qv-ollama-sdk 1.0.0

A simple SDK for interacting with the Ollama API by automatically creating a conversation (chat history)

QV Ollama SDK

A simple SDK for interacting with the Ollama API with thinking mode and tool calling support.

Features

• 🧠 Thinking Mode - See AI reasoning process before answers
• 🛠️ Tool Calling - Execute Python functions automatically
• 💬 Simple Conversation - Easy chat interface
• ⚡ Streaming Support - Real-time responses
• 🔧 Explicit Parameters - No unnecessary defaults
• 🛡️ Model Compatibility - Auto-fallback for unsupported features

Installation

pip install qv-ollama-sdk

Quick Start

from qv_ollama_sdk import OllamaChatClient

# Create a client with a system message
client = OllamaChatClient(
    model_name="qwen3:8b",
    system_message="You are a helpful assistant."
)

# Simple chat - uses Ollama's default parameters
response = client.chat("What is the capital of France?")
print(response.content)

# Continue the conversation
response = client.chat("And what is its population?")
print(response.content)

# Set specific parameters only when you need them
client.temperature = 1.0  # Using property setter
client.max_tokens = 500   # Using property setter
client.set_parameters(num_ctx=2048)  # For multiple parameters

# Get conversation history
history = client.get_history()

🧠 Thinking Mode

# Enable thinking globally
client.enable_thinking()
response = client.chat("Solve this complex problem...")
print(f"🧠 Thinking: {response.thinking}")
print(f"💬 Answer: {response.content}")

# Disable when you want fast responses
client.disable_thinking()

🛠️ Tool Calling

def add_numbers(a: str, b: str) -> str:
    """Add two numbers."""
    return str(int(a) + int(b))

def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Sunny in {city}, 23°C"

tools = [add_numbers, get_weather]

# AI automatically calls functions when needed
response = client.chat("What's 15+27? And weather in Berlin?", tools=tools)
print(response.content)

🎯 Thinking + Tools

client.enable_thinking()
response = client.chat("Calculate 25 + 18", tools=tools)

print(f"🧠 Thinking: {response.thinking}")
print(f"💬 Answer: {response.content}")
print(f"🛠️ Tools used: {len(response.tool_calls)}")

⚡ Streaming

# Stream with thinking and tools
for chunk in client.stream_chat("Add 12 + 8", tools=tools):
    if chunk.thinking:
        print(chunk.thinking, end="")
    if chunk.tool_calls:
        print(f"🛠️ Using: {chunk.tool_calls[0].function.name}")
    if chunk.content:
        print(chunk.content, end="")

API Reference

Main Methods

• chat(message, tools=None, auto_execute=True) - Get response
• stream_chat(message, tools=None, auto_execute=True) - Stream response

Thinking Control

• enable_thinking() - Enable thinking globally
• disable_thinking() - Disable thinking globally

Response Object

• response.content - The answer
• response.thinking - AI's thought process
• response.tool_calls - Tools that were called
• response.tool_results - Tool execution results

Parameters

• tools=None - List of Python functions
• auto_execute=True - Auto-run tools (default)
• auto_execute=False - Raw tool calls only

🛡️ Model Compatibility

The SDK automatically handles different model capabilities:

# Works with any model - features auto-disabled if unsupported
client = OllamaChatClient(model_name="gemma2:2b")  # No tool/thinking support
client.enable_thinking()  # Will be ignored if not supported
tools = [add_numbers]

# This still works! Falls back to normal chat
response = client.chat("What is 15 + 27?", tools=tools)
# → "15 + 27 equals 42" (calculated by model, no tools used)

Supported Models:

• ✅ Modern models (e.g., qwen3:8b) - Full features
• ✅ Tool-only models (e.g., llama3:8b) - Tools but no thinking
• ✅ Thinking-only models - Thinking but no tools
• ✅ Basic models (e.g., gemma2:2b) - Normal chat only

Graceful Degradation:

• Unsupported features are automatically disabled
• No errors or exceptions thrown
• Always provides a response

Advanced Usage

For more control, you can use the lower-level API:

from qv_ollama_sdk import Conversation, OllamaConversationService, ModelParameters

# Create a conversation
conversation = Conversation(model_name="qwen3:8b")
conversation.add_system_message("You are a helpful assistant.")
conversation.add_user_message("What is the capital of France?")

# Generate a response with specific parameters including thinking
service = OllamaConversationService()
parameters = ModelParameters(temperature=0.7, num_ctx=2048, think=True)
response = service.generate_response(conversation, parameters)

print(f"Thinking: {response.thinking}")
print(f"Answer: {response.content}")