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

Connecting to OpenAI models

📖 Scenario: You want to build a simple program that connects to OpenAI's language models using LangChain. This will help you send a prompt and get a response from the AI.

🎯 Goal: Create a Python script that sets up the OpenAI API client with LangChain, configures the model, sends a prompt, and receives the AI's response.

📋 What You'll Learn

Create a variable called api_key with your OpenAI API key as a string

Create an OpenAI client instance called llm using LangChain's OpenAI class with the api_key

Create a prompt string variable called prompt with the text 'Hello, how are you?'

Use the llm instance to generate a response from the prompt and store it in a variable called response

💡 Why This Matters

🌍 Real World

Connecting to OpenAI models is essential for building AI-powered chatbots, content generators, and other intelligent applications.

💼 Career

Many software development roles require integrating AI services like OpenAI to enhance user experiences and automate tasks.

1

Set up the OpenAI API key

Create a variable called api_key and assign it the string value 'your-openai-api-key'.

LangChain

# Your code here

Hint

Use a string to store your API key exactly as 'your-openai-api-key'.

2

Create the OpenAI client instance

Import OpenAI from langchain.llms and create a variable called llm by calling OpenAI(openai_api_key=api_key).

LangChain

api_key = 'your-openai-api-key'
# Your code here

Hint

Make sure to import OpenAI from langchain.llms before creating the client.

3

Create the prompt string

Create a variable called prompt and assign it the string 'Hello, how are you?'.

LangChain

api_key = 'your-openai-api-key'
from langchain.llms import OpenAI

llm = OpenAI(openai_api_key=api_key)
# Your code here

Hint

Use a simple string variable to hold the prompt text.

4

Generate the response from the model

Call the llm instance with the prompt and assign the result to a variable called response.

LangChain

api_key = 'your-openai-api-key'
from langchain.llms import OpenAI

llm = OpenAI(openai_api_key=api_key)

prompt = 'Hello, how are you?'
# Your code here

Hint

Call the llm like a function with the prompt string to get the response.

Practice

1. What is the main purpose of creating a ChatOpenAI object in Langchain?

easy

A. To store user data securely in a database

B. To connect and interact with OpenAI's chat models for generating responses

C. To create a graphical user interface for chat applications

D. To compile Python code into machine language

Solution

1. Step 1: Understand the role of ChatOpenAI

   The ChatOpenAI object is designed to connect your program to OpenAI's chat models.

2. Step 2: Identify its main use

   It allows sending prompts and receiving AI-generated chat responses, enabling conversational AI features.

3. Final Answer:

   To connect and interact with OpenAI's chat models for generating responses -> Option B

4. Quick Check:

   ChatOpenAI connects to OpenAI chat models = A [OK]

Hint: ChatOpenAI is for chatting with AI models, not data storage [OK]

Common Mistakes:

• Thinking ChatOpenAI stores data
• Confusing it with UI creation
• Assuming it compiles code

2. Which of the following is the correct way to create a ChatOpenAI instance with the model name "gpt-4" in Langchain?

easy

A. chat = ChatOpenAI.new(modelName='gpt-4')

B. chat = ChatOpenAI('gpt-4')

C. chat = ChatOpenAI.create(model='gpt-4')

D. chat = ChatOpenAI(model_name="gpt-4")

Solution

1. Step 1: Recall Langchain ChatOpenAI constructor syntax

   The correct way is to pass the model name as a keyword argument model_name.

2. Step 2: Match options to syntax

   chat = ChatOpenAI(model_name="gpt-4") uses model_name="gpt-4", which is correct. Others use incorrect method calls or argument names.

3. Final Answer:

   chat = ChatOpenAI(model_name="gpt-4") -> Option D

4. Quick Check:

   Use model_name keyword for model in ChatOpenAI = D [OK]

Hint: Use model_name keyword, not positional or create/new methods [OK]

Common Mistakes:

• Passing model name as positional argument
• Using .create() or .new() methods which don't exist
• Using wrong argument names like model or modelName

3. Given this code snippet, what will be the output?

from langchain.chat_models import ChatOpenAI
chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
response = chat.predict("Hello, how are you?")
print(response)

medium

A. A string with a friendly AI response to the greeting

B. An error because temperature must be between 1 and 10

C. None, because predict returns nothing

D. A list of tokens generated by the model

Solution

1. Step 1: Understand ChatOpenAI.predict behavior

   The predict method sends the prompt to the model and returns the AI's text response as a string.

2. Step 2: Check temperature and output type

   Temperature 0 is valid and means deterministic output. The method returns a string, not None or a list.

3. Final Answer:

   A string with a friendly AI response to the greeting -> Option A

4. Quick Check:

   predict returns AI text response string = C [OK]

Hint: predict returns text response string, temperature 0 is valid [OK]

Common Mistakes:

• Thinking temperature must be >0
• Assuming predict returns None or list
• Expecting an error from this code

4. What is wrong with this code snippet for connecting to an OpenAI model using Langchain?

from langchain.chat_models import ChatOpenAI
chat = ChatOpenAI(model="gpt-4")
response = chat.predict("Tell me a joke.")
print(response)

medium

A. The argument should be model_name, not model

B. The predict method requires an async call

C. ChatOpenAI cannot be imported from langchain.chat_models

D. The print statement should be inside a function

Solution

1. Step 1: Check constructor argument names

   The correct argument to specify the model is model_name, not model.

2. Step 2: Verify other code parts

   Import and usage of predict are correct and synchronous, print can be outside a function.

3. Final Answer:

   The argument should be model_name, not model -> Option A

4. Quick Check:

   Use model_name keyword, not model = B [OK]

Hint: Use model_name keyword exactly for model in ChatOpenAI [OK]

Common Mistakes:

• Using 'model' instead of 'model_name'
• Thinking predict is async by default
• Assuming import path is wrong

5. You want to create a Langchain ChatOpenAI instance that uses the "gpt-4" model with a temperature of 0.7 and a maximum token limit of 100. Which code snippet correctly sets all these parameters?

hard

A. chat = ChatOpenAI(model="gpt-4", temp=0.7, max_tokens=100)

B. chat = ChatOpenAI(model_name="gpt-4", temperature=0.7, maxToken=100)

C. chat = ChatOpenAI(model_name="gpt-4", temperature=0.7, max_tokens=100)

D. chat = ChatOpenAI(model_name="gpt-4", temperature=0.7, max_tokens=1000)

Solution

1. Step 1: Identify correct parameter names

   The correct parameters are model_name, temperature, and max_tokens.

2. Step 2: Check values and spelling

   chat = ChatOpenAI(model_name="gpt-4", temperature=0.7, max_tokens=100) uses correct names and values: temperature 0.7 and max_tokens 100. Others have wrong names or wrong token limit.

3. Final Answer:

   chat = ChatOpenAI(model_name="gpt-4", temperature=0.7, max_tokens=100) -> Option C

4. Quick Check:

   Use model_name, temperature, max_tokens correctly = A [OK]

Hint: Use exact parameter names: model_name, temperature, max_tokens [OK]

Common Mistakes:

• Using 'model' instead of 'model_name'
• Wrong parameter names like maxToken or temp
• Setting max_tokens too high or wrong value