Ollama Tool Calling

ollama pull qwen3.5:0.8b
pip install ollama parallel-web
export PARALLEL_API_KEY="your-parallel-api-key"

import os
from parallel import Parallel

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

def search_web(objective: str, search_queries: list[str]) -> dict:
    """Searches the web for current and factual information using Parallel.

    Args:
      objective: A concise, self-contained search query. Must include the
        key entity or topic being searched for.
      search_queries: Exactly 3 keyword queries, each 3-6 words. Must be
        diverse — vary entity names, synonyms, and angles. Each query must
        include the key entity. Never write sentences or use site: operators.

    Returns:
      A dict with a `results` list, each containing url, title, and excerpts.
    """
    response = parallel_client.search(
        objective=objective,
        search_queries=search_queries,
    )
    return {
        "results": [
            {"url": r.url, "title": r.title, "excerpts": r.excerpts[:3] if r.excerpts else []}
            for r in response.results
        ]
    }

available_tools = {"search_web": search_web}

def process_tool_calls(tool_calls):
    """Execute each tool call and return tool-result messages."""
    results = []
    for call in tool_calls:
        fn = available_tools.get(call.function.name)
        if fn is None:
            continue
        # Ollama returns arguments as a parsed dict, not a JSON string.
        result = fn(**call.function.arguments)
        results.append({
            "role": "tool",
            "tool_name": call.function.name,
            "content": str(result),
        })
    return results

import os
from ollama import chat
from parallel import Parallel

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

def search_web(objective: str, search_queries: list[str]) -> dict:
    """Searches the web for current and factual information using Parallel.

    Args:
      objective: A concise, self-contained search query. Must include the
        key entity or topic being searched for.
      search_queries: Exactly 3 keyword queries, each 3-6 words. Must be
        diverse — vary entity names, synonyms, and angles. Each query must
        include the key entity. Never write sentences or use site: operators.
    """
    response = parallel_client.search(
        objective=objective,
        search_queries=search_queries,
    )
    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, model: str = "qwen3.5:0.8b") -> str:
    messages = [
        {
            "role": "system",
            "content": "You are a helpful research assistant. Use search_web "
                       "for current information. Cite sources with URLs.",
        },
        {"role": "user", "content": user_message},
    ]
    available = {"search_web": search_web}

    while True:
        response = chat(model=model, messages=messages, tools=[search_web])
        messages.append(response.message)

        if not response.message.tool_calls:
            return response.message.content

        for call in response.message.tool_calls:
            fn = available.get(call.function.name)
            result = fn(**call.function.arguments) if fn else "unknown tool"
            messages.append({
                "role": "tool",
                "tool_name": call.function.name,
                "content": str(result),
            })

if __name__ == "__main__":
    print(chat_with_search("What's new with Parallel Web Systems?"))