确定 RAG 系统的最佳chunk_size不仅需要直觉，还需要实证证据。借助 LlamaIndex 的响应评估模块，您可以实验各种大小，并基于具体数据做出决策。在构建 RAG 系统时，请始终记住，chunk_size是一个关键参数。请投入时间来仔细评估和调整您的chunk_size，以获得最有价值的结果。参考链接：Jupter Notebook：https://github...
text_splitter = SentenceSplitter( separator=" ", chunk_size=1024, chunk_overlap=20, paragraph_separator="\n\n\n", secondary_chunking_regex="[^,.;。]+[,.;。]?", tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode ) node_parser = SimpleNodeParser.from_defaults(text_splitter=...
service_context= ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model="local:BAAI/bge-large-en") index=VectorStoreIndex.from_documents( documents, service_context=service_context ) 0x3:Query Index 将输入query通过embedding大模型生成嵌入空间向量,然后通过向量相似度搜索算法,在向量知识库里...
#setup the service context service_context = ServiceContext.from_defaults( chunk_size=256, ...
from_defaults(llm=llm, chunk_size=512) graph_store = SimpleGraphStore() # Neo4jGraphStore, NebulaGraphStore storage_context = StorageContext.from_defaults(graph_store=graph_store) # Create Index index = KnowledgeGraphIndex.from_documents( documents, max_triplets_per_chunk=2, storage_context=...
model_name="meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", stopping_ids=stopping_ids, tokenizer_kwargs={"max_length": 4096}, # uncomment this if using CUDA to reduce memory usage # model_kwargs={"torch_dtype": torch.float16} ) Settings.llm = llm Settings.chunk_size = 51...
( chunk_size=256, llm=llm, embed_model=embed_model ) #setupthestoragecontext graph_store=SimpleGraphStore() storage_context=StorageContext.from_defaults(graph_store=graph_store) #ConstructtheKnowlegeGraphUndex index=KnowledgeGraphIndex.from_documents(documents=documents, max_triplets_per_chunk=3, ...
这里的Embedding和Vector指的都是通过ChatGPT转换后的数据;当前版本嵌入的维度是 1536(问题的和Node一样长);假设将每个块设成最大600(Node: chunk size),如果是18K大小的文本文件,存储了utf-8的中文文字,每字占3字节,6000多字,约使用10来个Node存储;每个Node被转换成1538个float值,存储在本地,即Vector store。
service_context = ServiceContext.from_defaults(chunk_size_limit=512) index_set = {} foryearinyears: storage_context = StorageContext.from_defaults() cur_index = GPTVectorStoreIndex.from_documents( documents=doc_set[year], service_context=service_context, ...
from llama_index.core.node_parserimportSentenceSplitter # chunk_sizeof1024is a gooddefaultvalue splitter=SentenceSplitter(chunk_size=1024)# Create nodes from documents nodes=splitter.get_nodes_from_documents(documents) 可以使用以下方法获取有关每个块的详细信息: ...