03. Model Serialization-Save and Call

Serialization

What is Serialization?

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• The process of converting a model to a storeable format

purpose

• Model reuse (without retraining)

• Easy to distribute and share models

• Saving computational resources

Advantages

• Fast model loading

• Version manageable

• Available in a variety of environments

Model serialization is an important step in the AI development and deployment process, enabling efficient model management and reuse.

is_lc_serializable You can run it as a class method to see if the LangChain class is serializable.

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# API KEY a configuration file for managing environment variables
from dotenv import load_dotenv

# API KEY load information
load_dotenv()

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True

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# LangSmith Set up tracking. https://smith.langchain.com
# !pip install langchain-teddynote
from langchain_teddynote import logging

# Enter a project name.
logging.langsmith("CH04-Models")

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 Start tracking LangSmith. 
[Project name] 
CH04-Models 

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import os
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate

# Create questions using prompt templates.
prompt = PromptTemplate.from_template("{fruit} What is the color of?")

Check for serialization for class.

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# Check if serialization is possible.
print(f"ChatOpenAI: {ChatOpenAI.is_lc_serializable()}")

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ChatOpenAI: True

Check whether it can be serialized against llm objects.

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llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Check if serialization is possible.
print(f"ChatOpenAI: {llm.is_lc_serializable()}")

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ChatOpenAI: True

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# Create a chain.
chain = prompt | llm

# Check if serialization is possible.
chain.is_lc_serializable()