Ollama Agent

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

📚Prerequisites:

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

First, let’s install the Ollama JavaScript library:

npm install ollama

First, let’s install the Ollama Python package:

pip install ollama

🧬 2. Create the Ollama Agent class:

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

TypeScript
Python

import {
    Agent,
    AgentOptions,
    ConversationMessage,
    ParticipantRole,
    Logger
} from "multi-agent-orchestrator";
import ollama from 'ollama'

export interface OllamaAgentOptions extends AgentOptions {
    streaming?: boolean;
    // Add other Ollama-specific options here (e.g., temperature, top_k, top_p)
}

export class OllamaAgent extends Agent {
    private options: OllamaAgentOptions;

    constructor(options: OllamaAgentOptions) {
      super(options);
      this.options = {
        name: options.name,
        description: options.description,
        modelId: options.modelId ?? "llama2",
        streaming: options.streaming ?? false
      };
    }

    private async *handleStreamingResponse(messages: any[]): AsyncIterable<string> {
      try {
        const response = await ollama.chat({
          model: this.options.modelId ?? "llama2",
          messages: messages,
          stream: true,
        });

        for await (const part of response) {
          yield part.message.content;
        }
      } catch (error) {
        Logger.logger.error("Error getting stream from Ollama model:", error);
        throw error;
      }
    }

    async processRequest(
      inputText: string,
      userId: string,
      sessionId: string,
      chatHistory: ConversationMessage[],
      additionalParams?: Record<string, string>
    ): Promise<ConversationMessage | AsyncIterable<any>> {
      const messages = chatHistory.map(item => ({
        role: item.role,
        content: item.content![0].text
      }));
      messages.push({role: ParticipantRole.USER, content: inputText});

      if (this.options.streaming) {
        return this.handleStreamingResponse(messages);
      } else {
        const response = await ollama.chat({
          model: this.options.modelId!,
          messages: messages,
        });
        const message: ConversationMessage = {
          role: ParticipantRole.ASSISTANT,
          content: [{text: response.message.content}]
        };
        return message;
      }
    }
}

from typing import List, Dict, Optional, AsyncIterable, Any
from multi_agent_orchestrator.agents import Agent, AgentOptions
from multi_agent_orchestrator.types import ConversationMessage, ParticipantRole
from multi_agent_orchestrator.utils import Logger
import ollama
from dataclasses import dataclass

@dataclass
class OllamaAgentOptions(AgentOptions):
    streaming: bool = False
    model_id: str = "llama2"
    # Add other Ollama-specific options here (e.g., temperature, top_k, top_p)

class OllamaAgent(Agent):
    def __init__(self, options: OllamaAgentOptions):
        super().__init__(options)
        self.model_id = options.model_id
        self.streaming = options.streaming

    async def handle_streaming_response(self, messages: List[Dict[str, str]]) -> ConversationMessage:
        text = ''
        try:
            response = ollama.chat(
                model=self.model_id,
                messages=messages,
                stream=self.streaming
            )
            for part in response:
                text += part['message']['content']
                self.callbacks.on_llm_new_token(part['message']['content'])

            return ConversationMessage(
                role=ParticipantRole.ASSISTANT.value,
                content=[{"text": text}]
            )

        except Exception as error:
            Logger.get_logger().error("Error getting stream from Ollama model:", error)
            raise error



    async def process_request(
        self,
        input_text: str,
        user_id: str,
        session_id: str,
        chat_history: List[ConversationMessage],
        additional_params: Optional[Dict[str, str]] = None
    ) -> ConversationMessage | AsyncIterable[Any]:
        messages = [
            {"role": msg.role, "content": msg.content[0]['text']}
            for msg in chat_history
        ]
        messages.append({"role": ParticipantRole.USER.value, "content": input_text})

        if self.streaming:
            return await self.handle_streaming_response(messages)
        else:
            response = ollama.chat(
                model=self.model_id,
                messages=messages
            )
            return ConversationMessage(
                role=ParticipantRole.ASSISTANT.value,
                content=[{"text": response['message']['content']}]
            )

Now that we have our OllamaAgent, let’s add it to the Multi-Agent Orchestrator:

🔗 3. Add OllamaAgent to the orchestrator:

If you have used the quickstarter sample program, you can add the Ollama agent and run it:

TypeScript
Python

import { OllamaAgent } from "./ollamaAgent";
import { MultiAgentOrchestrator } from "multi-agent-orchestrator"

const orchestrator = new MultiAgentOrchestrator();

// Add a text summarization agent using Ollama and Llama 2
orchestrator.addAgent(
  new OllamaAgent({
    name: "Text Summarization Wizard",
    modelId: "llama2",
    description: "I'm your go-to agent for concise and accurate text summaries!",
    streaming: true
  })
);

from ollamaAgent import OllamaAgent, OllamaAgentOptions
from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator

orchestrator = MultiAgentOrchestrator()

# Add a text summarization agent using Ollama and Llama 2
orchestrator.add_agent(
    OllamaAgent(OllamaAgentOptions(
        name="Text Summarization Wizard",
        model_id="llama2",
        description="I'm your go-to agent for concise and accurate text summaries!",
        streaming=True
    ))
)

And you are done!

🏃 4. Run Your Ollama Model Locally:

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

ollama run llama2

If you haven’t downloaded the Llama 2 model yet, it will be downloaded automatically before running.

🎉 You’re All Set!

Congratulations! You’ve successfully integrated an Ollama agent into your Multi-Agent Orchestrator System. Now you can start summarizing text and leveraging the power of Llama 2 in your applications!

5.🔗 Useful Links:

6.💡 Next Steps:

Experiment with different Ollama models
Customize the agent’s behavior by adjusting parameters
Create specialized agents for various tasks using Ollama

Happy coding! 🚀