To chain multiple models in LangChain, you use the framework’s core abstraction: the Chain. Chains allow you to connect components like models, prompts, and other tools into a structured workflow. A common approach is to create a sequence where the output of one model becomes the input for the next. For example, you might use one model to summarize text and another to analyze the sentiment of that summary. LangChain provides pre-built chain classes like SequentialChain or SimpleSequentialChain to simplify this process. Each model in the chain is typically wrapped in an LLMChain, which combines the model with a prompt template to format inputs and outputs.
Here’s a practical example: Suppose you want to generate a product review and then translate it into French. First, define an LLMChain with a prompt like "Write a review for {product}:". The output from this chain is passed to a second LLMChain with a prompt like "Translate this to French: {text}". Using SimpleSequentialChain, you can link these two steps with minimal code. The framework handles passing the output from the first model to the second, ensuring the data flows correctly. This approach works for linear workflows where each step depends directly on the previous result.
For more complex scenarios, LangChain supports dynamic routing and conditional logic. The RouterChain class lets you direct outputs to different models based on predefined rules. For instance, you might route technical questions to a specialized model and general queries to a default model. Additionally, you can use TransformChain to preprocess data between steps, such as extracting keywords or validating formats. To manage dependencies between steps, the SequentialChain class allows specifying input/output variables explicitly, enabling non-linear workflows. Always test individual components first, handle errors (e.g., retries for API failures), and monitor costs since each model call may incur charges.