Pydantic Output Parser with LangChain: A nice and simple example

In the world of AI and natural language processing, structuring and parsing outputs from language models is crucial.

Why structured output is so hard 🤔

LLMs are largely based on the transformer architecture, which uses an auto-regressive generator. Transformer treats each word as a token and generates one token at a time. The LLM cannot go back and correct the output once generated, which makes consistent JSON outputs very difficult.

Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed JSON.

Here is a code demonstration with a combination of LangChain and Pydantic to generate and structure business names for an AI startup.

The Core Components

At the heart of this code are three key elements:

1. LangChain: A framework for developing applications powered by language models.

2. Pydantic: A data validation library that uses Python type annotations.

3. OpenAI's ChatGPT: The underlying language model driving the name generation.

   

The PydanticOutputParser from LangChain is a powerful tool designed to bridge the gap between the unstructured output of language models and the strictly typed, validated structures expected by Pydantic models. The parser seamlessly integrates with Pydantic models, allowing for the validation and structuring of data. This is crucial for applications requiring reliable and consistent data formats.

📌 Format Instructions: The get_format_instructions() method provides instructions on how the language model's output should be formatted. This ensures that the output adheres to the expected schema, making it easier to parse and validate.

📌 Error Handling: If the input data cannot be parsed to form a valid model, the parser raises a ValidationError. This helps in catching and handling errors early in the data processing pipeline.

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The Workflow

The code sets up a system to generate business names following specific principles. Here's how it works:

1. Defining the Structure: Two Pydantic models, BusinessName and BusinessNames, define the expected output structure.

2. Setting the Rules: A principles string outlines the criteria for name generation, focusing on innovation, brevity, and industry relevance.

3. Preparing the Prompt: A ChatPromptTemplate is created, incorporating the principles and industry context.

4. Generating Names: The code demonstrates three methods to generate names:

   • Using prompt_and_model

   • Using a complete chain

   • Using model.invoke() with format_prompt()

5. Parsing the Output: The PydanticOutputParser ensures the model's output adheres to the defined structure.

📌 The parse() method takes the raw text output from the language model and attempts to parse it into the specified Pydantic model. If the output does not conform to the expected structure, a ValidationError is raised. python

result = model.invoke(prompt)
parsed_result = parser.parse(result.content)

The smooth Integration

The simpleness of this code is its seamless integration of multiple tools:

• LangChain manages the interaction with the language model and structures the workflow.

• Pydantic ensures type safety and provides a clear structure for the output.

• The OpenAI model brings the creative power to generate relevant names.

So overall this code is a sophisticated approach to working with LLM outputs in a structured, programmable way.