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Documentation Index

Fetch the complete documentation index at: https://docs.insitechat.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Definition

An embedding is a list of numbers (a vector) that represents the meaning of a piece of text. Two texts with similar meaning produce vectors that are close together in vector space; two unrelated texts produce vectors that are far apart. Embeddings are the foundation of modern semantic search and retrieval-augmented generation (RAG) — they let a computer find content that means what the user asked, even when the exact words don’t match.

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How embeddings work

An embedding model is a neural network trained to compress the meaning of any sentence, paragraph, or document into a fixed-length vector — typically 384, 768, or 1,536 numbers. The training objective is simple: similar sentences should produce vectors that are mathematically close (low cosine distance), and dissimilar sentences should produce vectors that are far apart. Once content is embedded, finding relevant passages becomes a math problem: given a question vector, find the K stored vectors with the smallest distance. This is fast even at scale — modern vector databases can search millions of vectors in milliseconds.

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Example

Imagine these three sentences become embeddings:
  • “How do I cancel my subscription?” → [0.21, -0.04, 0.81, ...]
  • “Where can I close my account?” → [0.19, -0.06, 0.79, ...]
  • “What is your refund policy?” → [-0.11, 0.42, 0.05, ...]
The first two vectors are nearly identical (close in vector space) even though they share zero common keywords. A keyword search would miss this match entirely. An embedding-based search catches it instantly.

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How InsiteChat uses embeddings

When you add content to InsiteChat, this happens behind the scenes:
  1. Chunking: Long documents are split into 512-token chunks with 50-token overlap.
  2. Embedding: Each chunk is passed through a modern multilingual embedding model.
  3. Storage: The vectors are stored in a dedicated vector store, indexed for fast nearest-neighbor search.
  4. Search at query time: A visitor’s question is embedded with the same model and matched against the stored vectors.
InsiteChat’s embedding model supports 95+ languages including English, Hindi, Spanish, French, Arabic, Mandarin, and Japanese. A visitor can ask in Hindi and match content originally written in English — the embeddings capture meaning, not surface form.

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Why embeddings alone aren’t enough

Pure embedding-based search is excellent for paraphrase matching but weaker on:
  • Proper nouns and product names (“Vercel”, “GST”, “Shopify”) — these embed close to many other tech terms
  • Numbers and codes (“error 429”, “$199”, “v2.1”) — semantic models treat these as noise
  • Rare terminology specific to your industry
InsiteChat solves this by combining embeddings with keyword (BM25) search and fusing results via Reciprocal Rank Fusion — see Hybrid search for details.

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