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Ask HN: How should I burn $8k for Google Gemini 1.5 Pro?
Was looking into applying LLMs onto entity extraction in earnings calls. (Around 400mb of text data). 1 Million tokes is around 1-3$.
Open for ideas and experiments.
- reneberlin 2y agoThat's not how you do it. You use a Vectordatabase and a Retriever. This way, not all tokens of a document are used with the prompt, just the relevant parts will end up in the conversation / prompts. This way you save a lot of money and you are NOT limited to use GoogleAI - you can use whatever AI you want.
- infecto 2y agoThis is probably how you would do entity extraction. Why would you rag on entity extraction?
- reneberlin 2y agoUse Flowise and a VectorDB like upstash or pinecone. Flowise Tutorial https://www.youtube.com/watch?v=V7uBy3VQJAc https://www.youtube.com/watch?v=V7uBy3VQJAc
- reneberlin 2y agoJust be careful with the "temperature" when dealing with numbers and financial data as well. You should check samples of the retrieved data by hand. Setting the temperature lower than 0.9 means reducing the "creativity" and making it less prone to hallucinate.
- deleted 2y ago[deleted]
- infecto 2y agoWhat’s the goal?
- dissahc 2y agoare you sure you need a powerful model for this? llama3-8b is at least 10 times cheaper and might suffice for something like this
- langcss 2y agoCan’t you pre-filter it with a vector lookup or something cheap first (gpt3.5?). Or even simply filter first by other things you are considering for the trading decision (price to earnings for example). That might save you a lot of cost.
- BOOSTERHIDROGEN 2y agoCould someone from hedge funds kindly share their current technology stack if they utilize LLMs?