LangChain is designed to work with multiple types of data commonly used in applications involving language models. It primarily handles unstructured text, structured data from databases or APIs, and specialized formats like code or chat histories. By providing tools to process, split, and transform these data types, LangChain enables developers to build workflows that integrate language models with diverse data sources efficiently.
For unstructured text—the most common input type—LangChain supports raw text from documents, web pages, or user inputs. It includes utilities to load text from formats like PDFs, HTML, or plain text files using document loaders (e.g., TextLoader, WebBaseLoader). Once loaded, the text can be split into chunks for processing by language models, which is useful for tasks like summarization or question answering. For example, a developer might split a 10,000-word article into smaller sections, embed each section using a model, and then query specific parts of the document. LangChain also handles embeddings, allowing text to be converted into numerical representations for tasks like similarity comparisons or retrieval-augmented generation (RAG).
Structured data, such as tables from databases or JSON from APIs, is another key focus. LangChain provides tools to interface with SQL databases (via SQLDatabase), pandas DataFrames, or REST APIs, enabling language models to interact with tabular or hierarchical data. For instance, a developer could use LangChain to translate a natural language query like “Show me sales data for Q3” into a SQL query, fetch the results, and format them into a natural language response. This bridges the gap between unstructured language model inputs and structured data systems. Additionally, LangChain supports templating for structured prompts, allowing dynamic injection of data (e.g., inserting user-specific values from a database into a prompt template).
Finally, LangChain handles specialized data types like code snippets or conversational histories. For code, it includes parsers and tools to validate syntax, making it easier to generate or debug code using models. For chat-based applications, it manages message chains—sequences of user and AI interactions—preserving context across multiple turns. For example, a chatbot built with LangChain can retain the last five messages in a conversation to maintain coherence. It also supports custom data types through document objects, which allow metadata (e.g., source URLs, timestamps) to be attached to text, enabling richer retrieval or filtering in applications like knowledge bases. By unifying these data types under a single framework, LangChain simplifies building complex pipelines that combine language models with external data sources.