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ReferenceRAGChromaVector

Chroma Vector Store

The ChromaVector class provides vector search using ChromaDB , an open-source embedding database. It offers efficient vector search with metadata filtering and hybrid search capabilities.

Constructor Options

path:

string
URL path to ChromaDB instance

auth?:

object
Authentication configuration

auth

provider:

string
Authentication provider

credentials:

string
Authentication credentials

Methods

createIndex()

indexName:

string
Name of the index to create

dimension:

number
Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'
= cosine
Distance metric for similarity search

upsert()

indexName:

string
Name of the index to upsert into

vectors:

number[][]
Array of embedding vectors

metadata?:

Record<string, any>[]
Metadata for each vector

ids?:

string[]
Optional vector IDs (auto-generated if not provided)

documents?:

string[]
Chroma-specific: Original text documents associated with the vectors

query()

indexName:

string
Name of the index to query

queryVector:

number[]
Query vector to find similar vectors

topK?:

number
= 10
Number of results to return

filter?:

Record<string, any>
Metadata filters for the query

includeVector?:

boolean
= false
Whether to include vectors in the results

documentFilter?:

Record<string, any>
Chroma-specific: Filter to apply on the document content

listIndexes()

Returns an array of index names as strings.

describeIndex()

indexName:

string
Name of the index to describe

Returns:

interface IndexStats {
  dimension: number;
  count: number;
  metric: "cosine" | "euclidean" | "dotproduct";
}

deleteIndex()

indexName:

string
Name of the index to delete

updateVector()

indexName:

string
Name of the index containing the vector to update

id:

string
ID of the vector to update

update:

object
Update parameters

The update object can contain:

vector?:

number[]
New vector to replace the existing one

metadata?:

Record<string, any>
New metadata to replace the existing metadata

deleteVector()

indexName:

string
Name of the index containing the vector to delete

id:

string
ID of the vector to delete

Response Types

Query results are returned in this format:

interface QueryResult {
  id: string;
  score: number;
  metadata: Record<string, any>;
  document?: string; // Chroma-specific: Original document if it was stored
  vector?: number[]; // Only included if includeVector is true
}

Error Handling

The store throws typed errors that can be caught:

try {
  await store.query({
    indexName: "index_name",
    queryVector: queryVector,
  });
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}
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