first commit

2023-02-28 07:02:58 -05:00
commit ffe41fb97c
4 changed files with 263 additions and 0 deletions
@@ -0,0 +1,83 @@
+SET 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:
+
+- [Openai](https://platform.openai.com/docs)
+- [Pinecone](https://docs.pinecone.io/docs/quickstart)
+- [HuggingFace](https://huggingface.co/models)
+
+Tasks:
+
+1. Load the text from the given docx file and split them in to some chunks. (A splitter is defined, you can use that.)
+2. Add all the splitted chunks to the vector database. (Use addData function)
+3. Create a prompt using the process discussed above.
+4. Get the answer from gpt-3 api.
+5. Get all the things together such that, we can pass a query using the function user_query and get a solid answer.
+6. The embedding model we used here is a basic embedding model, change the model and use openai's embedding model 'text-embedding-ada-002'
+7. Can we improve something in this process? Any suggestion you think of list it down.
+8. 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.
+
+SET C:
+Problem:
+Given these rules:
+
+```
+We have 5 ingredient:
+oranges
+apples
+pears
+grapes
+watermelon
+lemon
+lime
+
+
+Questions we ask client:
+1.Do you go out to party on weekends? (yes or no)
+2.What flavours do you like? (cider, sweet, waterlike)
+3.What texture you don't like? (smooth, slimy, rough)
+4.What price range will you buy drink for? ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)
+
+If they party on weekends, apples, pears, grapes, watermelon are allowed.
+If they like cider, show apples, oranges, lemon, lime.
+If they like sweet, show watermelon, orange.
+If they like waterlike, show watermelon.
+If grapes is chosen, remove watermelon from the list.
+If texture you don't like is smooth, remove pears.
+If texture you don't like is slimy, remove watermelon, lime and grape.
+If texture you don't like is waterlike, remove watermelon.
+If price < $3 remove lime, watermelon.
+If price > $4 and < $7 remove pears, apples.
+```
+
+Make a function passing in the answer to the 4 questions and structure GPT3 prompt given these rules to give you the list of recommeded fruits.
+
+Make a simple flask POST API where we return the answers given the input in POST Body with content type application/json