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Updated 2 months ago

what is difference between semantic embedding and semantic semantic chunking

what is difference between semantic embedding and semantic semantic chunking
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Please stand by @andysingal as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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Semantic Embedding and Semantic Chunking are two different concepts used in the context of LlamaIndex.

Semantic Embedding refers to the process of converting text into numerical vectors that capture the semantic meaning of the text. This is typically done using machine learning models trained on large text corpora. In the context of LlamaIndex, the OpenAIEmbedding class is used for semantic embedding (source ()).

Semantic Chunking, on the other hand, is a technique for splitting text into chunks in a way that preserves semantic coherence within each chunk. Instead of splitting text into chunks of a fixed size, the Semantic Chunker in LlamaIndex adaptively picks the breakpoint in-between sentences using embedding similarity. This ensures that a "chunk" contains sentences that are semantically related to each other (source ()).

Here is an example of how to use the Semantic Chunker in LlamaIndex:

Plain Text
from llama_index.core.node_parser import SemanticSplitterNodeParser
from llama_index.embeddings.openai import OpenAIEmbedding

embed_model = OpenAIEmbedding()
splitter = SemanticSplitterNodeParser(
    buffer_size=1, breakpoint_percentile_threshold=95, embed_model=embed_model
)

(source ())

In this code, SemanticSplitterNodeParser is the class that implements semantic chunking, and it uses OpenAIEmbedding for semantic embedding to determine where to split the text.
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@kapa.ai how to implement semantic chunking like python code does using typescript
Let me check the knowledge base for you. One moment! :books:
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I'm sorry, but the provided knowledge sources do not contain information on how to implement semantic chunking in TypeScript. The examples and code snippets provided are all in Python, specifically for use with the LlamaIndex project.

However, the general approach to semantic chunking would involve the following steps:

  1. Use a machine learning model to generate embeddings for sentences in your text. In TypeScript, you might use a library like TensorFlow.js for this.
  2. Compare the embeddings of adjacent sentences to determine their semantic similarity.
  3. Choose breakpoints between sentences where the semantic similarity falls below a certain threshold.
Please note that implementing this in TypeScript would require a good understanding of both TypeScript and the underlying machine learning concepts. If you're not already familiar with these, you might find it helpful to start with some tutorials or courses on machine learning with TensorFlow.js.
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