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from langchain.chains.combine_documents.refine import RefineDocumentsChain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from template import template, template_many, template_many_out_put
from langchain import LLMChain
from langchain.llms import OpenAI, AzureOpenAI
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate
def LLMChain_test_outputs(context, result_count):
llm = OpenAI(model_name="text-davinci-003", engine="text-davinci-003",temperature=0)
prompt = PromptTemplate(template=template, input_variables=['context'])
chain = LLMChain(llm=llm, prompt=prompt)
return chain.run(
{
'context': context,
}
)
def LLMChain_test(context):
llm = OpenAI(model_name="text-davinci-003", engine="text-davinci-003",temperature=0)
prompt = PromptTemplate(template=template_many, input_variables=['context','positive', 'negative','indifference'])
chain = LLMChain(llm=llm, prompt=prompt)
return chain.run(
{
'context': context,
'positive': '正面',
'negative': '负面',
'indifference': '无关'
}
)
def LLMChain_test_many(context,count):
llm = OpenAI(model_name="text-davinci-003", engine="text-davinci-003", temperature=0,max_tokens=3000,verbose=True)
prompt = PromptTemplate(template=template_many,
input_variables=['count','context', 'positive', 'negative', 'indifference'],
)
chain = LLMChain(llm=llm, prompt=prompt)
return chain.run(
{
'count': count,
'context': context,
'positive': '正面',
'negative': '负面',
'indifference': '无关'
})
def LLMChain_test_many_output(context,count):
llm = OpenAI(model_name="text-davinci-003", engine="text-davinci-003", temperature=0,max_tokens=3000,verbose=True)
response_schemas = [
ResponseSchema(name="index", description="句子的序号作为数组的下标"),
ResponseSchema(name="emotion", description="每个句子的情绪，枚举值：正面、负面、无关"),
ResponseSchema(name="description", description="对每个句子的总结"),
ResponseSchema(name="count", description="单独key整体的正面、负面、无关的条数，并且只对整体"),
ResponseSchema(name="summary", description="给出所有句子整体的中文简体总结")
]
# 初始化解析器
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(template=template_many_out_put,
input_variables=['count','context', 'positive', 'negative', 'indifference'],
partial_variables={"format_instructions": format_instructions},)
promptValue = prompt.format(**{
'count': count,
'context': context,
'positive': '正面',
'negative': '负面',
'indifference': '无关'
})
llm_output = llm(promptValue)
# return output_parser.parse(llm_output)
return llm_output
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