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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"with open(\".inner/军事新闻.txt\") as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Chain.dict of MapReduceDocumentsChain(memory=None, callbacks=None, callback_manager=None, verbose=False, input_key='input_documents', output_key='output_text', llm_chain=LLMChain(memory=None, callbacks=None, callback_manager=None, verbose=False, prompt=PromptTemplate(input_variables=['text'], output_parser=None, partial_variables={}, template='对以下内容进行简要总结：\\n\\n\"{text}\"\\n\\n\\n简明摘要:', template_format='f-string', validate_template=True), llm=OpenAI(cache=None, verbose=False, callbacks=None, callback_manager=None, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), output_key='text'), combine_document_chain=StuffDocumentsChain(memory=None, callbacks=None, callback_manager=None, verbose=False, input_key='input_documents', output_key='output_text', llm_chain=LLMChain(memory=None, callbacks=None, callback_manager=None, verbose=False, prompt=PromptTemplate(input_variables=['text'], output_parser=None, partial_variables={}, template='对以下内容进行简要总结：\\n\\n\"{text}\"\\n\\n\\n简明摘要:', template_format='f-string', validate_template=True), llm=OpenAI(cache=None, verbose=False, callbacks=None, callback_manager=None, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), output_key='text'), document_prompt=PromptTemplate(input_variables=['page_content'], output_parser=None, partial_variables={}, template='{page_content}', template_format='f-string', validate_template=True), document_variable_name='text', document_separator='\\n\\n'), collapse_document_chain=None, document_variable_name='text', return_intermediate_steps=False)>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import OpenAI\n",
"from langchain.chains.summarize import load_summarize_chain\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"summary_chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
"summary_chain.dict"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import AnalyzeDocumentChain"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Chain.dict of AnalyzeDocumentChain(memory=None, callbacks=None, callback_manager=None, verbose=False, input_key='input_document', text_splitter=<langchain.text_splitter.RecursiveCharacterTextSplitter object at 0x10dfc97e0>, combine_docs_chain=MapReduceDocumentsChain(memory=None, callbacks=None, callback_manager=None, verbose=False, input_key='input_documents', output_key='output_text', llm_chain=LLMChain(memory=None, callbacks=None, callback_manager=None, verbose=False, prompt=PromptTemplate(input_variables=['text'], output_parser=None, partial_variables={}, template='对以下内容进行简要总结：\\n\\n\"{text}\"\\n\\n\\n简明摘要:', template_format='f-string', validate_template=True), llm=OpenAI(cache=None, verbose=False, callbacks=None, callback_manager=None, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), output_key='text'), combine_document_chain=StuffDocumentsChain(memory=None, callbacks=None, callback_manager=None, verbose=False, input_key='input_documents', output_key='output_text', llm_chain=LLMChain(memory=None, callbacks=None, callback_manager=None, verbose=False, prompt=PromptTemplate(input_variables=['text'], output_parser=None, partial_variables={}, template='对以下内容进行简要总结：\\n\\n\"{text}\"\\n\\n\\n简明摘要:', template_format='f-string', validate_template=True), llm=OpenAI(cache=None, verbose=False, callbacks=None, callback_manager=None, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), output_key='text'), document_prompt=PromptTemplate(input_variables=['page_content'], output_parser=None, partial_variables={}, template='{page_content}', template_format='f-string', validate_template=True), document_variable_name='text', document_separator='\\n\\n'), collapse_document_chain=None, document_variable_name='text', return_intermediate_steps=False))>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=summary_chain)\n",
"summarize_document_chain.dict"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' 美国陆军日前下令大部分飞行员暂停飞行，以确保飞行员安全，并审查风险批准和风险管理过程。自3月以来，已有12名士兵死于直升机坠毁事故，最近一起事故发生在4月27日，导致3名士兵死亡，另有1人受伤，原因正在调查中。'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summarize_document_chain.run(state_of_the_union)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
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}
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