Why is the OpenAI Assistant API Retrieval Tool so slow, and how can I speed it up?

Based on OpenAI’s latency optimization guidance, what you’re experiencing isn’t just due to the document-reading process but more about how the retrieval workflow is structured. Some observations and tips:

• Multiple sequential steps:
  The process of contextualizing your query and then performing a retrieval can incur additional round trips. If you’re using consecutive calls (for example, one to check for retrieval needs and another to handle the response), each call adds latency.

• Optimization strategies:
  OpenAI suggests a few strategies that might help:

  • Combine steps: Instead of making separate calls for contextualization and retrieval, you can input a combined prompt. This reduces the number of API requests.
  • Parallelization: For parts of the pipeline that can run independently (like retrieval checks and reasoning), executing them in parallel can reduce the overall wait time.
  • Switching or fine-tuning models: If possible, using a smaller or fine-tuned version (as mentioned in the examples where a smaller GPT-3.5 is used for specific tasks) can process tokens faster.

• Testing is key:
  The documentation emphasizes testing with production examples since what works best can vary from case to case. The combined prompt approach or parallelizing the checks might improve perceived response times.

In summary, while document reading itself might add some overhead, the main factors affecting speed are the way calls are structured and executed. Optimizing by reducing the number of calls or running some parts concurrently can lead to noticeable improvements.

Below is a brief illustrative example in pseudocode showing how you might combine steps:

PYTHONCopied
# Before: Consecutive API calls
context_result = call_api("contextualize query and check retrieval", query)
final_result = call_api("provide answer using", context_result)

# After: Combined single API call
combined_prompt = f"Contextualize: {query} and process retrieval check, then answer based on it."
final_result = call_api("combined prompt", combined_prompt)

This approach is in line with the optimization techniques suggested in the documentation to minimize latency.

Remember that testing variations with your specific workload is essential to identify the best approach for your use case.