La météorite GPT annonce t’elle la disparition des dinosaures “moteur de recherche” ?
A presentation at DevFest Nantes in in Nantes, France by David Pilato
La météorite GPT annonce t’elle la disparition des dinosaures “moteur de recherche” ?
The searchenginerosaur
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 droids you looking
“filter”: “snowball” droids you looking droid you 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
2016 David Pilato @dadoonet 2018
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”, “num_candidates”: 50, “query_vector”: [0.123, 0.244,…] } }], “filter”: { “term”: { “department”: “women” } } } } }, “size”: 10
knn query (with Elastic ML ) Transformer model Co m m er ci al GET product-catalog/_search { “query” : { “bool”: { “must”: [{ “knn”: { “field”: “desc_embbeding”, “num_candidates”: 50, “query_vector_builder”: { “text_embedding”: { “model_text”: “summer clothes”, “model_id”: <text-embedding-model> } } } }], “filter”: { “term”: { “department”: “women” } } } }, “size”: 10 }
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
- Needs lots of memory
- Avoid searches during indexing
- Indexing is slower
- Exclude vectors from _source
- Merging is slow
- 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
- 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,…], “num_candidates”: 50, “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 LLMs
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
La météorite GPT annonce t’elle la disparition des dinosaures “moteur de recherche” ? David Pilato | @dadoonet
L’arrivée des LLMs (Large Language Models) tels que ChatGPT ont fait couler beaucoup d’encre en annonçant l’extinction à venir des moteurs de recherche.
Au cours de cette session, je vous démontrerai que c’est bien l’inverse qui se joue. La grosse conséquence de la “révolution” GPT est, selon moi, le retour en grâce de cette question fondamentale :
Comment je peux rechercher dans mes données ?
Et donc, le retour au devant de la scène des moteurs de recherche.
Je vous expliquerai au cours de cette session, comment fonctionne la recherche lexicale (mot à mot), comment la recherche vectorielle permet d’implémenter la recherche sémantique ou encore la recherche de contenu binaire, comment nous pouvons combiner tout cela et enfin comment ces fonctionnalités permettent de construire un prompt de qualité avec vos vraies données métier à destination de votre LLM favori…
Non. Le monde des moteurs de recherche n’est pas prêt de s’éteindre !
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