Ollama classifier with llama3.1

Welcome to the Ollama Classifier guide! This example will walk you through creating an Ollama classifier and integrating it into your Multi-Agent Orchestrator System. Let’s dive in!

📚 Prerequisites:

Basic knowledge of Python
Familiarity with the Multi-Agent Orchestrator System
Ollama installed on your machine

💾 1. Ollama installation:

Python

First, let’s install the Ollama Python package:

pip install ollama

🧬 2. Create the Ollama Classifier class:

Now, let’s create our OllamaClassifier class. This class extends the Classifier abstract class from the Multi-Agent Orchestrator. The process_request method must be implemented by the OllamaClassifier

Python

from typing import List, Dict, Optional, Any
from multi_agent_orchestrator.classifiers import Classifier, ClassifierResult
from multi_agent_orchestrator.types import ConversationMessage, ParticipantRole
from multi_agent_orchestrator.utils import Logger
import ollama


class OllamaClassifierOptions:
    def __init__(self,
                model_id: Optional[str] = None,
                inference_config: Optional[Dict[str, Any]] = None,
                host: Optional[str] = None
                ):
        self.model_id = model_id
        self.inference_config = inference_config or {}
        self.host = host

class OllamaClassifier(Classifier):
    def __init__(self, options: OllamaClassifierOptions):
        super().__init__()

        self.model_id = options.model_id or 'llama3.1'
        self.inference_config = options.inference_config
        self.streaming = False
        self.temperature = options.inference_config.get('temperature', 0.0)
        self.client = ollama.Client(host=options.host or None)

    async def process_request(self,
                            input_text: str,
                            chat_history: List[ConversationMessage]) -> ClassifierResult:
        messages = [
            {"role": msg.role, "content": msg.content[0]['text']}
            for msg in chat_history
        ]
        self.system_prompt = self.system_prompt + f'\n question: {input_text}'
        messages.append({"role": ParticipantRole.USER.value, "content": self.system_prompt})

        try:
            response = self.client.chat(
                    model=self.model_id,
                    messages=messages,
                    options={'temperature':self.temperature},
                    tools=[{
                        'type': 'function',
                        'function': {
                            'name': 'analyzePrompt',
                            'description': 'Analyze the user input and provide structured output',
                            'parameters': {
                                'type': 'object',
                                'properties': {
                                    'userinput': {
                                        'type': 'string',
                                        'description': 'The original user input',
                                    },
                                    'selected_agent': {
                                        'type': 'string',
                                        'description': 'The name of the selected agent',
                                    },
                                    'confidence': {
                                        'type': 'number',
                                        'description': 'Confidence level between 0 and 1',
                                    },
                                },
                                'required': ['userinput', 'selected_agent', 'confidence'],
                            },
                        }
                    }]
                )
            # Check if the model decided to use the provided function
            if not response['message'].get('tool_calls'):
                Logger.get_logger().info(f"The model didn't use the function. Its response was:{response['message']['content']}")
                raise Exception(f'Ollama model {self.model_id} did not use tools')
            else:
                tool_result = response['message'].get('tool_calls')[0].get('function', {}).get('arguments', {})
                return ClassifierResult(
                    selected_agent=self.get_agent_by_id(tool_result.get('selected_agent', None)),
                    confidence=float(tool_result.get('confidence', 0.0))
                )

        except Exception as e:
            Logger.get_logger().error(f'Error in Ollama Classifier :{str(e)}')
            raise e

Now that we have our OllamaClassifier, let’s use it in the Multi-Agent Orchestrator:

🔗 3. Use OllamaClassifier in the orchestrator:

If you have used the quickstarter sample program, you can use the Ollama classifier and run it like this:

Python

from ollamaClassifier import OllamaClassifier, OllamaClassifierOptions
from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator

classifier = OllamaClassifier(OllamaClassifierOptions(
        model_id='llama3.1',
        inference_config={'temperature':0.0}
    ))

orchestrator = MultiAgentOrchestrator()

# Use our newly created classifier within the orchestrator
orchestrator.set_classifier(classifier)

And you are done!

🏃 4. Run Your Ollama Model Locally:

Before running your program, make sure to start the Ollama model locally:

ollama run llama3.1

If you haven’t downloaded the Llama3.1 model yet, it will be downloaded automatically before running.

🎉 You’re All Set!

Congratulations! You’ve successfully integrated an Ollama classifier into your Multi-Agent Orchestrator System. Now you can start classifiying user requests and leveraging the power of Llama3.1 in your applications!

5.🔗 Useful Links:

6.💡 Next Steps:

Experiment with different Ollama models
Build a complete multi agent system in an offline environment

Happy coding! 🚀