How can I reduce latency when using the OpenAI API with a Microsoft Cognitive Search-based knowledge base?

Below are some approaches you can experiment with to reduce latency when calling the OpenAI API:

1. Reduce Prompt Token Count

• Trim or Optimize System Instructions:
  Evaluate if the lengthy system message can be shortened without affecting response quality. Small improvements here can save tokens overall.

• Prune Retrieved Context:
  Instead of passing the entire Cognitive Search response, filter or summarize it so only the most relevant parts remain. This reduces the number of prompt tokens sent to the API.

2. Reorder and Combine Prompt Elements

• Shared Prompt Prefix Technique:
  Place static elements (constant text) at the beginning of the prompt and append the dynamic or variable parts (like search query results) later. This can help reduce the processing overhead by maximizing the benefits of caching mechanisms on the API provider's side.

• Combine Tasks in a Single Call:
  Instead of making multiple API calls (for example, one for context retrieval and another for response generation), combine them into a single call. This is especially useful when tasks such as query contextualization are clearly defined and do not require separate processing steps.

3. Evaluate Model Choices and Configurations

• Fine-tuning a Smaller Model:
  For well-defined tasks like implementing structured responses or frequently asked question answers, consider fine-tuning a smaller model (like GPT-3.5) for your specific task. This can help speed up processing times compared to using larger models.

• Testing Different Configurations:
  Since each use case is unique, try out different prompt layouts and configurations with production examples. Measure the token usage and processing time for each setup to empirically determine which configuration is fastest while still delivering quality responses.

Example Adjustments

For example, you might adjust your code like this:

JAVASCRIPTCopied
const system = "You are an enthusiastic (NAMEOFAPP) rep. Use only information from our documentation; be brief and helpful.";
const trimmedContext = getRelevantContext(responseFromCognitiveSearch.value[0].formattedText); // Implement context filtering

const prompt = `Query: ${query}\n\n` +
               `Relevant Info:\n\n${trimmedContext}`;
               
const requestBody = {
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "system",
      "content": system
    },
    {
      "role": "user",
      "content": prompt
    }
  ]
};

// Send requestBody to the OpenAI API

4. Monitor and Adjust Other Factors

While reducing token count is a significant factor:

• Keep an eye on network latency and overall server performance, as these also influence response times.
• Measure and profile each change so you can compare the improvements and iterate further.

By applying these techniques, you should be better able to reduce processing time while still providing robust responses based on your indexed knowledge base. Happy coding!