LLM -Based Agents
LLMを拡張するフレームワーク
LlamaIndex
That’s where the LlamaIndex comes in. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion:
* Offers data connectors to your existing data sources and data formats (API’s, PDF’s, docs, SQL, etc.)
* Provides indices over your unstructured and structured data for use with LLM’s. These indices help to abstract away common boilerplate and pain points for in-context learning:
- Storing context in an easy-to-access format for prompt insertion.
- Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when context is too big.
- Dealing with text splitting.
* Provides users an interface to query the index (feed in an input prompt) and obtain a knowledge-augmented output.
* Offers you a comprehensive toolset trading off cost and performance.
エージェント系フレームワーク
LangChain
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model, but will also be:
* Data-aware: connect a language model to other sources of data
* Agentic: allow a language model to interact with its environment
The LangChain framework is designed around these principles.
LlamaIndexとの大きな相違点は:
エージェント機能や、汎用的なツール機能を持っているところ。
Auto-GPT
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
LangChainとの違いは:
ゴール指向であること
ゴールを達成するために必要なアクションを複数のタスクにブレークダウンすること
それらのタスクを実行することで、ゴールを実現すること
BabyAGI
This Python script is an example of an AI-powered task management system. The system uses OpenAI and vector databases such as Chroma or Weaviate to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI's natural language processing (NLP) capabilities to create new tasks based on the objective, and Chroma/Weaviate to store and retrieve task results for context. This is a pared-down version of the original Task-Driven Autonomous Agent (Mar 28, 2023).
Auto-GPTとの違いは:
Webで試せるサービス
その他
https://files.speakerdeck.com/presentations/ee776ccad48f410fafef4865347b05ab/slide_7.jpg