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Feat: Q&A format segmentation support (#668)

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Co-authored-by: jyong <718720800@qq.com>
Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
This commit is contained in:
KVOJJJin
2023-07-28 20:47:15 +08:00
committed by GitHub
parent aae2fb8a30
commit cf93d8d6e2
52 changed files with 2038 additions and 274 deletions
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33
api/core/generator/llm_generator.py
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@@ -2,7 +2,7 @@ import logging
from langchain import PromptTemplate
from langchain.chat_models.base import BaseChatModel
from langchain.schema import HumanMessage, OutputParserException, BaseMessage
from langchain.schema import HumanMessage, OutputParserException, BaseMessage, SystemMessage
from core.constant import llm_constant
from core.llm.llm_builder import LLMBuilder
@@ -12,8 +12,8 @@ from core.prompt.output_parser.rule_config_generator import RuleConfigGeneratorO
from core.prompt.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
from core.prompt.prompt_template import JinjaPromptTemplate, OutLinePromptTemplate
from core.prompt.prompts import CONVERSATION_TITLE_PROMPT, CONVERSATION_SUMMARY_PROMPT, INTRODUCTION_GENERATE_PROMPT
from core.prompt.prompts import CONVERSATION_TITLE_PROMPT, CONVERSATION_SUMMARY_PROMPT, INTRODUCTION_GENERATE_PROMPT, \
GENERATOR_QA_PROMPT
# gpt-3.5-turbo works not well
generate_base_model = 'text-davinci-003'
@@ -31,7 +31,8 @@ class LLMGenerator:
llm: StreamableOpenAI = LLMBuilder.to_llm(
tenant_id=tenant_id,
model_name='gpt-3.5-turbo',
max_tokens=50
max_tokens=50,
timeout=600
)
if isinstance(llm, BaseChatModel):
@@ -185,3 +186,27 @@ class LLMGenerator:
}
return rule_config
@classmethod
async def generate_qa_document(cls, llm: StreamableOpenAI, query):
prompt = GENERATOR_QA_PROMPT
if isinstance(llm, BaseChatModel):
prompt = [SystemMessage(content=prompt), HumanMessage(content=query)]
response = llm.generate([prompt])
answer = response.generations[0][0].text
return answer.strip()
@classmethod
def generate_qa_document_sync(cls, llm: StreamableOpenAI, query):
prompt = GENERATOR_QA_PROMPT
if isinstance(llm, BaseChatModel):
prompt = [SystemMessage(content=prompt), HumanMessage(content=query)]
response = llm.generate([prompt])
answer = response.generations[0][0].text
return answer.strip()