LangChain – Models

What Is A Model ?
Basic Uses Of Model.
Parameters To The Model
Model Invocation
Tool Calling
Structured Output
Model Profilling
Multimodal Model
Reasoning Model
Local Models
Prompt Catching
Server Side Tool Uses
Rate Limiting
Base URL & Proxy Setting
Log Probabilities
Token Uses
Model Invocation Config
Configurable Model
Dynamic Model Selection

(1) What Is A Model ?

(2) Basic Uses Of Model.

pip install -U "langchain[openai]"

import os
from langchain.chat_models import init_chat_model

os.environ["OPENAI_API_KEY"] = "sk-..."

model = init_chat_model("gpt-5.5")

response = model.invoke("How Are You Doing?")

(3) Model Parameters

model = init_chat_model(
        model = "qwen3:0.6b",
        temperature = 0.7,
        timeout=30,
        max_token=1000,
        max_retries=6,
        )

(4) Model Invocation

(5) Model Streaming:

(6) Model Batch Processing:

(5) Model Tool Calling

from langchain.tools import tool


@tool
def get_weather(location:str)->str:
    """Get Weather At A Location"""
    return f"It's Sunny In {city}"
    
model_with_tools = model.bind_tools([get_weather])

response = model_with_tools.invoke("What Is The Weather In Bhubaneswar?")


for tool_call in response.tool_calls:
    # View tool calls made by the model
    print(f"Tool: {tool_call['name']}")
    print(f"Args: {tool_call['args']}")

(6) Model With Structured Output

from pydentic import BaseModel, Field

class Movie(BaseModel):
     """A Movie With Details."""
     title:str = Field(description="The Title Of The Movie")
     year:int = Field(description="The Year The Movie Was Released")
     director:str= Field(description="The Director Of The Movie")
     rating:float= Field(description="The Movies Rating Out Of 10")
     
model_with_structure = model.with_structured_output(Movie)

response = model_with_structure.invoke("Provide Details About The Movie Inception")
print(response)