Elasticsearch Vector Store

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Node Details

• Name: Elasticsearch_VectorStores
• Type: Elasticsearch
• Version: 2.0
• Category: Vector Stores

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Base Classes

• Elasticsearch
• VectorStoreRetriever
• BaseRetriever

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Credentials

• elasticsearchApi
• elasticSearchUserPassword

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Input Parameters

1. Document (optional, list)
   • Type: Document
   • Description: List of documents to be added to the vector store
2. Embeddings
   • Type: Embeddings
   • Description: Embedding model to use for vectorizing documents
3. Record Manager (optional)
   • Type: RecordManager
   • Description: Manages records to prevent duplication
4. Index Name
   • Type: string
   • Description: Name of the Elasticsearch index to use
5. Top K (optional)
   • Type: number
   • Default: 4
   • Description: Number of top results to fetch
6. Similarity (optional)
   • Type: options
   • Default: l2_norm
   • Options: l2_norm, dot_product, cosine
   • Description: Similarity measure used in Elasticsearch

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Outputs

1. Elasticsearch Retriever
   • Type: retriever
   • Base Classes: [Elasticsearch, VectorStoreRetriever, BaseRetriever]
2. Elasticsearch Vector Store
   • Type: vectorStore
   • Base Classes: [Elasticsearch, ElasticVectorSearch]

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Functionality

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Upsert Method

• Adds or updates documents in the Elasticsearch index
• Handles document flattening and metadata cleaning
• Supports record management to prevent duplication

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Delete Method

• Deletes documents from the Elasticsearch index
• Supports deletion by IDs or using a record manager

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Init Method

• Initializes the Elasticsearch vector store
• Creates a retriever or returns the vector store based on the output type

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Usage

1. Storing document embeddings in Elasticsearch
2. Performing similarity searches on stored embeddings
3. Retrieving relevant documents based on vector similarity

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Note