For the realtime API, how can I feed it a vector store for additional context? I have historically trained an OpenAI Assistant with a vector store but the latency and lack of speech-to-speech is a drawback.
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Okay, so for feeding a vector store into the real-time API, you’re basically creating a system that can retrieve relevant context dynamically during a conversation. If you’ve already trained an OpenAI assistant with a vector store, you’re familiar with embeddings, right? What you’d do is this:
- Embed the Query: When a user asks something, you take their input and generate a vector (embedding) from it.
- Search the Vector Store: That vector gets matched to the closest entries in your vector store—this could be documents, past interactions, or knowledge you’ve embedded previously.
- Feed Context to API: Once you’ve got the relevant chunks, you feed them back into the API’s prompt. So essentially, the context retrieved from the vector store becomes part of the API’s input, making responses more informed.
The Latency Issue:
Now, about the latency and lack of speech-to-speech—that’s tough because the real-time speech capabilities aren’t native to OpenAI (at least not speech-to-speech directly). You’d have to integrate an external speech-to-text service (like Whisper or Google’s API), then feed the text into the model. For speech output, you can use text-to-speech tools like AWS Polly or Google TTS. But yeah, these extra layers add latency, which can be annoying, especially if you need real-time interaction.
A possible hack around latency? You could run the vector store locally, or close to your environment, to reduce any lag in retrieving context. Also, using async calls in your setup could help speed things up.
Let me know if that makes sense or if you’ve got more questions
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