पाइथन में ओलामा वेब सर्च एपीआई का उपयोग

pip install 'ollama>=0.6.0'

export OLLAMA_API_KEY="your_api_key"

$env:OLLAMA_API_KEY = "your_api_key"

import ollama

# Simple web search
response = ollama.web_search("What is Ollama?")
print(response)

results = [
    {
        "title": "Ollama",
        "url": "https://ollama.com/",
        "content": "Cloud models are now available in Ollama..."
    },
    {
        "title": "What is Ollama? Features, Pricing, and Use Cases",
        "url": "https://www.walturn.com/insights/what-is-ollama",
        "content": "Our services..."
    },
    {
        "title": "Complete Ollama Guide: Installation, Usage & Code Examples",
        "url": "https://collabnix.com/complete-ollama-guide",
        "content": "Join our Discord Server..."
    }
]

import ollama

# Get more results
response = ollama.web_search("latest AI news", max_results=10)

for result in response.results:
    print(f"📌 {result.title}")
    print(f"   {result.url}")
    print(f"   {result.content[:100]}...")
    print()

from ollama import web_fetch

result = web_fetch('https://ollama.com')
print(result)

WebFetchResponse(
    title='Ollama',
    content='[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama\n\n**Chat & build with open models**\n\n[Download](https://ollama.com/download) [Explore models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
    links=['https://ollama.com/', 'https://ollama.com/models', 'https://github.com/ollama/ollama']
)

from ollama import web_search, web_fetch

# Search for information
search_results = web_search("Ollama new features 2025")

# Fetch full content from the first result
if search_results.results:
    first_url = search_results.results[0].url
    full_content = web_fetch(first_url)

    print(f"Title: {full_content.title}")
    print(f"Content: {full_content.content[:500]}...")
    print(f"Links found: {len(full_content.links)}")

ollama pull qwen3:4b

from ollama import chat, web_fetch, web_search

available_tools = {'web_search': web_search, 'web_fetch': web_fetch}

messages = [{'role': 'user', 'content': "what is ollama's new engine"}]

while True:
    response = chat(
        model='qwen3:4b',
        messages=messages,
        tools=[web_search, web_fetch],
        think=True
    )

    if response.message.thinking:
        print('🧠 Thinking:', response.message.thinking[:200], '...')

    if response.message.content:
        print('💬 Response:', response.message.content)

    messages.append(response.message)

    if response.message.tool_calls:
        print('🔧 Tool calls:', response.message.tool_calls)
        for tool_call in response.message.tool_calls:
            function_to_call = available_tools.get(tool_call.function.name)
            if function_to_call:
                args = tool_call.function.arguments
                result = function_to_call(**args)
                print('📥 Result:', str(result)[:200], '...')
                # Truncate result for limited context lengths
                messages.append({
                    'role': 'tool',
                    'content': str(result)[:2000 * 4],
                    'tool_name': tool_call.function.name
                })
            else:
                messages.append({
                    'role': 'tool',
                    'content': f'Tool {tool_call.function.name} not found',
                    'tool_name': tool_call.function.name
                })
    else:
        break

from ollama import chat, web_fetch, web_search
import json

class SearchAgent:
    def __init__(self, model: str = 'qwen3:4b'):
        self.model = model
        self.tools = {'web_search': web_search, 'web_fetch': web_fetch}
        self.messages = []
        self.max_iterations = 10

    def query(self, question: str) -> str:
        self.messages = [{'role': 'user', 'content': question}]

        for iteration in range(self.max_iterations):
            try:
                response = chat(
                    model=self.model,
                    messages=self.messages,
                    tools=[web_search, web_fetch],
                    think=True
                )
            except Exception as e:
                return f"Error during chat: {e}"

            self.messages.append(response.message)

            # If no tool calls, we have a final answer
            if not response.message.tool_calls:
                return response.message.content or "No response generated"

            # Execute tool calls
            for tool_call in response.message.tool_calls:
                result = self._execute_tool(tool_call)
                self.messages.append({
                    'role': 'tool',
                    'content': result,
                    'tool_name': tool_call.function.name
                })

        return "Max iterations reached without final answer"

    def _execute_tool(self, tool_call) -> str:
        func_name = tool_call.function.name
        args = tool_call.function.arguments

        if func_name not in self.tools:
            return f"Unknown tool: {func_name}"

        try:
            result = self.tools[func_name](**args)
            # Truncate for context limits
            result_str = str(result)
            if len(result_str) > 8000:
                result_str = result_str[:8000] + "... [truncated]"
            return result_str
        except Exception as e:
            return f"Tool error: {e}"

# Usage
agent = SearchAgent(model='qwen3:4b')
answer = agent.query("What are the latest features in Ollama?")
print(answer)

import asyncio
from ollama import AsyncClient

async def async_search():
    client = AsyncClient()

    # Perform multiple searches concurrently
    tasks = [
        client.web_search("Ollama features"),
        client.web_search("local LLM tools"),
        client.web_search("AI search agents"),
    ]

    results = await asyncio.gather(*tasks)

    for i, result in enumerate(results):
        print(f"Search {i + 1}:")
        for r in result.results[:2]:
            print(f"  - {r.title}")
        print()

# Run async search
asyncio.run(async_search())

import asyncio
from ollama import AsyncClient

async def async_research_agent(question: str):
    client = AsyncClient()
    messages = [{'role': 'user', 'content': question}]

    while True:
        response = await client.chat(
            model='qwen3:4b',
            messages=messages,
            tools=[client.web_search, client.web_fetch],
        )

        messages.append(response.message)

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

        # Execute tool calls concurrently
        tool_tasks = []
        for tool_call in response.message.tool_calls:
            if tool_call.function.name == 'web_search':
                task = client.web_search(**tool_call.function.arguments)
            elif tool_call.function.name == 'web_fetch':
                task = client.web_fetch(**tool_call.function.arguments)
            else:
                continue
            tool_tasks.append((tool_call.function.name, task))

        # Gather results
        for tool_name, task in tool_tasks:
            result = await task
            messages.append({
                'role': 'tool',
                'content': str(result)[:8000],
                'tool_name': tool_name
            })

# Run
answer = asyncio.run(async_research_agent("What's new in Python 3.13?"))
print(answer)

from ollama import chat, web_search

# सर्च-हैवी टास्क्स के लिए उच्च संदर्भ सेट करें
response = chat(
    model='qwen3:4b',
    messages=[{'role': 'user', 'content': 'नवीनतम AI विकासों का अनुसंधान करें'}],
    tools=[web_search],
    options={
        'num_ctx': 32768,  # 32K संदर्भ
    }
)

{
  "mcpServers": {
    "web_search_and_fetch": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "path/to/web-search-mcp.py"],
      "env": { "OLLAMA_API_KEY": "your_api_key_here" }
    }
  }
}

[mcp_servers.web_search]
command = "uv"
args = ["run", "path/to/web-search-mcp.py"]
env = { "OLLAMA_API_KEY" = "your_api_key_here" }

#!/usr/bin/env python3
"""Ollama वेब सर्च के लिए सरल MCP सर्वर।"""

import os
from mcp.server import Server
from mcp.types import Tool, TextContent
from ollama import web_search, web_fetch

app = Server("ollama-web-search")

@app.tool()
async def search_web(query: str, max_results: int = 5) -> str:
    """इंटरनेट पर जानकारी खोजें।"""
    results = web_search(query, max_results=max_results)

    output = []
    for r in results.results:
        output.append(f"**{r.title}**\n{r.url}\n{r.content}\n")

    return "\n---\n".join(output)

@app.tool()
async def fetch_page(url: str) -> str:
    """एक वेब पेज की पूर्ण सामग्री प्राप्त करें।"""
    result = web_fetch(url)
    return f"# {result.title}\n\n{result.content}"

if __name__ == "__main__":
    app.run()

from ollama import chat, web_search

def summarize_news(topic: str) -> str:
    # हाल के समाचारों की खोज करें
    results = web_search(f"{topic} latest news", max_results=5)

    # सर्च परिणामों को मॉडल के लिए फॉर्मेट करें
    news_content = "\n\n".join([
        f"**{r.title}**\n{r.content}"
        for r in results.results
    ])

    # मॉडल से सारांश करने के लिए कहें
    response = chat(
        model='qwen3:4b',
        messages=[{
            'role': 'user',
            'content': f"इन {topic} के बारे में समाचार आइटम्स का सारांश दें:\n\n{news_content}"
        }]
    )

    return response.message.content

summary = summarize_news("artificial intelligence")
print(summary)

from ollama import chat, web_search, web_fetch
from dataclasses import dataclass

@dataclass
class ResearchResult:
    question: str
    sources: list
    answer: str

def research(question: str) -> ResearchResult:
    # संबंधित जानकारी की खोज करें
    search_results = web_search(question, max_results=3)

    # शीर्ष स्रोतों से पूर्ण सामग्री प्राप्त करें
    sources = []
    full_content = []

    for result in search_results.results[:3]:
        try:
            page = web_fetch(result.url)
            sources.append(result.url)
            full_content.append(f"Source: {result.url}\n{page.content[:2000]}")
        except:
            continue

    # व्यापक उत्तर जनरेट करें
    context = "\n\n---\n\n".join(full_content)

    response = chat(
        model='qwen3:4b',
        messages=[{
            'role': 'user',
            'content': f"""निम्नलिखित स्रोतों के आधार पर इस प्रश्न का उत्तर दें: {question}

स्रोत:
{context}

स्रोतों के संदर्भ के साथ एक व्यापक उत्तर प्रदान करें।"""
        }]
    )

    return ResearchResult(
        question=question,
        sources=sources,
        answer=response.message.content
    )

# उपयोग
result = research("Ollama के नए मॉडल शेड्यूलिंग का काम कैसे चलता है?")
print(f"प्रश्न: {result.question}")
print(f"स्रोत: {result.sources}")
print(f"उत्तर: {result.answer}")