Captainspa/grounded-code

1The components of our project:2 3- A UI frontent that accepts user input4    - This will use Gradio Components5    - A chat window, an agent workspace, and a dropdown to select the desired model(s)6- Function to process urls into LangChain Document objects7- Function to create a vector database (vectorstore) from a list of Documents using an embedding model8- A RAG pipeline (Using a LangChain LCEL chain)9    - Retriever uses your vectorstore + prompt to retrieve the most relevant Documents: This is the "context"10    - Chain to a LLM (Claude Opus) to generate response based on the context11    - Parse the output if the format should follow specific JSON, or if a specific schema is needed for the next step12 13Helpful links:141. https://python.langchain.com/docs/expression_language/cookbook/retrieval - Super helpful for RAG152. https://python.langchain.com/docs/modules/data_connection/ - high level overview163. https://python.langchain.com/docs/integrations/text_embedding/together174. https://python.langchain.com/docs/integrations/chat/anthropic185. https://python.langchain.com/docs/integrations/chat/anthropic_functions