Question: How do i add memory to RetrievalQA.from_chain_type? or, how do I add a custom prompt to ConversationalRetrievalChain? For the past 2 weeks ive been trying to make a chatbot that can chat over documents (so not in just a semantic search/qa so with memory) but also with a custom prompt. I've tried every combination of all the chains and so far the closest I've gotten is ConversationalRetrievalChain, but without custom prompts, and RetrievalQA.from_chain_type but without memory

Answer: Here's a solution with ConversationalRetrievalChain, with memory and custom prompts, using the default 'stuff' chain type. There are two prompts that can be customized here. First, the prompt that condenses conversation history plus current user input (condense_question_prompt), and second, the prompt that instructs the Chain on how to return a final response to the user (which happens in the combine_docs_chain). from langchain import PromptTemplate # note that the input variables ('question', etc) are defaults, and can be changed condense_prompt = PromptTemplate.from_template( ('Do X with user input ({question}), and do Y with chat history ({chat_history}).') ) combine_docs_custom_prompt = PromptTemplate.from_template( ('Write a haiku about a dolphin.\n\n' 'Completely ignore any context, such as {context}, or the question ({question}).') )  Now we can initialize the ConversationalRetrievalChain with the custom prompts. from langchain.llms import OpenAI from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) chain = ConversationalRetrievalChain.from_llm( OpenAI(temperature=0), vectorstore.as_retriever(), # see below for vectorstore definition memory=memory, condense_question_prompt=condense_prompt, combine_docs_chain_kwargs=dict(prompt=combine_docs_custom_prompt) )  Note that this calls _load_stuff_chain() under the hood, which allows for an optional prompt kwarg (that's what we can customize). This is used to set the LLMChain , which then goes to initialize the StuffDocumentsChain. We can test the setup with a simple query to the vectorstore (see below for example vectorstore data) - you can see how the output is determined completely by the custom prompt: chain("What color is mentioned in the document about cats?")['answer'] #'\n\nDolphin leaps in sea\nGraceful and playful in blue\nJoyful in the waves'  And memory is working correctly: chain.memory #ConversationBufferMemory(chat_memory=ChatMessageHistory(messages=[HumanMessage(content='What color is mentioned in the document about cats?', additional_kwargs={}), AIMessage(content='\n\nDolphin leaps in sea\nGraceful and playful in blue\nJoyful in the waves', additional_kwargs={})]), output_key=None, input_key=None, return_messages=True, human_prefix='Human', ai_prefix='AI', memory_key='chat_history')  Example vectorstore dataset with ephemeral ChromaDB instance: from langchain.vectorstores import Chroma from langchain.document_loaders import DataFrameLoader from langchain.embeddings.openai import OpenAIEmbeddings data = { 'index': ['001', '002', '003'], 'text': [ 'title: cat friend\ni like cats and the color blue.', 'title: dog friend\ni like dogs and the smell of rain.', 'title: bird friend\ni like birds and the feel of sunshine.' ] } df = pd.DataFrame(data) loader = DataFrameLoader(df, page_content_column="text") docs = loader.load() embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(docs, embeddings)