When we want to find similarities in text using vector embeddings, especially for longer documents, how we create those embeddings matters a lot. Let’s compare two common approaches:
Approach 1: Single Embedding for the Entire Text
How it works: You take the entire piece of text (e.g., a whole document, a long paragraph) and generate one single vector embedding to represent it. If your model outputs 1024-dimensional vectors, this will be a single array of 1024 numbers.
Example Text: [This is a sample sentence to show how a sliding window works in its most basic form.]
Resulting Embedding: [vector_of_1024_dimensions_for_the_WHOLE_sentence]
Pros:
Simple to implement.
Represents the overall “gist” or dominant themes of the entire text.
Cons & Considerations:
Loss of Granularity: This approach “averages out” the meaning of the entire text. If a query (what you’re searching for) is only relevant to a small part of a long document, the single embedding for the whole document might not show high similarity. The specific information gets ‘diluted’ by the rest of the content.
Consequently, even if a part of the text is a strong match, the overall embedding might not be close enough to the query vector to exceed a similarity threshold, potentially causing relevant information to be missed.
Approach 2: Multiple Embeddings using a Sliding Window with Overlap
How it works: Instead of one embedding for everything, you break the text into smaller “chunks.” A “sliding window” moves across the text, creating these chunks. Crucially, these chunks overlap by a certain percentage. Each of these (potentially overlapping) chunks then gets its own vector embedding.
Example Text: [This is a sample sentence to show how a sliding window works in its most basic form.]
Window Size (example): 5 words
Step/Stride (example): 2 words (this creates overlap)
Resulting Chunks & their Embeddings:
[This is a sample sentence to] → [vector_1_for_chunk_1 (1024 dimensions)]
[sample sentence to show how] → [vector_2_for_chunk_2 (1024 dimensions)]
[to show how a sliding window] → [vector_3_for_chunk_3 (1024 dimensions)]
[a sliding window works in its] → [vector_4_for_chunk_4 (1024 dimensions)]
[works in its most basic form.] → [vector_5_for_chunk_5 (1024 dimensions)]
You now have an array of embeddings, where each embedding represents a smaller, more focused piece of the original text.
Pros:
Preserves Local Context: Each chunk embedding strongly represents the meaning of that specific part of the text. This makes it much more likely to find matches for queries targeting specific details or phrases.
Robustness to Phrasing: Overlap ensures that key concepts aren’t accidentally split between chunks and lost. If an important idea spans the boundary of one chunking method, the overlap ensures it’s captured whole in another.
This approach generally results in a greater number of potential similarity matches because the query is compared against many specific segments rather than one general representation.
Cons/Considerations:
More Embeddings: You generate and store more data.
Post-processing for Relevance: Because a query might match multiple chunks from the same original document, a strategy is required to interpret these results. This often involves calculating the significance of these matches, such as counting the number of chunks from a document that meet or exceed a given similarity threshold. Other methods include taking the highest similarity score among the matched chunks or averaging their scores to determine the overall relevance of the original document.
Why Overlap is Important:
Imagine your window doesn’t overlap, and you chunk by sentences:
Sentence 1: “The cat sat.”
Sentence 2: “On the mat.”
A query for “cat on the mat” might not strongly match either embedding alone. With overlap, a chunk like “The cat sat. On the mat.” (or parts of it combined by a sliding window) would capture the full concept.
In Summary:
Single Embedding: Good for short texts or when you only care about the very broad, overall topic of a long text. Prone to missing nuanced or localized information, especially if similarity thresholds are strict.
Sliding Window with Overlap: Better for longer texts where specific details matter. It provides more granular matching, increasing the likelihood of finding relevant information. However, it necessitates a clear strategy for handling and aggregating multiple chunk matches from the same source document.