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from langchain.chains import create_history_aware_retriever
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
def history_aware_retriever(llm, retriever):
"""
Create a chain that takes conversation history and returns documents.
If there is no chat_history, then the input is just passed directly to the retriever.
If there is chat_history, then the prompt and LLM will be used to generate a search query.
That search query is then passed to the retriever.
Args:
llm: The language model.
retriever: The retriever to use for finding relevant documents.
"""
contextualize_q_system_prompt = (
"Given a chat history and the latest user question "
"which might reference context in the chat history, "
"formulate a standalone question which can be understood "
"without the chat history. Do NOT answer the question, just "
"reformulate it if needed and otherwise return it as is."
)
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
history_aware_retriever = create_history_aware_retriever(
llm, retriever, contextualize_q_prompt
)
return history_aware_retriever
def documents_retriever(llm):
"""
Create a chain for passing a list of Documents to a model.
Args:
llm: The language model.
"""
system_prompt = (
"You are an helpfull assistant. "
"Use the following pieces of retrieved context to answer the question. "
"If you don't know the answer or the context is not retrieved, SAY THAT YOU DON'T KNOW!!. "
"Always response in Bahasa Indonesia or Indonesian Language. "
"Context: {context}"
)
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
return question_answer_chain |