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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",
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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
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LangChain – Ollama Model Running On Local
LangChain – Ollama Model Installation How To Install Ollama Model In Local ? (2) How To Test The Installation https://ollama.com/ ollama – version ollama pull smollm:135m (3) Making Of Agentic AI from langchain.agents import create_agent def get_weather(city:'str')->str: "Get The Weather Of The Given City" return f"Its Sunny In {city}" agent = create_agent( model="ollama:smollm:135m", tools=[get_weather] system_prompt = "You are an helpful Assistant" ) result = agent.invoke( {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]} ) print(result.content) (4) Install qwen3:0.6b Model Which Support Tool Calling ollama pull qwen3:0.6b from langchain.agents import create_agent def get_weather(city:str)->str: """Get The Weather Of The Given
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LangChain – Quick Guide
LangChain – Quick Guide Table Of Contents: Quick Start Build A Basic Agent Building A Real World Agent. (1) Quickstart pip install -U langchain deepagents export OPENAI_API_KEY = "your-api-key" (2) Building A Basic Agent from langchain.agents import create_agent def get_weather(city:str)->str: """Get Weather For A Given City""" return f"It's Always Sunny In {city}!" agent = create_agent( model = "openai:gpt-5.5", tools = [get_weather], system_prompt = "You Are A Helpful Assistant", ) result = agent.invoke( {"message":[{"role":"user", "content":"What Is The Weather In Bhubaneswar"}]} ) print(result["message"][-1].content_blocks) (3) Building A Real World Agent SYSTEM_PROMPT = """ You are a literary data assistant. IMPORTANT RULES: 1. If
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LangChain – Installation
LangChain – Installation pip install -U langchain # Installing Open AI Integration pip install -U langchain-openai # Installing The Anthropic Integration pip install -U langchain-anthropic
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LangChain – Introduction To LangChain
Introduction To LangChain
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Agentic AI – Practical Projects
Agentic AI – Practical Projects https://github.com/ashishpatel26/500-AI-Agents-Projects https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-from-scratch-functional.ipynb https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/concepts/multi_agent
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Agentic AI – Interview Question Set – 6
Agentic AI – Interview Questions Set – 6
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Agentic AI – Interview Questions Set – 5
Agentic AI – Interview Questions Set – 5
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Agentic AI – Interview Questions Set -4
Agentic AI – Interview Questions Set-4
