Vectara Vector Store

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Node Details

• Name: Vectara_VectorStores

• Type: Vectara

• Version: 2.0

• Category: Vector Stores

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Base Classes

• Vectara

• VectorStoreRetriever

• BaseRetriever

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Credentials

• Type: vectaraApi

• Required Parameters:

  • API Key

  • Customer ID

  • Corpus ID

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Input Parameters

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Main Inputs

1. Document (optional, list)

   • Type: Document

   • Description: List of documents to be indexed in Vectara.

2. File (optional)

   • Type: file

   • Description: File to upload to Vectara. Supports various file types as per Vectara documentation.

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Additional Parameters

1. Metadata Filter (optional)

   • Type: string

   • Description: Filter to apply to Vectara metadata.

2. Sentences Before (optional)

   • Type: number

   • Default: 2

   • Description: Number of sentences to fetch before the matched sentence.

3. Sentences After (optional)

   • Type: number

   • Default: 2

   • Description: Number of sentences to fetch after the matched sentence.

4. Lambda (optional)

   • Type: number

   • Default: 0.0

   • Description: Balance between neural search and keyword-based search (0 to 1).

5. Top K (optional)

   • Type: number

   • Default: 5

   • Description: Number of top results to fetch.

6. MMR K (optional)

   • Type: number

   • Default: 50

   • Description: Number of top results to fetch for MMR (Maximal Marginal Relevance).

7. MMR diversity bias (optional)

   • Type: number

   • Default: 0.0

   • Description: Diversity bias for MMR (0.0 to 1.0).

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Outputs

1. Vectara Retriever

   • Type: retriever

   • Base Classes: [Vectara, VectorStoreRetriever, BaseRetriever]

2. Vectara Vector Store

   • Type: vectorStore

   • Base Classes: [Vectara, VectaraStore]

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Functionality

• Upsert: Allows adding new documents or files to the Vectara index.

• Retrieval: Provides similarity search capabilities with various configuration options.

• File Handling: Supports both direct file uploads and file retrieval from storage.

• Filtering: Enables metadata filtering for more precise searches.

• Context Configuration: Allows setting the number of sentences before and after the matched sentence.

• Hybrid Search: Supports balancing between neural and keyword-based search through the lambda parameter.

• MMR: Implements Maximal Marginal Relevance for diverse result sets.

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Use Cases

• Document indexing and retrieval

• Similarity search in large document collections

• Integration of advanced search capabilities in AI applications

• Enhancing chatbots or question-answering systems with relevant document retrieval

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Notes

• Requires Vectara API credentials for operation.

• Supports various file types for indexing.

• Offers flexible configuration for search behavior and result formatting.