This is a LLM chatbot coded with LangChain. The web interface is coded with Streamlit. It implements a hybrid RAG (keyword and semantic search) and chat memory.
from langchain.chains import create_history_aware_retriever # To create the retriever chain (predefined chain)
from langchain.chains import create_retrieval_chain # To create the main chain (predefined chain)
from langchain.chains.combine_documents import create_stuff_documents_chain # To create a predefined chain
from langchain.chains import create_history_aware_retriever # To create the retriever chain (predefined chain) from langchain.chains import create_retrieval_chain # To create the main chain (predefined chain) from langchain.chains.combine_documents import create_stuff_documents_chain # To create a predefined chain
FROM: history_aware_retriever = create_history_aware_retriever(llm, ensemble_retriever, contextualize_q_prompt) question_answer_chain = create_stuff_documents_chain(llm, qa_prompt) ai_assistant_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
TO: history_awareretrieverchain = create_history_aware_retriever(llm, ensemble_retriever, contextualize_q_prompt) question_answer_chain = create_stuff_documents_chain(llm, qa_prompt) ai_assistant_chain = create_retrieval_chain(history_awareretrieverchain, question_answer_chain)