langchain/templates/rag-elasticsearch/rag_elasticsearch/prompts.py

from langchain.prompts import ChatPromptTemplate, PromptTemplate

# Used to condense a question and chat history into a single question
condense_question_prompt_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. If there is no chat history, just rephrase the question to be a standalone question.

Chat History:
{chat_history}
Follow Up Input: {question}
"""  # noqa: E501
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(
    condense_question_prompt_template
)

# RAG Prompt to provide the context and question for LLM to answer
# We also ask the LLM to cite the source of the passage it is answering from
llm_context_prompt_template = """
Use the following passages to answer the user's question.
Each passage has a SOURCE which is the title of the document. When answering, cite source name of the passages you are answering from below the answer in a unique bullet point list.

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}
"""  # noqa: E501

LLM_CONTEXT_PROMPT = ChatPromptTemplate.from_template(llm_context_prompt_template)

# Used to build a context window from passages retrieved
document_prompt_template = """
---
NAME: {name}
PASSAGE:
{page_content}
---
"""

DOCUMENT_PROMPT = PromptTemplate.from_template(document_prompt_template)