The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Build a small semantic search prototype by embedding a handful of passages once, embedding each incoming query, and ranking the passages by vector similarity. The example below uses Sentence Transformers locally; it returns likely matches, not guaranteed answers, and its similarity scores are ranking signals rather than probabilities.
How semantic search finds related passages
Semantic search represents text as vectors: encode each corpus passage, encode a query into the same vector space, then retrieve nearby passage vectors. Because the model learns patterns in language, it can find related wording even when a query and passage do not share exact keywords. What counts as “related” depends on the embedding model.
For a short query against longer answer passages, use the model’s query and document encoding methods where supported: encode_query for the query and encode_document for passages. These methods can apply different prompts or task routing. Follow the selected model’s intended usage. This is asymmetric retrieval; comparing items of similar length, such as question to question, is symmetric retrieval. Sentence Transformers’ semantic search guide explains this distinction.
Build a minimal Python search engine
1. Install the library
In an environment where Python and pip are available, install Sentence Transformers:
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →#1 Best Overall
- Read Before You Buy — No Video Output: These adapters support charging and USB 2.0 data transfer, but cannot transmit video signals. Except for standard USB webcams (which use USB data only), they are not compatible with HDMI/DisplayPort cables, video-capable USB-C hubs, or docking stations with video output.
- Convert USB-A Ports to USB-C: Designed to connect USB-C earphones, cables, flash drives, card readers, and other USB-C accessories to standard USB-A ports. Plug-and-play with no drivers or software required.
- Aluminum Alloy Housing: Built with a sturdy aluminum alloy shell that aids in heat dissipation and protects against daily wear and scratches. Designed to maintain a stable and secure connection.
- Compact & Travel-Friendly: The ultra-compact design allows the adapter to stay plugged into your device without blocking adjacent ports or adding bulk, reducing wear and tear on your original USB ports.
- 12-Month Warranty: Backed by a 12-month manufacturer warranty for peace of mind. Designed to meet strict quality control standards for reliable everyday performance.
python -m pip install -U sentence-transformersThe code below follows the documented Sentence Transformers workflow. It is an illustrative example, not a tested or benchmarked snippet; confirm API compatibility with your installed library version and chosen model.
2. Encode passages and search
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
# Keep each stable ID beside its original passage.
corpus = [
("p1", "A semantic search system compares text embeddings."),
("p2", "Cosine similarity compares vector directions."),
("p3", "A bicycle uses two wheels."),
]
texts = [text for _, text in corpus]
# Compute passage embeddings once, then reuse them for searches.
corpus_embeddings = model.encode_document(texts, convert_to_tensor=True)
query = "How can I compare the meaning of two passages?"
query_embedding = model.encode_query(query, convert_to_tensor=True)
scores = model.similarity(query_embedding, corpus_embeddings)[0]
requested_k = 5
k = min(requested_k, len(corpus))
values, indices = scores.topk(k)
results = [
(corpus[int(i)][0], corpus[int(i)][1], float(score))
for score, i in zip(values, indices)
]
for passage_id, text, score in results:
print(passage_id, score, text)The model example comes from the Sentence Transformers quickstart. Its current guide shows three sample texts producing embeddings with shape [3, 384]; that is an example output for this model, not a universal embedding size. Sentence Transformers quickstart
Rank #2
- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
- 4K HDMI Display: Enhance your visual experience with a hub capable of delivering 4K resolution at 30Hz in both mirror and extend modes. Please note that this hub is compatible with MacBook (macOS 12 and newer), Windows 10 and 11, ChromeOS, and laptops equipped with DP Alt Mode and Power Delivery. Note: This device is not compatible with Linux.
- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
3. Keep vectors connected to their passages
Embedding row order must stay aligned with passage IDs and original text. If you reorder or filter passages, apply the same change to the corresponding vectors; otherwise a correctly ranked vector can display the wrong passage. For a small prototype, storing the ID and text in the same ordered list is a simple safeguard.
How to interpret similarity scores
The example ranks by the model’s similarity function, using cosine similarity by default in Sentence Transformers’ semantic-search utility. Cosine similarity compares vector directions using the normalized dot product. A higher score means the model ranked a passage as more similar to the query; it does not mean the passage is correct, complete, or relevant with that probability.
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
Cosine similarity also works with sparse document vectors, as documented by scikit-learn. That makes it useful for a TF-IDF baseline, but TF-IDF represents lexical feature overlap rather than learned sentence-level semantic representations. When vectors are already normalized to unit length, dot product gives the same ranking as cosine similarity and avoids repeated normalization.
When should you add an index or reranker?
Stay with a direct scan for a tiny corpus
Comparing a query against every stored vector is the easiest baseline to understand and maintain. Sentence Transformers’ guide says a manual exact search can be used for corpora “up to about 1 million entries,” but that is project guidance, not a capacity guarantee. Model dimensions, available memory, batching, query rate, hardware, and latency needs all affect whether a direct scan is practical.
Rank #4
- Dual Converters, Infinite Potential：Includes 2× USB C male to USB A female adapters and 2× USB A male to USB C female adapters. Perfect for a wide range of uses—tablets with Bluetooth keyboards, expand USB ports on macbook, and more. Two different converters for all your daily needs
- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
Consider approximate nearest neighbors at larger scale
When exact comparisons over millions of vectors take too long, the Sentence Transformers guide identifies FAISS, Annoy, and hnswlib as approximate-nearest-neighbor options. ANN can trade exactness for speed and may miss relevant nearest neighbors. Evaluate it against representative queries and your corpus, then choose an acceptable recall and latency balance rather than assuming an index will improve every workload.
Rerank a shortlist when relevance matters more
A two-stage design first uses a bi-encoder to retrieve a shortlist, then uses a cross-encoder to score each query-passage pair. The cross-encoder can improve ranking quality, but it is slower because it computes each pair individually. Applying it only to the shortlist limits that extra work. Sentence Transformers’ quickstart describes this retrieve-and-rerank pattern.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
What to evaluate before relying on results
- Representative queries: Check whether the passages ranked near the top are useful for the kinds of questions people will actually ask.
- Exact terms: Names, codes, and exact phrases may still need lexical matching alongside semantic retrieval.
- Operational fit: Compare latency, memory use, index-building complexity, and retrieval quality on the intended corpus.
- Result handling: Return original passage text and stable IDs, and present scores as relative ranking information rather than confidence.
This prototype needs a text corpus and the software library/model workflow shown here; the cited implementation path does not establish a need for dedicated hardware, a paid database, or a separate utility.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
