Class PGVectorStore

constructor

• new PGVectorStore(embeddings, config): PGVectorStore

• Parameters

  • embeddings: EmbeddingsInterface
  • config: PGVectorStoreArgs

  Returns PGVectorStore

FilterType

chunkSize

collectionMetadata

collectionName

contentColumnName

extensionSchemaName

idColumnName

metadataColumnName

pool

schemaName

tableName

vectorColumnName

Optionalclient

OptionalcollectionTableName

OptionaldistanceStrategy

Optionalfilter

computedCollectionTableName

• get computedCollectionTableName(): string

• Returns string

computedOperatorString

• get computedOperatorString(): string

• Returns string

computedTableName

• get computedTableName(): string

• Returns string

addDocuments

• addDocuments(documents, options?): Promise<void>

• Method to add documents to the vector store. It converts the documents into vectors, and adds them to the store.

  Parameters

  • documents: Document[]

    Array of Document instances.
  • Optionaloptions: {
        ids?: string[];
    }

    Optional arguments for adding documents

    • Optionalids?: string[]

  Returns Promise<void>

  Promise that resolves when the documents have been added.

addVectors

• addVectors(vectors, documents, options?): Promise<void>

• Method to add vectors to the vector store. It converts the vectors into rows and inserts them into the database.

  Parameters

  • vectors: number[][]

    Array of vectors.
  • documents: Document[]

    Array of Document instances.
  • Optionaloptions: {
        ids?: string[];
    }

    Optional arguments for adding documents

    • Optionalids?: string[]

  Returns Promise<void>

  Promise that resolves when the vectors have been added.

createHnswIndex

• createHnswIndex(config): Promise<void>

• Method to create the HNSW index on the vector column.

  Parameters

  • config: {
        dimensions: number;
        distanceFunction?: string;
        efConstruction?: number;
        m?: number;
        namespace?: string;
    }

    • dimensions: number

    • OptionaldistanceFunction?: string

    • OptionalefConstruction?: number

    • Optionalm?: number

    • Optionalnamespace?: string

  Returns Promise<void>

  Promise that resolves with the query response of creating the index.

delete

• delete(params): Promise<void>

• Method to delete documents from the vector store. It deletes the documents that match the provided ids or metadata filter. Matches ids exactly and metadata filter according to postgres jsonb containment. Ids and filter are mutually exclusive.

  Parameters

  • params: {
        filter?: Metadata;
        ids?: string[];
    }

    Object containing either an array of ids or a metadata filter object.

    • Optionalfilter?: Metadata

    • Optionalids?: string[]

  Returns Promise<void>

  Promise that resolves when the documents have been deleted.

  Throws

  Error if neither ids nor filter are provided, or if both are provided.

  Example: Delete by ids

  await vectorStore.delete({ ids: ["id1", "id2"] });
  Copy

  Example: Delete by filter

  await vectorStore.delete({ filter: { a: 1, b: 2 } });
  Copy

end

• end(): Promise<void>

• Closes all the clients in the pool and terminates the pool.

  Returns Promise<void>

  Promise that resolves when all clients are closed and the pool is terminated.

ensureCollectionTableInDatabase

• ensureCollectionTableInDatabase(): Promise<void>

• Method to ensure the existence of the collection table in the database. It creates the table if it does not already exist.

  Returns Promise<void>

  Promise that resolves when the collection table has been ensured.

ensureTableInDatabase

• ensureTableInDatabase(dimensions?): Promise<void>

• Method to ensure the existence of the table in the database. It creates the table if it does not already exist.

  Parameters

  • Optionaldimensions: number

    Number of dimensions in your vector data type. For example, use 1536 for OpenAI's text-embedding-3-small. If not set, indexes like HNSW might not be used during query time.

  Returns Promise<void>

  Promise that resolves when the table has been ensured.

getOrCreateCollection

• getOrCreateCollection(): Promise<string>

• Inserts a row for the collectionName provided at initialization if it does not exist and returns the collectionId.

  Returns Promise<string>

  The collectionId for the given collectionName.

similaritySearchVectorWithScore

• similaritySearchVectorWithScore(query, k, filter?): Promise<[Document, number][]>

• Method to perform a similarity search in the vector store. It returns the k most similar documents to the query vector, along with their similarity scores.

  Parameters

  • query: number[]

    Query vector.
  • k: number

    Number of most similar documents to return.
  • Optionalfilter: Metadata

    Optional filter to apply to the search.

  Returns Promise<[Document, number][]>

  Promise that resolves with an array of tuples, each containing a Document and its similarity score.

StaticfromDocuments

• fromDocuments(docs, embeddings, dbConfig): Promise<PGVectorStore>

• Static method to create a new PGVectorStore instance from an array of Document instances. It adds the documents to the store.

  Parameters

  • docs: Document[]

    Array of Document instances.
  • embeddings: EmbeddingsInterface

    Embeddings instance.
  • dbConfig: PGVectorStoreArgs & {
        dimensions?: number;
    }

    PGVectorStoreArgs instance.

  Returns Promise<PGVectorStore>

  Promise that resolves with a new instance of PGVectorStore.

StaticfromTexts

• fromTexts(texts, metadatas, embeddings, dbConfig): Promise<PGVectorStore>

• Static method to create a new PGVectorStore instance from an array of texts and their metadata. It converts the texts into Document instances and adds them to the store.

  Parameters

  • texts: string[]

    Array of texts.
  • metadatas: object | object[]

    Array of metadata objects or a single metadata object.
  • embeddings: EmbeddingsInterface

    Embeddings instance.
  • dbConfig: PGVectorStoreArgs & {
        dimensions?: number;
    }

    PGVectorStoreArgs instance.

  Returns Promise<PGVectorStore>

  Promise that resolves with a new instance of PGVectorStore.

Staticinitialize

• initialize(embeddings, config): Promise<PGVectorStore>

• Static method to create a new PGVectorStore instance from a connection. It creates a table if one does not exist, and calls connect to return a new instance of PGVectorStore.

  Parameters

  • embeddings: EmbeddingsInterface

    Embeddings instance.
  • config: PGVectorStoreArgs & {
        dimensions?: number;
    }

  Returns Promise<PGVectorStore>

  A new instance of PGVectorStore.