Agents: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
Callbacks: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.
Use Cases#
Best practices and built-in implementations for common LangChain use cases:
Autonomous Agents: Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
Agent Simulations: Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.
Personal Assistants: One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
Question Answering: Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
Chatbots: Language models love to chat, making this a very natural use of them.
Querying Tabular Data: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).
Code Understanding: Recommended reading if you want to use language models to analyze code.
Interacting with APIs: Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.
Extraction: Extract structured information from text.
Summarization: Compressing longer documents. A type of Data-Augmented Generation.
Evaluation: Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.
Reference Docs#
rtdocs_stable/api.python.langchain.com/en/stable/index.html