# Currently working on... ## UI implementation for viewing sources in chat - [x] Extracting sources from RAG chain - [x] Adding sources to the UI (references drop down menu inspiration: https://github.com/shashankdeshpande/langchain-chatbot/tree/master) - [x] Add references in the chat with nested expanders - [x] Deploy to Ploomber (done: https://bot-de-continuonus.ploomberapp.io/) ## LLM with memory (Bot de Continuonus) ### Embeddings - [x] Research on how the embedding works in detail - [x] Make decision: one or two indexes - [x] Figure code to upsert new vector ### Summarize and upsert - [x] Look into summarization techniques (Abstractive vs. Extractive Summarization) - [x] Design and implement the chat summarization pipeline - [x] Extract and process chat data for summarization - [x] Populate and update the vectorstore with summarized chat data - [x] Deploy the summarization pipeline to be included in the RAG chain #### Questions to ask - How does the embeddings / vector DB work? - Do the past queries and the original data live in two different DBs? - Or do they live in same one with different metadata tag? If latter, how does the DB/ embeddings get updated after a new query comes in? - Another question: Does the app start to ask the user to elaborate on their thoughts / reactions to the answer? - Asking for clarification is a bit false because LLMs aren't capable of real understanding. - But asking the human user to elaborate on some aspects of their interpretation. - Maybe we should set the focus on whatever is interesting ― i.e. emotional reaction, or something else?
Currently working on...
UI implementation for viewing sources in chat
LLM with memory (Bot de Continuonus)
Embeddings
Summarize and upsert
Questions to ask
does the DB/ embeddings get updated after a new query comes in?
answer?