In vectordb.py, we have a function upload_vectors that can upload a list of 100 embeddings max to the vector database.

Write a function that can take in a very large list of embedding data, break this into batches of maximum size 100 and call the upload_vectors function on each batch.

Assume the structure of embedding data is the same as in upload_vectors: https://github.com/minerva-university/TeleQuest/blob/dev/db/vectordb.py#L33

Create a first page that gives info about the application. Use simple styles with white as the main background color. @andriikorotun24 and @casscruzz are working on this.

Use the correct create chat completion in ask function in get_answers.py

Create a basic setup of using OpenAI's APIs for embeddings and Q/A.

These are used to store embeddings for LLMs. Vector embeddings are representations of text as lists of numbers that help capture semantic similarities. We will use it to store embeddings of telegram messages.

Pinecone is a popular Vector database, but there might be others. Research and suggest a good one @makoohara .

In the database code, we have a function to store a single message to MongoDB. Add a function to take in a list of messages and store everything to MongoDB. The messages will all belong to the same group chat. The function should take in the chat_id of the group chat as well.

See https://community.openai.com/t/completion-endpoint-randomly-freezes/107206

We need to store chat information in databases to be able to use them. For a single message, we likely need to store

• ID of the group it was sent in
• User ID
• date/time it was sent
• Text content of message
• ID of the message it was replying to, if it is a reply
• Other metadata eg links in message or if it's solely media

We could also use a Relational DB, but Non-relational / NoSQL might be better. See [https://www.mongodb.com/nosql-explained/nosql-vs-sql](MongoDB NoSQL vs SQL)

Run the landing page and make any additions you deem needed. If you do not have any additions to make, update @Liet3 promptly so you can also look at the backend code.

Given a list of message IDs, we want to get those messages from MongoDB database. Add this (or these) functions to the database code.

The answer in this StackOverflow post is a nice starting point: https://stackoverflow.com/questions/23137870/mongodb-check-if-field-is-one-of-many-values

Once the bot is added to a telegram group newly, it needs to read the last 30,000 messages and store them in MongoDB. It should also convert them to embeddings and store them in Pinecone.
We need a listener that can determine when bot is added to a group and read the chat history.

OpenAI sets some strict rate limits on their APIs. After telegram bot is created, use it to test out rate limits on the embedding and GPT APIs.

We have a start handler for the bot, so it responds when someone types /start. Add a question handler, maybe /q, so when someone sends a message, for example /q what time is the event? the bot knows it's a question and can respond.