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README.md

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+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 quer 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 quer and find matches with the data we have on ector 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.
+
+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 summarry about the alternative approach.