Just 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.

That'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.

This is probably how you would do entity extraction. Why would you rag on entity extraction?

Use Flowise and a VectorDB like upstash or pinecone.

Flowise Tutorial https://www.youtube.com/watch?v=V7uBy3VQJAc

What’s the goal?

are you sure you need a powerful model for this? llama3-8b is at least 10 times cheaper and might suffice for something like this

I am a survivor please contact me

if your not a survivor with the intention of finding each other and communicating, you should take dissahc out of your name and stop making these posts.

Can’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.

Could someone from hedge funds kindly share their current technology stack if they utilize LLMs?