AI & intelligence
LangChain development
How does AonHive use LangChain?
LangChain development for retrieval and tool-using model workflows.
LangChain is a way to compose retrieval, a prompt, and a tool call so the steps stay in code we can read. It is not the product. The product is the decision the workflow serves, and the path it takes when the model call fails.Discuss this stackHow we use it
A pipeline you can trace.
- 01
Keep the chain short
Retrieve, then answer, or call one tool. A chain that calls five things because the library allows it is harder to evaluate than the task required.
- 02
Log the step that ran
The retrieved ids, the tool name, and the model response are stored for that request. A bad answer can be traced to the step that caused it.
- 03
Name the fallback
A timeout, an empty retrieval, or a schema failure takes a path that does not pretend the model succeeded.
LangChain
Bring the system this has to fit.
Tell us the product, the estate you already run, and what your team needs to own. We will say whether this belongs in the stack.