Developing an AI Assistant using the OpenAI Assistant API and Streamlit

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Leah Sirama
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Creating an Assistant with OpenAI Assistant API and Streamlit

A step-by-step guide


Image by author: assistant done with assistant api and streamlit

OpenAI has recently introduced new features that showcase an agent-like architecture, such as the Assistant API. According to OpenAI:

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and files to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, File Search, and Function calling.

While these advancements are promising, they still lag behind LangChain. LangChain enables the creation of agent-like systems powered by LLMs with greater flexibility in processing natural language input and executing context-based actions.

However, this is only the beginning.

At a high level, interaction with the Assistant API can be envisioned as a loop:

  • Given a user input, an LLM is called to determine whether to provide a response or take specific actions.
  • If the LLM’s decision suffices to answer the query, the loop ends.
  • If an action leads to a new observation, this observation is included in the prompt, and the LLM is called again.
  • The loop then restarts.

Image by author: LLM agent loop

OpenAI has recently introduced new features that showcase an agent-like architecture, such as the Assistant API. According to OpenAI:

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and files to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, File Search, and Function calling.

While these advancements are promising, they still lag behind LangChain. LangChain enables the creation of agent-like systems powered by LLMs with greater flexibility in processing natural language input and executing context-based actions.

However, this is only the beginning.

At a high level, interaction with the Assistant API can be envisioned as a loop:

  • Given a user input, an LLM is called to determine whether to provide a response or take specific actions.
  • If the LLM’s decision suffices to answer the query, the loop ends.
  • If an action leads to a new observation, this observation is included in the prompt, and the LLM is called again.
  • The loop then restarts.
Image by author: LLM agent loop

OpenAI has recently introduced new features that showcase an agent-like architecture, such as the Assistant API. According to OpenAI:

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Leah Sirama
Leah Sirama
https://ainewsera.com/
Leah Sirama, a lifelong enthusiast of Artificial Intelligence, has been exploring technology and the digital realm since childhood. Known for his creative thinking, he's dedicated to improving AI experiences for all, making him a respected figure in the field. His passion, curiosity, and creativity drive advancements in the AI world.
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