LangChain ecosystem (LangSmith, LangGraph, LangServe) - Mini Project: Build & Apply

Building a Simple LangChain App with LangSmith, LangGraph, and LangServe

📖 Scenario: You are creating a small LangChain app that uses the LangChain ecosystem tools: LangSmith for tracking, LangGraph for visualizing chains, and LangServe for serving your chain as an API.This project will guide you step-by-step to set up a chain, configure LangSmith tracking, visualize the chain with LangGraph, and finally serve it with LangServe.

🎯 Goal: Build a LangChain app that creates a simple chain, tracks it with LangSmith, visualizes it with LangGraph, and serves it using LangServe.

📋 What You'll Learn

Create a simple LangChain chain with a prompt template

Configure LangSmith tracking for the chain

Use LangGraph to visualize the chain structure

Serve the chain as an API endpoint using LangServe

💡 Why This Matters

🌍 Real World

LangChain ecosystem tools help developers build, track, visualize, and serve language model applications efficiently.

💼 Career

Understanding LangChain and its ecosystem is valuable for AI developers, ML engineers, and software engineers working with language models and conversational AI.

1

Create a simple LangChain chain

Create a variable called prompt_template with the value 'Hello, {name}!'. Then create a PromptTemplate named prompt using prompt_template with an input variable 'name'. Finally, create a LLMChain called chain using the prompt and a dummy llm object.

LangChain

from langchain import LLMChain, PromptTemplate

llm = None  # Dummy LLM placeholder

# Create prompt_template, prompt, and chain here
# Your code here

Need a hint?

Use PromptTemplate with template and input_variables. Then create LLMChain with llm and prompt.

2

Configure LangSmith tracking for the chain

Import LangSmithTracer from langchain_experimental.langsmith. Create a variable called tracer as an instance of LangSmithTracer. Then set chain.callback_manager to tracer. This will enable LangSmith tracking for the chain.

LangChain

from langchain_experimental.langsmith import LangSmithTracer

# Create tracer and assign to chain.callback_manager
# Your code here

Need a hint?

Import LangSmithTracer, create an instance, and assign it to chain.callback_manager.

3

Visualize the chain with LangGraph

Import LangGraph from langchain_experimental.langgraph. Create a variable called graph as an instance of LangGraph with the chain passed as the chain argument. Then call graph.render() to generate the visualization.

LangChain

from langchain_experimental.langgraph import LangGraph

# Create LangGraph instance and render the graph
# Your code here

Need a hint?

Import LangGraph, create it with chain=chain, then call render().

4

Serve the chain using LangServe

Import LangServe from langchain_experimental.langserve. Create a variable called serve as an instance of LangServe with the chain passed as the chain argument. Then call serve.start() to start serving the chain as an API.

LangChain

from langchain_experimental.langserve import LangServe

# Create LangServe instance and start serving
# Your code here

Need a hint?

Import LangServe, create it with chain=chain, then call start().