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@@ -54,7 +54,7 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
|
||||
- 📄 YAML Configuration
|
||||
- 💯 100+ LLM Support
|
||||
|
||||
## Using Code
|
||||
## Using Python Code
|
||||
|
||||
Light weight package dedicated for coding:
|
||||
```bash
|
||||
@@ -105,6 +105,19 @@ export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
praisonai --auto create a movie script about Robots in Mars
|
||||
```
|
||||
|
||||
## Using JavaScript Code
|
||||
|
||||
```bash
|
||||
npm install praisonai
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
|
||||
```javascript
|
||||
const { Agent } = require('praisonai');
|
||||
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
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agent.start('Write a movie script about a robot in Mars');
|
||||
```
|
||||
|
||||
## AI Agents Flow
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||||
|
||||
```mermaid
|
||||
@@ -551,6 +564,7 @@ uv pip install -r pyproject.toml --extra "crewai,autogen"
|
||||
| Topic | Video |
|
||||
|-------|--------|
|
||||
| AI Agents with Self Reflection | [](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
|
||||
| Reasoning Data Generating Agent | [](https://www.youtube.com/watch?v=fUT332Y2zA8) |
|
||||
| AI Agents with Reasoning | [](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
|
||||
| Multimodal AI Agents | [](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
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||||
| AI Agents Workflow | [](https://www.youtube.com/watch?v=yWTH44QPl2A) |
|
||||
@@ -570,4 +584,5 @@ uv pip install -r pyproject.toml --extra "crewai,autogen"
|
||||
| Training | [](https://www.youtube.com/watch?v=aLawE8kwCrI) |
|
||||
| Realtime Voice Interface | [](https://www.youtube.com/watch?v=frRHfevTCSw) |
|
||||
| Call Interface | [](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
|
||||
| Reasoning Extract Agents | [](https://www.youtube.com/watch?v=2PPamsADjJA) |
|
||||
|
||||
@@ -1,4 +0,0 @@
|
||||
"""Agent module for AI agents"""
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from .agent import Agent
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__all__ = ['Agent']
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@@ -1,6 +1,6 @@
|
||||
FROM python:3.11-slim
|
||||
WORKDIR /app
|
||||
COPY . .
|
||||
RUN pip install flask praisonai==2.0.60 gunicorn markdown
|
||||
RUN pip install flask praisonai==2.0.63 gunicorn markdown
|
||||
EXPOSE 8080
|
||||
CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
|
||||
@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
|
||||
file.write("FROM python:3.11-slim\n")
|
||||
file.write("WORKDIR /app\n")
|
||||
file.write("COPY . .\n")
|
||||
file.write("RUN pip install flask praisonai==2.0.60 gunicorn markdown\n")
|
||||
file.write("RUN pip install flask praisonai==2.0.63 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
|
||||
|
||||
|
||||
@@ -0,0 +1,113 @@
|
||||
---
|
||||
title: "Command Line Interface"
|
||||
sidebarTitle: "CLI"
|
||||
description: "Use PraisonAI directly from your terminal with simple commands"
|
||||
icon: "terminal"
|
||||
---
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
Start((🚀)) --> CLI[CLI Command]
|
||||
CLI --> Agent[AI Agent]
|
||||
Agent --> Config[Config/YAML]
|
||||
Config --> Out((🏁))
|
||||
|
||||
style Start fill:#8B0000,color:#fff,stroke:#8B0000
|
||||
style CLI fill:#2E8B57,color:#fff
|
||||
style Agent fill:#2E8B57,color:#fff
|
||||
style Config fill:#2E8B57,color:#fff
|
||||
style Out fill:#8B0000,color:#fff,stroke:#8B0000
|
||||
```
|
||||
|
||||
PraisonAI CLI provides a simple way to interact with AI agents directly from your terminal. You can run quick commands, specify LLM options, or use YAML configuration files for more complex scenarios.
|
||||
|
||||
## Quick Start
|
||||
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
Install the PraisonAI package:
|
||||
```bash
|
||||
pip install praisonai
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Set API Key">
|
||||
Set your OpenAI API key as an environment variable:
|
||||
```bash
|
||||
export OPENAI_API_KEY=your_api_key_here
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Usage Examples
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Simple Command" icon="terminal" defaultOpen>
|
||||
Run a simple command directly:
|
||||
```bash
|
||||
praisonai "write a movie script in 3 lines"
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="With LLM Option" icon="wand-magic-sparkles" defaultOpen>
|
||||
Specify a different LLM model:
|
||||
```bash
|
||||
praisonai "write a movie script in 3 lines" --llm gpt-4-mini
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Using YAML Config" icon="file-code" defaultOpen>
|
||||
Run agents defined in a YAML file:
|
||||
```bash
|
||||
praisonai agents.yaml
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Configuration
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Create Config">
|
||||
Initialize a new agents.yaml file for your project:
|
||||
```bash
|
||||
praisonai --init "Create a movie script about AI"
|
||||
```
|
||||
This will create an `agents.yaml` file with predefined configuration for your task.
|
||||
</Tab>
|
||||
|
||||
<Tab title="Auto Mode">
|
||||
Let PraisonAI automatically create and configure agents:
|
||||
```bash
|
||||
praisonai --auto "Create a movie script about AI"
|
||||
```
|
||||
This mode will analyze your task and set up appropriate agents automatically.
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Features
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Simple Commands" icon="terminal">
|
||||
Run AI tasks directly from your terminal with simple commands.
|
||||
</Card>
|
||||
<Card title="LLM Options" icon="wand-magic-sparkles">
|
||||
Choose from different LLM models for your specific needs.
|
||||
</Card>
|
||||
<Card title="YAML Support" icon="file-code">
|
||||
Use YAML files for complex agent configurations and workflows.
|
||||
</Card>
|
||||
<Card title="Auto Configuration" icon="robot">
|
||||
Automatic agent setup based on task requirements.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Next Steps
|
||||
|
||||
<CardGroup>
|
||||
<Card title="YAML Configuration" icon="file-code" href="/config">
|
||||
Learn more about YAML configuration options
|
||||
</Card>
|
||||
<Card title="API Reference" icon="code" href="/api-reference">
|
||||
View the complete API documentation
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -40,6 +40,7 @@ Chain-of-Thought (CoT) Generation is a process where AI agents create detailed,
|
||||
Set your OpenAI API key as an environment variable in your terminal:
|
||||
```bash
|
||||
export OPENAI_API_KEY=your_api_key_here
|
||||
export HF_TOKEN=your_huggingface_token_here
|
||||
```
|
||||
</Step>
|
||||
|
||||
@@ -190,6 +191,14 @@ agents = PraisonAIAgents(
|
||||
agents.start()
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Run the application">
|
||||
Execute the Python script to start generating chain-of-thought data:
|
||||
```bash
|
||||
python app.py
|
||||
```
|
||||
</Step>
|
||||
|
||||
</Steps>
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,126 @@
|
||||
---
|
||||
title: "Image Generation AI Agents"
|
||||
sidebarTitle: "Image Generation"
|
||||
description: "Generate high-quality images using PraisonAI Image Agents with both synchronous and asynchronous capabilities."
|
||||
icon: "image"
|
||||
---
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
In[Start] --> Process[Image Description]
|
||||
Process --> Agent[Image Agent]
|
||||
Agent --> Out[Image URL]
|
||||
|
||||
style In fill:#8B0000,color:#fff
|
||||
style Process fill:#2E8B57,color:#fff
|
||||
style Agent fill:#2E8B57,color:#fff
|
||||
style Out fill:#8B0000,color:#fff
|
||||
```
|
||||
|
||||
Image Generation in PraisonAI allows you to create high-quality images using natural language descriptions. The Image Agent supports both synchronous and asynchronous operations, making it flexible for various use cases.
|
||||
|
||||
## Quick Start
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Code">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
First, install the PraisonAI Agents package with LLM support:
|
||||
```bash
|
||||
pip install "praisonaiagents[llm]"
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Set API Key">
|
||||
Set your OpenAI API key as an environment variable:
|
||||
```bash
|
||||
export OPENAI_API_KEY=your_api_key_here
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Create Agent">
|
||||
Create a new file `image_gen.py` with the basic setup:
|
||||
```python
|
||||
from praisonaiagents.agent.image_agent import ImageAgent
|
||||
|
||||
# Create an image agent
|
||||
agent = ImageAgent(
|
||||
llm="dall-e-3",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Generate an image
|
||||
result = agent.chat("A cute baby sea otter playing with a laptop")
|
||||
print("Image generation result:", result)
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
|
||||
<Tab title="Async">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
First, install the PraisonAI Agents package with LLM support:
|
||||
```bash
|
||||
pip install "praisonaiagents[llm]"
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Set API Key">
|
||||
Set your OpenAI API key as an environment variable:
|
||||
```bash
|
||||
export OPENAI_API_KEY=your_api_key_here
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Create Async Agent">
|
||||
Create a new file `image_gen_async.py` with the async setup:
|
||||
```python
|
||||
import asyncio
|
||||
from praisonaiagents import ImageAgent
|
||||
|
||||
async def main():
|
||||
agent = ImageAgent(
|
||||
name="ImageCreator",
|
||||
llm="dall-e-3",
|
||||
style="natural"
|
||||
)
|
||||
|
||||
result = await agent.achat("A cute baby sea otter playing with a laptop")
|
||||
print(f"Image generation result: {result}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Features
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="DALL-E Integration" icon="image">
|
||||
Seamless integration with DALL-E for high-quality image generation.
|
||||
</Card>
|
||||
<Card title="Async Support" icon="bolt">
|
||||
Asynchronous operations for better performance in concurrent environments.
|
||||
</Card>
|
||||
<Card title="Natural Style" icon="paintbrush">
|
||||
Generate images with natural, realistic styling options.
|
||||
</Card>
|
||||
<Card title="Verbose Mode" icon="message">
|
||||
Detailed output logging for better debugging and monitoring.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Next Steps
|
||||
|
||||
<CardGroup>
|
||||
<Card title="Agents Overview" icon="robot" href="/agents">
|
||||
Learn more about PraisonAI Agents and their capabilities
|
||||
</Card>
|
||||
<Card title="API Reference" icon="code" href="/api-reference">
|
||||
View the complete API documentation
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -138,6 +138,48 @@ agents.start()
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
```bash
|
||||
npm install praisonai
|
||||
```
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
```bash
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
</Step>
|
||||
<Step title="Create File">
|
||||
Create `app.js` file
|
||||
|
||||
## Code Example
|
||||
|
||||
<CodeGroup>
|
||||
```javascript Single Agent
|
||||
const { Agent } = require('praisonai');
|
||||
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
|
||||
agent.start('Write a movie script about a robot in Mars');
|
||||
```
|
||||
|
||||
```javascript Multi Agents
|
||||
const { Agent, PraisonAIAgents } = require('praisonai');
|
||||
|
||||
const researchAgent = new Agent({ instructions: 'Research about AI' });
|
||||
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });
|
||||
|
||||
const agents = new PraisonAIAgents({ agents: [researchAgent, summariseAgent] });
|
||||
agents.start();
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Run Script">
|
||||
```bash
|
||||
node app.js
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## AI Agents Flow
|
||||
@@ -620,6 +662,7 @@ flowchart LR
|
||||
| Topic | Video |
|
||||
|-------|--------|
|
||||
| AI Agents with Self Reflection | [](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
|
||||
| Reasoning Data Generating Agent | [](https://www.youtube.com/watch?v=fUT332Y2zA8) |
|
||||
| AI Agents with Reasoning | [](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
|
||||
| Multimodal AI Agents | [](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
|
||||
| AI Agents Workflow | [](https://www.youtube.com/watch?v=yWTH44QPl2A) |
|
||||
@@ -639,3 +682,4 @@ flowchart LR
|
||||
| Training | [](https://www.youtube.com/watch?v=aLawE8kwCrI) |
|
||||
| Realtime Voice Interface | [](https://www.youtube.com/watch?v=frRHfevTCSw) |
|
||||
| Call Interface | [](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
|
||||
| Reasoning Extract Agents | [](https://www.youtube.com/watch?v=2PPamsADjJA) |
|
||||
@@ -11,6 +11,9 @@ pip install praisonaiagents
|
||||
```bash No Code
|
||||
pip install praisonai
|
||||
```
|
||||
```bash JavaScript
|
||||
npm install praisonai
|
||||
```
|
||||
</RequestExample>
|
||||
|
||||
<Note>
|
||||
@@ -90,6 +93,22 @@ Follow these steps to set up PraisonAI in your development environment.
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
<Steps>
|
||||
<Step title="Install PraisonAI">
|
||||
Install the PraisonAI package:
|
||||
```bash
|
||||
npm install praisonai
|
||||
```
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
Set your OpenAI API key as an environment variable in your terminal:
|
||||
```bash
|
||||
export OPENAI_API_KEY=your_openai_key
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
|
||||
|
||||
@@ -206,6 +206,48 @@ praisonai --init "your task description"
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
```bash
|
||||
npm install praisonai
|
||||
```
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
```bash
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
</Step>
|
||||
<Step title="Create File">
|
||||
Create `app.js` file
|
||||
|
||||
## Code Example
|
||||
|
||||
<CodeGroup>
|
||||
```javascript Single Agent
|
||||
const { Agent } = require('praisonai');
|
||||
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
|
||||
agent.start('Write a movie script about a robot in Mars');
|
||||
```
|
||||
|
||||
```javascript Multi Agents
|
||||
const { Agent, PraisonAIAgents } = require('praisonai');
|
||||
|
||||
const researchAgent = new Agent({ instructions: 'Research about AI' });
|
||||
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });
|
||||
|
||||
const agents = new PraisonAIAgents({ agents: [researchAgent, summariseAgent] });
|
||||
agents.start();
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Run Script">
|
||||
```bash
|
||||
node app.js
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Key Features
|
||||
|
||||
@@ -0,0 +1,38 @@
|
||||
---
|
||||
title: "Development Setup"
|
||||
sidebarTitle: "Development"
|
||||
description: "Set up your development environment for PraisonAI JavaScript framework"
|
||||
icon: "code"
|
||||
---
|
||||
|
||||
## Development Setup
|
||||
|
||||
<Steps>
|
||||
<Step title="Clone Repository">
|
||||
```bash
|
||||
git clone https://github.com/MervinPraison/PraisonAI.git
|
||||
cd src/praisonai-ts
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Install Dependencies">
|
||||
```bash
|
||||
npm install
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Build Package">
|
||||
```bash
|
||||
npm run build
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Package Structure
|
||||
|
||||
```
|
||||
src/
|
||||
├── agent/ # Agent-related interfaces and implementations
|
||||
├── task/ # Task management and execution
|
||||
├── utils/ # Utility functions and helpers
|
||||
└── types/ # TypeScript type definitions
|
||||
+214
@@ -0,0 +1,214 @@
|
||||
---
|
||||
title: "JavaScript AI Agents Framework"
|
||||
sidebarTitle: "JavaScript Agents"
|
||||
description: "A production-ready Multi AI Agents framework for JavaScript"
|
||||
icon: "js"
|
||||
---
|
||||
|
||||
PraisonAI is a production-ready Multi AI Agents framework for JavaScript, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
%% Define the main flow
|
||||
Start([▶ Start]) --> Agent1
|
||||
Agent1 --> Process[⚙ Process]
|
||||
Process --> Agent2
|
||||
Agent2 --> Output([✓ Output])
|
||||
Process -.-> Agent1
|
||||
|
||||
%% Define subgraphs for agents and their tasks
|
||||
subgraph Agent1[ ]
|
||||
Task1[📋 Task]
|
||||
AgentIcon1[🤖 AI Agent]
|
||||
Tools1[🔧 Tools]
|
||||
|
||||
Task1 --- AgentIcon1
|
||||
AgentIcon1 --- Tools1
|
||||
end
|
||||
|
||||
subgraph Agent2[ ]
|
||||
Task2[📋 Task]
|
||||
AgentIcon2[🤖 AI Agent]
|
||||
Tools2[🔧 Tools]
|
||||
|
||||
Task2 --- AgentIcon2
|
||||
AgentIcon2 --- Tools2
|
||||
end
|
||||
|
||||
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
|
||||
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
|
||||
classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
|
||||
classDef transparent fill:none,stroke:none
|
||||
|
||||
class Start,Output,Task1,Task2 input
|
||||
class Process,AgentIcon1,AgentIcon2 process
|
||||
class Tools1,Tools2 tools
|
||||
class Agent1,Agent2 transparent
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="JavaScript">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
<CodeGroup>
|
||||
```bash npm
|
||||
npm install praisonai
|
||||
```
|
||||
```bash yarn
|
||||
yarn add praisonai
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
```bash
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
</Step>
|
||||
<Step title="Create File">
|
||||
Create `app.js` file
|
||||
|
||||
## Code Example
|
||||
|
||||
<CodeGroup>
|
||||
```javascript Single Agent
|
||||
const { Agent } = require('praisonai');
|
||||
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
|
||||
agent.start('Write a movie script about a robot in Mars');
|
||||
```
|
||||
|
||||
```javascript Multi Agents
|
||||
const { Agent, PraisonAIAgents } = require('praisonai');
|
||||
|
||||
const researchAgent = new Agent({ instructions: 'Research about AI' });
|
||||
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });
|
||||
|
||||
const agents = new PraisonAIAgents({ agents: [researchAgent, summariseAgent] });
|
||||
agents.start();
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Run Script">
|
||||
```bash
|
||||
node app.js
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
## Usage Examples
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Single Agent Example" icon="user" defaultOpen>
|
||||
Create and run a single agent to perform a specific task:
|
||||
|
||||
```javascript
|
||||
const { Agent } = require('praisonai');
|
||||
|
||||
// Create a simple science explainer agent
|
||||
const agent = new Agent({
|
||||
instructions: "You are a science expert who explains complex phenomena in simple terms.",
|
||||
name: "ScienceExplainer",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Ask a question
|
||||
agent.start("Why is the sky blue?")
|
||||
.then(response => {
|
||||
console.log('\nExplanation:');
|
||||
console.log(response);
|
||||
})
|
||||
.catch(error => console.error('Error:', error));
|
||||
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Multi-Agent Example" icon="users" defaultOpen>
|
||||
Create and run multiple agents working together:
|
||||
|
||||
```javascript
|
||||
const { PraisonAIAgents, Agent } = require('praisonai');
|
||||
|
||||
// Create a story agent and a summary agent
|
||||
const storyAgent = new Agent({
|
||||
instructions: "You are a creative storyteller. Create engaging stories.",
|
||||
name: "Storyteller"
|
||||
});
|
||||
|
||||
const summaryAgent = new Agent({
|
||||
instructions: "You summarize stories into brief, engaging summaries.",
|
||||
name: "Summarizer"
|
||||
});
|
||||
|
||||
// Create multi-agent system
|
||||
const agents = new PraisonAIAgents({
|
||||
agents: [storyAgent, summaryAgent],
|
||||
tasks: [
|
||||
"Create a short story about a magical forest",
|
||||
"Summarize the story in 2 sentences"
|
||||
]
|
||||
});
|
||||
|
||||
// Run the agents
|
||||
agents.start()
|
||||
.then(responses => {
|
||||
console.log('\nStory:');
|
||||
console.log(responses[0]);
|
||||
console.log('\nSummary:');
|
||||
console.log(responses[1]);
|
||||
})
|
||||
.catch(error => console.error('Error:', error));
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Task-Based Agent Example" icon="list-check" defaultOpen>
|
||||
Create agents with specific tasks and dependencies:
|
||||
|
||||
```javascript
|
||||
const { Agent, Task } = require('praisonai');
|
||||
|
||||
// Create a task-based agent
|
||||
const agent = new Agent({
|
||||
name: "TaskMaster",
|
||||
role: "Assistant",
|
||||
goal: "Complete tasks efficiently",
|
||||
backstory: "You are an AI assistant that helps complete tasks step by step."
|
||||
});
|
||||
|
||||
// Create a task with dependencies
|
||||
const mainTask = new Task({
|
||||
name: "Write Blog Post",
|
||||
description: "Write a blog post about artificial intelligence",
|
||||
expected_output: "A complete blog post",
|
||||
dependencies: []
|
||||
});
|
||||
|
||||
// Execute the task
|
||||
agent.execute(mainTask)
|
||||
.then(response => {
|
||||
console.log('\nBlog Post:');
|
||||
console.log(response);
|
||||
})
|
||||
.catch(error => console.error('Error:', error));
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Running the Examples
|
||||
|
||||
<Steps>
|
||||
<Step title="Set Environment Variables">
|
||||
```bash
|
||||
export OPENAI_API_KEY='your-api-key'
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Create Example File">
|
||||
Create a new JavaScript file (e.g., `app.js`) with any of the above examples.
|
||||
</Step>
|
||||
|
||||
<Step title="Run the Example">
|
||||
```bash
|
||||
node app.js
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
@@ -0,0 +1,251 @@
|
||||
---
|
||||
title: "Node.js AI Agents Framework"
|
||||
sidebarTitle: "Node.js Agents"
|
||||
description: "A production-ready Multi AI Agents framework for Node.js"
|
||||
icon: "node-js"
|
||||
---
|
||||
|
||||
PraisonAI is a production-ready Multi AI Agents framework for Node.js, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
%% Define the main flow
|
||||
Start([▶ Start]) --> Agent1
|
||||
Agent1 --> Process[⚙ Process]
|
||||
Process --> Agent2
|
||||
Agent2 --> Output([✓ Output])
|
||||
Process -.-> Agent1
|
||||
|
||||
%% Define subgraphs for agents and their tasks
|
||||
subgraph Agent1[ ]
|
||||
Task1[📋 Task]
|
||||
AgentIcon1[🤖 AI Agent]
|
||||
Tools1[🔧 Tools]
|
||||
|
||||
Task1 --- AgentIcon1
|
||||
AgentIcon1 --- Tools1
|
||||
end
|
||||
|
||||
subgraph Agent2[ ]
|
||||
Task2[📋 Task]
|
||||
AgentIcon2[🤖 AI Agent]
|
||||
Tools2[🔧 Tools]
|
||||
|
||||
Task2 --- AgentIcon2
|
||||
AgentIcon2 --- Tools2
|
||||
end
|
||||
|
||||
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
|
||||
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
|
||||
classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
|
||||
classDef transparent fill:none,stroke:none
|
||||
|
||||
class Start,Output,Task1,Task2 input
|
||||
class Process,AgentIcon1,AgentIcon2 process
|
||||
class Tools1,Tools2 tools
|
||||
class Agent1,Agent2 transparent
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Node.js">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
<CodeGroup>
|
||||
```bash npm
|
||||
npm install praisonai
|
||||
```
|
||||
```bash yarn
|
||||
yarn add praisonai
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
```bash
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
</Step>
|
||||
<Step title="Create File">
|
||||
Create `app.js` file
|
||||
|
||||
## Code Example
|
||||
|
||||
<CodeGroup>
|
||||
```javascript Single Agent
|
||||
const { Agent } = require('praisonai');
|
||||
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
|
||||
agent.start('Write a movie script about a robot in Mars');
|
||||
```
|
||||
|
||||
```javascript Multi Agents
|
||||
const { Agent, PraisonAIAgents } = require('praisonai');
|
||||
|
||||
const researchAgent = new Agent({ instructions: 'Research about AI' });
|
||||
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });
|
||||
|
||||
const agents = new PraisonAIAgents({ agents: [researchAgent, summariseAgent] });
|
||||
agents.start();
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Run Script">
|
||||
```bash
|
||||
node app.js
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Usage Examples
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Single Agent Example" icon="user" defaultOpen>
|
||||
Create and run a single agent to perform a specific task:
|
||||
|
||||
```typescript
|
||||
import { Agent } from 'praisonai';
|
||||
|
||||
// Single agent example - Science Explainer
|
||||
const agent = new Agent({
|
||||
instructions: `You are a science expert who explains complex phenomena in simple terms.
|
||||
Provide clear, accurate, and easy-to-understand explanations.`,
|
||||
name: "ScienceExplainer",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
agent.start("Why is the sky blue?")
|
||||
.then(response => {
|
||||
console.log('\nExplanation:');
|
||||
console.log(response);
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error:', error);
|
||||
});
|
||||
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Multi-Agent Example" icon="users" defaultOpen>
|
||||
Create and run multiple agents working together:
|
||||
|
||||
```typescript
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
// Create story agent
|
||||
const storyAgent = new Agent({
|
||||
instructions: "You are a storyteller. Write a very short story (2-3 sentences) about a given topic.",
|
||||
name: "StoryAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create summary agent
|
||||
const summaryAgent = new Agent({
|
||||
instructions: "You are an editor. Create a one-sentence summary of the given story.",
|
||||
name: "SummaryAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create and start agents
|
||||
const agents = new PraisonAIAgents({
|
||||
agents: [storyAgent, summaryAgent],
|
||||
tasks: [
|
||||
"Write a short story about a cat",
|
||||
"{previous_result}" // This will be replaced with the story
|
||||
],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
agents.start()
|
||||
.then(results => {
|
||||
console.log('\nStory:', results[0]);
|
||||
console.log('\nSummary:', results[1]);
|
||||
})
|
||||
.catch(error => console.error('Error:', error));
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Task-Based Agent Example" icon="list-check" defaultOpen>
|
||||
Create agents with specific tasks and dependencies:
|
||||
|
||||
```typescript
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
// Create recipe agent
|
||||
const recipeAgent = new Agent({
|
||||
instructions: `You are a professional chef and nutritionist. Create 5 healthy food recipes that are both nutritious and delicious.
|
||||
Each recipe should include:
|
||||
1. Recipe name
|
||||
2. List of ingredients with quantities
|
||||
3. Step-by-step cooking instructions
|
||||
4. Nutritional information
|
||||
5. Health benefits
|
||||
|
||||
Format your response in markdown.`,
|
||||
name: "RecipeAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create blog agent
|
||||
const blogAgent = new Agent({
|
||||
instructions: `You are a food and health blogger. Write an engaging blog post about the provided recipes.
|
||||
The blog post should:
|
||||
1. Have an engaging title
|
||||
2. Include an introduction about healthy eating
|
||||
3. Discuss each recipe and its unique health benefits
|
||||
4. Include tips for meal planning and preparation
|
||||
5. End with a conclusion encouraging healthy eating habits
|
||||
|
||||
Here are the recipes to write about:
|
||||
{previous_result}
|
||||
|
||||
Format your response in markdown.`,
|
||||
name: "BlogAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create PraisonAIAgents instance with tasks
|
||||
const agents = new PraisonAIAgents({
|
||||
agents: [recipeAgent, blogAgent],
|
||||
tasks: [
|
||||
"Create 5 healthy and delicious recipes",
|
||||
"Write a blog post about the recipes"
|
||||
],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Start the agents
|
||||
agents.start()
|
||||
.then(results => {
|
||||
console.log('\nFinal Results:');
|
||||
console.log('\nRecipe Task Results:');
|
||||
console.log(results[0]);
|
||||
console.log('\nBlog Task Results:');
|
||||
console.log(results[1]);
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error:', error);
|
||||
});
|
||||
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Running the Examples
|
||||
|
||||
<Steps>
|
||||
<Step title="Set Environment Variables">
|
||||
```bash
|
||||
export OPENAI_API_KEY='your-api-key'
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Create Example File">
|
||||
Create a new TypeScript file (e.g., `app.ts`) with any of the above examples.
|
||||
</Step>
|
||||
|
||||
<Step title="Run the Example">
|
||||
```bash
|
||||
npx ts-node app.ts
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
@@ -0,0 +1,198 @@
|
||||
---
|
||||
title: "TypeScript Async AI Agents"
|
||||
sidebarTitle: "TypeScript Async"
|
||||
description: "A production-ready Multi AI Agents framework for TypeScript Async"
|
||||
icon: "rotate"
|
||||
---
|
||||
|
||||
PraisonAI is a production-ready Multi AI Agents framework for TypeScript Async, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
|
||||
|
||||
## Installation
|
||||
|
||||
<CodeGroup>
|
||||
```bash npm
|
||||
npm install praisonai
|
||||
```
|
||||
|
||||
```bash yarn
|
||||
yarn add praisonai
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
## Usage Examples
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Single Agent Example" icon="user" defaultOpen>
|
||||
Create and run a single agent to perform a specific task:
|
||||
|
||||
```typescript
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
async function main() {
|
||||
// Create a simple agent (no task specified)
|
||||
const agent = new Agent({
|
||||
name: "BiologyExpert",
|
||||
instructions: "Explain the process of photosynthesis in detail.",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Run the agent
|
||||
const praisonAI = new PraisonAIAgents({
|
||||
agents: [agent],
|
||||
tasks: ["Explain the process of photosynthesis in detail."],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
try {
|
||||
console.log('Starting single agent example...');
|
||||
const results = await praisonAI.start();
|
||||
console.log('\nFinal Results:', results);
|
||||
} catch (error) {
|
||||
console.error('Error:', error);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Multi-Agent Example" icon="users" defaultOpen>
|
||||
Create and run multiple agents working together:
|
||||
|
||||
```typescript
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
async function main() {
|
||||
// Create multiple agents with different roles
|
||||
const researchAgent = new Agent({
|
||||
name: "ResearchAgent",
|
||||
instructions: "Research and provide detailed information about renewable energy sources.",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
const summaryAgent = new Agent({
|
||||
name: "SummaryAgent",
|
||||
instructions: "Create a concise summary of the research findings about renewable energy sources. Use {previous_result} as input.",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
const recommendationAgent = new Agent({
|
||||
name: "RecommendationAgent",
|
||||
instructions: "Based on the summary in {previous_result}, provide specific recommendations for implementing renewable energy solutions.",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Run the agents in sequence
|
||||
const praisonAI = new PraisonAIAgents({
|
||||
agents: [researchAgent, summaryAgent, recommendationAgent],
|
||||
tasks: [
|
||||
"Research and analyze current renewable energy technologies and their implementation.",
|
||||
"Summarize the key findings from the research.",
|
||||
"Provide actionable recommendations based on the summary."
|
||||
],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
try {
|
||||
console.log('Starting multi-agent example...');
|
||||
const results = await praisonAI.start();
|
||||
console.log('\nFinal Results:', results);
|
||||
} catch (error) {
|
||||
console.error('Error:', error);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Task-Based Agent Example" icon="list-check" defaultOpen>
|
||||
Create agents with specific tasks and dependencies:
|
||||
|
||||
```typescript
|
||||
import { Agent, Task, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
async function main() {
|
||||
// Create agents first
|
||||
const dietAgent = new Agent({
|
||||
name: "DietAgent",
|
||||
role: "Nutrition Expert",
|
||||
goal: "Create healthy and delicious recipes",
|
||||
backstory: "You are a certified nutritionist with years of experience in creating balanced meal plans.",
|
||||
verbose: true,
|
||||
instructions: `You are a professional chef and nutritionist. Create 5 healthy food recipes that are both nutritious and delicious.
|
||||
Each recipe should include:
|
||||
1. Recipe name
|
||||
2. List of ingredients with quantities
|
||||
3. Step-by-step cooking instructions
|
||||
4. Nutritional information
|
||||
5. Health benefits
|
||||
|
||||
Format your response in markdown.`
|
||||
});
|
||||
|
||||
const blogAgent = new Agent({
|
||||
name: "BlogAgent",
|
||||
role: "Food Blogger",
|
||||
goal: "Write engaging blog posts about food and recipes",
|
||||
backstory: "You are a successful food blogger known for your ability to make recipes sound delicious and approachable.",
|
||||
verbose: true,
|
||||
instructions: `You are a food and health blogger. Write an engaging blog post about the provided recipes.
|
||||
The blog post should:
|
||||
1. Have an engaging title
|
||||
2. Include an introduction about healthy eating`
|
||||
});
|
||||
|
||||
// Create tasks
|
||||
const createRecipesTask = new Task({
|
||||
name: "Create Recipes",
|
||||
description: "Create 5 healthy and delicious recipes",
|
||||
agent: dietAgent
|
||||
});
|
||||
|
||||
const writeBlogTask = new Task({
|
||||
name: "Write Blog",
|
||||
description: "Write a blog post about the recipes",
|
||||
agent: blogAgent,
|
||||
dependencies: [createRecipesTask] // This task depends on the recipes being created first
|
||||
});
|
||||
|
||||
// Run the tasks
|
||||
const praisonAI = new PraisonAIAgents({
|
||||
tasks: [createRecipesTask, writeBlogTask],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
try {
|
||||
console.log('Starting task-based example...');
|
||||
const results = await praisonAI.start();
|
||||
console.log('\nFinal Results:', results);
|
||||
} catch (error) {
|
||||
console.error('Error:', error);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Running the Examples
|
||||
|
||||
<Steps>
|
||||
<Step title="Set Environment Variables">
|
||||
```bash
|
||||
export OPENAI_API_KEY='your-api-key'
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Create Example File">
|
||||
Create a new TypeScript file (e.g., `app.ts`) with any of the above examples.
|
||||
</Step>
|
||||
|
||||
<Step title="Run the Example">
|
||||
```bash
|
||||
npx ts-node app.ts
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
@@ -0,0 +1,266 @@
|
||||
---
|
||||
title: "TypeScript AI Agents Framework"
|
||||
sidebarTitle: "TypeScript Agents"
|
||||
description: "A production-ready Multi AI Agents framework for TypeScript"
|
||||
icon: "scroll"
|
||||
---
|
||||
|
||||
PraisonAI is a production-ready Multi AI Agents framework for TypeScript, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
%% Define the main flow
|
||||
Start([▶ Start]) --> Agent1
|
||||
Agent1 --> Process[⚙ Process]
|
||||
Process --> Agent2
|
||||
Agent2 --> Output([✓ Output])
|
||||
Process -.-> Agent1
|
||||
|
||||
%% Define subgraphs for agents and their tasks
|
||||
subgraph Agent1[ ]
|
||||
Task1[📋 Task]
|
||||
AgentIcon1[🤖 AI Agent]
|
||||
Tools1[🔧 Tools]
|
||||
|
||||
Task1 --- AgentIcon1
|
||||
AgentIcon1 --- Tools1
|
||||
end
|
||||
|
||||
subgraph Agent2[ ]
|
||||
Task2[📋 Task]
|
||||
AgentIcon2[🤖 AI Agent]
|
||||
Tools2[🔧 Tools]
|
||||
|
||||
Task2 --- AgentIcon2
|
||||
AgentIcon2 --- Tools2
|
||||
end
|
||||
|
||||
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
|
||||
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
|
||||
classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
|
||||
classDef transparent fill:none,stroke:none
|
||||
|
||||
class Start,Output,Task1,Task2 input
|
||||
class Process,AgentIcon1,AgentIcon2 process
|
||||
class Tools1,Tools2 tools
|
||||
class Agent1,Agent2 transparent
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="TypeScript">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
<CodeGroup>
|
||||
```bash npm
|
||||
npm install praisonai
|
||||
```
|
||||
```bash yarn
|
||||
yarn add praisonai
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
```bash
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
</Step>
|
||||
<Step title="Create File">
|
||||
Create `app.ts` file
|
||||
|
||||
## Code Example
|
||||
|
||||
<CodeGroup>
|
||||
```javascript Single Agent
|
||||
import { Agent } from 'praisonai';
|
||||
|
||||
const agent = new Agent({
|
||||
instructions: `You are a creative writer who writes short stories with emojis.`,
|
||||
name: "StoryWriter"
|
||||
});
|
||||
|
||||
agent.start("Write a story about a time traveler")
|
||||
```
|
||||
|
||||
```javascript Multi Agents
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
const storyAgent = new Agent({
|
||||
instructions: "Generate a very short story (2-3 sentences) about artificial intelligence with emojis.",
|
||||
name: "StoryAgent"
|
||||
});
|
||||
|
||||
const summaryAgent = new Agent({
|
||||
instructions: "Summarize the provided AI story in one sentence with emojis.",
|
||||
name: "SummaryAgent"
|
||||
});
|
||||
|
||||
const agents = new PraisonAIAgents({
|
||||
agents: [storyAgent, summaryAgent]
|
||||
});
|
||||
|
||||
agents.start()
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Run Script">
|
||||
```bash
|
||||
npx ts-node app.ts
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Usage Examples
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Single Agent Example" icon="user" defaultOpen>
|
||||
Create and run a single agent to perform a specific task:
|
||||
|
||||
```typescript
|
||||
import { Agent } from 'praisonai';
|
||||
|
||||
// Single agent example - Science Explainer
|
||||
const agent = new Agent({
|
||||
instructions: `You are a science expert who explains complex phenomena in simple terms.
|
||||
Provide clear, accurate, and easy-to-understand explanations.`,
|
||||
name: "ScienceExplainer",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
agent.start("Why is the sky blue?")
|
||||
.then(response => {
|
||||
console.log('\nExplanation:');
|
||||
console.log(response);
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error:', error);
|
||||
});
|
||||
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Multi-Agent Example" icon="users" defaultOpen>
|
||||
Create and run multiple agents working together:
|
||||
|
||||
```typescript
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
// Create story agent
|
||||
const storyAgent = new Agent({
|
||||
instructions: "You are a storyteller. Write a very short story (2-3 sentences) about a given topic.",
|
||||
name: "StoryAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create summary agent
|
||||
const summaryAgent = new Agent({
|
||||
instructions: "You are an editor. Create a one-sentence summary of the given story.",
|
||||
name: "SummaryAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create and start agents
|
||||
const agents = new PraisonAIAgents({
|
||||
agents: [storyAgent, summaryAgent],
|
||||
tasks: [
|
||||
"Write a short story about a cat",
|
||||
"{previous_result}" // This will be replaced with the story
|
||||
],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
agents.start()
|
||||
.then(results => {
|
||||
console.log('\nStory:', results[0]);
|
||||
console.log('\nSummary:', results[1]);
|
||||
})
|
||||
.catch(error => console.error('Error:', error));
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Task-Based Agent Example" icon="list-check" defaultOpen>
|
||||
Create agents with specific tasks and dependencies:
|
||||
|
||||
```typescript
|
||||
import { Agent, PraisonAIAgents } from 'praisonai';
|
||||
|
||||
// Create recipe agent
|
||||
const recipeAgent = new Agent({
|
||||
instructions: `You are a professional chef and nutritionist. Create 5 healthy food recipes that are both nutritious and delicious.
|
||||
Each recipe should include:
|
||||
1. Recipe name
|
||||
2. List of ingredients with quantities
|
||||
3. Step-by-step cooking instructions
|
||||
4. Nutritional information
|
||||
5. Health benefits
|
||||
|
||||
Format your response in markdown.`,
|
||||
name: "RecipeAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create blog agent
|
||||
const blogAgent = new Agent({
|
||||
instructions: `You are a food and health blogger. Write an engaging blog post about the provided recipes.
|
||||
The blog post should:
|
||||
1. Have an engaging title
|
||||
2. Include an introduction about healthy eating
|
||||
3. Discuss each recipe and its unique health benefits
|
||||
4. Include tips for meal planning and preparation
|
||||
5. End with a conclusion encouraging healthy eating habits
|
||||
|
||||
Here are the recipes to write about:
|
||||
{previous_result}
|
||||
|
||||
Format your response in markdown.`,
|
||||
name: "BlogAgent",
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Create PraisonAIAgents instance with tasks
|
||||
const agents = new PraisonAIAgents({
|
||||
agents: [recipeAgent, blogAgent],
|
||||
tasks: [
|
||||
"Create 5 healthy and delicious recipes",
|
||||
"Write a blog post about the recipes"
|
||||
],
|
||||
verbose: true
|
||||
});
|
||||
|
||||
// Start the agents
|
||||
agents.start()
|
||||
.then(results => {
|
||||
console.log('\nFinal Results:');
|
||||
console.log('\nRecipe Task Results:');
|
||||
console.log(results[0]);
|
||||
console.log('\nBlog Task Results:');
|
||||
console.log(results[1]);
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error:', error);
|
||||
});
|
||||
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Running the Examples
|
||||
|
||||
<Steps>
|
||||
<Step title="Set Environment Variables">
|
||||
```bash
|
||||
export OPENAI_API_KEY='your-api-key'
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Create Example File">
|
||||
Create a new TypeScript file (e.g., `app.ts`) with any of the above examples.
|
||||
</Step>
|
||||
|
||||
<Step title="Run the Example">
|
||||
```bash
|
||||
npx ts-node app.ts
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
@@ -74,6 +74,10 @@
|
||||
{
|
||||
"name": "Tools",
|
||||
"url": "/tools"
|
||||
},
|
||||
{
|
||||
"name": "JS",
|
||||
"url": "/js"
|
||||
}
|
||||
],
|
||||
"navigation": [
|
||||
@@ -115,7 +119,9 @@
|
||||
{
|
||||
"group": "Features",
|
||||
"pages": [
|
||||
"features/cli",
|
||||
"features/autoagents",
|
||||
"features/image-generation",
|
||||
"features/selfreflection",
|
||||
"features/rag",
|
||||
"features/reasoning-extract",
|
||||
@@ -234,6 +240,16 @@
|
||||
"tools/googlecalendar"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "JavaScript",
|
||||
"pages": [
|
||||
"js/js",
|
||||
"js/typescript",
|
||||
"js/nodejs",
|
||||
"js/typescript-async",
|
||||
"js/development"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Other Features",
|
||||
"pages": [
|
||||
|
||||
@@ -125,6 +125,48 @@ praisonai --init "your task description"
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
<Steps>
|
||||
<Step title="Install Package">
|
||||
```bash
|
||||
npm install praisonai
|
||||
```
|
||||
</Step>
|
||||
<Step title="Set API Key">
|
||||
```bash
|
||||
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
|
||||
```
|
||||
</Step>
|
||||
<Step title="Create File">
|
||||
Create `app.js` file
|
||||
|
||||
## Code Example
|
||||
|
||||
<CodeGroup>
|
||||
```javascript Single Agent
|
||||
const { Agent } = require('praisonai');
|
||||
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
|
||||
agent.start('Write a movie script about a robot in Mars');
|
||||
```
|
||||
|
||||
```javascript Multi Agents
|
||||
const { Agent, PraisonAIAgents } = require('praisonai');
|
||||
|
||||
const researchAgent = new Agent({ instructions: 'Research about AI' });
|
||||
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });
|
||||
|
||||
const agents = new PraisonAIAgents({ agents: [researchAgent, summariseAgent] });
|
||||
agents.start();
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Run Script">
|
||||
```bash
|
||||
node app.js
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Note>
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
import asyncio
|
||||
from praisonaiagents import ImageAgent
|
||||
|
||||
async def main():
|
||||
agent = ImageAgent(
|
||||
name="ImageCreator",
|
||||
llm="dall-e-3",
|
||||
style="natural"
|
||||
)
|
||||
|
||||
result = await agent.achat("A cute baby sea otter playing with a laptop")
|
||||
print(f"Image generation result: {result}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,8 @@
|
||||
from praisonaiagents.agent.image_agent import ImageAgent
|
||||
|
||||
# Create an image agent with normal mode
|
||||
agent = ImageAgent(llm="dall-e-3")
|
||||
|
||||
# Generate an image
|
||||
result = agent.chat("A cute baby sea otter playing with a laptop")
|
||||
print("Image generation result:", result)
|
||||
+1
-1
@@ -3,7 +3,7 @@ class Praisonai < Formula
|
||||
|
||||
desc "AI tools for various AI applications"
|
||||
homepage "https://github.com/MervinPraison/PraisonAI"
|
||||
url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.60.tar.gz"
|
||||
url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.63.tar.gz"
|
||||
sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32" # Replace with actual SHA256 checksum
|
||||
license "MIT"
|
||||
|
||||
|
||||
+87
-4
@@ -140,6 +140,8 @@ class PraisonAI:
|
||||
provided arguments.
|
||||
"""
|
||||
args = self.parse_args()
|
||||
# Store args for use in handle_direct_prompt
|
||||
self.args = args
|
||||
invocation_cmd = "praisonai"
|
||||
version_string = f"PraisonAI version {__version__}"
|
||||
|
||||
@@ -148,8 +150,16 @@ class PraisonAI:
|
||||
if args.command:
|
||||
if args.command.startswith("tests.test"): # Argument used for testing purposes
|
||||
print("test")
|
||||
return "test"
|
||||
else:
|
||||
self.agent_file = args.command
|
||||
elif hasattr(args, 'direct_prompt') and args.direct_prompt:
|
||||
result = self.handle_direct_prompt(args.direct_prompt)
|
||||
print(result)
|
||||
return result
|
||||
else:
|
||||
# Default to agents.yaml if no command provided
|
||||
self.agent_file = "agents.yaml"
|
||||
|
||||
if args.deploy:
|
||||
from .deploy import CloudDeployer
|
||||
@@ -285,14 +295,18 @@ class PraisonAI:
|
||||
"""
|
||||
Parse the command-line arguments for the PraisonAI CLI.
|
||||
"""
|
||||
# Define special commands
|
||||
special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui']
|
||||
|
||||
parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
|
||||
parser.add_argument("--framework", choices=["crewai", "autogen", "praisonai"], help="Specify the framework")
|
||||
parser.add_argument("--ui", choices=["chainlit", "gradio"], help="Specify the UI framework (gradio or chainlit).")
|
||||
parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("--init", nargs=argparse.REMAINDER, help="Initialize agents with optional topic")
|
||||
parser.add_argument("command", nargs="?", help="Command to run")
|
||||
parser.add_argument("command", nargs="?", help="Command to run or direct prompt")
|
||||
parser.add_argument("--deploy", action="store_true", help="Deploy the application")
|
||||
parser.add_argument("--model", type=str, help="Model name")
|
||||
parser.add_argument("--llm", type=str, help="LLM model to use for direct prompts")
|
||||
parser.add_argument("--hf", type=str, help="Hugging Face model name")
|
||||
parser.add_argument("--ollama", type=str, help="Ollama model name")
|
||||
parser.add_argument("--dataset", type=str, help="Dataset name for training", default="yahma/alpaca-cleaned")
|
||||
@@ -301,6 +315,7 @@ class PraisonAI:
|
||||
parser.add_argument("--public", action="store_true", help="Use ngrok to expose the server publicly (only with --call)")
|
||||
args, unknown_args = parser.parse_known_args()
|
||||
|
||||
# Handle special cases first
|
||||
if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
|
||||
args.command = 'agents.yaml'
|
||||
if args.command == 'api:app' or args.command == '/app/api:app':
|
||||
@@ -331,9 +346,7 @@ class PraisonAI:
|
||||
call_module.main(call_args)
|
||||
sys.exit(0)
|
||||
|
||||
# Handle special commands first
|
||||
special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui']
|
||||
|
||||
# Handle special commands
|
||||
if args.command in special_commands:
|
||||
if args.command == 'chat':
|
||||
if not CHAINLIT_AVAILABLE:
|
||||
@@ -402,8 +415,78 @@ class PraisonAI:
|
||||
print("pip install praisonaiagents # For PraisonAIAgents\n")
|
||||
sys.exit(1)
|
||||
|
||||
# Handle direct prompt if command is not a special command or file
|
||||
if args.command and not args.command.endswith('.yaml') and args.command not in special_commands:
|
||||
args.direct_prompt = args.command
|
||||
args.command = None
|
||||
|
||||
return args
|
||||
|
||||
def handle_direct_prompt(self, prompt):
|
||||
"""
|
||||
Handle direct prompt by creating a single agent and running it.
|
||||
"""
|
||||
if PRAISONAI_AVAILABLE:
|
||||
agent_config = {
|
||||
"name": "DirectAgent",
|
||||
"role": "Assistant",
|
||||
"goal": "Complete the given task",
|
||||
"backstory": "You are a helpful AI assistant"
|
||||
}
|
||||
|
||||
# Add llm if specified
|
||||
if hasattr(self, 'args') and self.args.llm:
|
||||
agent_config["llm"] = self.args.llm
|
||||
|
||||
agent = PraisonAgent(**agent_config)
|
||||
result = agent.start(prompt)
|
||||
return ""
|
||||
elif CREWAI_AVAILABLE:
|
||||
agent_config = {
|
||||
"name": "DirectAgent",
|
||||
"role": "Assistant",
|
||||
"goal": "Complete the given task",
|
||||
"backstory": "You are a helpful AI assistant"
|
||||
}
|
||||
|
||||
# Add llm if specified
|
||||
if hasattr(self, 'args') and self.args.llm:
|
||||
agent_config["llm"] = self.args.llm
|
||||
|
||||
agent = Agent(**agent_config)
|
||||
task = Task(
|
||||
description=prompt,
|
||||
agent=agent
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task]
|
||||
)
|
||||
return crew.kickoff()
|
||||
elif AUTOGEN_AVAILABLE:
|
||||
config_list = self.config_list
|
||||
# Add llm if specified
|
||||
if hasattr(self, 'args') and self.args.llm:
|
||||
config_list[0]['model'] = self.args.llm
|
||||
|
||||
assistant = autogen.AssistantAgent(
|
||||
name="DirectAgent",
|
||||
llm_config={"config_list": config_list}
|
||||
)
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="UserProxy",
|
||||
code_execution_config={"work_dir": "coding"}
|
||||
)
|
||||
user_proxy.initiate_chat(assistant, message=prompt)
|
||||
return "Task completed"
|
||||
else:
|
||||
print("[red]ERROR: No framework is installed. Please install at least one framework:[/red]")
|
||||
print("\npip install \"praisonai\\[crewai]\" # For CrewAI")
|
||||
print("pip install \"praisonai\\[autogen]\" # For AutoGen")
|
||||
print("pip install \"praisonai\\[crewai,autogen]\" # For both frameworks\n")
|
||||
print("pip install praisonaiagents # For PraisonAIAgents\n")
|
||||
sys.exit(1)
|
||||
|
||||
def create_chainlit_chat_interface(self):
|
||||
"""
|
||||
Create a Chainlit interface for the chat application.
|
||||
|
||||
+1
-1
@@ -56,7 +56,7 @@ class CloudDeployer:
|
||||
file.write("FROM python:3.11-slim\n")
|
||||
file.write("WORKDIR /app\n")
|
||||
file.write("COPY . .\n")
|
||||
file.write("RUN pip install flask praisonai==2.0.60 gunicorn markdown\n")
|
||||
file.write("RUN pip install flask praisonai==2.0.63 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
|
||||
|
||||
|
||||
+4
-4
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "PraisonAI"
|
||||
version = "2.0.60"
|
||||
version = "2.0.63"
|
||||
description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
|
||||
readme = "README.md"
|
||||
license = ""
|
||||
@@ -12,7 +12,7 @@ dependencies = [
|
||||
"rich>=13.7",
|
||||
"markdown>=3.5",
|
||||
"pyparsing>=3.0.0",
|
||||
"praisonaiagents>=0.0.60",
|
||||
"praisonaiagents>=0.0.61",
|
||||
"python-dotenv>=0.19.0",
|
||||
"instructor>=1.3.3",
|
||||
"PyYAML>=6.0",
|
||||
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
|
||||
|
||||
[tool.poetry]
|
||||
name = "PraisonAI"
|
||||
version = "2.0.60"
|
||||
version = "2.0.63"
|
||||
description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
|
||||
authors = ["Mervin Praison"]
|
||||
license = ""
|
||||
@@ -102,7 +102,7 @@ python = ">=3.10,<3.13"
|
||||
rich = ">=13.7"
|
||||
markdown = ">=3.5"
|
||||
pyparsing = ">=3.0.0"
|
||||
praisonaiagents = ">=0.0.60"
|
||||
praisonaiagents = ">=0.0.61"
|
||||
python-dotenv = ">=0.19.0"
|
||||
instructor = ">=1.3.3"
|
||||
PyYAML = ">=6.0"
|
||||
|
||||
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@@ -0,0 +1,142 @@
|
||||
from praisonaiagents import Agent, Task, PraisonAIAgents
|
||||
from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
|
||||
from pydantic import BaseModel
|
||||
import os
|
||||
|
||||
# Define Pydantic model for structured output
|
||||
class DecisionModel(BaseModel):
|
||||
response: str
|
||||
decision: str
|
||||
|
||||
def write_csv(file_path, data):
|
||||
"""Write data to CSV file."""
|
||||
if not os.path.exists(file_path):
|
||||
with open(file_path, 'w') as file:
|
||||
file.write(data + '\n')
|
||||
else:
|
||||
with open(file_path, 'a') as file:
|
||||
file.write(data + '\n')
|
||||
return f"Data appended to {file_path}"
|
||||
|
||||
def count_questions(file_path):
|
||||
"""Count lines in file."""
|
||||
with open(file_path, 'r') as file:
|
||||
return sum(1 for _ in file)
|
||||
|
||||
# Keep existing agents with minimal changes
|
||||
qa_generator = Agent(
|
||||
name="Generator",
|
||||
role="Question Creator",
|
||||
goal="Create challenging math and logic questions",
|
||||
backstory="Expert in educational content creation",
|
||||
llm="deepseek/deepseek-reasoner",
|
||||
tools=[write_csv, count_questions]
|
||||
)
|
||||
|
||||
total_questions_evaluator = Agent(
|
||||
name="TotalQuestionsEvaluator",
|
||||
role="Total Questions Evaluator",
|
||||
goal="Evaluate the total number of questions in qa_pairs.csv file",
|
||||
backstory="Expert in evaluating the total number of questions in a file",
|
||||
llm="deepseek/deepseek-reasoner",
|
||||
tools=[count_questions],
|
||||
verbose=False
|
||||
)
|
||||
|
||||
cot_generator = Agent(
|
||||
name="COTGenerator",
|
||||
role="Chain of Thought Specialist",
|
||||
goal="Generate and manage chain of thought solutions for Q&A pairs",
|
||||
backstory="Expert in breaking down problems and generating detailed solution steps",
|
||||
tools=[cot_save],
|
||||
llm="deepseek/deepseek-reasoner",
|
||||
verbose=False
|
||||
)
|
||||
|
||||
upload_to_huggingface = Agent(
|
||||
name="UploadToHuggingface",
|
||||
role="Upload to Huggingface",
|
||||
goal="Upload the generated chain of thought solutions to a Huggingface dataset",
|
||||
backstory="Expert in saving data to Huggingface",
|
||||
tools=[cot_upload_to_huggingface],
|
||||
llm="deepseek/deepseek-reasoner",
|
||||
verbose=False
|
||||
)
|
||||
|
||||
# Define tasks with workflow improvements
|
||||
generate_task = Task(
|
||||
description="""Generate question and answer in csv format without headers: question, answer and append to qa_pairs.csv file
|
||||
generate 10 unique questions and answers and don't repeat on the same question and answer. Reponse with 'done' when done
|
||||
with append mode as 'a'
|
||||
Example question and answer:
|
||||
question, answer
|
||||
What is the sum of numbers from 1 to 10?, 55
|
||||
Number of r's in the word strawberry, 3
|
||||
""",
|
||||
expected_output="append to qa_pairs.csv file with questions and answers and move to next task",
|
||||
agent=qa_generator,
|
||||
name="generate_task",
|
||||
is_start=True,
|
||||
next_tasks=["evaluate_total_questions"],
|
||||
task_type="decision",
|
||||
condition={
|
||||
"more": "generate_task",
|
||||
"done": "evaluate_total_questions"
|
||||
}
|
||||
)
|
||||
|
||||
evaluate_total_questions_task = Task(
|
||||
description="Evaluate the total number of questions in qa_pairs.csv file is 1",
|
||||
expected_output="Total number of questions in qa_pairs.csv file",
|
||||
agent=total_questions_evaluator,
|
||||
task_type="decision",
|
||||
name="evaluate_total_questions",
|
||||
condition={
|
||||
"more": "generate_task",
|
||||
"done": "generate_cot"
|
||||
}
|
||||
)
|
||||
|
||||
generate_cot_task = Task(
|
||||
name="generate_cot",
|
||||
description="""Generate chain of thought solutions for each question in the input file.
|
||||
Save to cot_solutions.csv file
|
||||
Don't generate chain of thought solutions again after receiving the response from Tool Call
|
||||
After calling the tool, respond with a JSON object:
|
||||
{
|
||||
"response": "done",
|
||||
"decision": "done"
|
||||
}
|
||||
""",
|
||||
expected_output="done",
|
||||
agent=cot_generator,
|
||||
input_file="qa_pairs.csv",
|
||||
task_type="loop",
|
||||
next_tasks=["upload_to_huggingface"],
|
||||
condition={
|
||||
"done": ["upload_to_huggingface"],
|
||||
"exit": [],
|
||||
},
|
||||
output_pydantic=DecisionModel # Use Pydantic model for output validation
|
||||
)
|
||||
|
||||
upload_to_huggingface_task = Task(
|
||||
name="upload_to_huggingface",
|
||||
description="""Upload to Huggingface:
|
||||
1. Save to cot_solutions.csv
|
||||
2. Upload to mervinpraison/cot-dataset""",
|
||||
expected_output="Dataset published successfully",
|
||||
agent=upload_to_huggingface,
|
||||
tools=[cot_upload_to_huggingface]
|
||||
)
|
||||
|
||||
# Initialize workflow
|
||||
agents = PraisonAIAgents(
|
||||
agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
|
||||
tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
|
||||
process="workflow",
|
||||
max_iter=30,
|
||||
verbose=False
|
||||
)
|
||||
|
||||
agents.start()
|
||||
@@ -0,0 +1,39 @@
|
||||
from praisonaiagents import Agent
|
||||
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "chroma",
|
||||
"config": {
|
||||
"collection_name": "praison",
|
||||
"path": ".praison"
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"provider": "ollama",
|
||||
"config": {
|
||||
"model": "deepseek-r1:latest",
|
||||
"temperature": 0,
|
||||
"max_tokens": 8000,
|
||||
"ollama_base_url": "http://localhost:11434",
|
||||
},
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "ollama",
|
||||
"config": {
|
||||
"model": "nomic-embed-text:latest",
|
||||
"ollama_base_url": "http://localhost:11434",
|
||||
"embedding_dims": 1536
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
agent = Agent(
|
||||
name="Knowledge Agent",
|
||||
instructions="You answer questions based on the provided knowledge.",
|
||||
knowledge=["kag-research-paper.pdf"], # Indexing
|
||||
knowledge_config=config,
|
||||
user_id="user1",
|
||||
llm="deepseek-r1"
|
||||
)
|
||||
|
||||
agent.start("What is KAG in one line?") # Querying
|
||||
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Loaded 100 of 301 files, more files were not shown because too many files have changed in this diff.
Show more
Reference in new issue
Block a user