
```python
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.yfinance import YFinanceTools
finance_agent = Agent(
name="Finance Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
instructions=["Use tables to display data"],
show_tool_calls=True,
markdown=True,
)
finance_agent.print_response("Summarize analyst recommendations for NVDA", stream=True)
```
Install libraries and run the Agent:
```shell
pip install yfinance
python finance_agent.py
```
## Team of Agents
Now lets create a team of agents using the agents above, create a file `agent_team.py`
```python
from phi.agent import Agent
from phi.model.openai import OpenAIChat
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=OpenAIChat(id="gpt-4o"),
tools=[DuckDuckGo()],
instructions=["Always include sources"],
show_tool_calls=True,
markdown=True,
)
finance_agent = Agent(
name="Finance Agent",
role="Get financial data",
model=OpenAIChat(id="gpt-4o"),
tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
instructions=["Use tables to display data"],
show_tool_calls=True,
markdown=True,
)
agent_team = Agent(
team=[web_agent, finance_agent],
instructions=["Always include sources", "Use tables to display data"],
show_tool_calls=True,
markdown=True,
)
agent_team.print_response("Summarize analyst recommendations and share the latest news for NVDA", stream=True)
```
Run the Agent team:
```shell
python agent_team.py
```
## Reasoning Agents
Reasoning is an experimental feature that helps agents work through a problem step-by-step, backtracking and correcting as needed. Create a file `reasoning_agent.py`.
```python
from phi.agent import Agent
from phi.model.openai import OpenAIChat
task = (
"Three missionaries and three cannibals need to cross a river. "
"They have a boat that can carry up to two people at a time. "
"If, at any time, the cannibals outnumber the missionaries on either side of the river, the cannibals will eat the missionaries. "
"How can all six people get across the river safely? Provide a step-by-step solution and show the solutions as an ascii diagram"
)
reasoning_agent = Agent(model=OpenAIChat(id="gpt-4o"), reasoning=True, markdown=True, structured_outputs=True)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)
```
Run the Reasoning Agent:
```shell
python reasoning_agent.py
```
> [!WARNING]
> Reasoning is an experimental feature and will break ~20% of the time. **It is not a replacement for o1.**
>
> It is an experiment fueled by curiosity, combining COT and tool use. Set your expectations very low for this initial release. For example: It will not be able to count ‘r’s in ‘strawberry’.
> [!TIP]
> If using tools with `reasoning=True`, set `structured_outputs=False` because gpt-4o doesnt support tools with structured outputs.
## RAG Agent
Instead of always inserting the "context" into the prompt, the RAG Agent can search its knowledge base (vector db) for the specific information it needs to achieve its task.
This saves tokens and improves response quality. Create a file `rag_agent.py`
```python
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.embedder.openai import OpenAIEmbedder
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.lancedb import LanceDb, SearchType
# Create a knowledge base from a PDF
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
# Use LanceDB as the vector database
vector_db=LanceDb(
table_name="recipes",
uri="tmp/lancedb",
search_type=SearchType.vector,
embedder=OpenAIEmbedder(model="text-embedding-3-small"),
),
)
# Comment out after first run as the knowledge base is loaded
knowledge_base.load()
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
# Add the knowledge base to the agent
knowledge=knowledge_base,
show_tool_calls=True,
markdown=True,
)
agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
```
Install libraries and run the Agent:
```shell
pip install lancedb tantivy pypdf sqlalchemy
python rag_agent.py
```
## Agent UI
Phidata provides a beautiful UI for interacting with your agents. Let's take it for a spin, create a file `playground.py`
> [!NOTE]
> Phidata does not store any data, all agent data is stored locally in a sqlite database.
```python
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.storage.agent.sqlite import SqlAgentStorage
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.yfinance import YFinanceTools
from phi.playground import Playground, serve_playground_app
web_agent = Agent(
name="Web Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[DuckDuckGo()],
instructions=["Always include sources"],
storage=SqlAgentStorage(table_name="web_agent", db_file="agents.db"),
add_history_to_messages=True,
markdown=True,
)
finance_agent = Agent(
name="Finance Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
instructions=["Use tables to display data"],
storage=SqlAgentStorage(table_name="finance_agent", db_file="agents.db"),
add_history_to_messages=True,
markdown=True,
)
app = Playground(agents=[finance_agent, web_agent]).get_app()
if __name__ == "__main__":
serve_playground_app("playground:app", reload=True)
```
Authenticate with phidata:
```
phi auth
```
> [!NOTE]
> If `phi auth` fails, you can set the `PHI_API_KEY` environment variable by copying it from [phidata.app](https://www.phidata.app)
Install dependencies and run the Agent Playground:
```
pip install 'fastapi[standard]' sqlalchemy
python playground.py
```
- Open the link provided or navigate to `http://phidata.app/playground`
- Select the `localhost:7777` endpoint and start chatting with your agents!
## Demo Agents
The Agent Playground includes a few demo agents that you can test with. If you have recommendations for other demo agents, please let us know in our [community forum](https://community.phidata.com/).
## Monitoring & Debugging
### Monitoring
Phidata comes with built-in monitoring. You can set `monitoring=True` on any agent to track sessions or set `PHI_MONITORING=true` in your environment.
> [!NOTE]
> Run `phi auth` to authenticate your local account or export the `PHI_API_KEY`
```python
from phi.agent import Agent
agent = Agent(markdown=True, monitoring=True)
agent.print_response("Share a 2 sentence horror story")
```
Run the agent and monitor the results on [phidata.app/sessions](https://www.phidata.app/sessions)
```shell
# You can also set the environment variable
# export PHI_MONITORING=true
python monitoring.py
```
View the agent session on [phidata.app/sessions](https://www.phidata.app/sessions)
### Debugging
Phidata also includes a built-in debugger that will show debug logs in the terminal. You can set `debug_mode=True` on any agent to track sessions or set `PHI_DEBUG=true` in your environment.
```python
from phi.agent import Agent
agent = Agent(markdown=True, debug_mode=True)
agent.print_response("Share a 2 sentence horror story")
```
## Getting help
- Read the docs at docs.phidata.com
- Post your questions on the [community forum](https://community.phidata.com/)
- Chat with us on discord
## More examples
### Agent that can write and run python code
Show code
The `PythonAgent` can achieve tasks by writing and running python code.
- Create a file `python_agent.py`
```python
from phi.agent.python import PythonAgent
from phi.model.openai import OpenAIChat
from phi.file.local.csv import CsvFile
python_agent = PythonAgent(
model=OpenAIChat(id="gpt-4o"),
files=[
CsvFile(
path="https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
description="Contains information about movies from IMDB.",
)
],
markdown=True,
pip_install=True,
show_tool_calls=True,
)
python_agent.print_response("What is the average rating of movies?")
```
- Run the `python_agent.py`
```shell
python python_agent.py
```
### Agent that can analyze data using SQL
Show code
The `DuckDbAgent` can perform data analysis using SQL.
- Create a file `data_analyst.py`
```python
import json
from phi.model.openai import OpenAIChat
from phi.agent.duckdb import DuckDbAgent
data_analyst = DuckDbAgent(
model=OpenAIChat(model="gpt-4o"),
markdown=True,
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",
}
]
},
indent=2,
),
)
data_analyst.print_response(
"Show me a histogram of ratings. "
"Choose an appropriate bucket size but share how you chose it. "
"Show me the result as a pretty ascii diagram",
stream=True,
)
```
- Install duckdb and run the `data_analyst.py` file
```shell
pip install duckdb
python data_analyst.py
```
### Agent that can generate structured outputs
Show code
One of our favorite LLM features is generating structured data (i.e. a pydantic model) from text. Use this feature to extract features, generate data etc.
Let's create a Movie Agent to write a `MovieScript` for us, create a file `structured_output.py`
```python
from typing import List
from pydantic import BaseModel, Field
from phi.agent import Agent
from phi.model.openai import OpenAIChat
# Define a Pydantic model to enforce the structure of the output
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!")
# Agent that uses JSON mode
json_mode_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
description="You write movie scripts.",
response_model=MovieScript,
)
# Agent that uses structured outputs
structured_output_agent = Agent(
model=OpenAIChat(id="gpt-4o-2024-08-06"),
description="You write movie scripts.",
response_model=MovieScript,
structured_outputs=True,
)
json_mode_agent.print_response("New York")
structured_output_agent.print_response("New York")
```
- Run the `structured_output.py` file
```shell
python structured_output.py
```
- The output is an object of the `MovieScript` class, here's how it looks:
```shell
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.'
)
```
### Checkout the [cookbook](https://github.com/phidatahq/phidata/tree/main/cookbook) for more examples.
## Contributions
We're an open-source project and welcome contributions, please read the [contributing guide](https://github.com/phidatahq/phidata/blob/main/CONTRIBUTING.md) 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.
## Telemetry
Phidata logs which model an agent used so we can prioritize features for the most popular models.
You can disable this by setting `PHI_TELEMETRY=false` in your environment.