Chat with OpenAI Assistant using function call in AG2: OSS Insights for Advanced GitHub Data Analysis#

This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining Function calls in AG2 for the OpenAI Assistant.

The notebook is structured into four main sections:

Function Schema and Implementation
Defining an OpenAI Assistant Agent in AG2
Fetching GitHub Insight Data using Function Call

Requirements#

AG2 requires Python>=3.9. To run this notebook example, please install:

Requirements

Install pyautogen:

pip install pyautogen[openai]

For more information, please refer to the installation guide.

%%capture --no-stderr
# %pip install "pyautogen>=0.2.3"

Function Schema and Implementation#

This section provides the function schema definition and their implementation details. These functions are tailored to fetch and process data from GitHub, utilizing OSS Insight’s capabilities.

import logging
import os

from autogen import LLMConfig, UserProxyAgent
from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent

logger = logging.getLogger(__name__)
logger.setLevel(logging.WARNING)

ossinsight_api_schema = {
    "name": "ossinsight_data_api",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {
                "type": "string",
                "description": (
                    "Enter your GitHub data question in the form of a clear and specific question to ensure the returned data is accurate and valuable. "
                    "For optimal results, specify the desired format for the data table in your request."
                ),
            }
        },
        "required": ["question"],
    },
    "description": "This is an API endpoint allowing users (analysts) to input question about GitHub in text format to retrieve the related and structured data.",
}

def get_ossinsight(question):
    """[Mock] Retrieve the top 10 developers with the most followers on GitHub."""
    report_components = [
        f"Question: {question}",
        "SQL: SELECT `login` AS `user_login`, `followers` AS `followers` FROM `github_users` ORDER BY `followers` DESC LIMIT 10",
        """Results:
  {'followers': 166730, 'user_login': 'torvalds'}
  {'followers': 86239, 'user_login': 'yyx990803'}
  {'followers': 77611, 'user_login': 'gaearon'}
  {'followers': 72668, 'user_login': 'ruanyf'}
  {'followers': 65415, 'user_login': 'JakeWharton'}
  {'followers': 60972, 'user_login': 'peng-zhihui'}
  {'followers': 58172, 'user_login': 'bradtraversy'}
  {'followers': 52143, 'user_login': 'gustavoguanabara'}
  {'followers': 51542, 'user_login': 'sindresorhus'}
  {'followers': 49621, 'user_login': 'tj'}""",
    ]
    return "\n" + "\n\n".join(report_components) + "\n"

Defining an OpenAI Assistant Agent in AG2#

Here, we explore how to define an OpenAI Assistant Agent within the AG2. This includes setting up the agent to make use of the previously defined function calls for data retrieval and analysis.

assistant_id = os.environ.get("ASSISTANT_ID", None)
llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")
assistant_config = {
    "assistant_id": assistant_id,
    "tools": [
        {
            "type": "function",
            "function": ossinsight_api_schema,
        }
    ],
}

oss_analyst = GPTAssistantAgent(
    name="OSS Analyst",
    instructions=(
        "Hello, Open Source Project Analyst. You'll conduct comprehensive evaluations of open source projects or organizations on the GitHub platform, "
        "analyzing project trajectories, contributor engagements, open source trends, and other vital parameters. "
        "Please carefully read the context of the conversation to identify the current analysis question or problem that needs addressing."
    ),
    llm_config=llm_config,
    assistant_config=assistant_config,
    verbose=True,
)
oss_analyst.register_function(
    function_map={
        "ossinsight_data_api": get_ossinsight,
    }
)

Tip

Learn more about configuring LLMs for agents here.

Fetching GitHub Insight Data using Function Call#

This part of the notebook demonstrates the practical application of the defined functions and the OpenAI Assistant Agent in fetching and interpreting GitHub Insight data.

user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={
        "work_dir": "coding",
        "use_docker": False,
    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
    is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1,
)

user_proxy.initiate_chat(oss_analyst, message="Top 10 developers with the most followers")
oss_analyst.delete_assistant()