Search a new era David Pilato | @dadoonet

Co m m er ci al Search a new era David Pilato | @dadoonet

Elasticsearch You Know, for Search

These are not the droids you are looking for.

GET /_analyze { “char_filter”: [ “html_strip” ], “tokenizer”: “standard”, “filter”: [ “lowercase”, “stop”, “snowball” ], “text”: “These are <em>not</em> the droids you are looking for.” }

“char_filter”: “html_strip” These are <em>not</em> the droids you are looking for. These are not the droids you are looking for.

“tokenizer”: “standard” These are not the droids you are looking for. These are not the droids you are looking for

“filter”: “lowercase” These are not the droids you are looking for these are not the droids you are looking for

“filter”: “stop” These are not the droids you are looking for these are not the droids you are looking for droids you looking

“filter”: “snowball” These are not the droids you are looking for these are not the droids you are looking for droids you droid you looking look

These are <em>not</em> the droids you are looking for. { “tokens”: [{ “token”: “droid”, “start_offset”: 27, “end_offset”: 33, “type”: “<ALPHANUM>”, “position”: 4 },{ “token”: “you”, “start_offset”: 34, “end_offset”: 37, “type”: “<ALPHANUM>”, “position”: 5 }, { “token”: “look”, “start_offset”: 42, “end_offset”: 49, “type”: “<ALPHANUM>”, “position”: 7 }]}

Semantic search ≠ Literal matches

Elasticsearch You Know, for Search

Elasticsearch You Know, for Vector Search

What is a Vector ?

Example: 1-dimensional vector Character Vector [ 1 ] ] Realistic

[ Embeddings represent your data Cartoon 1

represent different data aspects Human Character Vector [ 1, 1 Realistic Cartoon ] ] Machine

[ Multiple dimensions 1, 0

is grouped together Human Character Vector [ 1.0, 1.0 1.0, 0.0 Realistic Cartoon [ 1.0, 0.8 1.0, 1.0 [ 1.0, 1.0 ] ] ] ] ]

Machine

[ [ Similar data

Vector search ranks objects by similarity (~relevance) to the query Human Rank Query 1 Realistic Cartoon 2 3 4 5 Machine Result

How do you index vectors ?

Architecture of Vector Search

dense_vector field type PUT ecommerce { “mappings”: { “properties”: { “description”: { “type”: “text” } “desc_embedding”: { “type”: “dense_vector” } } } }

Data Ingestion and Embedding Generation POST ecommerce/_doc { “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, “fabric”:”cotton” “desc_embedding”:[0.452,0.3242,…], } “desc_embedding”:[0.452,0.3242,…] } “img_embedding”:[0.012,0.0,…] } Source data POST /ecommerce/_doc

Co m m er ci With Elastic ML al { } Source data { } “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, POST /ecommerce/_doc “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, “desc_embedding”:[0.452,0.3242,…]

Eland Imports PyTorch Models Co m m er ci al $ eland_import_hub_model —url https://cluster_URL —hubmodel-id BERT-MiniLM-L6 —tasktype text_embedding —start BERT-MiniLM-L6 Select the appropriate model Load it Manage models

Elastic’s range of supported NLP models Co m m er ci ● Fill mask model Mask some of the words in a sentence and predict words that replace masks ● Named entity recognition model NLP method that extracts information from text ● Text embedding model Represent individual words as numerical vectors in a predefined vector space ● Text classification model Assign a set of predefined categories to open-ended text ● Question answering model Model that can answer questions given some or no context ● Zero-shot text classification model Model trained on a set of labeled examples, that is able to classify previously unseen examples Full list at: ela.st/nlp-supported-models al

How do you search vectors ?

Architecture of Vector Search

knn query GET ecommerce/_search { “query” : { “bool”: { “must”: [{ “knn”: { “field”: “desc_embbeding”, “query_vector”: [0.123, 0.244,…] } }], “filter”: { “term”: { “department”: “women” } } } } }, “size”: 10

knn query (with Elastic ML Co m m er ci al GET ecommerce/_search { “query” : { “bool”: { “must”: [{ “knn”: { “field”: “desc_embbeding”, “query_vector_builder”: { “text_embedding”: { “model_text”: “summer clothes”, “model_id”: <text-embedding-model> } } } }], “filter”: { “term”: { “department”: “women” } } } }, “size”: 10 } ) Transformer model

semantic_text field type PUT /_inference/text_embedding/e5-small-multilingual { “service”: “elasticsearch”, “service_settings”: { “num_allocations”: 1, “num_threads”: 1, “model_id”: “.multilingual-e5-small_linux-x86_64” } } POST ecommerce/_doc { “description”: “Our best-selling…” } ne w in 8. 15 PUT ecommerce { “mappings”: { “properties”: { “description”: { “type”: “text”, “copy_to”: [ “desc_embedding” ] } “desc_embedding”: { “type”: “semantic_text”, “inference_id”: “e5-small-multilingual” } } } } GET ecommerce/_search { “query”: { “semantic”: { “field”: “desc_embedding” “query” : “I’m looking for a red dress for a DJ party” }}}

Architecture of Vector Search

Choice of Embedding Model Start with Off-the Shelf Models Extend to Higher Relevance ●Text data: Hugging Face (like Microsoft’s E5 ●Apply hybrid scoring ) ●Images: OpenAI’s CLIP ●Bring Your Own Model: requires expertise + labeled data

Problem training vs actual use-case

But how does it really work?

Similarity Human q cos(θ) = d1 d2 Realistic θ q⃗ × d ⃗ | q⃗ | × | d |⃗ _score = 1 + cos(θ) 2

Similarity: cosine (cosine) θ Similar vectors θ close to 0 cos(θ) close to 1 1+1 _score = =1 2 θ Orthogonal vectors θ close to 90° cos(θ) close to 0 1+0 _score = = 0.5 2 θ Opposite vectors θ close to 180° cos(θ) close to -1 1−1 _score = =0 2

Similarity: Dot Product (dot_product or max_inner_product) q⃗ × d ⃗ = | q⃗ | × cos(θ) × | d |⃗ q d θ | q⃗ | × co s (θ ) 1 + dot_ product(q, d) scorefloat = 2 0.5 + dot product(q, d) _scorebyte = 32768 × dims

Similarity: Euclidean distance (l2_norm) y 2 n i (x ∑ 1 i= − y i) q l2_normq,d = y1 d x1 y2 x2 n ∑ i=1 (xi − yi) 1 _score = 1 + (l2_normq,d )2 x 2

Brute Force

Hierarchical Navigable Small Worlds (HNSW One popular approach HNSW: a layered approach that simplifies access to the nearest neighbor Tiered: from coarse to fine approximation over a few steps Balance: Bartering a little accuracy for a lot of scalability ) Speed: Excellent query latency on large scale indices

Scaling Vector Search Vector search Best practices

1. Needs lots of memory
2. Avoid searches during indexing
3. Indexing is slower
4. Exclude vectors from _source
5. Merging is slow
6. Reduce vector dimensionality 4. Use byte rather than float

• Continuous improvements in Lucene + Elasticsearch

Reduce Required Memory 2. Reduce of number of dimensions per vector

1. Vector element size reduction (“quantize”)

Benchmarketing

https://djdadoo.pilato.fr/

https://github.com/dadoonet/music-search/

Elasticsearch You Know, for Hybrid Search

Hybrid scoring Term-based score Linear Combination manual boosting Vector similarity score Combine

GET ecommerce/_search { “query” : { “bool” : { “must” : [{ “match”: { “description”: { “query”: “summer clothes”, “boost”: 0.1 } } },{ “knn”: { “field”: “desc_embbeding”, “query_vector”: [0.123, 0.244,…], “boost”: 2.0, “filter”: { “term”: { “department”: “women” } } } }], “filter” : { “range” : { “price”: { “lte”: 30 } } } } } } summer clothes pre-filter post-filter

PUT starwars { “mappings”: { “properties”: { “text.tokens”: { “type”: “sparse_vector” } } } “These are not the droids you are looking for.”, } “Obi-Wan never told you what happened to your father.” GET starwars/_search { “query”:{ “sparse_vector”: { “field”: “text.tokens”, “query_vector”: { “lucas”: 0.50047517, “ship”: 0.29860738, “dragon”: 0.5300422, “quest”: 0.5974301, … } } } }

ELSER Elastic Learned Sparse EncodER sparse_vector Not BM25 or (dense) vector Sparse vector like BM25 Stored as inverted index Co m m er ci al

Hybrid ranking Dense vector score Reciprocal Rank Fusion (RRF blend multiple ranking methods Combine ) Term-based score Sparse vector score

Reciprocal Rank Fusion (RRF D set of docs R set of rankings as permutation on 1..|D| k - typically set to 60 by default Dense Vector r(d) k+r(d) A 1 1 B 0.7 C D Score r(d) k+r(d) 61 C 1,341 1 61 2 62 A 739 2 62 0.5 3 63 F 732 3 63 0.2 4 64 G 192 4 64 0.01

=

= +

E Doc 5 65 H 183 5 65 ) Score

Doc BM25 Doc RRF Score A 1/61 1/62 0,0325 C 1/63 1/61 0,0323 B 1/62 0,0161 F 1/63 0,0159 D 1/64 0,0156

GET index/_search { “retriever”: { “rrf”: { “retrievers”: [{ “standard” { “query”: { “match”: {…} } } },{ “standard” { “query”: { “sparse_vector”: {…} } } },{ “knn”: { … } } ] } } } Hybrid Ranking BM25f + Sparse Vector + Dense Vector Co m m er ci al

ChatGPT Elastic and LLM

Gen AI Search engines

LLM opportunities and limits your question one answer your question GAI / LLM : public internet data

Retrieval Augmented Generation your question the right answer your question + context window GAI / LLM public internet data your business data documents images audio

Demo Elastic Playground

Elasticsearch You Know, for Semantic Search

Search a new era David Pilato | @dadoonet