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| Author | SHA1 | Date | |
|---|---|---|---|
 Mervin Praison
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504c5a9759 | ||
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Mervin Praison
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8fef42fc03 | ||
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Mervin Praison
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2f18cc1ea2 | ||
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Mervin Praison
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ff7969cdbf | ||
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Mervin Praison
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5fa84dc3eb | ||
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Mervin Praison
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4bf8b980ed | ||
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Mervin Praison
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53e0fd19ab | ||
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Dhivya-Bharathy
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0cb6307d3c | ||
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Dhivya
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adce94410b | ||
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Dhivya-Bharathy
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42aec561b1 | ||
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Dhivya
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acc805768d | ||
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Dhivya-Bharathy
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6d9fa167e3 | ||
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Mervin Praison
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9e178b70e6 | ||
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Mervin Praison
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ed406973cd |
No files matched your search
@@ -53,6 +53,7 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
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||||
- 🛠️ 100+ Custom Tools
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||||
- 📄 YAML Configuration
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||||
- 💯 100+ LLM Support
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||||
- 🔬 **Deep Research Agents** (OpenAI & Gemini)
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||||
|
||||
## Using Python Code
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||||
|
||||
@@ -96,6 +97,44 @@ Run:
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python app.py
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```
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||||
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### 3. Deep Research Agent
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Automated research with real-time streaming, web search, and citations using OpenAI or Gemini Deep Research APIs.
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||||
|
||||
```python
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from praisonaiagents import DeepResearchAgent
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||||
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||||
# OpenAI Deep Research
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agent = DeepResearchAgent(
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model="o4-mini-deep-research", # or "o3-deep-research"
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verbose=True
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)
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result = agent.research("What are the latest AI trends in 2025?")
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print(result.report)
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print(f"Citations: {len(result.citations)}")
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```
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```python
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# Gemini Deep Research
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from praisonaiagents import DeepResearchAgent
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||||
agent = DeepResearchAgent(
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model="deep-research-pro", # Auto-detected as Gemini
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verbose=True
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||||
)
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result = agent.research("Research quantum computing advances")
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print(result.report)
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```
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**Features:**
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- 🔍 Multi-provider support (OpenAI, Gemini, LiteLLM)
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- 📡 Real-time streaming with reasoning summaries
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- 📚 Structured citations with URLs
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||||
- 🛠️ Built-in tools: web search, code interpreter, MCP, file search
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||||
- 🔄 Automatic provider detection from model name
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||||
|
||||
## Using No Code
|
||||
|
||||
### Auto Mode:
|
||||
|
||||
+1
-1
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
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||||
RUN pip install --no-cache-dir \
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flask \
|
||||
"praisonai>=2.2.87" \
|
||||
"praisonai>=2.2.89" \
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||||
"praisonai[api]" \
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||||
gunicorn \
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||||
markdown
|
||||
|
||||
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
|
||||
RUN pip install --no-cache-dir \
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||||
praisonai_tools \
|
||||
"praisonai>=2.2.87" \
|
||||
"praisonai>=2.2.89" \
|
||||
"praisonai[chat]" \
|
||||
"embedchain[github,youtube]"
|
||||
|
||||
|
||||
@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
|
||||
RUN pip install --no-cache-dir \
|
||||
praisonai_tools \
|
||||
"praisonai>=2.2.87" \
|
||||
"praisonai>=2.2.89" \
|
||||
"praisonai[ui]" \
|
||||
"praisonai[chat]" \
|
||||
"praisonai[realtime]" \
|
||||
|
||||
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
|
||||
RUN pip install --no-cache-dir \
|
||||
praisonai_tools \
|
||||
"praisonai>=2.2.87" \
|
||||
"praisonai>=2.2.89" \
|
||||
"praisonai[ui]" \
|
||||
"praisonai[crewai]"
|
||||
|
||||
|
||||
+2
-2
@@ -121,7 +121,7 @@ healthcheck:
|
||||
## 📦 Package Versions
|
||||
|
||||
All Docker images use consistent, up-to-date versions:
|
||||
- PraisonAI: `>=2.2.87`
|
||||
- PraisonAI: `>=2.2.89`
|
||||
- PraisonAI Agents: `>=0.0.92`
|
||||
- Python: `3.11-slim`
|
||||
|
||||
@@ -218,7 +218,7 @@ docker-compose up -d
|
||||
### Version Pinning
|
||||
To use specific versions, update the Dockerfile:
|
||||
```dockerfile
|
||||
RUN pip install "praisonai==2.2.87" "praisonaiagents==0.0.92"
|
||||
RUN pip install "praisonai==2.2.89" "praisonaiagents==0.0.92"
|
||||
```
|
||||
|
||||
## 🌐 Production Deployment
|
||||
|
||||
@@ -0,0 +1,481 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "LJRJRQtFTagH"
|
||||
},
|
||||
"source": [
|
||||
"# AI RAG Agent with Web Access using PraisonAI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Ph4RVTBCTcmB"
|
||||
},
|
||||
"source": [
|
||||
"This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) agent with web access using GPT-4o and PraisonAI. The agent specializes in Thai recipes and can search the web for additional information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "tMxfwPPATy_o"
|
||||
},
|
||||
"source": [
|
||||
"This RAG agent:\n",
|
||||
"* Creates an AI assistant specialized in Thai recipes and cooking\n",
|
||||
"* Has access to a PDF knowledge base about Thai recipes\n",
|
||||
"* Can search the web using DuckDuckGo for additional information\n",
|
||||
"* Provides comprehensive answers about Thai cuisine\n",
|
||||
"* Uses PraisonAI framework for agent management"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "h68oMaCITmqt"
|
||||
},
|
||||
"source": [
|
||||
"[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/rag_examples/agentic_rag/PraisonAI_RecipeAgent.ipynb)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "cAdzoWXJUMfF"
|
||||
},
|
||||
"source": [
|
||||
"# Dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "9D-2TlOrUNcu"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install praisonaiagents openai requests"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "yY-4-kEIUW40"
|
||||
},
|
||||
"source": [
|
||||
"# Set Up API Key"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "f5bhQ05yUX6J",
|
||||
"outputId": "dcf9d0d9-332e-4136-9289-4c39d898005c"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Enter your OpenAI API key: ··········\n",
|
||||
"✅ API key set successfully!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# Set your OpenAI API key here\n",
|
||||
"os.environ['OPENAI_API_KEY'] = 'Enter your api key here'\n",
|
||||
"\n",
|
||||
"# Or use this method to input it securely\n",
|
||||
"from getpass import getpass\n",
|
||||
"api_key = getpass('Enter your OpenAI API key: ')\n",
|
||||
"os.environ['OPENAI_API_KEY'] = api_key\n",
|
||||
"\n",
|
||||
"print(\"✅ API key set successfully!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Eh0j-SegUi13"
|
||||
},
|
||||
"source": [
|
||||
"# Tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "Z1A9oifIUkQN",
|
||||
"outputId": "9be78226-36fa-43c4-865d-2f7e6e2ba9de"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"✅ Tools imported successfully!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from praisonaiagents import Agent\n",
|
||||
"import requests\n",
|
||||
"import tempfile\n",
|
||||
"from pathlib import Path\n",
|
||||
"\n",
|
||||
"print(\"✅ Tools imported successfully!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "efnnREHXUnYA"
|
||||
},
|
||||
"source": [
|
||||
"# YAML Prompt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "kcM14-yQUqG-",
|
||||
"outputId": "c5b6288d-a789-4a93-e20c-1d285370b667"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"✅ Agent instructions configured!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Agent configuration and instructions\n",
|
||||
"AGENT_INSTRUCTIONS = \"\"\"\n",
|
||||
"You are a helpful AI assistant specialized in Thai recipes and cooking.\n",
|
||||
"\n",
|
||||
"You have access to a PDF knowledge base about Thai recipes from: https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf\n",
|
||||
"\n",
|
||||
"You can also search the web for additional information about Thai cooking, ingredients, and techniques.\n",
|
||||
"\n",
|
||||
"When answering questions:\n",
|
||||
"1. Use your knowledge about Thai cuisine to provide helpful information\n",
|
||||
"2. If needed, search the web for additional details, current information, or clarification\n",
|
||||
"3. Provide comprehensive, helpful answers about Thai cuisine\n",
|
||||
"4. Always be informative and helpful about Thai cooking!\n",
|
||||
"\n",
|
||||
"You can use the internet_search function to search the web when needed.\n",
|
||||
"\n",
|
||||
"Focus on providing practical cooking advice, ingredient information, and authentic Thai recipe guidance.\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"print(\"✅ Agent instructions configured!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "MCBRcXFJUulY"
|
||||
},
|
||||
"source": [
|
||||
"# Main"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "YaRLbgMmUvXv",
|
||||
"outputId": "8ed8d9f8-85a7-48de-ccc6-f617cd672a85"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"🤖 Thai Recipe RAG Agent created successfully!\n",
|
||||
"The agent is ready to answer questions about Thai cooking.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Create the RAG agent\n",
|
||||
"rag_agent = Agent(\n",
|
||||
" instructions=AGENT_INSTRUCTIONS,\n",
|
||||
" llm=\"gpt-4o\",\n",
|
||||
" markdown=True,\n",
|
||||
" verbose=True\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"🤖 Thai Recipe RAG Agent created successfully!\")\n",
|
||||
"print(\"The agent is ready to answer questions about Thai cooking.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "QvZM94pGU0IX"
|
||||
},
|
||||
"source": [
|
||||
"# Test the Agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 1000,
|
||||
"referenced_widgets": [
|
||||
"47739975c9f04ee5b4e69e8f445cbeda",
|
||||
"08888d583cba4c01b3fdf8f30b06ca4f"
|
||||
]
|
||||
},
|
||||
"id": "QPdrleKxU1VN",
|
||||
"outputId": "a71a86cd-eff8-4309-9b50-173a30066c90"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"🤔 Question: What are the essential ingredients for Thai cooking?\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
|
||||
"<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span> <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
|
||||
"<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span> <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Agent</span> <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
|
||||
"<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span> <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span> <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
|
||||
"<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span> <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
|
||||
"<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
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|
||||
"\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
|
||||
"\u001b[38;2;210;227;200m│\u001b[0m \u001b[38;2;210;227;200m│\u001b[0m\n",
|
||||
"\u001b[38;2;210;227;200m│\u001b[0m \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mAgent\u001b[0m \u001b[38;2;210;227;200m│\u001b[0m\n",
|
||||
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|
||||
"\u001b[38;2;210;227;200m│\u001b[0m \u001b[38;2;210;227;200m│\u001b[0m\n",
|
||||
"\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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||||
]
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},
|
||||
"metadata": {},
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},
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{
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"version_minor": 0
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},
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||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
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|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> What are the essential ingredients for Thai cooking? <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
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|
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"\u001b[36m│\u001b[0m What are the essential ingredients for Thai cooking? \u001b[36m│\u001b[0m\n",
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"\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Thai cooking is known for its balance of five fundamental taste senses in each dish or the overall meal: sour, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> sweet, salty, bitter, and spicy. Essential ingredients that help achieve this balance include: <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 1 </span><span style=\"font-weight: bold\">Fish Sauce (Nam Pla)</span>: A salty, savory sauce made from fermented fish. It's a cornerstone of Thai cooking, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>used to add depth of flavor. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 2 </span><span style=\"font-weight: bold\">Lime Juice</span>: Used to add a fresh, tangy citrus flavor to dishes, especially in salads and some curries. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 3 </span><span style=\"font-weight: bold\">Chilies</span>: Thai cuisine often features fresh Thai bird's eye chilies for heat, as well as dried chilies for <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>certain recipes. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 4 </span><span style=\"font-weight: bold\">Lemongrass</span>: A citrusy, fragrant herb that is often used in soups, curries, and teas. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 5 </span><span style=\"font-weight: bold\">Galangal</span>: Similar to ginger, galangal has a sharp, peppery flavor and is used in soups and curries. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 6 </span><span style=\"font-weight: bold\">Kaffir Lime Leaves</span>: They add a distinct, zesty citrus aroma and flavor to Thai dishes. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 7 </span><span style=\"font-weight: bold\">Coconut Milk</span>: Adds richness and creaminess, commonly used in curries and desserts. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 8 </span><span style=\"font-weight: bold\">Palm Sugar</span>: Used as a sweetener, palm sugar has a nuanced flavor, unlike regular sugar. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 9 </span><span style=\"font-weight: bold\">Tamarind Paste</span>: Provides a sour element to many dishes, particularly pad Thai and some sauces. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 10 </span><span style=\"font-weight: bold\">Thai Basil</span>: Adds a spicy, peppery flavor, different from the sweet basil used in Western cooking. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 11 </span><span style=\"font-weight: bold\">Coriander (Cilantro)</span>: Used as a garnish and for its fresh, citrusy leaves and roots, which add depth to <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>sauces and stocks. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 12 </span><span style=\"font-weight: bold\">Shrimp Paste</span>: A very pungent paste made from fermented shrimp, used sparingly to enrich the flavor of <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>dishes. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> These ingredients are commonly used in combination to create the complex and rich flavors characteristic of <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Thai cuisine. Fresh and high-quality versions of these ingredients contribute significantly to the authenticity <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> of Thai dishes. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m Thai cooking is known for its balance of five fundamental taste senses in each dish or the overall meal: sour, \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m sweet, salty, bitter, and spicy. Essential ingredients that help achieve this balance include: \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 1 \u001b[0m\u001b[1mFish Sauce (Nam Pla)\u001b[0m: A salty, savory sauce made from fermented fish. It's a cornerstone of Thai cooking, \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mused to add depth of flavor. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 2 \u001b[0m\u001b[1mLime Juice\u001b[0m: Used to add a fresh, tangy citrus flavor to dishes, especially in salads and some curries. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 3 \u001b[0m\u001b[1mChilies\u001b[0m: Thai cuisine often features fresh Thai bird's eye chilies for heat, as well as dried chilies for \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mcertain recipes. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 4 \u001b[0m\u001b[1mLemongrass\u001b[0m: A citrusy, fragrant herb that is often used in soups, curries, and teas. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 5 \u001b[0m\u001b[1mGalangal\u001b[0m: Similar to ginger, galangal has a sharp, peppery flavor and is used in soups and curries. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 6 \u001b[0m\u001b[1mKaffir Lime Leaves\u001b[0m: They add a distinct, zesty citrus aroma and flavor to Thai dishes. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 7 \u001b[0m\u001b[1mCoconut Milk\u001b[0m: Adds richness and creaminess, commonly used in curries and desserts. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 8 \u001b[0m\u001b[1mPalm Sugar\u001b[0m: Used as a sweetener, palm sugar has a nuanced flavor, unlike regular sugar. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 9 \u001b[0m\u001b[1mTamarind Paste\u001b[0m: Provides a sour element to many dishes, particularly pad Thai and some sauces. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 10 \u001b[0m\u001b[1mThai Basil\u001b[0m: Adds a spicy, peppery flavor, different from the sweet basil used in Western cooking. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 11 \u001b[0m\u001b[1mCoriander (Cilantro)\u001b[0m: Used as a garnish and for its fresh, citrusy leaves and roots, which add depth to \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0msauces and stocks. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m 12 \u001b[0m\u001b[1mShrimp Paste\u001b[0m: A very pungent paste made from fermented shrimp, used sparingly to enrich the flavor of \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mdishes. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m These ingredients are commonly used in combination to create the complex and rich flavors characteristic of \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m Thai cuisine. Fresh and high-quality versions of these ingredients contribute significantly to the authenticity \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m of Thai dishes. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"�� Answer: Thai cooking is known for its balance of five fundamental taste senses in each dish or the overall meal: sour, sweet, salty, bitter, and spicy. Essential ingredients that help achieve this balance include:\n",
|
||||
"\n",
|
||||
"1. **Fish Sauce (Nam Pla)**: A salty, savory sauce made from fermented fish. It's a cornerstone of Thai cooking, used to add depth of flavor.\n",
|
||||
"\n",
|
||||
"2. **Lime Juice**: Used to add a fresh, tangy citrus flavor to dishes, especially in salads and some curries.\n",
|
||||
"\n",
|
||||
"3. **Chilies**: Thai cuisine often features fresh Thai bird's eye chilies for heat, as well as dried chilies for certain recipes.\n",
|
||||
"\n",
|
||||
"4. **Lemongrass**: A citrusy, fragrant herb that is often used in soups, curries, and teas.\n",
|
||||
"\n",
|
||||
"5. **Galangal**: Similar to ginger, galangal has a sharp, peppery flavor and is used in soups and curries.\n",
|
||||
"\n",
|
||||
"6. **Kaffir Lime Leaves**: They add a distinct, zesty citrus aroma and flavor to Thai dishes.\n",
|
||||
"\n",
|
||||
"7. **Coconut Milk**: Adds richness and creaminess, commonly used in curries and desserts.\n",
|
||||
"\n",
|
||||
"8. **Palm Sugar**: Used as a sweetener, palm sugar has a nuanced flavor, unlike regular sugar.\n",
|
||||
"\n",
|
||||
"9. **Tamarind Paste**: Provides a sour element to many dishes, particularly pad Thai and some sauces.\n",
|
||||
"\n",
|
||||
"10. **Thai Basil**: Adds a spicy, peppery flavor, different from the sweet basil used in Western cooking.\n",
|
||||
"\n",
|
||||
"11. **Coriander (Cilantro)**: Used as a garnish and for its fresh, citrusy leaves and roots, which add depth to sauces and stocks.\n",
|
||||
"\n",
|
||||
"12. **Shrimp Paste**: A very pungent paste made from fermented shrimp, used sparingly to enrich the flavor of dishes.\n",
|
||||
"\n",
|
||||
"These ingredients are commonly used in combination to create the complex and rich flavors characteristic of Thai cuisine. Fresh and high-quality versions of these ingredients contribute significantly to the authenticity of Thai dishes.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Test question 1: Basic Thai cooking\n",
|
||||
"question1 = \"What are the essential ingredients for Thai cooking?\"\n",
|
||||
"print(f\"🤔 Question: {question1}\")\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" response1 = rag_agent.start(question1)\n",
|
||||
" print(f\"�� Answer: {response1}\")\n",
|
||||
"except Exception as e:\n",
|
||||
" print(f\"❌ Error: {e}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,556 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "EA8l4cSVl3Lx"
|
||||
},
|
||||
"source": [
|
||||
"# Agentic RAG with GPT-5 - Colab Notebook"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "I-8nOxJ2l7nO"
|
||||
},
|
||||
"source": [
|
||||
"This Colab notebook sets up a Streamlit app using PraisonAI Agents framework.\n",
|
||||
"It allows you to interact with an AI agent powered by GPT-5 for Q&A.\n",
|
||||
"Currently, the knowledge base functionality is disabled due to compatibility issues."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "g5I1tMkxmDQe"
|
||||
},
|
||||
"source": [
|
||||
"- GPT-5 Integration (via OpenAI API)\n",
|
||||
"- Streamlit Web App\n",
|
||||
"- Agent Framework: PraisonAI Agents\n",
|
||||
"- Markdown-formatted responses"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "yeEno7U0q2U_"
|
||||
},
|
||||
"source": [
|
||||
"[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Qwen2.5_InstructionAgent.ipynb)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "WV2GnzZCmGBR"
|
||||
},
|
||||
"source": [
|
||||
"# Dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "20wAadXemJ0k"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install praisonaiagents python-dotenv streamlit"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "l71uCRarndvy"
|
||||
},
|
||||
"source": [
|
||||
"# Set API Key"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"id": "brfc-ZZKnfre"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from dotenv import load_dotenv\n",
|
||||
"\n",
|
||||
"load_dotenv()\n",
|
||||
"\n",
|
||||
"OPENAI_API_KEY = \"Enter your api key here\"\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "d1fGzyQCn1aQ"
|
||||
},
|
||||
"source": [
|
||||
"# Tools and Imports"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"id": "GcNPLkZXntIS"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import streamlit as st\n",
|
||||
"from praisonaiagents import Agent\n",
|
||||
"\n",
|
||||
"# YAML Prompt Configuration\n",
|
||||
"agent_config = {\n",
|
||||
" \"name\": \"Knowledge Agent\",\n",
|
||||
" \"instructions\": [\n",
|
||||
" \"You are a helpful AI assistant. Answer questions based on your general knowledge.\",\n",
|
||||
" \"Provide clear, well-structured answers in markdown format.\",\n",
|
||||
" \"Use proper markdown formatting with headers, lists, and emphasis where appropriate.\",\n",
|
||||
" \"Structure your response with clear sections and bullet points when helpful.\",\n",
|
||||
" ],\n",
|
||||
" \"llm\": \"gpt-5-nano\",\n",
|
||||
" \"markdown\": True,\n",
|
||||
" \"verbose\": True\n",
|
||||
"}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "jkmrh0gIn_4a"
|
||||
},
|
||||
"source": [
|
||||
"# Initialize Agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"id": "qaJvf8RyoAsB"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def create_agent():\n",
|
||||
" \"\"\"Create an agent with reasoning capabilities\"\"\"\n",
|
||||
" return Agent(\n",
|
||||
" name=agent_config[\"name\"],\n",
|
||||
" instructions=agent_config[\"instructions\"],\n",
|
||||
" llm=agent_config[\"llm\"],\n",
|
||||
" markdown=agent_config[\"markdown\"],\n",
|
||||
" verbose=agent_config[\"verbose\"]\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "kvGJh3bbqTmh"
|
||||
},
|
||||
"source": [
|
||||
"# Main Function"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 1000,
|
||||
"referenced_widgets": [
|
||||
"ad296d97510246de8eb0138ce8a8a166",
|
||||
"f6e502b3dca442f0ae6c5739f5a6d159"
|
||||
]
|
||||
},
|
||||
"id": "_QyxzqKKqUrW",
|
||||
"outputId": "97be7e02-b034-4004-e9e3-56337e3f2887"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"�� Initializing Agentic RAG with GPT-5...\n",
|
||||
"📚 Loading agent...\n",
|
||||
"✅ Agent loaded successfully!\n",
|
||||
"\n",
|
||||
"🎯 Example queries you can try:\n",
|
||||
"1. What is PraisonAI and how do Agents work?\n",
|
||||
"2. What are Teams in PraisonAI and how do they work?\n",
|
||||
"3. Give me a step-by-step guide to building a RAG system.\n",
|
||||
"4. What are AI Agents?\n",
|
||||
"\n",
|
||||
"============================================================\n",
|
||||
"\n",
|
||||
"🤔 Enter your question (or 'quit' to exit):\n",
|
||||
"> What is PraisonAI and how do Agents work?\n",
|
||||
"🔍 Processing your question...\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
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|
||||
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|
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
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|
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|
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|
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|
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"\u001b[2mResponse generated in 9.4s\u001b[0m\n"
|
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|
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|
||||
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|
||||
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|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> What is PraisonAI and how do Agents work? <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
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|
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|
||||
"\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
|
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|
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|
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|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┃ <span style=\"font-weight: bold\">What is PraisonAI?</span> ┃ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> PraisonAI is a platform designed to leverage artificial intelligence to enhance personal and business <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> productivity. It typically involves developing AI models that can assist in various tasks, automate processes, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> and provide insights based on data analysis. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold; text-decoration: underline\">Key Features of PraisonAI</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Task Automation</span>: Automates repetitive tasks, allowing users to focus on more strategic activities. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Data Insights</span>: Analyzes large sets of data to provide actionable insights. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Natural Language Processing</span>: Enables users to interact with the AI through natural language, making it <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>user-friendly. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Customizable Solutions</span>: Tailors AI applications to meet specific user needs across various industries. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┃ <span style=\"font-weight: bold\">How Do Agents Work in PraisonAI?</span> ┃ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Agents in PraisonAI refer to autonomous or semi-autonomous AI systems that perform designated tasks or <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> functions. Here’s how they typically operate: <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold; text-decoration: underline\">1. Types of Agents</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Reactive Agents</span>: Respond to the current environment without internal memory. They act solely based on <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>current inputs. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Deliberative Agents</span>: Have a model of their environment, plan their actions, and retain memories of past <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>interactions. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Learning Agents</span>: Improve their performance over time through experience, adapting to new information. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold; text-decoration: underline\">2. Functionality of Agents</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Task Execution</span>: Agents can carry out specific tasks as programmed, such as scheduling, data entry, or <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>customer support. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Interaction</span>: They often interact with users through chat interfaces or APIs, providing a seamless user <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>experience. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Integration</span>: Can be integrated with other systems or software to enhance functionality, such as CRM systems, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>databases, or other AI tools. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Continuous Learning</span>: Agents may utilize machine learning algorithms to adapt and evolve based on user <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> </span>interactions and data received. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold; text-decoration: underline\">3. Benefits of Using Agents</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Increased Efficiency</span>: Automate routine tasks to reduce workload. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">24/7 Availability</span>: Provide consistent support and functionality without downtime. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Scalability</span>: Easily scale operations by deploying more agents as needed. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Data-Driven Decisions</span>: Use data insights to inform strategy and operations. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold; text-decoration: underline\">Conclusion</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> PraisonAI represents a robust platform for enhancing productivity through AI, with agents serving as the heart <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> of this solution, performing various tasks and supporting users in achieving their goals efficiently. Whether <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> they are simple reactive agents or complex learning entities, their design focuses on making operations <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">│</span> smoother and more effective. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
|
||||
"<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
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"\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
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"\u001b[36m│\u001b[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ \u001b[36m│\u001b[0m\n",
|
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"\u001b[36m│\u001b[0m ┃ \u001b[1mWhat is PraisonAI?\u001b[0m ┃ \u001b[36m│\u001b[0m\n",
|
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"\u001b[36m│\u001b[0m ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ \u001b[36m│\u001b[0m\n",
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"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
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"\u001b[36m│\u001b[0m PraisonAI is a platform designed to leverage artificial intelligence to enhance personal and business \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m productivity. It typically involves developing AI models that can assist in various tasks, automate processes, \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m and provide insights based on data analysis. \u001b[36m│\u001b[0m\n",
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"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
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"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;4mKey Features of PraisonAI\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
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||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mTask Automation\u001b[0m: Automates repetitive tasks, allowing users to focus on more strategic activities. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mData Insights\u001b[0m: Analyzes large sets of data to provide actionable insights. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mNatural Language Processing\u001b[0m: Enables users to interact with the AI through natural language, making it \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0muser-friendly. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mCustomizable Solutions\u001b[0m: Tailors AI applications to meet specific user needs across various industries. \u001b[36m│\u001b[0m\n",
|
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"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
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"\u001b[36m│\u001b[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m ┃ \u001b[1mHow Do Agents Work in PraisonAI?\u001b[0m ┃ \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m Agents in PraisonAI refer to autonomous or semi-autonomous AI systems that perform designated tasks or \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m functions. Here’s how they typically operate: \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
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"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;4m1. \u001b[0m\u001b[1;4mTypes of Agents\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mReactive Agents\u001b[0m: Respond to the current environment without internal memory. They act solely based on \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mcurrent inputs. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mDeliberative Agents\u001b[0m: Have a model of their environment, plan their actions, and retain memories of past \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0minteractions. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mLearning Agents\u001b[0m: Improve their performance over time through experience, adapting to new information. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;4m2. \u001b[0m\u001b[1;4mFunctionality of Agents\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mTask Execution\u001b[0m: Agents can carry out specific tasks as programmed, such as scheduling, data entry, or \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mcustomer support. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mInteraction\u001b[0m: They often interact with users through chat interfaces or APIs, providing a seamless user \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mexperience. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mIntegration\u001b[0m: Can be integrated with other systems or software to enhance functionality, such as CRM systems, \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0mdatabases, or other AI tools. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mContinuous Learning\u001b[0m: Agents may utilize machine learning algorithms to adapt and evolve based on user \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m \u001b[0minteractions and data received. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;4m3. \u001b[0m\u001b[1;4mBenefits of Using Agents\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mIncreased Efficiency\u001b[0m: Automate routine tasks to reduce workload. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1m24/7 Availability\u001b[0m: Provide consistent support and functionality without downtime. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mScalability\u001b[0m: Easily scale operations by deploying more agents as needed. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mData-Driven Decisions\u001b[0m: Use data insights to inform strategy and operations. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[1;4mConclusion\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m PraisonAI represents a robust platform for enhancing productivity through AI, with agents serving as the heart \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m of this solution, performing various tasks and supporting users in achieving their goals efficiently. Whether \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m they are simple reactive agents or complex learning entities, their design focuses on making operations \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m│\u001b[0m smoother and more effective. \u001b[36m│\u001b[0m\n",
|
||||
"\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"💡 Answer:\n",
|
||||
"========================================\n",
|
||||
"# What is PraisonAI?\n",
|
||||
"\n",
|
||||
"PraisonAI is a platform designed to leverage artificial intelligence to enhance personal and business productivity. It typically involves developing AI models that can assist in various tasks, automate processes, and provide insights based on data analysis.\n",
|
||||
"\n",
|
||||
"## Key Features of PraisonAI\n",
|
||||
"\n",
|
||||
"- **Task Automation**: Automates repetitive tasks, allowing users to focus on more strategic activities.\n",
|
||||
"- **Data Insights**: Analyzes large sets of data to provide actionable insights.\n",
|
||||
"- **Natural Language Processing**: Enables users to interact with the AI through natural language, making it user-friendly.\n",
|
||||
"- **Customizable Solutions**: Tailors AI applications to meet specific user needs across various industries.\n",
|
||||
"\n",
|
||||
"# How Do Agents Work in PraisonAI?\n",
|
||||
"\n",
|
||||
"Agents in PraisonAI refer to autonomous or semi-autonomous AI systems that perform designated tasks or functions. Here’s how they typically operate:\n",
|
||||
"\n",
|
||||
"## 1. **Types of Agents**\n",
|
||||
"\n",
|
||||
"- **Reactive Agents**: Respond to the current environment without internal memory. They act solely based on current inputs.\n",
|
||||
"- **Deliberative Agents**: Have a model of their environment, plan their actions, and retain memories of past interactions.\n",
|
||||
"- **Learning Agents**: Improve their performance over time through experience, adapting to new information.\n",
|
||||
"\n",
|
||||
"## 2. **Functionality of Agents**\n",
|
||||
"\n",
|
||||
"- **Task Execution**: Agents can carry out specific tasks as programmed, such as scheduling, data entry, or customer support.\n",
|
||||
"- **Interaction**: They often interact with users through chat interfaces or APIs, providing a seamless user experience.\n",
|
||||
"- **Integration**: Can be integrated with other systems or software to enhance functionality, such as CRM systems, databases, or other AI tools.\n",
|
||||
"- **Continuous Learning**: Agents may utilize machine learning algorithms to adapt and evolve based on user interactions and data received.\n",
|
||||
"\n",
|
||||
"## 3. **Benefits of Using Agents**\n",
|
||||
"\n",
|
||||
"- **Increased Efficiency**: Automate routine tasks to reduce workload.\n",
|
||||
"- **24/7 Availability**: Provide consistent support and functionality without downtime.\n",
|
||||
"- **Scalability**: Easily scale operations by deploying more agents as needed.\n",
|
||||
"- **Data-Driven Decisions**: Use data insights to inform strategy and operations.\n",
|
||||
"\n",
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"PraisonAI represents a robust platform for enhancing productivity through AI, with agents serving as the heart of this solution, performing various tasks and supporting users in achieving their goals efficiently. Whether they are simple reactive agents or complex learning entities, their design focuses on making operations smoother and more effective.\n",
|
||||
"========================================\n",
|
||||
"\n",
|
||||
"🤔 Enter your question (or 'quit' to exit):\n",
|
||||
"> quit\n",
|
||||
"👋 Goodbye!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def main():\n",
|
||||
" print(\"�� Initializing Agentic RAG with GPT-5...\")\n",
|
||||
"\n",
|
||||
" # Check if API key is set\n",
|
||||
" if not OPENAI_API_KEY or OPENAI_API_KEY == \"your_openai_api_key_here\":\n",
|
||||
" print(\"❌ Please set your OpenAI API key first!\")\n",
|
||||
" print(\"�� Get your key from: https://platform.openai.com/\")\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" try:\n",
|
||||
" # Create agent\n",
|
||||
" print(\"📚 Loading agent...\")\n",
|
||||
" agent = create_agent()\n",
|
||||
" print(\"✅ Agent loaded successfully!\")\n",
|
||||
"\n",
|
||||
" # Example queries\n",
|
||||
" example_queries = [\n",
|
||||
" \"What is PraisonAI and how do Agents work?\",\n",
|
||||
" \"What are Teams in PraisonAI and how do they work?\",\n",
|
||||
" \"Give me a step-by-step guide to building a RAG system.\",\n",
|
||||
" \"What are AI Agents?\"\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" print(\"\\n🎯 Example queries you can try:\")\n",
|
||||
" for i, query in enumerate(example_queries, 1):\n",
|
||||
" print(f\"{i}. {query}\")\n",
|
||||
"\n",
|
||||
" print(\"\\n\" + \"=\"*60)\n",
|
||||
"\n",
|
||||
" # Interactive query loop\n",
|
||||
" while True:\n",
|
||||
" print(\"\\n🤔 Enter your question (or 'quit' to exit):\")\n",
|
||||
" user_query = input(\"> \")\n",
|
||||
"\n",
|
||||
" if user_query.lower() in ['quit', 'exit', 'q']:\n",
|
||||
" print(\"👋 Goodbye!\")\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" if user_query.strip():\n",
|
||||
" print(\"🔍 Processing your question...\")\n",
|
||||
" try:\n",
|
||||
" response = agent.start(user_query)\n",
|
||||
" print(\"\\n💡 Answer:\")\n",
|
||||
" print(\"=\"*40)\n",
|
||||
" print(response)\n",
|
||||
" print(\"=\"*40)\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"❌ Error: {str(e)}\")\n",
|
||||
" print(f\"🔍 Error type: {type(e).__name__}\")\n",
|
||||
" print(\"💡 This might be due to API issues or model access problems.\")\n",
|
||||
" else:\n",
|
||||
" print(\"❌ Please enter a valid question.\")\n",
|
||||
"\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"❌ Failed to initialize agent: {str(e)}\")\n",
|
||||
" print(\"�� Make sure your API key is correct and you have sufficient credits.\")\n",
|
||||
"\n",
|
||||
"# Run the main function\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" main()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
from praisonaiagents import Agent
|
||||
import os
|
||||
|
||||
# Set OpenAI API key if not already set
|
||||
if not os.getenv("OPENAI_API_KEY"):
|
||||
print("Please set your OpenAI API key: export OPENAI_API_KEY='your-api-key-here'")
|
||||
exit(1)
|
||||
|
||||
# Create the RAG agent with web search capabilities
|
||||
rag_agent = Agent(
|
||||
instructions="""You are a helpful AI assistant specialized in Thai recipes and cooking.
|
||||
|
||||
You have access to a PDF knowledge base about Thai recipes from: https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf
|
||||
|
||||
You can also search the web for additional information about Thai cooking, ingredients, and techniques.
|
||||
|
||||
When answering questions:
|
||||
1. Use your knowledge about Thai cuisine to provide helpful information
|
||||
2. If needed, search the web for additional details, current information, or clarification
|
||||
3. Provide comprehensive, helpful answers about Thai cuisine
|
||||
4. Always be informative and helpful about Thai cooking!
|
||||
|
||||
You can use the internet_search function to search the web when needed.""",
|
||||
llm="gpt-4o",
|
||||
markdown=True,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🤖 Thai Recipe RAG Agent is ready!")
|
||||
print("Ask me anything about Thai recipes or cooking!")
|
||||
print("Type 'quit' to exit.\n")
|
||||
|
||||
while True:
|
||||
user_input = input("You: ")
|
||||
if user_input.lower() in ['quit', 'exit', 'bye']:
|
||||
print("👋 Goodbye! Happy cooking!")
|
||||
break
|
||||
|
||||
try:
|
||||
response = rag_agent.start(user_input)
|
||||
print(f"\n🤖 Assistant: {response}\n")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}\n")
|
||||
@@ -0,0 +1,154 @@
|
||||
# 🧠 Agentic RAG with GPT-5
|
||||
|
||||
An agentic RAG application built with the PraisonAI Agents framework, featuring GPT-5 and built-in vector search for efficient knowledge retrieval and question answering.
|
||||
|
||||
## ✨ Features
|
||||
|
||||
- **🤖 GPT-5**: Latest OpenAI model for intelligent responses
|
||||
- **🗄️ Built-in Vector Search**: ChromaDB integration for fast similarity search
|
||||
- **🔍 Agentic RAG**: Intelligent retrieval augmented generation
|
||||
- **📝 Markdown Formatting**: Beautiful, structured responses
|
||||
- **🌐 Dynamic Knowledge**: Add URLs to expand knowledge base
|
||||
- **⚡ Real-time Responses**: Fast answer generation- **🎯 Clean Interface**: Simplified UI without configuration complexity
|
||||
|
||||
## 🚀 Quick Start
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Python 3.11+
|
||||
- OpenAI API key with GPT-5 access
|
||||
|
||||
### Installation
|
||||
|
||||
1. **Clone and navigate to the project**
|
||||
```bash
|
||||
cd rag_examples/agentic_rag_gpt5
|
||||
```
|
||||
|
||||
2. **Install dependencies**
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
3. **Set up your OpenAI API key**
|
||||
```bash
|
||||
export OPENAI_API_KEY="your-api-key-here"
|
||||
```
|
||||
Or create a `.env` file:
|
||||
```
|
||||
OPENAI_API_KEY=your-api-key-here
|
||||
```
|
||||
|
||||
4. **Run the application**
|
||||
```bash
|
||||
streamlit run agentic_rag_gpt5.py
|
||||
```
|
||||
|
||||
## 🎯 How to Use
|
||||
|
||||
1. **Enter your OpenAI API key** in the sidebar
|
||||
2. **Add knowledge sources** by entering URLs in the sidebar
|
||||
3. **Ask questions** using the text area or suggested prompts
|
||||
4. **Get answers** with markdown formatting
|
||||
|
||||
### Suggested Questions
|
||||
|
||||
- **"What is PraisonAI?"** - Learn about the PraisonAI Agents framework
|
||||
- **"Teams in PraisonAI"** - Understand how teams work in PraisonAI
|
||||
- **"Build RAG system"** - Get a step-by-step guide to building RAG systems
|
||||
|
||||
## 🏗️ Architecture
|
||||
|
||||
### Core Components
|
||||
|
||||
- **`Agent`**: PraisonAI Agents framework for intelligent Q&A
|
||||
- **`knowledge`**: Built-in knowledge base that handles URLs and documents
|
||||
- **`llm`**: OpenAI GPT-5-nano for generating responses
|
||||
- **Built-in Vector Search**: Automatic similarity search without external setup
|
||||
|
||||
### Data Flow
|
||||
|
||||
1. **Knowledge Loading**: URLs are processed and stored in the built-in vector database
|
||||
2. **Vector Search**: OpenAI embeddings enable semantic search
|
||||
3. **Response Generation**: GPT-5-nano processes information and generates answers
|
||||
4. **Formatted Output**: Markdown-formatted responses
|
||||
|
||||
## 🔧 Configuration
|
||||
|
||||
### Database Settings
|
||||
- **Vector DB**: Built-in vector database with automatic indexing
|
||||
- **Storage**: Local storage managed by PraisonAI Agents
|
||||
- **Search**: Automatic similarity search
|
||||
|
||||
### Model Configuration
|
||||
- **LLM**: OpenAI GPT-5-nano
|
||||
- **Embeddings**: Automatic handling by PraisonAI Agents
|
||||
- **Vector Store**: Built-in with automatic document processing
|
||||
|
||||
## 📚 Knowledge Management
|
||||
|
||||
### Adding Sources
|
||||
- Use the sidebar to add new URLs
|
||||
- Sources are automatically processed and indexed
|
||||
- Current sources are displayed as numbered list
|
||||
|
||||
### Default Knowledge
|
||||
- Starts with PraisonAI documentation: `https://docs.praisonai.com/introduction/agents.md`
|
||||
- Expandable with any web-based documentation
|
||||
|
||||
## 🎨 UI Features
|
||||
|
||||
### Sidebar
|
||||
- **API Key Management**: Secure input for OpenAI credentials
|
||||
- **URL Addition**: Dynamic knowledge base expansion
|
||||
- **Current Sources**: Numbered list of loaded URLs
|
||||
|
||||
### Main Interface
|
||||
- **Suggested Prompts**: Quick access to common questions
|
||||
- **Query Input**: Large text area for custom questions
|
||||
- **Fast Responses**: Quick answer generation
|
||||
- **Markdown Rendering**: Beautiful formatted responses
|
||||
|
||||
## 🛠️ Technical Details
|
||||
|
||||
### Dependencies
|
||||
```
|
||||
streamlit>=1.28.0
|
||||
praisonaiagents>=0.1.0
|
||||
openai>=1.0.0
|
||||
python-dotenv>=1.0.0
|
||||
```
|
||||
|
||||
### Key Features
|
||||
- **Built-in Knowledge Base**: Automatic document processing and indexing
|
||||
- **Vector Search**: Efficient similarity search with built-in database
|
||||
- **Caching**: Efficient resource loading with Streamlit caching
|
||||
- **Error Handling**: Graceful handling of API and processing errors
|
||||
|
||||
## 🔍 Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
**Knowledge base not loading**
|
||||
- Check OpenAI API key is valid
|
||||
- Ensure URLs are accessible
|
||||
- Verify internet connection
|
||||
|
||||
**Agent initialization errors**
|
||||
- Check if PraisonAI Agents is properly installed
|
||||
- Verify OpenAI API key has sufficient credits
|
||||
- Ensure Python version is 3.11+
|
||||
|
||||
### Performance Tips
|
||||
- **Cache Resources**: Knowledge base and agent are cached for efficiency
|
||||
- **Built-in Vector Search**: Fast similarity search without external setup
|
||||
- **Local Storage**: Optimized local storage for optimal performance
|
||||
|
||||
## 🎯 Use Cases
|
||||
|
||||
- **Documentation Q&A**: Ask questions about technical documentation
|
||||
- **Research Assistant**: Get answers from multiple knowledge sources
|
||||
- **Learning Tool**: Interactive exploration of complex topics
|
||||
- **Content Discovery**: Find relevant information across multiple sources
|
||||
|
||||
**Built with ❤️ using PraisonAI Agents and GPT-5**
|
||||
@@ -0,0 +1,201 @@
|
||||
import streamlit as st
|
||||
import os
|
||||
from praisonaiagents import Agent
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Page configuration
|
||||
st.set_page_config(
|
||||
page_title="Agentic RAG with GPT-5",
|
||||
page_icon="🧠",
|
||||
layout="wide"
|
||||
)
|
||||
|
||||
# Main title and description
|
||||
st.title("🧠 Agentic RAG with GPT-5")
|
||||
st.markdown("""
|
||||
This app demonstrates an intelligent AI agent that:
|
||||
1. **Answers** your questions clearly and concisely using GPT-5
|
||||
|
||||
⚠️ **Note**: Knowledge base functionality is temporarily disabled due to a compatibility issue with the current version of PraisonAI Agents.
|
||||
|
||||
Enter your OpenAI API key in the sidebar to get started!
|
||||
""")
|
||||
|
||||
# Sidebar for API key and settings
|
||||
with st.sidebar:
|
||||
st.header("🔧 Configuration")
|
||||
|
||||
# OpenAI API Key
|
||||
openai_key = st.text_input(
|
||||
"OpenAI API Key",
|
||||
type="password",
|
||||
value=os.getenv("OPENAI_API_KEY", ""),
|
||||
help="Get your key from https://platform.openai.com/"
|
||||
)
|
||||
|
||||
# Add URLs to knowledge base
|
||||
st.subheader("🌐 Add Knowledge Sources")
|
||||
st.info("⚠️ Knowledge base functionality is temporarily disabled due to compatibility issues.")
|
||||
new_url = st.text_input(
|
||||
"Add URL",
|
||||
placeholder="https://docs.praisonai.com/introduction",
|
||||
help="Enter a URL to add to the knowledge base (currently disabled)"
|
||||
)
|
||||
|
||||
if st.button("➕ Add URL", type="primary", disabled=True):
|
||||
st.info("Knowledge base functionality is temporarily disabled.")
|
||||
|
||||
# Check if API key is provided
|
||||
if openai_key:
|
||||
# Initialize knowledge base (cached to avoid reloading)
|
||||
@st.cache_resource(show_spinner="📚 Loading knowledge base...")
|
||||
def load_knowledge() -> list:
|
||||
"""Load and initialize the knowledge base with default URL"""
|
||||
return ["https://docs.praisonai.com/introduction/agents.md"] # Default URL
|
||||
|
||||
# Initialize agent (cached to avoid reloading)
|
||||
@st.cache_resource(show_spinner="🤖 Loading agent...")
|
||||
def load_agent(_knowledge: list) -> Agent:
|
||||
"""Create an agent with reasoning capabilities"""
|
||||
# Note: Temporarily removed knowledge parameter to avoid rerank error
|
||||
# TODO: Re-enable when PraisonAI Agents knowledge issue is resolved
|
||||
return Agent(
|
||||
name="Knowledge Agent",
|
||||
instructions=[
|
||||
"You are a helpful AI assistant. Answer questions based on your general knowledge.",
|
||||
"Provide clear, well-structured answers in markdown format.",
|
||||
"Use proper markdown formatting with headers, lists, and emphasis where appropriate.",
|
||||
"Structure your response with clear sections and bullet points when helpful.",
|
||||
],
|
||||
llm="gpt-5-nano
|
||||
markdown=True,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Load knowledge and agent
|
||||
knowledge = load_knowledge()
|
||||
agent = load_agent(knowledge)
|
||||
|
||||
# Display current URLs in knowledge base
|
||||
if knowledge:
|
||||
st.sidebar.subheader("📚 Current Knowledge Sources")
|
||||
for i, url in enumerate(knowledge, 1):
|
||||
st.sidebar.markdown(f"{i}. {url}")
|
||||
|
||||
# Handle URL additions
|
||||
if hasattr(st.session_state, 'urls_to_add') and st.session_state.urls_to_add:
|
||||
with st.spinner("📥 Loading new documents..."):
|
||||
knowledge.append(st.session_state.urls_to_add)
|
||||
# Reinitialize agent with new knowledge
|
||||
agent = load_agent(knowledge)
|
||||
st.success(f"✅ Added: {st.session_state.urls_to_add}")
|
||||
del st.session_state.urls_to_add
|
||||
st.rerun()
|
||||
|
||||
# Main query section
|
||||
st.divider()
|
||||
st.subheader("🤔 Ask a Question")
|
||||
|
||||
# Suggested prompts
|
||||
st.markdown("**Try these prompts:**")
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
if st.button("What is PraisonAI?", use_container_width=True):
|
||||
st.session_state.query = "What is PraisonAI and how do Agents work?"
|
||||
with col2:
|
||||
if st.button("Teams in PraisonAI", use_container_width=True):
|
||||
st.session_state.query = "What are Teams in PraisonAI and how do they work?"
|
||||
with col3:
|
||||
if st.button("Build RAG system", use_container_width=True):
|
||||
st.session_state.query = "Give me a step-by-step guide to building a RAG system."
|
||||
|
||||
# Query input
|
||||
query = st.text_area(
|
||||
"Your question:",
|
||||
value=st.session_state.get("query", "What are AI Agents?"),
|
||||
height=100,
|
||||
help="Ask anything about the loaded knowledge sources"
|
||||
)
|
||||
|
||||
# Run button
|
||||
if st.button("🚀 Get Answer", type="primary"):
|
||||
if query:
|
||||
# Create container for answer
|
||||
st.markdown("### 💡 Answer")
|
||||
answer_container = st.container()
|
||||
answer_placeholder = answer_container.empty()
|
||||
|
||||
# Get the agent's response
|
||||
with st.spinner("🔍 Searching and generating answer..."):
|
||||
try:
|
||||
st.info("🤖 Agent is processing your question...")
|
||||
response = agent.start(query)
|
||||
answer_placeholder.markdown(
|
||||
response,
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
except Exception as e:
|
||||
st.error(f"Error getting response: {str(e)}")
|
||||
st.error(f"Error type: {type(e).__name__}")
|
||||
st.error(f"Full error details: {repr(e)}")
|
||||
# Try to provide a helpful response
|
||||
answer_placeholder.markdown("""
|
||||
**⚠️ Error occurred while processing your question.**
|
||||
|
||||
This might be due to:
|
||||
- Knowledge base configuration issues
|
||||
- Model access problems
|
||||
- Network connectivity issues
|
||||
|
||||
Please try again or check your OpenAI API key.
|
||||
""")
|
||||
else:
|
||||
st.error("Please enter a question")
|
||||
|
||||
else:
|
||||
# Show instructions if API key is missing
|
||||
st.info("""
|
||||
👋 **Welcome! To use this app, you need:**
|
||||
|
||||
- **OpenAI API Key** (set it in the sidebar)
|
||||
- Sign up at [platform.openai.com](https://platform.openai.com/)
|
||||
- Generate a new API key
|
||||
|
||||
Once you enter the key, the app will load the knowledge base and agent.
|
||||
""")
|
||||
|
||||
# Footer with explanation
|
||||
st.divider()
|
||||
with st.expander("📖 How This Works"):
|
||||
st.markdown("""
|
||||
**This app uses the PraisonAI Agents framework to create an intelligent Q&A system:**
|
||||
|
||||
⚠️ **Current Status**: Knowledge base functionality is temporarily disabled due to compatibility issues with the current version of PraisonAI Agents (v0.0.157).
|
||||
|
||||
**What Works Now:**
|
||||
1. **GPT-5 Integration**: OpenAI's GPT-5 model for generating intelligent responses
|
||||
2. **Markdown Formatting**: Beautiful, structured responses
|
||||
3. **Agent Framework**: PraisonAI Agents for orchestration
|
||||
|
||||
**What's Temporarily Disabled:**
|
||||
- URL-based knowledge base
|
||||
- Vector search and retrieval
|
||||
- Document processing
|
||||
|
||||
**Key Components:**
|
||||
- `Agent`: PraisonAI Agents framework for creating intelligent agents
|
||||
- `llm`: OpenAI GPT-5-nano for generating responses
|
||||
|
||||
**Why PraisonAI Agents?**
|
||||
- Easy-to-use agent framework
|
||||
- Built-in error handling
|
||||
- Clean API design
|
||||
- Perfect for prototyping and production applications
|
||||
|
||||
**Next Steps:**
|
||||
- Monitor PraisonAI Agents updates for knowledge base fixes
|
||||
- Re-enable knowledge functionality when compatible version is available
|
||||
""")
|
||||
+533
-54
@@ -1,83 +1,562 @@
|
||||
# MCP SSE Server and Client Implementation
|
||||
# PraisonAI Agents
|
||||
|
||||
This project demonstrates a working pattern for SSE-based MCP (Model Context Protocol) servers and clients. It consists of three main components:
|
||||
A powerful Python framework for building AI agents with self-reflection, tool use, and multi-agent orchestration capabilities.
|
||||
|
||||
1. **server.py**: An SSE-based MCP server that provides simple tools
|
||||
2. **client.py**: A standalone client that connects to the server and uses its tools with Claude
|
||||
3. **mcp-sse.py**: A client using praisonaiagents that connects to the server and uses its tools with OpenAI
|
||||
[](https://badge.fury.io/py/praisonaiagents)
|
||||
[](https://www.python.org/downloads/)
|
||||
|
||||
## Tools Provided by the Server
|
||||
|
||||
The server implements two simple tools:
|
||||
|
||||
- **get_greeting**: Returns a personalized greeting for a given name
|
||||
- **get_weather**: Returns simulated weather data for a given city
|
||||
|
||||
## Setup and Usage
|
||||
|
||||
### Prerequisites
|
||||
|
||||
Make sure you have the required packages installed:
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install praisonaiagents mcp httpx starlette uvicorn anthropic python-dotenv
|
||||
# Basic installation
|
||||
pip install praisonaiagents
|
||||
|
||||
# With all features
|
||||
pip install "praisonaiagents[all]"
|
||||
|
||||
# Specific features
|
||||
pip install "praisonaiagents[memory]" # Memory support
|
||||
pip install "praisonaiagents[knowledge]" # Knowledge base
|
||||
pip install "praisonaiagents[mcp]" # Model Context Protocol
|
||||
pip install "praisonaiagents[llm]" # LiteLLM support
|
||||
pip install "praisonaiagents[mongodb]" # MongoDB integration
|
||||
```
|
||||
|
||||
### Running the Server
|
||||
## Quick Start
|
||||
|
||||
First, start the MCP SSE server:
|
||||
### Basic Agent
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(
|
||||
name="Assistant",
|
||||
instructions="You are a helpful assistant.",
|
||||
model="gpt-4o-mini"
|
||||
)
|
||||
|
||||
response = agent.chat("What is the capital of France?")
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Agent with Tools
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent
|
||||
|
||||
def get_weather(city: str) -> str:
|
||||
"""Get weather for a city."""
|
||||
return f"Weather in {city}: Sunny, 22°C"
|
||||
|
||||
agent = Agent(
|
||||
name="Weather Agent",
|
||||
instructions="You help users check the weather.",
|
||||
tools=[get_weather]
|
||||
)
|
||||
|
||||
response = agent.chat("What's the weather in Paris?")
|
||||
```
|
||||
|
||||
### Multi-Agent Workflow
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent, Task, PraisonAIAgents
|
||||
|
||||
# Define agents
|
||||
researcher = Agent(name="Researcher", instructions="Research topics thoroughly")
|
||||
writer = Agent(name="Writer", instructions="Write clear, engaging content")
|
||||
|
||||
# Define tasks
|
||||
research_task = Task(
|
||||
description="Research AI trends in 2025",
|
||||
agent=researcher,
|
||||
expected_output="Research findings"
|
||||
)
|
||||
|
||||
write_task = Task(
|
||||
description="Write an article based on research",
|
||||
agent=writer,
|
||||
expected_output="Article draft"
|
||||
)
|
||||
|
||||
# Run workflow
|
||||
agents = PraisonAIAgents(agents=[researcher, writer], tasks=[research_task, write_task])
|
||||
result = agents.start()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Agent Types
|
||||
|
||||
### 1. Agent (Base)
|
||||
The core agent class with self-reflection, tool use, and streaming support.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(
|
||||
name="Assistant",
|
||||
instructions="You are helpful.",
|
||||
model="gpt-4o-mini",
|
||||
verbose=True,
|
||||
self_reflect=True, # Enable self-reflection
|
||||
max_reflect=3 # Max reflection iterations
|
||||
)
|
||||
```
|
||||
|
||||
### 2. DeepResearchAgent 🆕
|
||||
Automated research agent using OpenAI or Gemini Deep Research APIs with real-time streaming.
|
||||
|
||||
```python
|
||||
from praisonaiagents import DeepResearchAgent
|
||||
|
||||
# OpenAI Deep Research
|
||||
agent = DeepResearchAgent(
|
||||
model="o4-mini-deep-research", # or "o3-deep-research"
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research("What are the latest AI trends?")
|
||||
print(result.report)
|
||||
print(f"Citations: {len(result.citations)}")
|
||||
|
||||
# Gemini Deep Research
|
||||
agent = DeepResearchAgent(
|
||||
model="deep-research-pro", # Auto-detected as Gemini
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research("Research quantum computing advances")
|
||||
```
|
||||
|
||||
**Features:**
|
||||
- **Multi-provider**: OpenAI, Gemini, and LiteLLM support
|
||||
- **Auto-detection**: Provider detected from model name
|
||||
- **Streaming**: Real-time progress with reasoning summaries (default: enabled)
|
||||
- **Citations**: Structured citations with URLs
|
||||
- **Tools**: Web search, code interpreter, MCP, file search
|
||||
|
||||
### 3. ImageAgent
|
||||
Agent specialized for image generation and analysis.
|
||||
|
||||
```python
|
||||
from praisonaiagents import ImageAgent
|
||||
|
||||
agent = ImageAgent(
|
||||
name="Artist",
|
||||
instructions="Generate creative images"
|
||||
)
|
||||
|
||||
result = agent.chat("Create an image of a sunset over mountains")
|
||||
```
|
||||
|
||||
### 4. ContextAgent
|
||||
Agent with persistent context across conversations.
|
||||
|
||||
```python
|
||||
from praisonaiagents import ContextAgent
|
||||
|
||||
agent = ContextAgent(
|
||||
name="Context Agent",
|
||||
instructions="Remember our conversation"
|
||||
)
|
||||
```
|
||||
|
||||
### 5. RouterAgent
|
||||
Routes requests to appropriate specialized agents.
|
||||
|
||||
```python
|
||||
from praisonaiagents.agent import RouterAgent
|
||||
|
||||
router = RouterAgent(
|
||||
agents=[weather_agent, math_agent, general_agent],
|
||||
instructions="Route to the best agent for each query"
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Memory
|
||||
|
||||
Persistent memory with short-term, long-term, and graph memory support.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent, Memory
|
||||
|
||||
memory = Memory(
|
||||
provider="chroma", # or "mongodb", "rag"
|
||||
use_short_term=True,
|
||||
use_long_term=True
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
name="Memory Agent",
|
||||
memory=memory
|
||||
)
|
||||
```
|
||||
|
||||
**Memory Types:**
|
||||
- **Short-term**: Recent conversation context
|
||||
- **Long-term**: Persistent storage across sessions
|
||||
- **Graph**: Relationship-based memory with Mem0
|
||||
|
||||
---
|
||||
|
||||
## Knowledge Base
|
||||
|
||||
Add documents and data sources to your agents.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent, Knowledge
|
||||
|
||||
knowledge = Knowledge(
|
||||
sources=["docs/", "data.pdf", "https://example.com"],
|
||||
chunking_method="semantic"
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
name="Knowledge Agent",
|
||||
knowledge=knowledge
|
||||
)
|
||||
```
|
||||
|
||||
**Supported Sources:**
|
||||
- PDF, Word, Excel, CSV files
|
||||
- Directories of documents
|
||||
- URLs and web pages
|
||||
- Databases
|
||||
|
||||
---
|
||||
|
||||
## Tools
|
||||
|
||||
### Built-in Tools
|
||||
|
||||
```python
|
||||
from praisonaiagents.tools import (
|
||||
# Search
|
||||
duckduckgo_search,
|
||||
searxng_search,
|
||||
|
||||
# Data
|
||||
read_csv, write_csv,
|
||||
read_json, write_json,
|
||||
read_yaml, write_yaml,
|
||||
read_excel, write_excel,
|
||||
|
||||
# Finance
|
||||
get_stock_price, get_stock_info,
|
||||
|
||||
# Research
|
||||
arxiv_search,
|
||||
wikipedia_search,
|
||||
|
||||
# Web
|
||||
spider_scrape,
|
||||
newspaper_extract,
|
||||
|
||||
# System
|
||||
execute_shell,
|
||||
run_python,
|
||||
|
||||
# Database
|
||||
duckdb_query,
|
||||
mongodb_query
|
||||
)
|
||||
```
|
||||
|
||||
### Custom Tools
|
||||
|
||||
```python
|
||||
def my_tool(param: str) -> str:
|
||||
"""Tool description for the agent."""
|
||||
return f"Result: {param}"
|
||||
|
||||
agent = Agent(tools=[my_tool])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## MCP (Model Context Protocol)
|
||||
|
||||
Connect to MCP servers for extended capabilities.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent, MCP
|
||||
|
||||
# Local MCP server
|
||||
agent = Agent(
|
||||
tools=MCP(
|
||||
command="npx",
|
||||
args=["-y", "@modelcontextprotocol/server-filesystem", "/path"]
|
||||
)
|
||||
)
|
||||
|
||||
# Remote MCP server (SSE)
|
||||
agent = Agent(
|
||||
tools=MCP(url="http://localhost:8080/sse")
|
||||
)
|
||||
|
||||
# With environment variables
|
||||
agent = Agent(
|
||||
tools=MCP(
|
||||
command="npx",
|
||||
args=["-y", "@modelcontextprotocol/server-brave-search"],
|
||||
env={"BRAVE_API_KEY": "your-key"}
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Guardrails
|
||||
|
||||
Add safety checks and validation to agent responses.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent, LLMGuardrail
|
||||
|
||||
# LLM-based guardrail
|
||||
guardrail = LLMGuardrail(
|
||||
instructions="Ensure responses are professional and accurate"
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
name="Safe Agent",
|
||||
guardrail=guardrail
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Handoffs
|
||||
|
||||
Transfer conversations between agents.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent, handoff
|
||||
|
||||
support_agent = Agent(name="Support", instructions="Handle support queries")
|
||||
sales_agent = Agent(name="Sales", instructions="Handle sales queries")
|
||||
|
||||
# Enable handoffs
|
||||
support_agent.handoffs = [handoff(sales_agent, "Transfer to sales")]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Streaming
|
||||
|
||||
Real-time streaming responses.
|
||||
|
||||
```python
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Streamer", verbose=True)
|
||||
|
||||
# Streaming is automatic with verbose=True
|
||||
response = agent.chat("Tell me a story")
|
||||
|
||||
# Or use the stream method
|
||||
for chunk in agent.stream("Tell me a story"):
|
||||
print(chunk, end="", flush=True)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Async Support
|
||||
|
||||
Full async/await support for all operations.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Async Agent")
|
||||
|
||||
async def main():
|
||||
response = await agent.achat("Hello!")
|
||||
print(response)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Telemetry
|
||||
|
||||
Built-in telemetry for monitoring and analytics.
|
||||
|
||||
```python
|
||||
from praisonaiagents import enable_telemetry, disable_telemetry
|
||||
|
||||
# Enable full telemetry
|
||||
enable_telemetry()
|
||||
|
||||
# Disable telemetry
|
||||
disable_telemetry()
|
||||
```
|
||||
|
||||
**Environment Variables:**
|
||||
- `PRAISONAI_DISABLE_TELEMETRY=true` - Disable all telemetry
|
||||
- `PRAISONAI_PERFORMANCE_MODE=true` - Minimal overhead mode
|
||||
- `DO_NOT_TRACK=true` - Respect DNT preference
|
||||
|
||||
---
|
||||
|
||||
## Supported LLM Providers
|
||||
|
||||
PraisonAI Agents works with any LLM provider via LiteLLM:
|
||||
|
||||
| Provider | Model Example |
|
||||
|----------|---------------|
|
||||
| OpenAI | `gpt-4o`, `gpt-4o-mini`, `o1`, `o3` |
|
||||
| Anthropic | `claude-3-5-sonnet`, `claude-3-opus` |
|
||||
| Google | `gemini-2.0-flash`, `gemini-pro` |
|
||||
| Ollama | `ollama/llama3`, `ollama/mistral` |
|
||||
| Azure | `azure/gpt-4` |
|
||||
| AWS Bedrock | `bedrock/claude-3` |
|
||||
| Groq | `groq/llama3-70b` |
|
||||
| Together | `together/mixtral-8x7b` |
|
||||
|
||||
```python
|
||||
# Use any provider
|
||||
agent = Agent(model="anthropic/claude-3-5-sonnet")
|
||||
agent = Agent(model="ollama/llama3")
|
||||
agent = Agent(model="gemini/gemini-2.0-flash")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Environment Variables
|
||||
|
||||
```bash
|
||||
python server.py
|
||||
# API Keys
|
||||
OPENAI_API_KEY=sk-...
|
||||
ANTHROPIC_API_KEY=sk-ant-...
|
||||
GEMINI_API_KEY=...
|
||||
GOOGLE_API_KEY=...
|
||||
|
||||
# Logging
|
||||
LOGLEVEL=DEBUG # DEBUG, INFO, WARNING, ERROR
|
||||
|
||||
# Telemetry
|
||||
PRAISONAI_DISABLE_TELEMETRY=true
|
||||
```
|
||||
|
||||
By default, the server runs on 0.0.0.0:8080, but you can customize the host and port:
|
||||
---
|
||||
|
||||
```bash
|
||||
python server.py --host 127.0.0.1 --port 8081
|
||||
## Examples
|
||||
|
||||
### Research Agent with Citations
|
||||
|
||||
```python
|
||||
from praisonaiagents import DeepResearchAgent
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
model="o4-mini-deep-research",
|
||||
instructions="Provide data-rich insights with citations",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research("Economic impact of AI on healthcare")
|
||||
|
||||
print(result.report)
|
||||
for citation in result.citations:
|
||||
print(f"- {citation.title}: {citation.url}")
|
||||
```
|
||||
|
||||
### Running the Standalone Client
|
||||
### Multi-Agent Research Team
|
||||
|
||||
The standalone client uses Claude to interact with the MCP server tools:
|
||||
```python
|
||||
from praisonaiagents import Agent, Task, PraisonAIAgents
|
||||
|
||||
```bash
|
||||
# Set your Anthropic API key
|
||||
export ANTHROPIC_API_KEY=your_api_key_here
|
||||
researcher = Agent(
|
||||
name="Researcher",
|
||||
instructions="Research topics thoroughly",
|
||||
tools=[duckduckgo_search]
|
||||
)
|
||||
|
||||
# Run the client
|
||||
python client.py http://0.0.0.0:8080/sse
|
||||
analyst = Agent(
|
||||
name="Analyst",
|
||||
instructions="Analyze data and provide insights"
|
||||
)
|
||||
|
||||
writer = Agent(
|
||||
name="Writer",
|
||||
instructions="Write clear reports"
|
||||
)
|
||||
|
||||
tasks = [
|
||||
Task(description="Research AI trends", agent=researcher),
|
||||
Task(description="Analyze findings", agent=analyst),
|
||||
Task(description="Write report", agent=writer)
|
||||
]
|
||||
|
||||
team = PraisonAIAgents(agents=[researcher, analyst, writer], tasks=tasks)
|
||||
result = team.start()
|
||||
```
|
||||
|
||||
You'll see a prompt where you can type queries for Claude to process using the MCP tools.
|
||||
### Agent with Memory and Knowledge
|
||||
|
||||
### Running the praisonaiagents Client
|
||||
```python
|
||||
from praisonaiagents import Agent, Memory, Knowledge
|
||||
|
||||
The praisonaiagents client uses OpenAI to interact with the MCP server tools:
|
||||
|
||||
```bash
|
||||
# Set your OpenAI API key
|
||||
export OPENAI_API_KEY=your_api_key_here
|
||||
|
||||
# Run the client
|
||||
python mcp-sse.py
|
||||
agent = Agent(
|
||||
name="Smart Assistant",
|
||||
memory=Memory(use_long_term=True),
|
||||
knowledge=Knowledge(sources=["docs/"]),
|
||||
instructions="Help users with their questions using available knowledge"
|
||||
)
|
||||
```
|
||||
|
||||
This will automatically send a query about the weather in Paris to the agent.
|
||||
---
|
||||
|
||||
## How It Works
|
||||
## API Reference
|
||||
|
||||
1. The server exposes MCP tools via an SSE endpoint
|
||||
2. Clients connect to this endpoint and discover available tools
|
||||
3. When a user makes a query, the client:
|
||||
- For client.py: Uses Claude to determine which tool to call
|
||||
- For mcp-sse.py: Uses OpenAI to determine which tool to call
|
||||
4. The client executes the tool call against the server
|
||||
5. The result is returned to the user
|
||||
### Agent
|
||||
|
||||
This pattern allows for decoupled processes where the MCP server can run independently of clients, making it suitable for cloud-native applications.
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `name` | str | "Agent" | Agent name |
|
||||
| `instructions` | str | "" | System instructions |
|
||||
| `model` | str | "gpt-4o-mini" | LLM model |
|
||||
| `tools` | list | [] | Available tools |
|
||||
| `memory` | Memory | None | Memory instance |
|
||||
| `knowledge` | Knowledge | None | Knowledge base |
|
||||
| `verbose` | bool | False | Enable verbose output |
|
||||
| `self_reflect` | bool | False | Enable self-reflection |
|
||||
| `max_reflect` | int | 3 | Max reflection iterations |
|
||||
|
||||
## Customizing
|
||||
### DeepResearchAgent
|
||||
|
||||
- To add more tools to the server, define new functions with the `@mcp.tool()` decorator in `server.py`
|
||||
- To change the client's behavior, update the instructions and query in `mcp-sse.py`
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `model` | str | "o3-deep-research" | Research model |
|
||||
| `provider` | str | None | Force provider (openai/gemini/litellm) |
|
||||
| `verbose` | bool | True | Show progress |
|
||||
| `poll_interval` | int | 5 | Gemini poll interval (seconds) |
|
||||
| `max_wait_time` | int | 3600 | Max research time (seconds) |
|
||||
|
||||
### Task
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `description` | str | required | Task description |
|
||||
| `agent` | Agent | required | Assigned agent |
|
||||
| `expected_output` | str | "" | Expected output format |
|
||||
| `context` | list | [] | Context from other tasks |
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
MIT License - see LICENSE file for details.
|
||||
|
||||
## Links
|
||||
|
||||
- [Documentation](https://docs.praison.ai)
|
||||
- [GitHub](https://github.com/MervinPraison/PraisonAI)
|
||||
- [PyPI](https://pypi.org/project/praisonaiagents/)
|
||||
@@ -0,0 +1,48 @@
|
||||
"""
|
||||
Gemini Deep Research Agent Example
|
||||
|
||||
Uses Google's deep-research-pro model via the Interactions API
|
||||
for automated research with web search.
|
||||
|
||||
Supports two modes:
|
||||
1. Polling (default): Checks status periodically, shows progress logs
|
||||
2. Streaming: Real-time output with thinking summaries (recommended)
|
||||
|
||||
Requirements:
|
||||
- GEMINI_API_KEY or GOOGLE_API_KEY environment variable
|
||||
- google-genai >= 1.55.0 (pip install google-genai)
|
||||
"""
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
from praisonaiagents import DeepResearchAgent
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Gemini Researcher",
|
||||
instructions="""
|
||||
You are a professional researcher. Focus on:
|
||||
- Comprehensive analysis with multiple perspectives
|
||||
- Data-backed insights and trends
|
||||
- Clear structure with executive summary
|
||||
""",
|
||||
model="deep-research-pro", # Auto-detected as Gemini
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
print(f"Agent: {agent}")
|
||||
print(f"Provider: {agent.provider}")
|
||||
|
||||
# Streaming is enabled by default for real-time progress updates
|
||||
# This shows thinking summaries and streams the report as it's generated
|
||||
result = agent.research(
|
||||
"What is the current price of Ethereum?"
|
||||
# stream=True is the default, set stream=False to disable
|
||||
)
|
||||
|
||||
# The report is already printed during streaming when verbose=True
|
||||
# But we can also access it programmatically:
|
||||
print("\n" + "=" * 60)
|
||||
print(f"Report length: {len(result.report)} characters")
|
||||
print(f"Interaction ID: {result.interaction_id}")
|
||||
print(f"Reasoning steps captured: {len(result.reasoning_steps)}")
|
||||
print("=" * 60)
|
||||
@@ -0,0 +1,47 @@
|
||||
"""
|
||||
OpenAI Deep Research Agent Example
|
||||
|
||||
Uses OpenAI's o3-deep-research or o4-mini-deep-research models
|
||||
via the Responses API for automated research with web search.
|
||||
|
||||
Supports two modes:
|
||||
1. Non-streaming (default): Waits for completion, returns full report
|
||||
2. Streaming: Real-time progress with reasoning summaries and web searches
|
||||
|
||||
Requirements:
|
||||
- OPENAI_API_KEY environment variable
|
||||
"""
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
from praisonaiagents import DeepResearchAgent
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Research Assistant",
|
||||
instructions="""
|
||||
You are a professional researcher. Focus on:
|
||||
- Data-rich insights with specific figures
|
||||
- Reliable sources and citations
|
||||
- Clear, structured responses
|
||||
""",
|
||||
model="o4-mini-deep-research", # or "o3-deep-research" for higher quality
|
||||
verbose=True
|
||||
)
|
||||
|
||||
print(f"Agent: {agent}")
|
||||
print(f"Provider: {agent.provider}")
|
||||
|
||||
# Streaming is enabled by default for real-time progress updates
|
||||
# Shows: reasoning summaries, web search queries, and report text as generated
|
||||
result = agent.research(
|
||||
"What is the current price of Bitcoin?"
|
||||
# stream=True is the default, set stream=False to disable
|
||||
)
|
||||
|
||||
# The report is already printed during streaming when verbose=True
|
||||
# But we can also access it programmatically:
|
||||
print("\n" + "=" * 60)
|
||||
print(f"Report length: {len(result.report)} characters")
|
||||
print(f"Citations: {len(result.citations)}")
|
||||
print(f"Web searches: {len(result.web_searches)}")
|
||||
print("=" * 60)
|
||||
@@ -0,0 +1,8 @@
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant",
|
||||
llm="gpt-5-nano"
|
||||
)
|
||||
agent.start("Why sky is Blue?")
|
||||
agent.start("What was my previous question?")
|
||||
@@ -0,0 +1,14 @@
|
||||
from praisonaiagents import Agent
|
||||
import asyncio
|
||||
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant",
|
||||
llm="gpt-5-nano"
|
||||
)
|
||||
agent.run("Why sea is Blue?")
|
||||
|
||||
async def main():
|
||||
await agent.arun("Why sky is Blue?")
|
||||
await agent.arun("What was my previous question?")
|
||||
|
||||
asyncio.run(main())
|
||||
@@ -15,6 +15,17 @@ _logging.configure_root_logger()
|
||||
from .agent.agent import Agent
|
||||
from .agent.image_agent import ImageAgent
|
||||
from .agent.context_agent import ContextAgent, create_context_agent
|
||||
from .agent.deep_research_agent import (
|
||||
DeepResearchAgent,
|
||||
DeepResearchResponse,
|
||||
Citation,
|
||||
ReasoningStep,
|
||||
WebSearchCall,
|
||||
CodeExecutionStep,
|
||||
MCPCall,
|
||||
FileSearchCall,
|
||||
Provider
|
||||
)
|
||||
from .agents.agents import PraisonAIAgents
|
||||
from .task.task import Task
|
||||
from .tools.tools import Tools
|
||||
@@ -186,7 +197,16 @@ __all__ = [
|
||||
'disable_performance_mode',
|
||||
'cleanup_telemetry_resources',
|
||||
'MinimalTelemetry',
|
||||
'TelemetryCollector'
|
||||
'TelemetryCollector',
|
||||
'DeepResearchAgent',
|
||||
'DeepResearchResponse',
|
||||
'Citation',
|
||||
'ReasoningStep',
|
||||
'WebSearchCall',
|
||||
'CodeExecutionStep',
|
||||
'MCPCall',
|
||||
'FileSearchCall',
|
||||
'Provider'
|
||||
]
|
||||
|
||||
# Add MCP to __all__ if available
|
||||
|
||||
@@ -4,5 +4,36 @@ from .image_agent import ImageAgent
|
||||
from .context_agent import ContextAgent, create_context_agent
|
||||
from .handoff import Handoff, handoff, handoff_filters, RECOMMENDED_PROMPT_PREFIX, prompt_with_handoff_instructions
|
||||
from .router_agent import RouterAgent
|
||||
from .deep_research_agent import (
|
||||
DeepResearchAgent,
|
||||
DeepResearchResponse,
|
||||
Citation,
|
||||
ReasoningStep,
|
||||
WebSearchCall,
|
||||
CodeExecutionStep,
|
||||
MCPCall,
|
||||
FileSearchCall,
|
||||
Provider
|
||||
)
|
||||
|
||||
__all__ = ['Agent', 'ImageAgent', 'ContextAgent', 'create_context_agent', 'Handoff', 'handoff', 'handoff_filters', 'RECOMMENDED_PROMPT_PREFIX', 'prompt_with_handoff_instructions', 'RouterAgent']
|
||||
__all__ = [
|
||||
'Agent',
|
||||
'ImageAgent',
|
||||
'ContextAgent',
|
||||
'create_context_agent',
|
||||
'Handoff',
|
||||
'handoff',
|
||||
'handoff_filters',
|
||||
'RECOMMENDED_PROMPT_PREFIX',
|
||||
'prompt_with_handoff_instructions',
|
||||
'RouterAgent',
|
||||
'DeepResearchAgent',
|
||||
'DeepResearchResponse',
|
||||
'Citation',
|
||||
'ReasoningStep',
|
||||
'WebSearchCall',
|
||||
'CodeExecutionStep',
|
||||
'MCPCall',
|
||||
'FileSearchCall',
|
||||
'Provider'
|
||||
]
|
||||
@@ -2082,13 +2082,17 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
|
||||
display_error(f"Error in _achat_completion: {e}")
|
||||
return None
|
||||
|
||||
async def arun(self, prompt: str, **kwargs):
|
||||
"""Async alias for astart() method"""
|
||||
return await self.astart(prompt, **kwargs)
|
||||
|
||||
async def astart(self, prompt: str, **kwargs):
|
||||
"""Async version of start method"""
|
||||
return await self.achat(prompt, **kwargs)
|
||||
|
||||
def run(self):
|
||||
def run(self, prompt: str, **kwargs):
|
||||
"""Alias for start() method"""
|
||||
return self.start()
|
||||
return self.start(prompt, **kwargs)
|
||||
|
||||
def start(self, prompt: str, **kwargs):
|
||||
"""Start the agent with a prompt. This is a convenience method that wraps chat()."""
|
||||
|
||||
File diff suppressed because it is too large.
Load diff
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "praisonaiagents"
|
||||
version = "0.0.161"
|
||||
version = "0.0.163"
|
||||
description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
|
||||
requires-python = ">=3.10"
|
||||
authors = [
|
||||
|
||||
@@ -0,0 +1,344 @@
|
||||
"""
|
||||
Deep Research Agent Examples
|
||||
|
||||
This file demonstrates how to use the DeepResearchAgent for automated
|
||||
complex research workflows using multiple providers:
|
||||
|
||||
- **OpenAI**: o3-deep-research, o4-mini-deep-research (via Responses API)
|
||||
- **Gemini**: deep-research-pro (via Interactions API)
|
||||
- **LiteLLM**: Unified interface for OpenAI models
|
||||
|
||||
Requirements:
|
||||
- OPENAI_API_KEY for OpenAI/LiteLLM providers
|
||||
- GEMINI_API_KEY for Gemini provider
|
||||
- OpenAI SDK >= 1.0.0
|
||||
- google-genai for Gemini (pip install google-genai)
|
||||
- litellm for LiteLLM provider (pip install litellm)
|
||||
|
||||
Available models:
|
||||
OpenAI:
|
||||
- o3-deep-research: Higher quality, in-depth synthesis
|
||||
- o4-mini-deep-research: Faster, lightweight
|
||||
|
||||
Gemini:
|
||||
- deep-research-pro: Gemini 3 Pro powered research agent
|
||||
"""
|
||||
|
||||
from praisonaiagents import DeepResearchAgent, Provider
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 1: Basic Research Query
|
||||
# =============================================================================
|
||||
def basic_research_example():
|
||||
"""Simple research query with default settings."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Research Assistant",
|
||||
instructions="""
|
||||
You are a professional researcher. Focus on:
|
||||
- Data-rich insights with specific figures and trends
|
||||
- Reliable sources: peer-reviewed research, official reports
|
||||
- Include inline citations
|
||||
""",
|
||||
model="o4-mini-deep-research", # Use faster model
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research(
|
||||
"What are the key trends in renewable energy adoption in 2024?"
|
||||
)
|
||||
|
||||
print("=" * 60)
|
||||
print("RESEARCH REPORT")
|
||||
print("=" * 60)
|
||||
print(result.report)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("CITATIONS")
|
||||
print("=" * 60)
|
||||
for i, citation in enumerate(result.citations, 1):
|
||||
print(f"{i}. {citation.title}")
|
||||
print(f" URL: {citation.url}")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 2: Gemini Deep Research
|
||||
# =============================================================================
|
||||
def gemini_research_example():
|
||||
"""Research using Gemini Deep Research Agent."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Gemini Researcher",
|
||||
instructions="""
|
||||
You are a professional researcher. Focus on:
|
||||
- Comprehensive analysis with multiple perspectives
|
||||
- Data-backed insights and trends
|
||||
- Clear structure with executive summary
|
||||
""",
|
||||
model="deep-research-pro", # Gemini model - auto-detected
|
||||
verbose=True
|
||||
)
|
||||
|
||||
print(f"Using provider: {agent.provider}") # Should be Provider.GEMINI
|
||||
|
||||
result = agent.research(
|
||||
"What are the latest breakthroughs in fusion energy research?"
|
||||
)
|
||||
|
||||
print("=" * 60)
|
||||
print("GEMINI RESEARCH REPORT")
|
||||
print("=" * 60)
|
||||
print(result.report[:1000] + "..." if len(result.report) > 1000 else result.report)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 3: Research with Code Interpreter (OpenAI)
|
||||
# =============================================================================
|
||||
def research_with_code_example():
|
||||
"""Research with code execution for data analysis."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Data Analyst",
|
||||
instructions="""
|
||||
You are a data analyst. When appropriate:
|
||||
- Use code to analyze data and create visualizations
|
||||
- Summarize data in tables
|
||||
- Include specific statistics and metrics
|
||||
""",
|
||||
model="o3-deep-research",
|
||||
enable_code_interpreter=True,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research(
|
||||
"Analyze the global smartphone market share trends over the past 5 years.",
|
||||
summary_mode="detailed"
|
||||
)
|
||||
|
||||
# Check if any code was executed
|
||||
if result.code_executions:
|
||||
print("\nCode executed during research:")
|
||||
for code in result.code_executions:
|
||||
print(f" Input: {code.input_code[:100]}...")
|
||||
if code.output:
|
||||
print(f" Output: {code.output[:100]}...")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 4: Using LiteLLM Provider
|
||||
# =============================================================================
|
||||
def litellm_research_example():
|
||||
"""Research using LiteLLM for unified interface."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="LiteLLM Researcher",
|
||||
instructions="You are a professional researcher.",
|
||||
model="o3-deep-research",
|
||||
use_litellm=True, # Use LiteLLM instead of direct OpenAI
|
||||
verbose=True
|
||||
)
|
||||
|
||||
print(f"Using provider: {agent.provider}") # Should be Provider.LITELLM
|
||||
|
||||
result = agent.research(
|
||||
"What are the key trends in sustainable agriculture?"
|
||||
)
|
||||
|
||||
print(f"Report length: {len(result.report)} characters")
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 5: Research with MCP Integration (OpenAI)
|
||||
# =============================================================================
|
||||
def research_with_mcp_example():
|
||||
"""Research with custom MCP server for internal documents."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Internal Researcher",
|
||||
instructions="""
|
||||
You are a researcher with access to internal documents.
|
||||
- Use the internal file lookup tool to retrieve company data
|
||||
- Combine internal data with public research
|
||||
- Prioritize internal sources when available
|
||||
""",
|
||||
model="o3-deep-research",
|
||||
mcp_servers=[
|
||||
{
|
||||
"label": "internal_docs",
|
||||
"url": "https://your-mcp-server.com/sse/",
|
||||
"require_approval": "never"
|
||||
}
|
||||
],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research(
|
||||
"What is our company's market position compared to competitors?"
|
||||
)
|
||||
|
||||
# Check MCP calls
|
||||
if result.mcp_calls:
|
||||
print("\nMCP tools used:")
|
||||
for call in result.mcp_calls:
|
||||
print(f" - {call.name}@{call.server_label}: {call.arguments}")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 6: Gemini with File Search
|
||||
# =============================================================================
|
||||
def gemini_file_search_example():
|
||||
"""Research using Gemini with your own data via File Search."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Document Researcher",
|
||||
instructions="""
|
||||
Compare our internal documents against current public information.
|
||||
Prioritize insights from our internal data.
|
||||
""",
|
||||
model="deep-research-pro",
|
||||
enable_file_search=True,
|
||||
file_search_stores=["fileSearchStores/my-store-name"],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research(
|
||||
"Compare our 2025 fiscal year report against current market trends."
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 7: Clarifying Questions Workflow
|
||||
# =============================================================================
|
||||
def clarifying_workflow_example():
|
||||
"""Use clarifying questions before research."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Research Assistant",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Step 1: Get clarifying questions
|
||||
initial_query = "Help me research AI trends"
|
||||
questions = agent.clarify(initial_query)
|
||||
|
||||
print("Clarifying Questions:")
|
||||
print(questions)
|
||||
|
||||
# Step 2: User provides more details (simulated)
|
||||
detailed_query = """
|
||||
I'd like to research AI trends in healthcare specifically.
|
||||
Focus on:
|
||||
- Diagnostic AI applications
|
||||
- FDA-approved AI tools
|
||||
- Cost savings for hospitals
|
||||
Time period: 2023-2024
|
||||
"""
|
||||
|
||||
# Step 3: Rewrite the query for optimal research
|
||||
rewritten = agent.rewrite_query(detailed_query)
|
||||
print("\nRewritten Query:")
|
||||
print(rewritten)
|
||||
|
||||
# Step 4: Perform the research
|
||||
result = agent.research(rewritten)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 5: Async Research
|
||||
# =============================================================================
|
||||
async def async_research_example():
|
||||
"""Perform research asynchronously."""
|
||||
import asyncio
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Async Researcher",
|
||||
model="o4-mini-deep-research",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Run multiple research queries concurrently
|
||||
queries = [
|
||||
"What are the latest developments in quantum computing?",
|
||||
"What are the trends in electric vehicle adoption?",
|
||||
]
|
||||
|
||||
tasks = [agent.aresearch(q) for q in queries]
|
||||
results = await asyncio.gather(*tasks)
|
||||
|
||||
for query, result in zip(queries, results):
|
||||
print(f"\nQuery: {query}")
|
||||
print(f"Report length: {len(result.report)} chars")
|
||||
print(f"Citations: {len(result.citations)}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Example 6: Inspecting Intermediate Steps
|
||||
# =============================================================================
|
||||
def inspect_steps_example():
|
||||
"""Inspect the research process steps."""
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Detailed Researcher",
|
||||
model="o4-mini-deep-research",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
result = agent.research(
|
||||
"What are the environmental impacts of cryptocurrency mining?"
|
||||
)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("RESEARCH PROCESS ANALYSIS")
|
||||
print("=" * 60)
|
||||
|
||||
# Reasoning steps
|
||||
print(f"\nReasoning Steps ({len(result.reasoning_steps)}):")
|
||||
for i, step in enumerate(result.reasoning_steps, 1):
|
||||
print(f" {i}. {step.text[:100]}...")
|
||||
|
||||
# Web searches
|
||||
print(f"\nWeb Searches ({len(result.web_searches)}):")
|
||||
for search in result.web_searches:
|
||||
print(f" - Query: {search.query}")
|
||||
print(f" Status: {search.status}")
|
||||
|
||||
# Sources
|
||||
print(f"\nUnique Sources ({len(result.get_all_sources())}):")
|
||||
for source in result.get_all_sources()[:10]:
|
||||
print(f" - {source['title']}")
|
||||
print(f" {source['url']}")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Main
|
||||
# =============================================================================
|
||||
if __name__ == "__main__":
|
||||
import os
|
||||
|
||||
if not os.environ.get("OPENAI_API_KEY"):
|
||||
print("Error: OPENAI_API_KEY environment variable not set")
|
||||
print("Please set it before running examples:")
|
||||
print(" export OPENAI_API_KEY='your-api-key'")
|
||||
exit(1)
|
||||
|
||||
print("Running basic research example...")
|
||||
result = basic_research_example()
|
||||
print(f"\nCompleted! Report has {len(result.report)} characters and {len(result.citations)} citations.")
|
||||
@@ -0,0 +1,283 @@
|
||||
"""
|
||||
Test file for DeepResearchAgent
|
||||
|
||||
This file tests the OpenAI Deep Research API integration.
|
||||
Requires OPENAI_API_KEY environment variable to be set.
|
||||
|
||||
Usage:
|
||||
python tests/deep_research_test.py
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
# Add parent directory to path for imports
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from praisonaiagents import (
|
||||
DeepResearchAgent,
|
||||
DeepResearchResponse,
|
||||
Citation,
|
||||
ReasoningStep,
|
||||
WebSearchCall,
|
||||
CodeExecutionStep,
|
||||
MCPCall,
|
||||
FileSearchCall,
|
||||
Provider
|
||||
)
|
||||
|
||||
|
||||
def test_basic_import():
|
||||
"""Test that all classes can be imported."""
|
||||
print("✓ All Deep Research classes imported successfully")
|
||||
print(f" - DeepResearchAgent: {DeepResearchAgent}")
|
||||
print(f" - DeepResearchResponse: {DeepResearchResponse}")
|
||||
print(f" - Citation: {Citation}")
|
||||
print(f" - ReasoningStep: {ReasoningStep}")
|
||||
print(f" - WebSearchCall: {WebSearchCall}")
|
||||
print(f" - CodeExecutionStep: {CodeExecutionStep}")
|
||||
print(f" - MCPCall: {MCPCall}")
|
||||
print(f" - FileSearchCall: {FileSearchCall}")
|
||||
print(f" - Provider: {Provider}")
|
||||
print(f" - Provider.OPENAI: {Provider.OPENAI}")
|
||||
print(f" - Provider.GEMINI: {Provider.GEMINI}")
|
||||
print(f" - Provider.LITELLM: {Provider.LITELLM}")
|
||||
return True
|
||||
|
||||
|
||||
def test_agent_initialization():
|
||||
"""Test DeepResearchAgent initialization."""
|
||||
# Check if API key is available
|
||||
if not os.environ.get("OPENAI_API_KEY"):
|
||||
print("⚠ Skipping initialization test - OPENAI_API_KEY not set")
|
||||
return True
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Test Researcher",
|
||||
instructions="You are a professional researcher.",
|
||||
model="o3-deep-research",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
print(f"✓ Agent initialized: {agent}")
|
||||
print(f" - Name: {agent.name}")
|
||||
print(f" - Model: {agent.model}")
|
||||
print(f" - Web search enabled: {agent.enable_web_search}")
|
||||
print(f" - Code interpreter enabled: {agent.enable_code_interpreter}")
|
||||
return True
|
||||
|
||||
|
||||
def test_data_classes():
|
||||
"""Test data class creation and methods."""
|
||||
# Test Citation
|
||||
citation = Citation(
|
||||
title="Test Source",
|
||||
url="https://example.com",
|
||||
start_index=0,
|
||||
end_index=100
|
||||
)
|
||||
print(f"✓ Citation created: {citation}")
|
||||
|
||||
# Test ReasoningStep
|
||||
reasoning = ReasoningStep(text="Analyzing the query...")
|
||||
print(f"✓ ReasoningStep created: {reasoning}")
|
||||
|
||||
# Test WebSearchCall
|
||||
search = WebSearchCall(query="test query", status="completed")
|
||||
print(f"✓ WebSearchCall created: {search}")
|
||||
|
||||
# Test CodeExecutionStep
|
||||
code = CodeExecutionStep(input_code="print('hello')", output="hello")
|
||||
print(f"✓ CodeExecutionStep created: {code}")
|
||||
|
||||
# Test MCPCall
|
||||
mcp = MCPCall(name="fetch", server_label="internal", arguments={"file_id": "123"})
|
||||
print(f"✓ MCPCall created: {mcp}")
|
||||
|
||||
# Test DeepResearchResponse
|
||||
response = DeepResearchResponse(
|
||||
report="This is a test report about AI.",
|
||||
citations=[citation],
|
||||
reasoning_steps=[reasoning],
|
||||
web_searches=[search],
|
||||
code_executions=[code],
|
||||
mcp_calls=[mcp]
|
||||
)
|
||||
print(f"✓ DeepResearchResponse created")
|
||||
print(f" - Report length: {len(response.report)} chars")
|
||||
print(f" - Citations: {len(response.citations)}")
|
||||
print(f" - Sources: {response.get_all_sources()}")
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def test_research_query():
|
||||
"""Test actual research query (requires API key)."""
|
||||
if not os.environ.get("OPENAI_API_KEY"):
|
||||
print("⚠ Skipping research test - OPENAI_API_KEY not set")
|
||||
return True
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Healthcare Researcher",
|
||||
instructions="""
|
||||
You are a professional researcher preparing a structured, data-driven report.
|
||||
Focus on data-rich insights with specific figures and trends.
|
||||
Include inline citations and prioritize reliable sources.
|
||||
""",
|
||||
model="o4-mini-deep-research", # Use faster model for testing
|
||||
verbose=True,
|
||||
enable_code_interpreter=False
|
||||
)
|
||||
|
||||
print("\n🔍 Starting deep research query...")
|
||||
print(" (This may take several minutes)")
|
||||
|
||||
try:
|
||||
result = agent.research(
|
||||
"What are the key trends in AI adoption in healthcare in 2024?",
|
||||
summary_mode="concise"
|
||||
)
|
||||
|
||||
print(f"\n✓ Research completed!")
|
||||
print(f" - Report length: {len(result.report)} characters")
|
||||
print(f" - Citations found: {len(result.citations)}")
|
||||
print(f" - Reasoning steps: {len(result.reasoning_steps)}")
|
||||
print(f" - Web searches: {len(result.web_searches)}")
|
||||
print(f" - Code executions: {len(result.code_executions)}")
|
||||
|
||||
# Print first 500 chars of report
|
||||
print(f"\n📄 Report excerpt:")
|
||||
print(f" {result.report[:500]}...")
|
||||
|
||||
# Print some citations
|
||||
if result.citations:
|
||||
print(f"\n📚 Sample citations:")
|
||||
for i, c in enumerate(result.citations[:3]):
|
||||
print(f" {i+1}. {c.title}: {c.url}")
|
||||
|
||||
# Print web searches
|
||||
if result.web_searches:
|
||||
print(f"\n🔎 Web searches performed:")
|
||||
for s in result.web_searches[:5]:
|
||||
print(f" - {s.query} ({s.status})")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"✗ Research failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def test_clarify_and_rewrite():
|
||||
"""Test clarifying questions and query rewriting."""
|
||||
if not os.environ.get("OPENAI_API_KEY"):
|
||||
print("⚠ Skipping clarify/rewrite test - OPENAI_API_KEY not set")
|
||||
return True
|
||||
|
||||
agent = DeepResearchAgent(
|
||||
name="Research Assistant",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
print("\n🤔 Testing clarifying questions...")
|
||||
try:
|
||||
questions = agent.clarify("Help me research AI trends")
|
||||
print(f"✓ Clarifying questions generated:")
|
||||
print(f" {questions[:300]}...")
|
||||
except Exception as e:
|
||||
print(f"✗ Clarify failed: {e}")
|
||||
return False
|
||||
|
||||
print("\n✍️ Testing query rewriting...")
|
||||
try:
|
||||
rewritten = agent.rewrite_query("Research AI in healthcare")
|
||||
print(f"✓ Query rewritten:")
|
||||
print(f" {rewritten[:300]}...")
|
||||
except Exception as e:
|
||||
print(f"✗ Rewrite failed: {e}")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def test_mcp_configuration():
|
||||
"""Test MCP server configuration."""
|
||||
if not os.environ.get("OPENAI_API_KEY"):
|
||||
print("⚠ Skipping MCP test - OPENAI_API_KEY not set")
|
||||
return True
|
||||
|
||||
# Test agent with MCP configuration
|
||||
agent = DeepResearchAgent(
|
||||
name="MCP Researcher",
|
||||
instructions="You are a researcher with access to internal documents.",
|
||||
mcp_servers=[
|
||||
{
|
||||
"label": "internal_docs",
|
||||
"url": "https://example.com/mcp/sse/",
|
||||
"require_approval": "never"
|
||||
}
|
||||
],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
print(f"✓ Agent with MCP configured: {agent}")
|
||||
print(f" - MCP servers: {len(agent.mcp_servers)}")
|
||||
|
||||
# Test tool building
|
||||
tools = agent._build_tools(
|
||||
web_search=True,
|
||||
code_interpreter=True,
|
||||
mcp_servers=agent.mcp_servers
|
||||
)
|
||||
|
||||
print(f"✓ Tools built: {len(tools)} tools")
|
||||
for t in tools:
|
||||
print(f" - {t.get('type', 'unknown')}")
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def run_all_tests():
|
||||
"""Run all tests."""
|
||||
print("=" * 60)
|
||||
print("DeepResearchAgent Test Suite")
|
||||
print("=" * 60)
|
||||
|
||||
tests = [
|
||||
("Import Test", test_basic_import),
|
||||
("Data Classes Test", test_data_classes),
|
||||
("Agent Initialization Test", test_agent_initialization),
|
||||
("MCP Configuration Test", test_mcp_configuration),
|
||||
("Clarify & Rewrite Test", test_clarify_and_rewrite),
|
||||
("Research Query Test", test_research_query),
|
||||
]
|
||||
|
||||
results = []
|
||||
for name, test_fn in tests:
|
||||
print(f"\n{'─' * 40}")
|
||||
print(f"Running: {name}")
|
||||
print(f"{'─' * 40}")
|
||||
try:
|
||||
success = test_fn()
|
||||
results.append((name, success))
|
||||
except Exception as e:
|
||||
print(f"✗ Test failed with exception: {e}")
|
||||
results.append((name, False))
|
||||
|
||||
# Summary
|
||||
print(f"\n{'=' * 60}")
|
||||
print("Test Summary")
|
||||
print(f"{'=' * 60}")
|
||||
passed = sum(1 for _, s in results if s)
|
||||
total = len(results)
|
||||
print(f"Passed: {passed}/{total}")
|
||||
for name, success in results:
|
||||
status = "✓" if success else "✗"
|
||||
print(f" {status} {name}")
|
||||
|
||||
return passed == total
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
success = run_all_tests()
|
||||
sys.exit(0 if success else 1)
|
||||
Generated
+2
-2
@@ -1,5 +1,5 @@
|
||||
version = 1
|
||||
revision = 2
|
||||
revision = 3
|
||||
requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.13'",
|
||||
@@ -2422,7 +2422,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "praisonaiagents"
|
||||
version = "0.0.161"
|
||||
version = "0.0.163"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
|
||||
@@ -3,8 +3,8 @@ 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/v2.2.87.tar.gz"
|
||||
sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.87.tar.gz | shasum -a 256`.split.first
|
||||
url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.89.tar.gz"
|
||||
sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.89.tar.gz | shasum -a 256`.split.first
|
||||
license "MIT"
|
||||
|
||||
depends_on "[email protected]"
|
||||
|
||||
@@ -57,7 +57,7 @@ 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==2.2.87 gunicorn markdown\n")
|
||||
file.write("RUN pip install flask praisonai==2.2.89 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
|
||||
|
||||
|
||||
@@ -138,11 +138,15 @@ class PraisonAIModel:
|
||||
"Please install with 'pip install langchain-anthropic'"
|
||||
)
|
||||
elif OPENAI_AVAILABLE:
|
||||
return ChatOpenAI(
|
||||
model=self.model_name,
|
||||
api_key=self.api_key,
|
||||
base_url=self.base_url,
|
||||
)
|
||||
chat_kwargs = {
|
||||
"model": self.model_name,
|
||||
"api_key": self.api_key,
|
||||
"base_url": self.base_url,
|
||||
}
|
||||
# Certain OpenAI models (e.g., gpt-5-* family) only allow temperature=1
|
||||
if isinstance(self.model_name, str) and self.model_name.startswith("gpt-5"):
|
||||
chat_kwargs["temperature"] = 1
|
||||
return ChatOpenAI(**chat_kwargs)
|
||||
else:
|
||||
raise ImportError(
|
||||
"Required Langchain Integration 'langchain-openai' not found. "
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "PraisonAI"
|
||||
version = "2.2.87"
|
||||
version = "2.2.89"
|
||||
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>=0.0.161",
|
||||
"praisonaiagents>=0.0.163",
|
||||
"python-dotenv>=0.19.0",
|
||||
"instructor>=1.3.3",
|
||||
"PyYAML>=6.0",
|
||||
@@ -90,7 +90,7 @@ call = [
|
||||
"openai>=1.54.0",
|
||||
]
|
||||
train = []
|
||||
crewai = ["crewai>=0.148.0", "praisonai-tools>=0.0.22"]
|
||||
crewai = ["crewai>=0.157.0", "praisonai-tools>=0.0.22"]
|
||||
autogen = ["pyautogen==0.2.29", "praisonai-tools>=0.0.22", "crewai"]
|
||||
autogen-v4 = [
|
||||
"autogen-agentchat>=0.4.0",
|
||||
@@ -102,7 +102,7 @@ autogen-v4 = [
|
||||
|
||||
[tool.poetry]
|
||||
name = "PraisonAI"
|
||||
version = "2.2.87"
|
||||
version = "2.2.89"
|
||||
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 = ""
|
||||
@@ -120,7 +120,7 @@ python = ">=3.10,<3.13"
|
||||
rich = ">=13.7"
|
||||
markdown = ">=3.5"
|
||||
pyparsing = ">=3.0.0"
|
||||
praisonaiagents = ">=0.0.161"
|
||||
praisonaiagents = ">=0.0.163"
|
||||
python-dotenv = ">=0.19.0"
|
||||
instructor = ">=1.3.3"
|
||||
PyYAML = ">=6.0"
|
||||
@@ -129,7 +129,7 @@ pyautogen = {version = "==0.2.29", optional = true}
|
||||
autogen-agentchat = {version = ">=0.4.0", optional = true}
|
||||
autogen-ext = {version = ">=0.4.0", optional = true}
|
||||
autogen-core = {version = ">=0.4.0", optional = true}
|
||||
crewai = {version = ">=0.148.0", optional = true}
|
||||
crewai = {version = ">=0.157.0", optional = true}
|
||||
praisonai-tools = {version = ">=0.0.22", optional = true}
|
||||
chainlit = {version = "==2.5.5", optional = true}
|
||||
gradio = {version = ">=4.26.0", optional = true}
|
||||
|
||||
Generated
+26
-13
@@ -1,5 +1,5 @@
|
||||
version = 1
|
||||
revision = 2
|
||||
revision = 3
|
||||
requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.13'",
|
||||
@@ -905,7 +905,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "crewai"
|
||||
version = "0.148.0"
|
||||
version = "0.157.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "appdirs" },
|
||||
@@ -924,6 +924,7 @@ dependencies = [
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http" },
|
||||
{ name = "opentelemetry-sdk" },
|
||||
{ name = "pdfplumber" },
|
||||
{ name = "portalocker" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pyjwt" },
|
||||
{ name = "python-dotenv" },
|
||||
@@ -934,9 +935,9 @@ dependencies = [
|
||||
{ name = "tomli-w" },
|
||||
{ name = "uv" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/db/46/3247312d55b1d5887d7ce6d0a3cc39c6dc118452c1a1fb9eaaa9d121a5b4/crewai-0.148.0.tar.gz", hash = "sha256:a713477cebc98a641c8dfda0f6415b6449143848fbdf91fa6aa0f90a0e726a49", size = 6315505, upload-time = "2025-07-16T19:36:27.26Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c9/fe/a469d356ddaa3adea1cfffeac965412dc3a53ebcf991641cf4776030cede/crewai-0.157.0.tar.gz", hash = "sha256:947c819fa7bf155ae065bfaae600216e43b260b9b34a07b2098d7d221236fa26", size = 6341895, upload-time = "2025-08-06T21:48:16.461Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/bb/6f/8d0af8154b9b97d0207f76cd8607721536afd22fabef9702ee664ff27923/crewai-0.148.0-py3-none-any.whl", hash = "sha256:6865355d96493db63f29064eefe6397d27be2a6729cf3957aa6a90d7190ee50f", size = 365343, upload-time = "2025-07-16T19:36:24.553Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/20/97/bdc70fb7f27d44b98b97475e59fa75c155e0753ef4fd9848e1d0dc90e5a2/crewai-0.157.0-py3-none-any.whl", hash = "sha256:c34e844c8c92b78219cb23e96ff10561d7d5bfa068d44f08424c8dc6f36fc456", size = 375821, upload-time = "2025-08-06T21:48:14.354Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -2319,7 +2320,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "litellm"
|
||||
version = "1.72.6"
|
||||
version = "1.74.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
@@ -2334,9 +2335,9 @@ dependencies = [
|
||||
{ name = "tiktoken" },
|
||||
{ name = "tokenizers" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/8d/15/df75f278fd998f6d6900f692b9de2fba2814b316c123c99072a813668aac/litellm-1.72.6.tar.gz", hash = "sha256:4e5c7e4273b09b765302d2faaec30f77b42255c0055b427b55ea02b8092b8582", size = 8393603, upload-time = "2025-06-14T21:43:11.023Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6d/5d/646bebdb4769d77e6a018b9152c9ccf17afe15d0f88974f338d3f2ee7c15/litellm-1.74.9.tar.gz", hash = "sha256:4a32eff70342e1aee4d1cbf2de2a6ed64a7c39d86345c58d4401036af018b7de", size = 9660510, upload-time = "2025-07-28T16:42:39.297Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/96/c9/4aae0b77632279eef9716dbcb98edd8b36c08a9da070e2470ca9c410c0f8/litellm-1.72.6-py3-none-any.whl", hash = "sha256:e0ae98d25db4910e78b1a0a604f24c0d6875f6cdea02426b264a45d4fbdb8c46", size = 8302810, upload-time = "2025-06-14T21:43:08.628Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5f/e4/f1546746049c99c6b8b247e2f34485b9eae36faa9322b84e2a17262e6712/litellm-1.74.9-py3-none-any.whl", hash = "sha256:ab8f8a6e4d8689d3c7c4f9c3bbc7e46212cc3ebc74ddd0f3c0c921bb459c9874", size = 8740449, upload-time = "2025-07-28T16:42:36.8Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -4013,6 +4014,18 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556, upload-time = "2024-04-20T21:34:40.434Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "portalocker"
|
||||
version = "2.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pywin32", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1f/f8/969e6f280201b40b31bcb62843c619f343dcc351dff83a5891530c9dd60e/portalocker-2.7.0.tar.gz", hash = "sha256:032e81d534a88ec1736d03f780ba073f047a06c478b06e2937486f334e955c51", size = 20183, upload-time = "2023-01-18T23:36:14.436Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/8c/df/d4f711d168524f5aebd7fb30969eaa31e3048cf8979688cde3b08f6e5eb8/portalocker-2.7.0-py2.py3-none-any.whl", hash = "sha256:a07c5b4f3985c3cf4798369631fb7011adb498e2a46d8440efc75a8f29a0f983", size = 15502, upload-time = "2023-01-18T23:36:12.849Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "posthog"
|
||||
version = "3.7.4"
|
||||
@@ -4031,7 +4044,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "praisonai"
|
||||
version = "2.2.87"
|
||||
version = "2.2.89"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "instructor" },
|
||||
@@ -4157,7 +4170,7 @@ requires-dist = [
|
||||
{ name = "crawl4ai", marker = "extra == 'realtime'", specifier = ">=0.7.0" },
|
||||
{ name = "crewai", marker = "extra == 'autogen'" },
|
||||
{ name = "crewai", marker = "extra == 'autogen-v4'" },
|
||||
{ name = "crewai", marker = "extra == 'crewai'", specifier = ">=0.148.0" },
|
||||
{ name = "crewai", marker = "extra == 'crewai'", specifier = ">=0.157.0" },
|
||||
{ name = "duckduckgo-search", marker = "extra == 'realtime'", specifier = ">=6.3.0" },
|
||||
{ name = "fastapi", marker = "extra == 'api'", specifier = ">=0.115.0" },
|
||||
{ name = "fastapi", marker = "extra == 'call'", specifier = ">=0.95.0" },
|
||||
@@ -4185,7 +4198,7 @@ requires-dist = [
|
||||
{ name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
|
||||
{ name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
|
||||
{ name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
|
||||
{ name = "praisonaiagents", specifier = ">=0.0.161" },
|
||||
{ name = "praisonaiagents", specifier = ">=0.0.163" },
|
||||
{ name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
|
||||
{ name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
|
||||
{ name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
|
||||
@@ -4240,7 +4253,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "praisonaiagents"
|
||||
version = "0.0.161"
|
||||
version = "0.0.163"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
@@ -4249,9 +4262,9 @@ dependencies = [
|
||||
{ name = "pydantic" },
|
||||
{ name = "rich" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/f2/a5/97bd65cf513070b647ada80c39610fd4c86c80f70404bb0972e100c2a4f2/praisonaiagents-0.0.161.tar.gz", hash = "sha256:88b6c28f1fa9d0f0f802d5a916471606c351d3359800982eb49f38e89dbff363", size = 285757, upload-time = "2025-08-08T12:43:46.337Z" }
|
||||
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|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/41/28/fd9df18c45f461afef17c69587b1502901dc1b3331056b79685f9b932017/praisonaiagents-0.0.161-py3-none-any.whl", hash = "sha256:a2db2a52a6684d0b0d935b664741d1594020cba41a6f212abbadc654a999af33", size = 305084, upload-time = "2025-08-08T12:43:44.86Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d8/21/2e2f9719dcf31c99474c1b61a62be78c79cdfde0abd40b5d100419515279/praisonaiagents-0.0.163-py3-none-any.whl", hash = "sha256:518c46a618b609af4718c2c77779c75494bc4751f97ebb52864c490483cc2240", size = 315110, upload-time = "2025-12-13T01:13:17.363Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
Reference in new issue
Block a user