Validating LLM Output with LangChain: A Python Framework

Introduction

When working with Language Learning Models (LLMs), it is crucial to ensure that their output is structured in a way that conforms to a specific schema. This validation process helps to guarantee that the LLM's response is in the desired format, such as JSON. In this blog post, we will explore how LangChain, a Python framework, can assist in validating LLM output and provide instructions for formatting the response.

Validating LLM Output with LangChain

LangChain is a powerful Python framework that simplifies the process of validating and formatting LLM output. One of its key features is the ability to generate JSON schemas using Pydantic, a Python library for data validation and parsing.

With LangChain, any input can be easily returned in JSON format. By leveraging Pydantic, LangChain can generate a JSON schema that serves as an instruction for the LLM on how to format its response. This ensures that the output adheres to the desired structure.

Using Pydantic Models for Formatting Instructions

To instruct the LLM on how to format its response, LangChain utilizes Pydantic models. Pydantic models are Python classes that define the structure and validation rules for data.

By creating a Pydantic model that represents the desired JSON structure, LangChain can generate a format instruction for the LLM. The model specifies the expected fields, their data types, and any additional constraints. This allows LangChain to validate the LLM's output and ensure it conforms to the specified schema.

Validating the Output

LangChain provides a seamless way to validate the LLM's output. By passing the LLM's response through the Pydantic model, LangChain can verify if the output matches the expected format.

If the output does not conform to the schema, LangChain will raise an error, indicating the inconsistencies. This validation step is crucial for debugging and ensuring the LLM's response meets the required specifications.

Debugging with LangChain

LangChain's validation capabilities also serve as a powerful debugging tool. When the LLM's output fails to validate against the specified schema, LangChain can provide valuable insights into the inconsistencies.

By identifying the specific fields or data types that do not match the schema, LangChain helps pinpoint the source of the issue. This significantly speeds up the debugging process, allowing developers to quickly resolve any formatting errors in the LLM's response.

Conclusion

Validating LLM output and ensuring it conforms to a specific schema is essential for maintaining data consistency and accuracy. LangChain, with its integration of Pydantic models, provides a straightforward solution for validating and formatting LLM responses.

By leveraging LangChain's capabilities, developers can easily instruct LLMs on how to structure their output and validate it against the desired schema. This not only ensures data integrity but also streamlines the debugging process by providing clear insights into any formatting inconsistencies.

LangChain is a valuable tool for any developer working with LLMs and seeking a reliable framework for validating and formatting their output. Give LangChain a try and experience the benefits it brings to your LLM development workflow.

Here’s an example:

``json

{ "properties":

{ "name": str },

{ "description": str },

{ "parts": [ { "part1": str, "quantity": float, "units": str}, { "part2": str, "quantity": float, "units": str} ] },

(etcetera)

}

```

We use an appliance parts store data keeping track of the parts on their shelf as an example:

```python

from langchain.pydantic import BaseModel, Field, field_validator

from typing import List

Class Part(BaseModel):

name: str = Field(description: "This is the name of the part")

quantity: float = Field(description: "This is the quantity of the part")

unit: str = Field(description: "This is the unit of the part's quantity")

Class Shelf(BaseModel):

naFme: str = Field(description: "This is the name of the shelf")

description: str = Field(description: "This is the description of the shelf")

Parts: list[part] = Field(description: "This is a list of parts, their quantities and their units")

```

Langchain will format it:

```python

from langchain.output_parsers import PydanticOutputParser

parser = PydanticOutputParser(pydantic_object=Part)

prompt = PromptTemplate(

template="Extract the relevant information from the following shelf.\n{format_instructions}\n{query}\n", input_variables=["query"],

partial_variables={"format_instructions": parser.get_format_instructions()}

)

```

The Shelf object is now assigned the root of the parsing:

The output should be formatted as a JSON instance that conforms to the JSON schema below. In\nAs an example, for the schema {"properties"： "foo"： "title"： "Foo", "description": "a list of strings" , "type": "array", "items": {"type": "string"}}}, "required"; ["foo"1}}\nthe object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted. \n\nhere is the output schema:\n'''\n{ "properties": { "name": str, { "description": str, { "ingredients": [ { "part1": str, "quantity": float, "units": str}, { "part2": str, "quantity": float, "units": str} ] }\n'"\n', additional _kwargs={}, example=False)]

Now you can validate as part of debugging as well