fe07d0ede1
refactor: 简化Embeddings和LLM类实现，移除不必要依赖 docs: 更新文档内容，添加硅基流动API使用说明 chore: 更新requirements.txt依赖版本
19 lines
805 B
Python
19 lines
805 B
Python
from VectorBase import VectorStore
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from utils import ReadFiles
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from LLM import OpenAIChat
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from Embeddings import OpenAIEmbedding
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# 没有保存数据库
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docs = ReadFiles('./data').get_content(max_token_len=600, cover_content=150) # 获得data目录下的所有文件内容并分割
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vector = VectorStore(docs)
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embedding = OpenAIEmbedding() # 创建EmbeddingModel
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vector.get_vector(EmbeddingModel=embedding)
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vector.persist(path='storage') # 将向量和文档内容保存到storage目录下，下次再用就可以直接加载本地的数据库
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# vector.load_vector('./storage') # 加载本地的数据库
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question = 'RAG的原理是什么？'
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content = vector.query(question, EmbeddingModel=embedding, k=1)[0]
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chat = OpenAIChat(model='Qwen/Qwen2.5-32B-Instruct')
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print(chat.chat(question, [], content)) |