Personal Assistants (Agents)#
We use “personal assistant” here in a very broad sense. Personal assistants have a few characteristics:
They can interact with the outside world
They have knowledge of your data
They remember your interactions
Really all of the functionality in LangChain is relevant for building a personal assistant. Highlighting specific parts:
Agent Documentation (for interacting with the outside world)
Index Documentation (for giving them knowledge of your data)
Memory (for helping them remember interactions)
Specific examples of this include:
Baby AGI: a notebook implementing BabyAGI by Yohei Nakajima as LLM Chains
Baby AGI with Tools: building off the above notebook, this example substitutes in an agent with tools as the execution tools, allowing it to actually take actions.
CAMEL: an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with eachother.
AI Plugins: an implementation of an agent that is designed to be able to use all AI Plugins.