How can I dynamically update function definitions during an Assistant API run?

Below is an explanation addressing your concerns.

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Dynamic Function Modification with Assistant API

The Assistant API is designed to run as a single “run” that persists until a final, non-function response is generated. This means the assistant's configuration—including its available functions—is set when the run is initiated and isn’t intended to change mid-run.

Key Points

• Static Configuration in a Run:
  Once you start a run using an Assistant API call, you can’t update the parameters (like the function definitions) dynamically within that same run. Unlike the Chat Completions API where you can adjust the function list between individual completions, the Assistant API run holds the configuration constant.

• Run Lifecycle and Dynamic Scenarios:
  If your workflow requires switching functions or models based on state (for example, generating multiple function calls before reaching a user-directed answer), you’ll need to interrupt the run, update the configuration, and then start a new run. This is how the current design intends to manage state changes rather than applying them in the midst of an ongoing run.

• Forcing Function Calls:
  At this time the ability to “force” the model to call a function—similar to using the function_call parameter in the Chat Completions endpoint—is not supported with the Assistant API. The run continues with the function calls based on the instructions and available functions that were provided when the run started. The API expects the run to end once a message that isn’t a function call is provided.

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What This Means for Your Use Case

If your implementation relies on dynamically updating function parameters and switching models on a per-message basis, the current Assistant API approach may not fully meet your needs without restarting runs between state changes. Maintaining the same thread by canceling and starting new runs may negate some of the benefits you’d hoped to gain from using runs for continuity.

For now, it appears that if your use case requires this kind of dynamic function behavior throughout a conversation, the Chat Completions API remains the better fit due to its flexibility in altering function call controls at each turn.

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Example Illustration

Below is a simplified example using curl for modifying an assistant configuration. Notice that once a run starts, the functions you pass in remain static for that run:

BASHCopied
curl https://api.openai.com/v1/assistants/asst_abc123 \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2" \
-d '{
  "instructions": "You are an HR bot with specific access to policy documents.",
  "tools": [{"type": "file_search"}],
  "model": "gpt-4o"
}'

Each run will complete only when a non-function call is output by the model. Modifications to the function list need to occur by starting a new run.

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Conclusion

In summary, the Assistant API does not currently support the kind of dynamic function calling you’ve been leveraging with the Chat Completions API—where you can modify the functions on the fly between message completions. If this dynamic behavior is essential, you may need to either stick with the Chat Completions API or manage stateful switching by methodically canceling runs and starting new ones, though that comes with its own trade-offs.

Hopefully, this clarifies the current limitations with respect to dynamic function calling in the Assistant API. Let me know if you have any more questions on this topic!