## 🚀🔗 Leveraging OpenAI with Composio
Facilitate the integration of OpenAI with Composio to empower OpenAI models to directly interact with external applications, broadening their capabilities and application scope.
### Objective
- **Automate starring a GitHub repository** using conversational instructions via OpenAI Function Calls.
### Installation and Setup
Ensure you have the necessary packages installed and connect your GitHub account to allow your agents to utilize GitHub functionalities.
```bash
# Install Composio LangChain package
pip install composio-openai
# Connect your GitHub account
composio-cli add github
# View available applications you can connect with
composio-cli show-apps
```
### Usage Steps
#### 1. Import Base Packages
Prepare your environment by initializing necessary imports from OpenAI and setting up your client.
```python
from openai import OpenAI
# Initialize OpenAI client
openai_client = OpenAI()
```
### Step 2: Integrating GitHub Tools with Composio
This step involves fetching and integrating GitHub tools provided by Composio, enabling enhanced functionality for LangChain operations.
```python
from composio_openai import App, ComposioToolSet
toolset = ComposioToolSet()
actions = toolset.get_tools(apps=[App.GITHUB])
```
### Step 3: Agent Execution
This step involves configuring and executing the agent to carry out actions, such as starring a GitHub repository.
```python
my_task = "Star a repo composiohq/composio on GitHub"
# Create a chat completion request to decide on the action
response = openai_client.chat.completions.create(model="gpt-4-turbo-preview",
tools=actions, # Passing actions we fetched earlier.
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": my_task}
]
)
pprint(response)
```
### Step 4: Validate Execution Response
Execute the following code to validate the response, ensuring that the intended task has been successfully completed.
```python
result = toolset.handle_tool_calls(response)
pprint(result)
```
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"description": "## \ud83d\ude80\ud83d\udd17 Leveraging OpenAI with Composio\n\nFacilitate the integration of OpenAI with Composio to empower OpenAI models to directly interact with external applications, broadening their capabilities and application scope.\n\n### Objective\n\n- **Automate starring a GitHub repository** using conversational instructions via OpenAI Function Calls.\n\n### Installation and Setup\n\nEnsure you have the necessary packages installed and connect your GitHub account to allow your agents to utilize GitHub functionalities.\n\n```bash\n# Install Composio LangChain package\npip install composio-openai\n\n# Connect your GitHub account\ncomposio-cli add github\n\n# View available applications you can connect with\ncomposio-cli show-apps\n```\n\n### Usage Steps\n\n#### 1. Import Base Packages\n\nPrepare your environment by initializing necessary imports from OpenAI and setting up your client.\n\n```python\nfrom openai import OpenAI\n\n# Initialize OpenAI client\nopenai_client = OpenAI()\n```\n\n### Step 2: Integrating GitHub Tools with Composio\n\nThis step involves fetching and integrating GitHub tools provided by Composio, enabling enhanced functionality for LangChain operations.\n```python\nfrom composio_openai import App, ComposioToolSet\n\ntoolset = ComposioToolSet()\nactions = toolset.get_tools(apps=[App.GITHUB])\n```\n\n### Step 3: Agent Execution\n\nThis step involves configuring and executing the agent to carry out actions, such as starring a GitHub repository.\n\n```python\nmy_task = \"Star a repo composiohq/composio on GitHub\"\n\n# Create a chat completion request to decide on the action\nresponse = openai_client.chat.completions.create(model=\"gpt-4-turbo-preview\",\n tools=actions, # Passing actions we fetched earlier.\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": my_task}\n ]\n )\n\npprint(response)\n```\n\n### Step 4: Validate Execution Response\n\nExecute the following code to validate the response, ensuring that the intended task has been successfully completed.\n\n```python\nresult = toolset.handle_tool_calls(response)\npprint(result)\n```\n",
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