-
开源的DeepSeek R1,我们会使用经过蒸馏并量化为 4 bit的版本,以方便我们在本地运行。 -
为方便展示,我们不再对推理模型进行额外微调,Agent 也不会回溯或调整方向。 -
囿于本地资源,查询范围,我们框定在 Wikipedia 页面,只执行单独的 RAG 查询(暂时不对全网数据进行搜寻)。 -
查找范围上,本篇只限定文本数据,不将图像、PDF 等数据纳入考量。
<think>
Alright, so I need to break down the question "How has the cast changed over time?" related to the evolution of The Simpsons. Let me think about what aspects are involved here.
First, I know that the cast has changed a lot, but I need to figure out the intermediate questions to approach this. The main question is about changes in the cast over time, so I should consider different areas that contribute to this change.
I guess the first sub-question would be about the original cast members. Who were the main voices and how did they evolve? Then, there might be new cast additions over the years, so another sub-question about that.
Also, some original voice actors have left, so I should include a sub-question about departures. Then, new voice actors joining would be another point.
The show has been popular for a long time, so recurring roles changing might be another aspect. Additionally, the role of the show in society might have influenced casting choices, so a sub-question about that.
Lastly, the overall impact on the cast's careers could be another angle. So, I should list these as sub-questions to cover all aspects.
</think>
wiki_wiki = wikipediaapi.Wikipedia(user_agent='MilvusDeepResearchBot (<insert your email>)', language='en')
page_py = wiki_wiki.page(page_title)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
docs = text_splitter.create_documents([page_py.text])
vectorstore = Milvus.from_documents(# or Zilliz.from_documents
documents=docs,
embedding=embeddings,
connection_args={
"uri": "./milvus_demo.db",
},
drop_old=True,
index_params={
"metric_type": "COSINE",
"index_type": "FLAT",
"params": {},
},
)
# Define the RAG chain for response generation
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
# Prompt the RAG for each question
answers = {}
total = len(leaves(breakdown))
pbar = tqdm(total=total)
for k, v in breakdown.items():
if v == []:
print(k)
answers[k] = rag_chain.invoke(k).split('</think>')[-1].strip()
pbar.update(1)
else:
for q in v:
print(q)
answers[q] = rag_chain.invoke(q).split('</think>')[-1].strip()
pbar.update(1)
04
结果展示

05
总结:从给出回答,到给出完整报告,大模型+向量数据库前景无限
版权声明:charles 发表于 2025年2月13日 am3:26。
转载请注明:教你本地复现Deep Research:DeepSeek R1+ LangChain+Milvus | AI工具大全&导航
转载请注明:教你本地复现Deep Research:DeepSeek R1+ LangChain+Milvus | AI工具大全&导航