OpenAI Tool Calling

pip install openai parallel-web
export PARALLEL_API_KEY="your-parallel-api-key"
export OPENAI_API_KEY="your-openai-api-key"

parallel_search_tool = {
    "type": "function",
    "function": {
        "name": "parallel_search",
        "description": "Search the web for current information using Parallel's AI-powered search. Returns relevant excerpts from web pages optimized for LLM consumption. Use this for finding up-to-date information, news, research, and facts. Provide at least one of objective or search_queries.",
        "parameters": {
            "type": "object",
            "properties": {
                "objective": {
                    "type": "string",
                    "description": "A natural language description of what you're searching for. Be specific about your research goal."
                },
                "search_queries": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Specific search queries to execute. Use multiple queries to cover different angles of the search."
                },
                "max_chars_total": {
                    "type": "integer",
                    "description": "Maximum total characters across all excerpts. Default 50000."
                }
            }
        }
    }
}

import os
import json
from parallel import Parallel

parallel_client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])

def parallel_search(objective: str = None, search_queries: list = None, max_chars_total: int = 50000) -> dict:
    """Execute a search using the Parallel Search API."""
    response = parallel_client.beta.search(
        objective=objective,
        search_queries=search_queries,
        excerpts={"max_chars_per_result": 5000, "max_chars_total": max_chars_total}
    )

    # Format results for the LLM
    return {
        "results": [
            {"url": r.url, "title": r.title, "excerpts": r.excerpts[:3] if r.excerpts else []}
            for r in response.results
        ]
    }

def process_tool_calls(tool_calls):
    """Process tool calls from OpenAI and return results."""
    results = []
    for tool_call in tool_calls:
        if tool_call.function.name == "parallel_search":
            args = json.loads(tool_call.function.arguments)
            search_result = parallel_search(
                objective=args.get("objective"),
                search_queries=args.get("search_queries"),
                max_chars_total=args.get("max_chars_total", 50000)
            )
            results.append({
                "tool_call_id": tool_call.id,
                "role": "tool",
                "content": json.dumps(search_result)
            })
    return results

import os
import json
from openai import OpenAI
from parallel import Parallel

# Initialize clients
openai_client = OpenAI()
parallel_client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])

# Tool definition
parallel_search_tool = {
    "type": "function",
    "function": {
        "name": "parallel_search",
        "description": "Search the web for current information using Parallel's AI-powered search. Provide at least one of objective or search_queries.",
        "parameters": {
            "type": "object",
            "properties": {
                "objective": {
                    "type": "string",
                    "description": "A natural language description of what you're searching for."
                },
                "search_queries": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Specific search queries to execute."
                },
                "max_results": {
                    "type": "integer",
                    "description": "Maximum results (1-20). Default 10."
                },
                "max_chars_total": {
                    "type": "integer",
                    "description": "Maximum total characters across all excerpts. Default 50000."
                }
            }
        }
    }
}


def parallel_search(objective=None, search_queries=None, max_results=10, max_chars_total=50000):
    response = parallel_client.beta.search(
        objective=objective,
        search_queries=search_queries,
        max_results=max_results,
        excerpts={"max_chars_per_result": 5000, "max_chars_total": max_chars_total}
    )
    return {
        "results": [
            {"url": r.url, "title": r.title, "excerpts": r.excerpts[:3] if r.excerpts else []}
            for r in response.results
        ]
    }


def chat_with_search(user_message: str) -> str:
    messages = [
        {
            "role": "system",
            "content": "You are a helpful research assistant. Use the parallel_search tool to find current information. Always cite sources with URLs."
        },
        {"role": "user", "content": user_message}
    ]

    # First API call - may trigger tool use
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=[parallel_search_tool],
        tool_choice="auto"
    )

    assistant_message = response.choices[0].message

    # Check if the model wants to use tools
    if assistant_message.tool_calls:
        messages.append(assistant_message)

        # Process each tool call
        for tool_call in assistant_message.tool_calls:
            if tool_call.function.name == "parallel_search":
                args = json.loads(tool_call.function.arguments)
                result = parallel_search(
                    objective=args.get("objective"),
                    search_queries=args.get("search_queries"),
                    max_results=args.get("max_results", 10),
                    max_chars_total=args.get("max_chars_total", 50000)
                )
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": json.dumps(result)
                })

        # Second API call with search results
        response = openai_client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=[parallel_search_tool],
            tool_choice="auto"
        )

    return response.choices[0].message.content


# Example usage
if __name__ == "__main__":
    answer = chat_with_search("What are the latest developments in quantum computing?")
    print(answer)