Meilisearch

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Node Details

• Name: meilisearch

• Type: Meilisearch

• Version: 1.0

• Category: Vector Stores

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Base Classes

• BaseRetriever

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Inputs

1. Document (optional, list)

   • Type: Document

   • Description: List of documents to be embedded and stored

2. Embeddings

   • Type: Embeddings

   • Description: Embedding model to use for vectorizing documents

3. Host

   • Type: string

   • Description: URL for the desired Meilisearch instance (must not end with a ’/’)

4. Index Uid

   • Type: string

   • Description: UID for the index to answer from

5. Delete Index if exists (optional)

   • Type: boolean

   • Description: Whether to delete the existing index before upserting

6. Top K (optional, additional param)

   • Type: number

   • Description: Number of top searches to return as context (default is 4)

7. Semantic Ratio (optional, additional param)

   • Type: number

   • Description: Percentage of semantic reasoning in Meilisearch hybrid search (default is 0.75)

8. Search Filter (optional, additional param)

   • Type: string

   • Description: Search filter to apply on searchable attributes

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Outputs

• Meilisearch Retriever

  • Name: retriever

  • Base Classes: [BaseRetriever]

  • Description: Retriever for getting answers from the Meilisearch index

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Credentials

• Credential Name: meilisearchApi

• Parameters:

  • meilisearchAdminApiKey

  • meilisearchSearchApiKey

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Functionality

1. Upsert:

   • Embeds documents using the provided embedding model

   • Creates or updates a Meilisearch index with the embedded documents

   • Handles index deletion if requested

   • Sets up necessary index settings for vector search

2. Initialization:

   • Sets up the Meilisearch client with the provided credentials

   • Enables vector store features in Meilisearch

   • Creates a MeilisearchRetriever instance for similarity search

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Usage

This node is particularly useful for:

• Creating and managing document embeddings in a Meilisearch index

• Performing hybrid (keyword + semantic) searches on stored documents

• Integrating Meilisearch’s vector search capabilities into a larger workflow

The node handles the complexities of document embedding, index management, and retrieval setup, making it easier to leverage Meilisearch’s capabilities in a vector store context.