Custom gpt vs assistant api

API
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My Personal Opinion on the Appropriate Usage of Custom GPTs vs Assistants API vs Chat Completions API

  1. Custom GPTs - All-in-one / Ready to Use / App-Packed
  • Preconfigured and optimized for conversation-type interactions.
  • Have built-in thread management with conversation context.
  • Include built-in tools like web search, image generation, vision, voice, etc.
  • Can connect to external APIs (via tools).
  • Cannot be called from outside!
  • Allow the use of knowledge files for specific domains, but you have limited control over how these files are used in context retrieval.
  1. Assistants API - Chat Completions API with Extra Capabilities
  • Supports persistent conversation threads (you need to create and manage threads manually).
  • Can access stored files and utilize OpenAI’s internal vector store for context retrieval (but you do not have direct control over this vector store).
  • Offers more control over model parameters (but you are responsible for testing and configuring them properly).
  • Can connect to external tools via function calling (you must configure and handle these connections).
  • Can be called from outside.
  • The built-in vector store makes it easier to manage context for ongoing conversations, but it comes at the cost of limited flexibility compared to custom vector DB solutions.
  1. Chat Completions API - Stateless Remote AI Functions Accessible Over an HTTP REST API
  • Offers full control over model parameters and context retrieval.
  • Is completely stateless (no built-in support for persistent threads, unless you build your own mechanism).
  • Supports external tool usage via function calling (with complete freedom to integrate with any system or database, including custom vector DBs).
  • Can be called from outside.
  • You can hook it up to any external vector database of your choice (Pinecone, Weaviate, etc.), providing maximum flexibility for managing context and knowledge retrieval.

Summary

As a rule of thumb, the more control you give away to OpenAI, the more general-purpose and ready-to-use the solution will be. If you need precision or specific configurations, the Chat Completions API will offer the most flexibility. For simple, out-of-the-box setups, Custom GPTs are the quickest way to deploy something useful. The Assistants API sits somewhere in the middle, offering more built-in features but with some trade-offs in control.

Vector Store Management

One important nuance to consider:

  • Assistants API: Comes with an internal vector store for managing conversation context, but this vector store is managed by OpenAI, and you do not have direct access to it. You can upload files, and the assistant will use them to retrieve relevant context automatically.
  • Chat Completions API: Requires you to manage your own vector database if you want to use one. This offers more control and flexibility, allowing you to use external solutions like Pinecone, Weaviate, or even self-hosted options.

My Personal Approach

When building a solution using these tools, I follow a step-by-step process:

  1. Quickly draft the system and user prompts in the playground to test the concept or task.
  2. Analyze the task to break it down into smaller subtasks, identifying opportunities to parallelize or simplify processes.
  3. Craft and test each subtask in the playground.
  4. Set them up as tools behind an API gateway to access them via REST API calls.
  5. Get the API definition with all tools documented.
  6. Pass the API definition along with a "master’ system configuration to a Custom GPT for quick interaction testing (most projects never go beyond this step, as it is often good enough).
  7. Review interactions through the Custom GPT to see if a full app (with a database, UI, etc.) is necessary. If not, stop there.
  8. If persistent threads or complex context management is required, decide between Assistants API and Chat Completions API based on the level of control needed.

Common Pitfall

I have seen several cases where developers chose the Assistants API for its built-in thread management, only to realize later that their application required more control over context retrieval. This often results in them converting to the Chat Completions API, which could have been a better fit from the start. The key takeaway: do not rush the decision—spend time analyzing your application’s requirements before committing to a specific API.

Hope that helps. (AI edited)

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