tahsin-protikSET A:
The objective of this project is to get better answers for user queries from gpt-3 on a specific matter. So, there can be some sectors, the data for those are not updated on gpt-3. To handle that, we tried to follow the following steps:
- First we'll read the data we want to use in a specific case.
- We will divide in to some chunks.
- Transform the chunks in to vector using embedding algorithm
- Save the vectors to a vector database.
- If an user query appears, we'll find some best matches. So, these are the steps we do s preparation of dataset. Then, If a query appeared, we do the following:
- We first take the query and find matches with the data we have on vector database, like a semantic serch.
- We take those contexts, and generate a prompt appropriate to the use case, including the contexts and the user's original question. We tell gpt-3 to answer based on the context.
Note: The embedding model used here has 384 dimensions.
Useful Docs:
Tasks:
- Load the text from the given docx file and split them in to some chunks. (A splitter is defined, you can use that.)
- Add all the splitted chunks to the vector database. (Use addData function)
- Create a prompt using the process discussed above.
- Get the answer from gpt-3 api.
- Get all the things together such that, we can pass a query using the function user_query and get a solid answer.
- The embedding model we used here is a basic embedding model, change the model and use openai's embedding model 'text-embedding-ada-002'
- Can we improve something in this process? Any suggestion you think of list it down.
- Do you think you have a better idea to handle the whole process? Write a summary about the alternative approach.
SET B:
Problem: We have a sets of rules for a specific game. Based on the rules, we will need to implement a system to predict the optimal next move of a player.
Use this as reference of the rules: https://gamerules.com/rules/7-wonders-duel/
Make some different scenerios to test the system you built.