How to Use Pydantic with LangChain

Pydantic is a powerful Python library used for data validation and settings management using Python type annotations. When combined with LangChain, a framework designed to build applications powered by large language models (LLMs), Pydantic can enhance the way you manage and validate structured data within your LangChain workflows.

Understanding Pydantic and LangChain Integration

LangChain leverages structured data extensively, such as prompts, configuration settings, and outputs from various chains or agents. Pydantic models bring type safety, validation, and clear data schemas to these parts, which improves reliability and maintainability.

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Why Use Pydantic with LangChain?

1. Data Validation: Ensures that inputs to chains, agents, or tools conform to expected types and formats.

2. Clear Interfaces: Define explicit schemas for your data structures, making code easier to understand.

3. Error Handling: Catch invalid data early through Pydantic’s validation mechanisms.

4. Serialization: Easy conversion between Python objects and JSON-compatible data for external APIs or storage.

5. Settings Management: Configure LangChain components with typed, validated settings.

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Setting Up Pydantic in a LangChain Project

Start by installing Pydantic if you haven’t:

bash
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pip install pydantic

LangChain already uses Pydantic extensively under the hood, but you can create custom models for your own chains, prompts, or agents.

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Example 1: Validating Inputs to a Custom Chain

Suppose you want to build a custom chain that expects a structured input with user details.

python
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from pydantic import BaseModel, Field, ValidationError
from langchain.chains import Chain
from typing import Dict, Any

class UserInput(BaseModel):
    name: str = Field(..., description="User's full name")
    age: int = Field(..., gt=0, description="User's age, must be positive")
    email: str

class CustomUserChain(Chain):
    input_keys = ["user_data"]
    output_keys = ["response"]

    def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        # Validate the input data using Pydantic
        try:
            user = UserInput(**inputs["user_data"])
        except ValidationError as e:
            return {"response": f"Invalid input: {e}"}
        
        # Use validated data to build a response
        response = f"Hello, {user.name}! Your email {user.email} is registered."
        return {"response": response}

# Example usage
chain = CustomUserChain()
input_data = {"user_data": {"name": "Alice", "age": 30, "email": "alice@example.com"}}
output = chain(input_data)
print(output)

This example validates input before processing, preventing errors downstream.

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Example 2: Creating Typed Prompt Templates

LangChain’s prompt templates can be more structured by defining inputs as Pydantic models.

python
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from pydantic import BaseModel
from langchain.prompts import PromptTemplate

class ProductInfo(BaseModel):
    product_name: str
    product_price: float

template = "Describe the product named {product_name} which costs ${product_price}."

prompt = PromptTemplate(
    input_variables=ProductInfo.__fields__.keys(),
    template=template
)

product = ProductInfo(product_name="SuperWidget", product_price=99.99)
formatted_prompt = prompt.format(**product.dict())
print(formatted_prompt)

Using Pydantic here makes it clear which inputs are required and ensures proper typing.

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Example 3: Structured Outputs with Pydantic

After calling an LLM, you may want to parse the response into a structured format:

python
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from pydantic import BaseModel, ValidationError
from langchain.llms import OpenAI

class ProductReview(BaseModel):
    rating: int
    review_text: str

llm = OpenAI()

prompt = "Write a product review with a rating from 1 to 5 and a short comment."

response = llm(prompt)
print("Raw LLM response:", response)

# Example output parsing (assuming JSON output)
try:
    review = ProductReview.parse_raw(response)
    print(f"Rating: {review.rating}, Comment: {review.review_text}")
except ValidationError:
    print("Failed to parse LLM output.")

This approach depends on your prompt instructing the model to output JSON that fits the Pydantic schema.

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Tips for Using Pydantic with LangChain

• Use Pydantic models for all inputs and outputs where possible to catch errors early.

• Combine Pydantic with LangChain’s native BaseModel inheritance for custom chains or agents.

• For complex nested data, Pydantic can model deeply nested structures, improving clarity.

• Validate settings and environment variables for LangChain integrations (API keys, etc.) with Pydantic’s BaseSettings.

• Leverage Pydantic’s parse_obj and parse_raw for flexible parsing of API or LLM responses.

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Conclusion

Integrating Pydantic with LangChain enables robust, maintainable applications by providing a typed contract for data passing through your chains, prompts, and agents. This improves error detection, clarifies code intent, and simplifies handling of structured data when working with large language models.

Mastering this combination will help you build scalable and reliable AI workflows with clear data schemas and solid validation.