Connecting to OpenAI models in LangChain - Step-by-Step Execution

Concept Flow - Connecting to OpenAI models

Import LangChain OpenAI class

↓

Create OpenAI instance with API key

↓

Call model with prompt

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Receive response from OpenAI

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Use or display the response

This flow shows how to import, create, call, and get a response from an OpenAI model using LangChain.

Execution Sample

LangChain

import os
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4", temperature=0.7)
response = llm([HumanMessage(content="Hello!")])
print(response.content)

This code connects to the OpenAI GPT-4 model, sends a greeting prompt, and prints the model's reply.

Execution Table

Step	Action	Input/State	Output/Result
1	Import ChatOpenAI class	None	ChatOpenAI class ready to use
2	Create OpenAI instance	openai_api_key=os.getenv('OPENAI_API_KEY'), model_name='gpt-4', temperature=0.7	llm object created with settings
3	Call model with prompt	[HumanMessage(content="Hello!")]	Request sent to OpenAI API
4	Receive response	OpenAI processes prompt	Response object with content text
5	Print response content	response.content	Printed text from model reply

💡 Execution stops after printing the model's response.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
llm	None	ChatOpenAI instance	Same instance	Same instance	Same instance
response	None	None	Response object	Response object	Response object with content

Key Moments - 3 Insights

Why do we need to create an instance of ChatOpenAI before calling the model?

Creating the instance sets up the model name and parameters like temperature, which configures how the model responds. This is shown in step 2 of the execution_table.

What does the 'call' method expect as input?

The 'call' method expects a list of BaseMessage objects, like [HumanMessage(content="Hello!")]. This is shown in step 3.

Where does the actual AI response come from?

The response comes from the OpenAI API after processing the prompt, shown in step 4 where the response object is received.

Visual Quiz - 3 Questions

Look at the execution_table, what is the state of 'llm' after step 2?

A It is None

B It is a ChatOpenAI instance configured with model and temperature

C It contains the response from the model

D It is a string with the prompt

💡 Hint

Check the variable_tracker row for 'llm' after step 2.

At which step does the program send the prompt to the OpenAI API?

A Step 3

B Step 4

C Step 1

D Step 5

💡 Hint

Look at the 'Action' and 'Output/Result' columns in the execution_table.

If you change the temperature parameter to 0.0 when creating 'llm', what changes in the execution?

A The model name changes

B The prompt sent changes

C The model's response becomes more deterministic

D The response is printed differently

💡 Hint

Temperature controls randomness in the model's output, set in step 2.

Concept Snapshot

Connecting to OpenAI models with LangChain:
- Import ChatOpenAI from langchain.chat_models and HumanMessage from langchain.schema
- Create instance: llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4", temperature=0.7)
- Call model with messages: llm([HumanMessage(content="Hello!")])
- Receive and use response.content
- Temperature controls response creativity

Full Transcript

To connect to OpenAI models using LangChain, first import the ChatOpenAI class and HumanMessage. Then create an instance with your API key, chosen model name and parameters like temperature. Next, call the model with a list of messages like [HumanMessage(content='Hello!')]. The model sends the prompt to OpenAI's API and returns a response object. Finally, access the response content to see the model's reply. This process sets up the connection, sends input, and receives output step-by-step.

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