How do i add memory to RetrievalQA.from_chain_type? or, how do I add a custom prompt to ConversationalRetrievalChain?
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
Answers:
Update:
This post answers the first part of OP’s question:
how do i add memory to RetrievalQA.from_chain_type?
For the second part, see @andrew_reece’s answer
or, how do I add a custom prompt to ConversationalRetrievalChain?
Original:
Have you tried passing in chain_type_kwargs (at the bottom is a screenshot from the source code for quick references)?
The documentation hasn’t make it very easy to unserstand what’s under the hood, but here is something that could achieve your goal.
You could find the notebook at this GitHub Link
setup
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain import PromptTemplate
from langchain.retrievers import TFIDFRetriever
retriever = TFIDFRetriever.from_texts(
["Our client, a gentleman named Jason, has a dog whose name is Dobby",
"Jason has a good friend called Emma",
"Emma has a cat whose name is Sullivan"])
Then define your customized prompt:
template = """
Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question:
------
<ctx>
{context}
</ctx>
------
<hs>
{history}
</hs>
------
{question}
Answer:
"""
prompt = PromptTemplate(
input_variables=["history", "context", "question"],
template=template,
)
Take the note of what you used for your input variables, especially 'history' and 'question', since you will need to match these when setting up the memory:
qa = RetrievalQA.from_chain_type(
llm=ChatOpenAI(),
chain_type='stuff',
retriever=retriever,
verbose=True,
chain_type_kwargs={
"verbose": True,
"prompt": prompt,
"memory": ConversationBufferMemory(
memory_key="history",
input_key="question"),
}
)
Now you can call qa.run({"query": "who's the client's friend?"})
"The client’s friend is Emma."
and then qa.run("and her pet's name is?")
"Emma’s pet’s name is Sullivan."
To check and verify the memory/chat history: qa.combine_documents_chain.memory
ConversationBufferMemory(chat_memory=ChatMessageHistory(messages=[HumanMessage(content="who’s the client’s friend?", additional_kwargs={}), AIMessage(content="The client’s friend is Emma.", additional_kwargs={}), HumanMessage(content="and her pet’s name is?", additional_kwargs={}), AIMessage(content="Emma’s pet’s name is Sullivan.", additional_kwargs={})]), output_key=None, input_key=’question’, return_messages=False, human_prefix=’Human’, ai_prefix=’AI’, memory_key=’history’)
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.nn'
'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']
#'nnDolphin leaps in seanGraceful and playful in bluenJoyful 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='nnDolphin leaps in seanGraceful and playful in bluenJoyful 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 friendni like cats and the color blue.',
'title: dog friendni like dogs and the smell of rain.',
'title: bird friendni 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)
When using the ConversationBufferMemory I am using a very simple test to confirm whether memory is working on my chatbot, which is asking the chatbot "What was the first question I asked".
I always seems to get the same incorrect answer:
I’m sorry, but I don’t have access to your initial question as I am an AI language model and I don’t have the capability to track previous interactions. Could you please repeat your question?
Apart from this though it does seem that memory is working to an extent, for example if I ask non-contextual questions, it does seem to be able to answer correctly.
Has anyone else encountered this anomaly.
I’m not sure if I need to work on the prompting or why else this anomaly is arising.
#This is the function
def qasystem(query):
loader = TextLoader("details.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
documents = text_splitter.split_documents(documents)
vectordb = Chroma.from_documents(
documents,
embedding=OpenAIEmbeddings(),
persist_directory='./data'
)
vectordb.persist()
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a
standalone question without changing the content in given question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
condense_question_prompt_template = PromptTemplate.from_template(_template)
prompt_template = """You are helpful information giving QA System and make sure you don't answer anything
not related to following context. You are always provide useful information & details available in the given context. Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:"""
qa_prompt = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = ChatOpenAI(temperature=0.1)
question_generator = LLMChain(llm=llm, prompt=condense_question_prompt_template, memory=memory)
doc_chain = load_qa_chain(llm, chain_type="stuff", prompt=qa_prompt)
qa_chain = ConversationalRetrievalChain(
retriever=vectordb.as_retriever(search_kwargs={'k': 6}),
question_generator=question_generator,
combine_docs_chain=doc_chain,
memory=memory,
)
chat_history = []
while True:
result = qa_chain({'question': question, 'chat_history': chat_history})
response = result['answer']
chat_history.append((question, response))
return result['answer'
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
Update:
This post answers the first part of OP’s question:
how do i add memory to RetrievalQA.from_chain_type?
For the second part, see @andrew_reece’s answer
or, how do I add a custom prompt to ConversationalRetrievalChain?
Original:
Have you tried passing in chain_type_kwargs (at the bottom is a screenshot from the source code for quick references)?
The documentation hasn’t make it very easy to unserstand what’s under the hood, but here is something that could achieve your goal.
You could find the notebook at this GitHub Link
setup
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain import PromptTemplate
from langchain.retrievers import TFIDFRetriever
retriever = TFIDFRetriever.from_texts(
["Our client, a gentleman named Jason, has a dog whose name is Dobby",
"Jason has a good friend called Emma",
"Emma has a cat whose name is Sullivan"])
Then define your customized prompt:
template = """
Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question:
------
<ctx>
{context}
</ctx>
------
<hs>
{history}
</hs>
------
{question}
Answer:
"""
prompt = PromptTemplate(
input_variables=["history", "context", "question"],
template=template,
)
Take the note of what you used for your input variables, especially 'history' and 'question', since you will need to match these when setting up the memory:
qa = RetrievalQA.from_chain_type(
llm=ChatOpenAI(),
chain_type='stuff',
retriever=retriever,
verbose=True,
chain_type_kwargs={
"verbose": True,
"prompt": prompt,
"memory": ConversationBufferMemory(
memory_key="history",
input_key="question"),
}
)
Now you can call qa.run({"query": "who's the client's friend?"})
"The client’s friend is Emma."
and then qa.run("and her pet's name is?")
"Emma’s pet’s name is Sullivan."
To check and verify the memory/chat history: qa.combine_documents_chain.memory
ConversationBufferMemory(chat_memory=ChatMessageHistory(messages=[HumanMessage(content="who’s the client’s friend?", additional_kwargs={}), AIMessage(content="The client’s friend is Emma.", additional_kwargs={}), HumanMessage(content="and her pet’s name is?", additional_kwargs={}), AIMessage(content="Emma’s pet’s name is Sullivan.", additional_kwargs={})]), output_key=None, input_key=’question’, return_messages=False, human_prefix=’Human’, ai_prefix=’AI’, memory_key=’history’)
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.nn'
'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']
#'nnDolphin leaps in seanGraceful and playful in bluenJoyful 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='nnDolphin leaps in seanGraceful and playful in bluenJoyful 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 friendni like cats and the color blue.',
'title: dog friendni like dogs and the smell of rain.',
'title: bird friendni 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)
When using the ConversationBufferMemory I am using a very simple test to confirm whether memory is working on my chatbot, which is asking the chatbot "What was the first question I asked".
I always seems to get the same incorrect answer:
I’m sorry, but I don’t have access to your initial question as I am an AI language model and I don’t have the capability to track previous interactions. Could you please repeat your question?
Apart from this though it does seem that memory is working to an extent, for example if I ask non-contextual questions, it does seem to be able to answer correctly.
Has anyone else encountered this anomaly.
I’m not sure if I need to work on the prompting or why else this anomaly is arising.
#This is the function
def qasystem(query):
loader = TextLoader("details.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
documents = text_splitter.split_documents(documents)
vectordb = Chroma.from_documents(
documents,
embedding=OpenAIEmbeddings(),
persist_directory='./data'
)
vectordb.persist()
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a
standalone question without changing the content in given question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
condense_question_prompt_template = PromptTemplate.from_template(_template)
prompt_template = """You are helpful information giving QA System and make sure you don't answer anything
not related to following context. You are always provide useful information & details available in the given context. Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:"""
qa_prompt = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = ChatOpenAI(temperature=0.1)
question_generator = LLMChain(llm=llm, prompt=condense_question_prompt_template, memory=memory)
doc_chain = load_qa_chain(llm, chain_type="stuff", prompt=qa_prompt)
qa_chain = ConversationalRetrievalChain(
retriever=vectordb.as_retriever(search_kwargs={'k': 6}),
question_generator=question_generator,
combine_docs_chain=doc_chain,
memory=memory,
)
chat_history = []
while True:
result = qa_chain({'question': question, 'chat_history': chat_history})
response = result['answer']
chat_history.append((question, response))
return result['answer'