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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.
- Generative Agents: This notebook implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Park, et. al.