How to Migrate Pydantic for LangChain Compatibility

Let’s talk about something that we all face during development: API Testing with Postman for your Development Team.

Yeah, I’ve heard of it as well, Postman is getting worse year by year, but, you are working as a team and you need some collaboration tools for your development process, right? So you paid Postman Enterprise for…. $49/month.

Now I am telling you: You Don’t Have to:

That’s right, APIDog gives you all the features that comes with Postman paid version, at a fraction of the cost. Migration has been so easily that you only need to click a few buttons, and APIDog will do everything for you.

How to Migrate Postman Collections/Environments to Apidog

APIDog has a comprehensive, easy to use GUI that makes you spend no time to get started working (If you have migrated from Postman). It’s elegant, collaborate, easy to use, with Dark Mode too!

Want a Good Alternative to Postman? APIDog is definitely worth a shot. But if you are the Tech Lead of a Dev Team that really want to dump Postman for something Better, and Cheaper, Check out APIDog!

Apidog An integrated platform for API design, debugging, development, mock, and testing

Understanding Pydantic and LangChain

What is Pydantic?

Pydantic is a data validation and settings management library in Python that relies on Python type annotations. The philosophy behind Pydantic is to provide simple and robust data validation for Python applications while promoting readability and type safety. It allows developers to define schemas using Python classes and validates them against input data, ensuring that the data conforms to the specified types and constraints. Features such as custom validators and aliases make Pydantic a flexible choice for various projects.

from pydantic import BaseModel, ValidationError

class User(BaseModel):
    name: str
    age: int
    email: str

try:
    user = User(name='John Doe', age='30', email='john.doe@example.com')  # Age is a string here
except ValidationError as e:
    print(e.json())

In this code, a ValidationError is raised when the incorrect type is provided for age, showcasing the automatic validation capabilities of Pydantic.

What is LangChain?

LangChain is a framework designed for building applications with large language models (LLMs). By simplifying interactions with LLMs, LangChain allows developers to create applications such as chatbots, summarizers, or intelligent agents. As large language models often require structured prompts and responses, integrating Pydantic can enhance the reliability and clarity when dealing with complex data structures.

Why Migrate Pydantic for LangChain Compatibility?

When working with LangChain, the seamless integration of Pydantic is essential due to the need for structured data handling, especially when constructing APIs and responding to user inputs. Migrating your Pydantic models for LangChain can lead to improved compatibility, making your application more robust and easier to maintain.

In this guide, we will walk through the steps required to successfully migrate Pydantic models for LangChain compatibility, ensuring that we can leverage both libraries to build efficient applications.

Step 1: Install Required Libraries

Before starting the migration process, it is crucial to have both Pydantic and LangChain installed in your Python environment. You can install these libraries using pip:

pip install pydantic langchain

Step 2: Assess Existing Pydantic Models

Take an inventory of your existing Pydantic models. Assess how they are currently used in your application and identify the changes needed to make them compatible with LangChain. Consider the data types, fields, and any custom validation logic.

For example, let’s assume you have a Pydantic model for a booking system as follows:

from pydantic import BaseModel

class Booking(BaseModel):
    user_id: int
    event_id: int
    quantity: int

Step 3: Understanding LangChain’s Requirements

LangChain utilizes the concept of “chains” that interpret and generate language models for conversations or text processing. This means that your data models must be easily serializable into a format readily understood by the language processing units. When you migrate your Pydantic models, you may need to ensure they can accommodate serialization formats such as JSON or key-value pairs.

Step 4: Modify Pydantic Models for Serialization

In this step, we will refine the existing Pydantic model to ensure it is compatible with LangChain’s data structures. You may want to implement methods for converting Pydantic models to dictionaries or JSON strings, as LangChain may require such formats.

Using our previous Booking example, we can implement a method to serialize our data:

class Booking(BaseModel):
    user_id: int
    event_id: int
    quantity: int

    def to_dict(self) -> dict:
        return self.dict()

This simple method to_dict() allows for converting instances of Booking into dictionaries neatly, making it easier to work with in LangChain.

Step 5: Creating a LangChain-Compatible Input Schema

LangChain works effectively with defined prompt structures. By establishing a compatible input schema based on our Pydantic model, we can ensure that inputs to the application are uniform and easy to manage.

For instance, you may want to convert Booking instances into a prompt suitable for the language model:

from langchain.prompts import PromptTemplate

prompt_template = PromptTemplate(
    input_variables=["user_id", "event_id", "quantity"],
    template="Create a booking for user {user_id} for event {event_id} with quantity {quantity}."
)

# Example usage
booking_instance = Booking(user_id=42, event_id=5, quantity=3)
prompt = prompt_template.format(**booking_instance.to_dict())
print(prompt)

Here, we define a prompt template that utilizes the keys from our dictionary to ensure inputs are properly formatted for the LangChain model. The method to_dict() aids in the structured format necessary for the prompt.

Step 6: Implementing Custom Validation and Error Handling

When integrating Pydantic with LangChain, it is vital to ensure that the input data is validated properly. Custom validation methods can be employed to ensure data integrity while also providing meaningful error messages that can enhance user experience.

For example, if you wish to ensure that the quantity for bookings is always positive, you can implement this constraint as follows:

from pydantic import Field, conint

class Booking(BaseModel):
    user_id: int
    event_id: int
    quantity: conint(gt=0)  # quantity must be greater than 0

    def to_dict(self) -> dict:
        return self.dict()

In this instance, conint(gt=0) ensures that any attempt to create a Booking with a non-positive quantity raises a validation error. This method of constraint validation is powerful, particularly when dealing with user inputs in an application.

Step 7: Serializing and Deserializing the Models

With the Pydantic models prepared, it is now necessary to ensure smooth serialization and deserialization between the model and the LangChain prompts. We can utilize JSON for serialization, which makes it easy to send data across APIs or save state.

Here is how you can serialize and deserialize a Booking instance:

import json

# Serialization
booking_instance = Booking(user_id=42, event_id=5, quantity=3)
json_booking = booking_instance.json()
print("Serialized:", json_booking)

# Deserialization
booking_data = json.loads(json_booking)
booking_instance_from_json = Booking(**booking_data)
print("Deserialized:", booking_instance_from_json)

The JSON methods provided by Pydantic simplify both the serialization and deserialization processes, allowing for easy transitions between different data formats.

Step 8: Integrating with LangChain Process Flow

Lastly, integrate your Pydantic models into your LangChain application’s flow, ensuring that they work seamlessly together. There may be various points in your app where you’ll need to convert inputs and outputs back and forth between your structured data model and the formats required by LangChain.

For example, let’s assume you send a prompt to the LangChain model and expect a response that also corresponds to your Pydantic model:

from langchain.llms import OpenAI

llm = OpenAI(model_name="text-davinci-003")  # Example model

response = llm(prompt)
# Parse response and convert to a new Pydantic model if applicable

At this stage, ensure that the inputs to the language model are extracted from your Pydantic models correctly. Likewise, you’ll want to parse returned results and check if they need to be transformed back into a Pydantic model.

Conclusion

In this document, we examined the migration process of Pydantic models to ensure compatibility with LangChain. By understanding the frameworks’ requirements and adjusting the data structures accordingly, developers can enhance the functionality and robustness of their applications. The seamless interaction between typed data and language processing not only improves the clarity and maintainability of the code but also leverages the powerful capabilities embedded in these libraries to enhance the overall user experience.

Let’s talk about something that we all face during development: API Testing with Postman for your Development Team.

Yeah, I’ve heard of it as well, Postman is getting worse year by year, but, you are working as a team and you need some collaboration tools for your development process, right? So you paid Postman Enterprise for…. $49/month.

Now I am telling you: You Don’t Have to:

That’s right, APIDog gives you all the features that comes with Postman paid version, at a fraction of the cost. Migration has been so easily that you only need to click a few buttons, and APIDog will do everything for you.

How to Migrate Postman Collections/Environments to Apidog

APIDog has a comprehensive, easy to use GUI that makes you spend no time to get started working (If you have migrated from Postman). It’s elegant, collaborate, easy to use, with Dark Mode too!

Want a Good Alternative to Postman? APIDog is definitely worth a shot. But if you are the Tech Lead of a Dev Team that really want to dump Postman for something Better, and Cheaper, Check out APIDog!

Apidog An integrated platform for API design, debugging, development, mock, and testing