Ailixir is an application that utilises LLMs and custom user input to generate AI agent prototypes specialised in fields such as health, economics, physics etc. The prototypes enable the user, which is an entrepreneur-developer, to compare the results produced by different LLMs.
Integrate chat history awareness into the system to enable a more coherent and context-aware conversational experience. The retrieval process manages and utilizes chat history to provide answers. This includes updating the promps to support historical messages by reformoulating questions based on past interactions.
Description
Integrate chat history awareness into the system to enable a more coherent and context-aware conversational experience. The retrieval process manages and utilizes chat history to provide answers. This includes updating the promps to support historical messages by reformoulating questions based on past interactions.
An overview of a history-aware retriever:
Links from the official documentation:
How to add chat history to a question-answering chain https://js.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/
How to add message history https://python.langchain.com/v0.2/docs/how_to/message_history/
How to add memory to chatbots https://python.langchain.com/v0.2/docs/how_to/chatbots_memory/
Other links
https://www.youtube.com/watch?v=X05uK0TZozM
https://www.pinecone.io/learn/series/langchain/langchain-conversational-memory/
https://k33g.hashnode.dev/make-a-genai-conversational-chatbot-with-memory
User Story
Acceptance Criteria
Definition of Done