CtrlK
BlogDocsLog inGet started
Tessl Logo

tessl/maven-com-embabel-agent--embabel-agent-rag-core

RAG (Retrieval-Augmented Generation) framework for the Embabel Agent platform providing content ingestion, chunking, hierarchical navigation, and semantic search capabilities

—

Pending
Overview
Eval results
Files

vector-search.mddocs/quickstart/

Quickstart: Vector Search

This guide covers semantic similarity search using vector embeddings, including filtering, result expansion, and performance optimization.

Overview

Vector search enables semantic similarity matching by:

  • Converting text to embeddings (dense vector representations)
  • Finding nearest neighbors in vector space
  • Ranking results by cosine similarity
  • Filtering by metadata and entity properties

Basic Vector Search

Perform a simple semantic search to find relevant chunks.

import com.embabel.agent.rag.service.VectorSearch
import com.embabel.agent.rag.model.*

val searchOps: VectorSearch = // implementation

// Simple vector search
val results = searchOps.vectorSearch(
    request = TextSimilaritySearchRequest(
        query = "machine learning algorithms",
        topK = 10,
        similarityThreshold = 0.7
    ),
    clazz = Chunk::class.java
)

// Process results
results.forEach { result ->
    println("Score: ${"%.3f".format(result.score)}")
    println("Content: ${result.content.text}")
    println("Metadata: ${result.content.metadata}")
    println()
}

Understanding Search Parameters

val request = TextSimilaritySearchRequest(
    query = "authentication methods",
    topK = 20,                    // Maximum results to return
    similarityThreshold = 0.75    // Minimum similarity (0.0-1.0)
)

// Higher threshold = stricter matching
// Lower threshold = more lenient matching
// topK limits results after threshold filtering

Filtered Vector Search

Apply metadata and entity filters to narrow search scope.

import com.embabel.agent.rag.service.FilteringVectorSearch
import com.embabel.agent.rag.filter.*

val searchOps: FilteringVectorSearch = // implementation

// Search with metadata filter
val results = searchOps.vectorSearchWithFilter(
    request = TextSimilaritySearchRequest(
        query = "authentication setup",
        topK = 10
    ),
    clazz = Chunk::class.java,
    metadataFilter = PropertyFilter.eq("category", "security")
        .and(PropertyFilter.gte("version", 2.0)),
    entityFilter = null
)

Property Filters

import com.embabel.agent.rag.filter.PropertyFilter

// Equality
val eqFilter = PropertyFilter.eq("status", "active")

// Comparison
val gtFilter = PropertyFilter.gt("priority", 5)
val gteFilter = PropertyFilter.gte("version", 2.0)
val ltFilter = PropertyFilter.lt("age", 30)
val lteFilter = PropertyFilter.lte("score", 100)

// Not equal
val neFilter = PropertyFilter.ne("status", "archived")

// Contains (substring)
val containsFilter = PropertyFilter.contains("title", "auth")

// In list
val inFilter = PropertyFilter.`in`("team", "platform", "infrastructure", "security")

// Not in list
val ninFilter = PropertyFilter.nin("status", "deleted", "archived")

Combining Filters

// AND operator
val andFilter = PropertyFilter.eq("category", "docs")
    .and(PropertyFilter.gte("version", 3.0))
    .and(PropertyFilter.ne("status", "draft"))

// OR operator
val orFilter = PropertyFilter.eq("type", "tutorial")
    .or(PropertyFilter.eq("type", "guide"))

// NOT operator
val notFilter = !PropertyFilter.eq("archived", true)

// Complex boolean logic
val complexFilter = PropertyFilter.and(
    PropertyFilter.or(
        PropertyFilter.eq("type", "documentation"),
        PropertyFilter.eq("type", "tutorial")
    ),
    PropertyFilter.gte("lastUpdated", System.currentTimeMillis() - 86400000),
    !PropertyFilter.eq("status", "archived")
)

Entity Filters

import com.embabel.agent.rag.filter.EntityFilter

// Filter by entity labels
val entityFilter = EntityFilter.hasAnyLabel("Person", "Employee")

// Combined with metadata filter
val results = searchOps.vectorSearchWithFilter(
    request = TextSimilaritySearchRequest(
        query = "software engineers",
        topK = 10
    ),
    clazz = NamedEntityData::class.java,
    metadataFilter = PropertyFilter.eq("department", "engineering"),
    entityFilter = EntityFilter.hasAnyLabel("Employee", "Contractor")
)

Search Multiple Types

Search across different content types simultaneously.

import com.embabel.agent.rag.model.*

// Define search types
val searchTypes = listOf(
    Chunk::class.java,
    Fact::class.java,
    NamedEntity::class.java
)

// Search each type
val allResults = searchTypes.flatMap { type ->
    searchOps.vectorSearch(
        request = TextSimilaritySearchRequest(
            query = "machine learning",
            topK = 5
        ),
        clazz = type
    )
}.sortedByDescending { it.score }

// Get top results across all types
val topResults = allResults.take(10)

Type-Specific Processing

results.forEach { result ->
    when (val content = result.content) {
        is Chunk -> {
            println("Chunk: ${content.text.take(100)}...")
            println("  Section: ${content.metadata["container_section_title"]}")
        }
        is Fact -> {
            println("Fact: ${content.assertion}")
            println("  Authority: ${content.authority}")
        }
        is NamedEntity -> {
            println("Entity: ${content.name}")
            println("  Description: ${content.description}")
        }
    }
}

Result Expansion

Get additional context by expanding search results.

import com.embabel.agent.rag.service.ResultExpander

val expander: ResultExpander = // implementation
val searchResults = searchOps.vectorSearch(...)

// Get first result
val topResult = searchResults.first()

// Expand to include surrounding chunks (sequence)
val sequenceContext = expander.expandResult(
    id = topResult.content.id,
    method = ResultExpander.Method.SEQUENCE,
    elementsToAdd = 2  // 2 chunks before and 2 after
)

println("Expanded to ${sequenceContext.size} chunks")
sequenceContext.forEach { element ->
    println("- ${element.id}")
}

Zoom Out to Parent Section

// Zoom out to containing section
val parentContent = expander.expandResult(
    id = topResult.content.id,
    method = ResultExpander.Method.ZOOM_OUT,
    elementsToAdd = 1
)

println("Parent section contains ${parentContent.size} elements")

Combining Search and Expansion

fun searchWithContext(
    query: String,
    searchOps: VectorSearch,
    expander: ResultExpander,
    contextChunks: Int = 1
): List<List<ContentElement>> {

    // Perform search
    val results = searchOps.vectorSearch(
        request = TextSimilaritySearchRequest(
            query = query,
            topK = 5
        ),
        clazz = Chunk::class.java
    )

    // Expand each result
    return results.map { result ->
        expander.expandResult(
            id = result.content.id,
            method = ResultExpander.Method.SEQUENCE,
            elementsToAdd = contextChunks
        )
    }
}

Finding by ID

Look up specific items when you know their identifier.

import com.embabel.agent.rag.service.FinderOperations

val finder: FinderOperations = // implementation

// Find by ID and class
val chunk = finder.findById("chunk-123", Chunk::class.java)
if (chunk != null) {
    println("Found chunk: ${chunk.text}")
}

// Find by ID and type string
val entity = finder.findById<NamedEntity>("person-456", "Person")
if (entity != null) {
    println("Found entity: ${entity.name}")
}

// Check type support
if (finder.supportsType("Chunk")) {
    println("Chunk type is supported")
}

Clustering Similar Items

Group similar items together using cluster analysis.

import com.embabel.agent.rag.service.*

val clusterFinder: ClusterFinder = // implementation

// Find clusters with custom parameters
val clusters = clusterFinder.findClusters(
    ClusterRetrievalRequest<NamedEntityData>()
        .withSimilarityThreshold(0.8)  // Higher = tighter clusters
        .withTopK(15)                   // Max similar items per cluster
)

// Process clusters
clusters.forEach { cluster ->
    println("Cluster anchor: ${cluster.anchor.name}")
    println("Similar items (${cluster.similar.size}):")

    cluster.similar.forEach { similar ->
        println("  - ${similar.content.name} (score: ${similar.score})")
    }
    println()
}

Formatting Results

Customize how search results are displayed.

import com.embabel.agent.rag.service.*

// Use default formatter
val formatter = SimpleRetrievableResultsFormatter
val results = searchOps.vectorSearch(...)

val formatted = formatter.formatResults(
    SimilarityResults.fromList(results)
)
println(formatted)

Custom Formatter

// Create custom formatter
val markdownFormatter = RetrievableResultsFormatter { results ->
    buildString {
        appendLine("# Search Results")
        appendLine()
        appendLine("Found ${results.results.size} matches")
        appendLine()

        results.results.forEachIndexed { index, result ->
            appendLine("## ${index + 1}. Score: ${"%.3f".format(result.score)}")

            when (val content = result.content) {
                is Chunk -> {
                    appendLine("**Type:** Chunk")
                    appendLine("**Section:** ${content.metadata["container_section_title"]}")
                    appendLine()
                    appendLine(content.text)
                }
                is NamedEntity -> {
                    appendLine("**Type:** Entity")
                    appendLine("**Name:** ${content.name}")
                    appendLine()
                    appendLine(content.description)
                }
            }
            appendLine()
            appendLine("---")
            appendLine()
        }
    }
}

val formatted = markdownFormatter.formatResults(
    SimilarityResults.fromList(results)
)

Performance Optimization

Adjust Top-K for Filtering

When using filters, you may need to fetch more results before filtering.

import com.embabel.agent.rag.tools.TopKInflationStrategy

// Multiplier strategy: fetch 3x more results before filtering
val strategy = TopKInflationStrategy.multiplier(
    multiplier = 3,
    maxTopK = 1000
)

// Offset strategy: fetch fixed extra amount
val offsetStrategy = TopKInflationStrategy.offset(
    offset = 50,
    maxTopK = 1000
)

// Expected pass rate strategy
val passRateStrategy = TopKInflationStrategy.expectedPassRate(
    percentage = 0.3,  // Expect 30% to pass filter
    maxTopK = 1000
)

Batch Processing

fun batchSearch(
    queries: List<String>,
    searchOps: VectorSearch
): Map<String, List<SimilarityResult<Chunk>>> {

    return queries.associateWith { query ->
        searchOps.vectorSearch(
            request = TextSimilaritySearchRequest(
                query = query,
                topK = 5
            ),
            clazz = Chunk::class.java
        )
    }
}

// Use
val queries = listOf(
    "authentication methods",
    "error handling",
    "database configuration"
)

val batchResults = batchSearch(queries, searchOps)
batchResults.forEach { (query, results) ->
    println("Query: $query → ${results.size} results")
}

Caching Search Results

import java.util.concurrent.ConcurrentHashMap

class CachedVectorSearch(
    private val delegate: VectorSearch
) : VectorSearch by delegate {

    private val cache = ConcurrentHashMap<String, List<SimilarityResult<*>>>()

    override fun <T : Retrievable> vectorSearch(
        request: TextSimilaritySearchRequest,
        clazz: Class<T>
    ): List<SimilarityResult<T>> {

        val cacheKey = "${request.query}:${clazz.simpleName}:${request.topK}"

        @Suppress("UNCHECKED_CAST")
        return cache.getOrPut(cacheKey) {
            delegate.vectorSearch(request, clazz)
        } as List<SimilarityResult<T>>
    }

    fun clearCache() {
        cache.clear()
    }
}

Common Variations

Search with Threshold Adjustment

fun adaptiveSearch(
    query: String,
    searchOps: VectorSearch,
    minResults: Int = 5
): List<SimilarityResult<Chunk>> {

    var threshold = 0.8
    var results = emptyList<SimilarityResult<Chunk>>()

    // Lower threshold until we get enough results
    while (results.size < minResults && threshold > 0.5) {
        results = searchOps.vectorSearch(
            request = TextSimilaritySearchRequest(
                query = query,
                topK = 20,
                similarityThreshold = threshold
            ),
            clazz = Chunk::class.java
        )

        if (results.size < minResults) {
            threshold -= 0.1
        }
    }

    return results
}

Deduplicate Results

fun deduplicateResults(
    results: List<SimilarityResult<Chunk>>,
    similarityThreshold: Double = 0.95
): List<SimilarityResult<Chunk>> {

    val deduplicated = mutableListOf<SimilarityResult<Chunk>>()
    val seen = mutableSetOf<String>()

    for (result in results) {
        // Use normalized text as key
        val normalized = result.content.text
            .lowercase()
            .replace(Regex("\\s+"), " ")
            .trim()

        if (!seen.contains(normalized)) {
            deduplicated.add(result)
            seen.add(normalized)
        }
    }

    return deduplicated
}

Multi-Query Search

fun multiQuerySearch(
    queries: List<String>,
    searchOps: VectorSearch,
    topK: Int = 5
): List<SimilarityResult<Chunk>> {

    // Search with each query
    val allResults = queries.flatMap { query ->
        searchOps.vectorSearch(
            request = TextSimilaritySearchRequest(
                query = query,
                topK = topK
            ),
            clazz = Chunk::class.java
        )
    }

    // Deduplicate and re-rank
    return allResults
        .distinctBy { it.content.id }
        .sortedByDescending { it.score }
        .take(topK)
}

// Use
val results = multiQuerySearch(
    queries = listOf(
        "how to authenticate",
        "authentication methods",
        "login and authorization"
    ),
    searchOps = searchOps,
    topK = 10
)

Edge Cases

No Results Found

val results = searchOps.vectorSearch(
    request = TextSimilaritySearchRequest(
        query = "very specific query",
        topK = 10
    ),
    clazz = Chunk::class.java
)

if (results.isEmpty()) {
    println("No results found")

    // Try with lower threshold
    val relaxedResults = searchOps.vectorSearch(
        request = TextSimilaritySearchRequest(
            query = "very specific query",
            topK = 10,
            similarityThreshold = 0.5
        ),
        clazz = Chunk::class.java
    )

    if (relaxedResults.isEmpty()) {
        println("Still no results with relaxed threshold")
        // Try text search as fallback
    }
}

Empty Query

fun safeVectorSearch(
    query: String,
    searchOps: VectorSearch
): List<SimilarityResult<Chunk>> {

    if (query.isBlank()) {
        println("Warning: Empty query provided")
        return emptyList()
    }

    return searchOps.vectorSearch(
        request = TextSimilaritySearchRequest(
            query = query.trim(),
            topK = 10
        ),
        clazz = Chunk::class.java
    )
}

Low-Quality Results

// Filter out low-quality results
fun filterQualityResults(
    results: List<SimilarityResult<Chunk>>,
    minScore: Double = 0.7,
    minTextLength: Int = 50
): List<SimilarityResult<Chunk>> {

    return results.filter { result ->
        result.score >= minScore &&
        result.content.text.length >= minTextLength
    }
}

Type Not Supported

fun <T : Retrievable> safeSearch(
    query: String,
    searchOps: VectorSearch,
    clazz: Class<T>
): List<SimilarityResult<T>> {

    // Check if type is supported (if searchOps implements TypeRetrievalOperations)
    if (searchOps is TypeRetrievalOperations) {
        val typeName = clazz.simpleName
        if (!searchOps.supportsType(typeName)) {
            println("Warning: Type $typeName not supported")
            return emptyList()
        }
    }

    return searchOps.vectorSearch(
        request = TextSimilaritySearchRequest(
            query = query,
            topK = 10
        ),
        clazz = clazz
    )
}

Next Steps

  • Entity Management - Search and manage named entities
  • LLM Integration - Expose search to language models
  • Basic RAG Pipeline - Complete pipeline setup

Install with Tessl CLI

npx tessl i tessl/maven-com-embabel-agent--embabel-agent-rag-core@0.3.0

docs

quickstart

basic-rag-pipeline.md

entity-management.md

llm-integration.md

vector-search.md

index.md

README.md

tile.json