ElasticsearchConfig

org.llm4s.vectorstore.elasticsearch.ElasticsearchConfig

See theElasticsearchConfig companion object

What ElasticsearchVectorStore needs to reach one index of an Elasticsearch cluster.

Load it from configuration with org.llm4s.config.ElasticsearchVectorStoreConfigLoader, which reads llm4s.vectorstore.elasticsearch, or build one in code:

val config = ElasticsearchConfig("https://es.example.com:9200")
 .withIndex("docs")
 .withAuth(ElasticsearchAuth.ApiKey(encodedKey))

The store checks it with ElasticsearchConfig#validate when it is built.

Value parameters

auth: the credentials, ElasticsearchAuth.NoAuth by default
baseUrl: the cluster's root URL, optionally with a path prefix (a proxy), without a query. It must be https when credentials are sent; plain http is accepted for a loopback host (localhost, 127.0.0.0/8, ::1) or when there are no credentials to protect.
dimensions: the vector size the index is created with; when unset, the size of the first record upserted. When set, a record of another size is refused before it is sent.
index: the index holding the vectors; Elasticsearch's index-name rules apply (lower case, no \ / * ? " < > | , # or space, not starting with -, _ or +)
numCandidates: kNN num_candidates, the candidates each shard considers; when unset, max(100, 10 * topK) capped at 10,000
refreshOnWrite: whether writes wait for a refresh (refresh=wait_for), so that a search straight after an upsert or delete sees it. Turn it off for bulk ingestion.
similarity: the dense_vector similarity the index is created with
timeout: how long each HTTP request may take

Companion: object
Graph
Supertypes: trait Serializable

trait Product

trait Equals

class Object

trait Matchable

class Any
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Checks the config; the first problem is reported, naming the setting.

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