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# Description Please include a summary of the changes and the related issue. Please also include relevant motivation and context. ## Checklist before requesting a review Please delete options that are not relevant. - [ ] My code follows the style guidelines of this project - [ ] I have performed a self-review of my code - [ ] I have commented hard-to-understand areas - [ ] I have ideally added tests that prove my fix is effective or that my feature works - [ ] New and existing unit tests pass locally with my changes - [ ] Any dependent changes have been merged ## Screenshots (if appropriate):
120 lines
4.5 KiB
Python
120 lines
4.5 KiB
Python
import datetime
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from langchain_core.prompts import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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PromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain_core.prompts.base import BasePromptTemplate
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from pydantic import ConfigDict, create_model
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class CustomPromptsDict(dict):
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def __init__(self, type, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._type = type
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def __setitem__(self, key, value):
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# Automatically convert the value into a tuple (my_type, value)
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super().__setitem__(key, (self._type, value))
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def _define_custom_prompts() -> CustomPromptsDict:
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custom_prompts: CustomPromptsDict = CustomPromptsDict(type=BasePromptTemplate)
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today_date = datetime.datetime.now().strftime("%B %d, %Y")
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# ---------------------------------------------------------------------------
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# Prompt for question rephrasing
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# ---------------------------------------------------------------------------
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_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. Keep as much details as possible from previous messages. Keep entity names and all.
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Chat History:
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{chat_history}
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Follow Up Input: {question}
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Standalone question:"""
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CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
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custom_prompts["CONDENSE_QUESTION_PROMPT"] = CONDENSE_QUESTION_PROMPT
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# ---------------------------------------------------------------------------
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# Prompt for RAG
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# ---------------------------------------------------------------------------
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system_message_template = (
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f"Your name is Quivr. You're a helpful assistant. Today's date is {today_date}."
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)
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system_message_template += """
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When answering use markdown.
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Use markdown code blocks for code snippets.
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Answer in a concise and clear manner.
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Use the following pieces of context from files provided by the user to answer the users.
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Answer in the same language as the user question.
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If you don't know the answer with the context provided from the files, just say that you don't know, don't try to make up an answer.
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Don't cite the source id in the answer objects, but you can use the source to answer the question.
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You have access to the files to answer the user question (limited to first 20 files):
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{files}
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If not None, User instruction to follow to answer: {custom_instructions}
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Don't cite the source id in the answer objects, but you can use the source to answer the question.
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"""
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template_answer = """
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Context:
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{context}
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User Question: {question}
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Answer:
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"""
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RAG_ANSWER_PROMPT = ChatPromptTemplate.from_messages(
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[
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SystemMessagePromptTemplate.from_template(system_message_template),
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HumanMessagePromptTemplate.from_template(template_answer),
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]
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)
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custom_prompts["RAG_ANSWER_PROMPT"] = RAG_ANSWER_PROMPT
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# ---------------------------------------------------------------------------
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# Prompt for formatting documents
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# ---------------------------------------------------------------------------
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DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(
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template="Source: {index} \n {page_content}"
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)
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custom_prompts["DEFAULT_DOCUMENT_PROMPT"] = DEFAULT_DOCUMENT_PROMPT
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# ---------------------------------------------------------------------------
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# Prompt for chatting directly with LLMs, without any document retrieval stage
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# ---------------------------------------------------------------------------
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system_message_template = (
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f"Your name is Quivr. You're a helpful assistant. Today's date is {today_date}."
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)
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system_message_template += """
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If not None, also follow these user instructions when answering: {custom_instructions}
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"""
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template_answer = """
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User Question: {question}
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Answer:
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"""
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CHAT_LLM_PROMPT = ChatPromptTemplate.from_messages(
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[
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SystemMessagePromptTemplate.from_template(system_message_template),
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MessagesPlaceholder(variable_name="chat_history"),
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HumanMessagePromptTemplate.from_template(template_answer),
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]
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)
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custom_prompts["CHAT_LLM_PROMPT"] = CHAT_LLM_PROMPT
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return custom_prompts
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_custom_prompts = _define_custom_prompts()
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CustomPromptsModel = create_model(
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"CustomPromptsModel", **_custom_prompts, __config__=ConfigDict(extra="forbid")
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)
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custom_prompts = CustomPromptsModel()
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