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40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
from langchain.prompts import ChatPromptTemplate, PromptTemplate
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# Used to condense a question and chat history into a single question
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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.
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Chat History:
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{chat_history}
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Follow Up Input: {question}
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""" # noqa: E501
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CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(
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condense_question_prompt_template
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)
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# RAG Prompt to provide the context and question for LLM to answer
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# We also ask the LLM to cite the source of the passage it is answering from
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llm_context_prompt_template = """
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Use the following passages to answer the user's question.
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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.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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----
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{context}
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----
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Question: {question}
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""" # noqa: E501
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LLM_CONTEXT_PROMPT = ChatPromptTemplate.from_template(llm_context_prompt_template)
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# Used to build a context window from passages retrieved
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document_prompt_template = """
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---
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NAME: {name}
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PASSAGE:
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{page_content}
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---
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"""
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DOCUMENT_PROMPT = PromptTemplate.from_template(document_prompt_template)
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