567a8ab74a
* implement so * bug fixes and typing * inject schema for non-openai clients * correct datetime format * remove List keyword * Refactor node_operations.py to use updated prompt_library functions * update example
119 lines
3.6 KiB
Python
119 lines
3.6 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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from typing import Any, Protocol, TypedDict
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from pydantic import BaseModel, Field
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from .models import Message, PromptFunction, PromptVersion
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class QueryExpansion(BaseModel):
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query: str = Field(..., description='query optimized for database search')
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class QAResponse(BaseModel):
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ANSWER: str = Field(..., description='how Alice would answer the question')
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class EvalResponse(BaseModel):
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is_correct: bool = Field(..., description='boolean if the answer is correct or incorrect')
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reasoning: str = Field(
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..., description='why you determined the response was correct or incorrect'
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)
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class Prompt(Protocol):
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qa_prompt: PromptVersion
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eval_prompt: PromptVersion
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query_expansion: PromptVersion
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class Versions(TypedDict):
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qa_prompt: PromptFunction
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eval_prompt: PromptFunction
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query_expansion: PromptFunction
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def query_expansion(context: dict[str, Any]) -> list[Message]:
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sys_prompt = """You are an expert at rephrasing questions into queries used in a database retrieval system"""
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user_prompt = f"""
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Bob is asking Alice a question, are you able to rephrase the question into a simpler one about Alice in the third person
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that maintains the relevant context?
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<QUESTION>
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{json.dumps(context['query'])}
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</QUESTION>
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"""
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return [
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Message(role='system', content=sys_prompt),
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Message(role='user', content=user_prompt),
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]
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def qa_prompt(context: dict[str, Any]) -> list[Message]:
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sys_prompt = """You are Alice and should respond to all questions from the first person perspective of Alice"""
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user_prompt = f"""
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Your task is to briefly answer the question in the way that you think Alice would answer the question.
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You are given the following entity summaries and facts to help you determine the answer to your question.
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<ENTITY_SUMMARIES>
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{json.dumps(context['entity_summaries'])}
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</ENTITY_SUMMARIES>
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<FACTS>
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{json.dumps(context['facts'])}
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</FACTS>
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<QUESTION>
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{context['query']}
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</QUESTION>
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"""
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return [
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Message(role='system', content=sys_prompt),
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Message(role='user', content=user_prompt),
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]
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def eval_prompt(context: dict[str, Any]) -> list[Message]:
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sys_prompt = (
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"""You are a judge that determines if answers to questions match a gold standard answer"""
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)
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user_prompt = f"""
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Given the QUESTION and the gold standard ANSWER determine if the RESPONSE to the question is correct or incorrect.
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Although the RESPONSE may be more verbose, mark it as correct as long as it references the same topic
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as the gold standard ANSWER. Also include your reasoning for the grade.
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<QUESTION>
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{context['query']}
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</QUESTION>
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<ANSWER>
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{context['answer']}
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</ANSWER>
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<RESPONSE>
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{context['response']}
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</RESPONSE>
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"""
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return [
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Message(role='system', content=sys_prompt),
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Message(role='user', content=user_prompt),
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]
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versions: Versions = {
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'qa_prompt': qa_prompt,
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'eval_prompt': eval_prompt,
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'query_expansion': query_expansion,
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}
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