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Vector store retrievers enable similarity-based document retrieval using vector embeddings. The VectorIndexRetriever searches for the most semantically similar nodes to your query.

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VectorStoreRetriever

The VectorIndexRetriever is created from a VectorStoreIndex and performs similarity search using embeddings: 
import { VectorStoreIndex, Document } from "llamaindex";

const documents = [
  new Document({ text: "LlamaIndex is a data framework for LLMs" }),
  new Document({ text: "Vector stores enable semantic search" }),
];

const index = await VectorStoreIndex.fromDocuments(documents);
const retriever = index.asRetriever();

const results = await retriever.retrieve({
  query: "What is LlamaIndex?"
});

results.forEach((result) => {
  console.log(`Score: ${result.score}`);
  console.log(`Text: ${result.node.getText()}`);
});
Vector store retrievers use cosine similarity (or other distance metrics) to find the most relevant documents: 
import { VectorStoreIndex } from "llamaindex";
import { OpenAIEmbedding } from "@llamaindex/openai";
import { Settings } from "llamaindex";

// Configure embedding model
Settings.embedModel = new OpenAIEmbedding();

const index = await VectorStoreIndex.fromDocuments(documents);

// Retrieve top-k most similar nodes
const retriever = index.asRetriever({
  similarityTopK: 5, // Return top 5 results
});

const nodes = await retriever.retrieve({ 
  query: "machine learning concepts"
});

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Top-k Retrieval

Control the number of results returned with the similarityTopK parameter: 
// Retrieve top 3 results
const retriever = index.asRetriever({
  similarityTopK: 3,
});

// Or configure at query engine level
const queryEngine = index.asQueryEngine({
  similarityTopK: 10,
});
Default Value: DEFAULT_SIMILARITY_TOP_K = 2

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Filters and Metadata

Filter results based on metadata to narrow your search: 
import { Document, MetadataFilters } from "llamaindex";

// Create documents with metadata
const docs = [
  new Document({ 
    text: "The dog is brown", 
    metadata: { category: "animals", color: "brown" } 
  }),
  new Document({ 
    text: "The cat is yellow", 
    metadata: { category: "animals", color: "yellow" } 
  }),
  new Document({ 
    text: "The car is red", 
    metadata: { category: "vehicles", color: "red" } 
  }),
];

const index = await VectorStoreIndex.fromDocuments(docs);

// Filter by metadata
const filters: MetadataFilters = {
  filters: [
    { key: "category", value: "animals", operator: "==" },
  ],
};

const retriever = index.asRetriever({ 
  similarityTopK: 3, 
  filters 
});

const results = await retriever.retrieve({ 
  query: "What color is it?" 
});

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Filter Operators

Supported filter operators:
  • == - Equal to (default)
  • != - Not equal to
  • >, < - Greater/less than (numbers)
  • >=, <= - Greater/less than or equal (numbers)
  • in - Value in array
  • nin - Value not in array
  • any - Contains any (array fields)
  • all - Contains all (array fields)
  • text_match - Full text match
  • contains - Array contains value
  • is_empty - Field is empty or doesn’t exist

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Multiple Filters

Combine filters with AND or OR conditions: 
const filters: MetadataFilters = {
  filters: [
    { key: "category", value: "animals", operator: "==" },
    { key: "color", value: ["brown", "yellow"], operator: "in" },
  ],
  condition: "and", // Default is "and"
};

// OR condition
const orFilters: MetadataFilters = {
  filters: [
    { key: "category", value: "animals", operator: "==" },
    { key: "category", value: "vehicles", operator: "==" },
  ],
  condition: "or",
};

​
Query Modes

Different query modes for specialized retrieval: 
import { VectorStoreQueryMode } from "@llamaindex/core/vector-store";

const retriever = index.asRetriever({
  similarityTopK: 5,
  mode: VectorStoreQueryMode.DEFAULT, // Default similarity search
});

// Hybrid search (if supported by vector store)
const hybridRetriever = index.asRetriever({
  similarityTopK: 5,
  mode: VectorStoreQueryMode.HYBRID,
});

// MMR (Maximum Marginal Relevance) for diversity
const mmrRetriever = index.asRetriever({
  similarityTopK: 5,
  mode: VectorStoreQueryMode.MMR,
});

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Complete Example

import { Document, VectorStoreIndex, MetadataFilters } from "llamaindex";
import { OpenAIEmbedding } from "@llamaindex/openai";
import { Settings } from "llamaindex";

Settings.embedModel = new OpenAIEmbedding();

async function main() {
  // Create documents with metadata
  const documents = [
    new Document({ 
      text: "LlamaIndex provides a simple interface for LLM applications",
      metadata: { topic: "overview", difficulty: "beginner" }
    }),
    new Document({ 
      text: "Vector stores enable efficient similarity search over embeddings",
      metadata: { topic: "vector-stores", difficulty: "intermediate" }
    }),
    new Document({ 
      text: "Advanced retrieval techniques include hybrid search and reranking",
      metadata: { topic: "retrieval", difficulty: "advanced" }
    }),
  ];

  // Build index
  const index = await VectorStoreIndex.fromDocuments(documents);

  // Create retriever with filters
  const filters: MetadataFilters = {
    filters: [
      { key: "difficulty", value: ["beginner", "intermediate"], operator: "in" },
    ],
  };

  const retriever = index.asRetriever({
    similarityTopK: 2,
    filters,
  });

  // Retrieve relevant nodes
  const results = await retriever.retrieve({ 
    query: "How do I build LLM applications?" 
  });

  // Display results
  results.forEach((result, idx) => {
    console.log(`\nResult ${idx + 1}:`);
    console.log(`Score: ${result.score?.toFixed(4)}`);
    console.log(`Text: ${result.node.getText()}`);
    console.log(`Metadata:`, result.node.metadata);
  });
}

main().catch(console.error);

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Custom Parameters

Pass provider-specific parameters through customParams: 
const retriever = index.asRetriever({
  similarityTopK: 5,
  customParams: {
    // Vector store specific parameters
    searchParams: { ef: 100 }, // Example for HNSW
  },
});

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