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AI Models & Assistants / Knowledge library
Semantic Search
Retrieving items by vector similarity to query meaning
Why learn Semantic Search?
It can match paraphrases that share few exact words. Its quality depends on the embedding model, document chunks, index, filters, and relevance evaluation.
Where it came from
Semantic search represents queries and documents in an embedding space and retrieves nearby candidates. Hybrid systems often combine dense semantic retrieval with lexical matching.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain semantic retrieval: What lets semantic search retrieve a paraphrase with few shared keywords?
- Use a compatible similarity: Document embeddings were normalized before indexing. Which score commonly ranks them?
- Choose chunking: Why can indexing entire long manuals as single vectors hurt retrieval?
- Combine retrieval signals: A query contains a rare product code plus a natural-language symptom. Which design is strongest?
- Evaluate retrieval: Which metric directly checks whether a relevant passage appears in the first k results?
Documentation and sources
- Sentence-BERT (arxiv.org)
Connect the ideas
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Real World Example
Automated Technical Documentation Retrieval for Aerospace Engineering
Aerospace engineering firms managing vast repositories of legacy technical manuals and maintenance logs.
The business problem
Engineers struggle to locate relevant maintenance procedures because traditional keyword-based search fails to capture the intent behind natural language queries, leading to inefficient manual browsing of thousands of documents.
How this topic solves it
By utilizing Sentence-BERT (SBERT) to transform textual documentation into fixed-length vector embeddings, the system enables semantic search. Instead of matching exact keywords, the system calculates the cosine similarity between the vector representation of a user's query and the pre-computed vectors of the document database, allowing for retrieval based on the underlying meaning of the technical request.
- Pre-process the corpus of technical manuals into individual sentence segments.
- Pass each segment through a Siamese BERT-network to generate high-dimensional vector embeddings.
- Store these embeddings in a vector database for efficient retrieval.
- Convert incoming user queries into the same vector space using the identical SBERT model.
- Perform a cosine similarity search to identify the most relevant document segments.
The flow from raw text to semantic search results.
Siamese network
Stored embeddings
Natural language input
Relevant segments
- Manuals → embeds → SBERT Model
- SBERT Model → indexes → Vector DB
- User Query → encodes → SBERT Model
- Vector DB → cosine similarity → Results
Expected business outcome
Engineers achieve faster access to precise maintenance instructions by querying with natural language, significantly reducing the time spent navigating legacy documentation.
Limitations and checks
A primary limitation is the loss of nuance in highly specialized technical jargon if the model was not fine-tuned on domain-specific aerospace corpora. This can be checked by evaluating the top-k retrieval results against a gold-standard set of manually curated relevant documents to identify false positives.