2 LLM with langchain

In [5]:

import json

import json

In [6]:

from langchain_ollama.llms import OllamaLLM
ollamaModel = "llama3.2:latest"
model = OllamaLLM(model=ollamaModel)

from langchain_ollama.llms import OllamaLLM ollamaModel = "llama3.2:latest" model = OllamaLLM(model=ollamaModel)

In [7]:

return_value = model.invoke("Assalamualaikum")
print(return_value)

return_value = model.invoke("Assalamualaikum") print(return_value)

Wa alaykums salam. How can I assist you today?

Konversasi menggunakan chat model¶

In [8]:

from langchain_core.messages import HumanMessage, AIMessage
from langchain_ollama.chat_models import ChatOllama

chat_model = ChatOllama(model=ollamaModel)

from langchain_core.messages import HumanMessage, AIMessage from langchain_ollama.chat_models import ChatOllama chat_model = ChatOllama(model=ollamaModel)

In [9]:

human_message = HumanMessage("Nama saya farras")

return_value_chage_model = chat_model.invoke([human_message])
print(type(return_value_chage_model))
print(json.dumps(return_value_chage_model.model_dump(), indent=4))

human_message = HumanMessage("Nama saya farras") return_value_chage_model = chat_model.invoke([human_message]) print(type(return_value_chage_model)) print(json.dumps(return_value_chage_model.model_dump(), indent=4))

<class 'langchain_core.messages.ai.AIMessage'>
{
    "content": "Halo Farras! Senang bertemu denganmu. Bagaimana hari ini?",
    "additional_kwargs": {},
    "response_metadata": {
        "model": "llama3.2:latest",
        "created_at": "2025-10-13T14:52:18.3048132Z",
        "done": true,
        "done_reason": "stop",
        "total_duration": 575399700,
        "load_duration": 270851100,
        "prompt_eval_count": 30,
        "prompt_eval_duration": 35991400,
        "eval_count": 19,
        "eval_duration": 230276900,
        "model_name": "llama3.2:latest"
    },
    "type": "ai",
    "name": null,
    "id": "run--e6d1b0df-7b89-4721-a51a-353a45bacfd3-0",
    "example": false,
    "tool_calls": [],
    "invalid_tool_calls": [],
    "usage_metadata": {
        "input_tokens": 30,
        "output_tokens": 19,
        "total_tokens": 49
    }
}

Build simple class to using chat models¶

In [10]:

from ChatModelsSimplifier import MyLLMChatModel

my_simple_model = MyLLMChatModel("llama3.2:latest")

from ChatModelsSimplifier import MyLLMChatModel my_simple_model = MyLLMChatModel("llama3.2:latest")

In [11]:

my_simple_model.chat_models("Halo nama saya muhammad farras ma'ruf")

my_simple_model.chat_models("Halo nama saya muhammad farras ma'ruf")

Selamat datang Muhammad Farras Ma'ruf XXX

In [12]:

my_simple_model.chat_models("Saya punya tiga anak, 2 laki-laku, dan satu perempuan")

my_simple_model.chat_models("Saya punya tiga anak, 2 laki-laku, dan satu perempuan")

Terima kasih atas informasinya, Muhammad Farras Ma'ruf XXX

In [13]:

my_simple_model.chat_models("Siapa nama saya ?")

my_simple_model.chat_models("Siapa nama saya ?")

Nama Anda adalah Muhammad Farras Ma'ruf XXX

In [14]:

print(my_simple_model.list_of_chats)

print(my_simple_model.list_of_chats)

[SystemMessage(content='You are assitant named NoSys, you always answert the question with sufix three XXX.', additional_kwargs={}, response_metadata={}), HumanMessage(content="Halo nama saya muhammad farras ma'ruf", additional_kwargs={}, response_metadata={}), AIMessage(content="Selamat datang Muhammad Farras Ma'ruf XXX", additional_kwargs={}, response_metadata={'model': 'llama3.2:latest', 'created_at': '2025-10-13T14:52:19.1976237Z', 'done': True, 'done_reason': 'stop', 'total_duration': 490199400, 'load_duration': 238550500, 'prompt_eval_count': 58, 'prompt_eval_duration': 21505000, 'eval_count': 13, 'eval_duration': 207168400, 'model_name': 'llama3.2:latest'}, id='run--caa341c6-ae8f-4bcf-89d0-fdf501b1cf46-0', usage_metadata={'input_tokens': 58, 'output_tokens': 13, 'total_tokens': 71}), HumanMessage(content='Saya punya tiga anak, 2 laki-laku, dan satu perempuan', additional_kwargs={}, response_metadata={}), AIMessage(content="Terima kasih atas informasinya, Muhammad Farras Ma'ruf XXX", additional_kwargs={}, response_metadata={'model': 'llama3.2:latest', 'created_at': '2025-10-13T14:52:19.9664717Z', 'done': True, 'done_reason': 'stop', 'total_duration': 760525300, 'load_duration': 410881300, 'prompt_eval_count': 99, 'prompt_eval_duration': 27383700, 'eval_count': 17, 'eval_duration': 277198600, 'model_name': 'llama3.2:latest'}, id='run--9655ac52-329b-4c99-919c-979544610714-0', usage_metadata={'input_tokens': 99, 'output_tokens': 17, 'total_tokens': 116}), HumanMessage(content='Siapa nama saya ?', additional_kwargs={}, response_metadata={}), AIMessage(content="Nama Anda adalah Muhammad Farras Ma'ruf XXX", additional_kwargs={}, response_metadata={'model': 'llama3.2:latest', 'created_at': '2025-10-13T14:52:20.4393638Z', 'done': True, 'done_reason': 'stop', 'total_duration': 466768400, 'load_duration': 223380100, 'prompt_eval_count': 130, 'prompt_eval_duration': 33110500, 'eval_count': 12, 'eval_duration': 183154500, 'model_name': 'llama3.2:latest'}, id='run--c1551cac-c285-4e99-ab2d-7e94ff8600d0-0', usage_metadata={'input_tokens': 130, 'output_tokens': 12, 'total_tokens': 142})]

Membuat MML yang dapat digunakan kembalim¶

In [ ]: