prompt+rag
时间: 2023-11-19 13:05:24 浏览: 386
根据提供的引用内容,可以得知prompt+RAG的流程如下:
1. 首先,使用Retriever部分在知识库中检索出top-k个匹配的文档zi。
2. 然后,将query和k个文档拼接起来作为QA的prompt,送入seq2seq模型。
3. seq2seq模型生成回复y。
4. 如果需要进行Re-rank,可以使用LLM来rerank,给LLM写好prompt即可。
下面是一个简单的示例代码,演示如何使用prompt+RAG:
```python
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
# 初始化tokenizer、retriever和seq2seq模型
tokenizer = RagTokenizer.from_pretrained('facebook/rag-token-base')
retriever = RagRetriever.from_pretrained('facebook/rag-token-base', index_name='exact', use_dummy_dataset=True)
model = RagSequenceForGeneration.from_pretrained('facebook/rag-token-base')
# 设置query和context
query = "What is the capital of France?"
context = "France is a country located in Western Europe. Paris, the capital city of France, is known for its romantic ambiance and iconic landmarks such as the Eiffel Tower."
# 使用Retriever部分检索top-k个匹配的文档
retrieved_docs = retriever(query)
# 将query和k个文档拼接起来作为QA的prompt
input_dict = tokenizer.prepare_seq2seq_batch(query, retrieved_docs[:2], return_tensors='pt')
generated = model.generate(input_ids=input_dict['input_ids'], attention_mask=input_dict['attention_mask'])
# 输出生成的回复
generated_text = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
print(generated_text)
```
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