代码来源
【大模型RAG】使用llama index实现RAG_哔哩哔哩_bilibili
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings from llama_index.llms.ollama import Ollama from llama_index.embeddings.ollama import OllamaEmbedding # 加载数据 documents = SimpleDirectoryReader("llama-index/docs").load_data() # Emb Settings.embed_model = OllamaEmbedding(model_name="wangshenzhi/llama3-8b-chinese-chat-ollama-q8") # Post processing Settings.llm = Ollama(model="wangshenzhi/llama3-8b-chinese-chat-ollama-q8", request_timeout=360) index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() # 执行查询并获取响应 response = query_engine.query("霸王茶姬香港店什么时候开业？店长薪酬多少？") print(response)
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