While both functions allow control over chunk size and overlap, the critical difference between these functions lies in their splitting strategies. The recursive version employs a more sophisticated approach aimed at preserving semantic meaning, which is what we recommend for most use cases. It split...
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If you need to customize your scenario beyond whatAzure OpenAI on your dataoffers out-of-the-box, use this repository. By default, this repo comes with one specific set of RAG configurations including but not limited to: chunk size, overlap, retrieval/search type and system prompt. It is ...
{ "tokenizer_model_name": None, # Defaults to the embedding model tokenizer "chunk_size": 64, # Number of tokens per document chunk "overlap": 8, # Overlap of tokens between chunks for context continuity "passes": 10, # Number of document passes for indexing "icl_kb": False, # ...
I have used thisHow to parse PDF docs for RAG | OpenAI Cookbook Hope the answers will be better jochenschultzJanuary 22, 2025, 2:50pm4 You might as well combine different strategies… like contextual analysis on each word where you put that into subgraphs of an information graph and find ...
This is a quick-start essay for LangChain and RAG which mainly refers to the Langchain chat with your data course which are taught by Harrison Chase and Andrew Ng. You can check the entire code in the rag101 repository. This article is also posted on my blog, feel free to check it....
需要检索的查询通过RAG模块进行;其他查询直接由llm处理。 2. 片段划分 主要有几种划分方式: token-level chunking:直接以token划分,但是影响检索质量 semantic-level chunking:有LLM决定划分的断点,能够保留上下文,但是非常耗时 Sentence-level chunking:在简单和语义保留之间做了平衡 本文使用Sentence-level chunking 片段...
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