To define custom logic for chains in LangChain, you create a subclass of the Chain class and implement its core methods. Start by defining your chain’s input and output structure using the input_variables and output_variables properties. The main logic goes into the _call method, where you process inputs, execute steps (like calling language models or external APIs), and return results. For example, if building a chain to generate and filter jokes, you might accept a topic as input, generate text via an LLM, then validate it using a moderation API. Override _chain_type to give your chain a unique identifier for debugging or serialization.
You can combine existing LangChain components (like prompts, models, or tools) within your custom chain. For instance, a chain might first format a prompt template with user input, pass it to a model, parse the response, and execute conditional logic based on the result. If building a customer support assistant, your chain could use a RetrievalQA component to fetch documentation, then a separate validation step to ensure the answer meets length constraints. Use SimpleSequentialChain or custom logic to link components sequentially or conditionally. This approach lets you reuse built-in functionality while injecting domain-specific checks or transformations.
For advanced use cases, override methods like _acall for async support or integrate memory and callbacks. For example, a chain that tracks conversation history could store past interactions in memory and reference them in subsequent calls. If your chain interacts with APIs, use error handling and retries within _call to improve reliability. Testing is critical: validate each step in isolation before combining them. Tools like LangSmith can help trace execution and debug inputs/outputs. By encapsulating logic in a Chain subclass, you create reusable, modular components that integrate cleanly with LangChain’s ecosystem.