-
LangChain – Models
LangChain – Models Table Of Contents: 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",
-
Langchain – Agents
LangChain – Agents Table Of Contents: What Is An Agent? Core Components Model Component Tools Component System Prompt Structured Output Invocation Streaming Configure The Harness (1) What Is An Agent ? from langchain.agents import create_agent agent = create_agent( model = "ollama:qwen3:0.6b", tools= tools, system_prompt="Your System Prompt" ) (2) Core Components Of Agent (3) Model Component from langchain.agents import create_agent agent = create_agent( model="qwen3:0.6b", tools=[tools], system_prompt = "System Prompt" ) (4) Tools Component from langchain.agents import create_agent from langchain.tools import tools @tool def search(query:str)->str: """Search For Information""" return f"Result For {query}" agent = create_agent( model="ollama:qwen3:0.6b", tools = [search], system_prompt = "You
