Retrievers examples
editRetrievers examples
editLearn how to combine different retrievers in these hands-on examples. To demonstrate the full functionality of retrievers, these examples require access to a semantic reranking model set up using the Elastic inference APIs.
Add example data
editTo begin with, we’ll set up the necessary services and have them in place for later use.
// Setup rerank task stored as `my-rerank-model`
PUT _inference/rerank/my-rerank-model
{
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}
Now that we have our reranking service in place, lets create the retrievers_example index, and add some documents to it.
PUT retrievers_example
{
"mappings": {
"properties": {
"vector": {
"type": "dense_vector",
"dims": 3,
"similarity": "l2_norm",
"index": true
},
"text": {
"type": "text"
},
"year": {
"type": "integer"
},
"topic": {
"type": "keyword"
}
}
}
}
POST /retrievers_example/_doc/1
{
"vector": [0.23, 0.67, 0.89],
"text": "Large language models are revolutionizing information retrieval by boosting search precision, deepening contextual understanding, and reshaping user experiences in data-rich environments.",
"year": 2024,
"topic": ["llm", "ai", "information_retrieval"]
}
POST /retrievers_example/_doc/2
{
"vector": [0.12, 0.56, 0.78],
"text": "Artificial intelligence is transforming medicine, from advancing diagnostics and tailoring treatment plans to empowering predictive patient care for improved health outcomes.",
"year": 2023,
"topic": ["ai", "medicine"]
}
POST /retrievers_example/_doc/3
{
"vector": [0.45, 0.32, 0.91],
"text": "AI is redefining security by enabling advanced threat detection, proactive risk analysis, and dynamic defenses against increasingly sophisticated cyber threats.",
"year": 2024,
"topic": ["ai", "security"]
}
POST /retrievers_example/_doc/4
{
"vector": [0.34, 0.21, 0.98],
"text": "Elastic introduces Elastic AI Assistant, the open, generative AI sidekick powered by ESRE to democratize cybersecurity and enable users of every skill level.",
"year": 2023,
"topic": ["ai", "elastic", "assistant"]
}
POST /retrievers_example/_doc/5
{
"vector": [0.11, 0.65, 0.47],
"text": "Learn how to spin up a deployment of our hosted Elasticsearch Service and use Elastic Observability to gain deeper insight into the behavior of your applications and systems.",
"year": 2024,
"topic": ["documentation", "observability", "elastic"]
}
Now that we also have our documents in place, let’s try to run some queries using retrievers.
Example: Combining query and kNN with RRF
editFirst, let’s examine how to combine two different types of queries: a kNN query and a
query_string query. While these queries may produce scores in different ranges, we can use
Reciprocal Rank Fusion (rrf) to combine the results and generate a merged final result
list.
To implement this in the retriever framework, we start with the top-level element: our rrf
retriever. This retriever operates on top of two other retrievers: a knn retriever and a
standard retriever. Our query structure would look like this:
GET /retrievers_example/_search
{
"retriever":{
"rrf": {
"retrievers":[
{
"standard":{
"query":{
"query_string":{
"query": "(information retrieval) OR (artificial intelligence)",
"default_field": "text"
}
}
}
},
{
"knn": {
"field": "vector",
"query_vector": [
0.23,
0.67,
0.89
],
"k": 3,
"num_candidates": 5
}
}
],
"rank_window_size": 10,
"rank_constant": 1
}
},
"_source": ["text", "topic"]
}
Example: Grouping results by year with collapse
editIn our result set, we have many documents with the same year value. We can clean this
up using the collapse parameter with our retriever. This enables grouping results by
any field and returns only the highest-scoring document from each group. In this example
we’ll collapse our results based on the year field.
GET /retrievers_example/_search
{
"retriever":{
"rrf": {
"retrievers":[
{
"standard":{
"query":{
"query_string":{
"query": "(information retrieval) OR (artificial intelligence)",
"default_field": "text"
}
}
}
},
{
"knn": {
"field": "vector",
"query_vector": [
0.23,
0.67,
0.89
],
"k": 3,
"num_candidates": 5
}
}
],
"rank_window_size": 10,
"rank_constant": 1
}
},
"collapse": {
"field": "year",
"inner_hits": {
"name": "topic related documents",
"_source": ["text", "year"]
}
},
"_source": ["text", "topic"]
}
Example: Rerank results of an RRF retriever
editPreviously, we used a text_similarity_reranker retriever within an rrf retriever.
Because retrievers support full composability, we can also rerank the results of an
rrf retriever. Let’s apply this to our first example.
GET retrievers_example/_search
{
"retriever": {
"text_similarity_reranker": {
"retriever": {
"rrf": {
"retrievers": [
{
"standard":{
"query":{
"query_string":{
"query": "(information retrieval) OR (artificial intelligence)",
"default_field": "text"
}
}
}
},
{
"knn": {
"field": "vector",
"query_vector": [
0.23,
0.67,
0.89
],
"k": 3,
"num_candidates": 5
}
}
],
"rank_window_size": 10,
"rank_constant": 1
}
},
"field": "text",
"inference_id": "my-rerank-model",
"inference_text": "What are the state of the art applications of AI in information retrieval?"
}
},
"_source": ["text", "topic"]
}
Example: RRF with semantic reranker
editFor this example, we’ll replace our semantic query with the my-rerank-model
reranker we previously configured. Since this is a reranker, it needs an initial pool of
documents to work with. In this case, we’ll filter for documents about ai topics.
GET /retrievers_example/_search
{
"retriever": {
"rrf": {
"retrievers": [
{
"knn": {
"field": "vector",
"query_vector": [
0.23,
0.67,
0.89
],
"k": 3,
"num_candidates": 5
}
},
{
"text_similarity_reranker": {
"retriever": {
"standard": {
"query": {
"term": {
"topic": "ai"
}
}
}
},
"field": "text",
"inference_id": "my-rerank-model",
"inference_text": "Can I use generative AI to identify user intent and improve search relevance?"
}
}
],
"rank_window_size": 10,
"rank_constant": 1
}
},
"_source": [
"text",
"topic"
]
}
Example: Chaining multiple semantic rerankers
editFull composability means we can chain together multiple retrievers of the same type. For instance, imagine we have a computationally expensive reranker that’s specialized for AI content. We can rerank the results of a text_similarity_reranker using another text_similarity_reranker retriever. Each reranker can operate on different fields and/or use different inference services.
GET retrievers_example/_search
{
"retriever": {
"text_similarity_reranker": {
"retriever": {
"text_similarity_reranker": {
"retriever": {
"knn": {
"field": "vector",
"query_vector": [
0.23,
0.67,
0.89
],
"k": 3,
"num_candidates": 5
}
},
"rank_window_size": 100,
"field": "text",
"inference_id": "my-rerank-model",
"inference_text": "What are the state of the art applications of AI in information retrieval?"
}
},
"rank_window_size": 10,
"field": "text",
"inference_id": "my-other-more-expensive-rerank-model",
"inference_text": "Applications of Large Language Models in technology and their impact on user satisfaction"
}
},
"_source": [
"text",
"topic"
]
}
Note that our example applies two reranking steps. First, we rerank the top 100
documents from the knn search using the my-rerank-model reranker. Then we
pick the top 10 results and rerank them using the more fine-grained
my-other-more-expensive-rerank-model.
Example: Combine RRF with aggregations
editRetrievers support both composability and most of the standard _search functionality. For instance,
we can compute aggregations with the rrf retriever. When using a compound retriever,
the aggregations are computed based on its nested retrievers. In the following example,
the terms aggregation for the topic field will include all results, not just the top rank_window_size,
from the 2 nested retrievers, i.e. all documents whose year field is greater than 2023, and whose topic field
matches the term elastic.
GET retrievers_example/_search
{
"retriever": {
"rrf": {
"retrievers": [
{
"standard": {
"query": {
"range": {
"year": {
"gt": 2023
}
}
}
}
},
{
"standard": {
"query": {
"term": {
"topic": "elastic"
}
}
}
}
],
"rank_window_size": 10,
"rank_constant": 1
}
},
"_source": [
"text",
"topic"
],
"aggs": {
"topics": {
"terms": {
"field": "topic"
}
}
}
}