Connecting to open-source models in LangChain - Mini Project: Build & Apply

Connecting to Open-Source Models with LangChain

📖 Scenario: You want to build a simple Python program that uses LangChain to connect to an open-source language model. This will help you understand how to set up the data, configure the connection, run the model, and get the output.

🎯 Goal: Build a LangChain script that connects to the llama_cpp model, sends a prompt, and prepares to get the response.

📋 What You'll Learn

Create a dictionary called model_params with the key model_path set to "/models/llama-7b.ggmlv3.q4_0.bin".

Create a variable called max_tokens and set it to 100.

Create a LlamaCpp object called llm using model_params and max_tokens.

Create a prompt string with the text "Hello, how are you?".

💡 Why This Matters

🌍 Real World

Connecting to open-source language models allows developers to build AI-powered applications without relying on paid APIs. This is useful for chatbots, content generation, and research.

💼 Career

Many AI and software engineering jobs require integrating language models into applications. Knowing how to configure and connect to models like LlamaCpp is a valuable skill.

1

Set up the model parameters dictionary

Create a dictionary called model_params with the key model_path set to the string "/models/llama-7b.ggmlv3.q4_0.bin".

LangChain

# Create the model_params dictionary with model_path
# Your code here

Need a hint?

Use curly braces {} to create a dictionary. The key is "model_path" and the value is the exact string "/models/llama-7b.ggmlv3.q4_0.bin".

2

Add max tokens configuration

Create a variable called max_tokens and set it to the integer 100.

LangChain

model_params = {"model_path": "/models/llama-7b.ggmlv3.q4_0.bin"}
# Create max_tokens variable and set to 100
# Your code here

Need a hint?

Just write max_tokens = 100 on a new line.

3

Create the LlamaCpp model object

Import LlamaCpp from langchain_community.llms. Then create a LlamaCpp object called llm using the model_params dictionary and the max_tokens variable as arguments.

LangChain

model_params = {"model_path": "/models/llama-7b.ggmlv3.q4_0.bin"}
max_tokens = 100
# Import LlamaCpp and create llm object with model_params and max_tokens
# Your code here

Need a hint?

Use from langchain_community.llms import LlamaCpp to import. Then create llm = LlamaCpp(model_path=model_params["model_path"], max_tokens=max_tokens).

4

Create the prompt string

Create a string variable called prompt and set it to "Hello, how are you?".

LangChain

from langchain_community.llms import LlamaCpp

model_params = {"model_path": "/models/llama-7b.ggmlv3.q4_0.bin"}
max_tokens = 100

llm = LlamaCpp(model_path=model_params["model_path"], max_tokens=max_tokens)
# Create prompt string with text "Hello, how are you?"
# Your code here

Need a hint?

Just assign the exact string to prompt.