How can LangChain be used for data extraction tasks?

LangChain simplifies data extraction by connecting language models to structured outputs and workflows. Developers can use its components to define schemas, process unstructured text, and validate results. This approach is useful for tasks like pulling specific fields from documents, transforming natural language into tables, or standardizing data formats. LangChain handles the complexity of interacting with language models while letting you focus on the extraction logic.

A typical setup involves three steps. First, define the data structure you want to extract using tools like Pydantic models. For example, to extract product details from an email, you might create a Product model with name, price, and features fields. Next, create a prompt template that instructs the language model to identify these elements in the input text. LangChain’s create_extraction_chain function can then process the text through the model and map the output to your schema. Finally, use output parsers to convert the model’s text response into your defined format (like JSON) and validate it against the schema. This structured approach reduces manual parsing and handles variations in input phrasing.

For more complex scenarios, LangChain offers additional tools. You can chain multiple extraction steps—like first identifying relevant paragraphs in a contract before extracting specific clauses. Document transformers can split large texts into manageable chunks for processing, while retry logic helps handle model errors or incomplete outputs. For instance, extracting invoice data might involve splitting a PDF into line items, using a model to categorize each item, then aggregating results into a spreadsheet. LangChain also integrates with external tools like OCR systems or databases, enabling end-to-end pipelines where raw documents are transformed into structured data ready for analysis or storage.