Tools allow language models to interact with external systems and perform actions beyond just generating text. In the LangSmith playground, you can use two types of tools:
LangSmith provides a collaborative interface to create, test, and iterate on prompts.
After running an experiment, you can use LangSmith's experiment view to analyze the results and draw insights about your experiment's performance.
Oftentimes, when you are iterating on your LLM application (such as changing the model or the prompt), you will want to compare the results of different experiments.
LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application. This guide covers both automatic instrumentation for LangChain applications and manual instrumentation for other frameworks.
Evaluate a target system on a given dataset. Parameters: target (TARGET_T | Runnable | EXPERIMENT_T | Tuple[EXPERIMENT_T, EXPERIMENT_T]) – The target system or experiment(s) to evaluate. Can be a function that takes a dict and returns a dict, a langchain Runnable, an existing experiment ID, or a...
Try “site:docs.smith.langchain.com” on: Marginalia · Mojeek · Wiby · DuckDuckGo · Bing · Google · Wikipedia · Internet Archive