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VectorDB Node

VectorDB Node

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Overview

The VectorDB Node is a comprehensive vector database management component that performs various operations including insertion, deletion, updating, and management of vector data. This node provides full control over vector database operations.

vector-actions.png

Node Type Information

TypeDescriptionStatus
Batch TriggerStarts the flow on a schedule or batch event. Ideal for periodic data processing.❌ False
Event TriggerStarts the flow based on external events (e.g., webhook, user interaction).✅ True
ActionExecutes a task or logic as part of the flow (e.g., API call, transformation).✅ True

This node is an **Action** node that performs various operations on vector databases for data management and manipulation.

Features

Key Functionalities
  1. Multi-Action Capability: Choose between indexing, filtering, or deleting records in a vector DB.
  2. Conditional Filtering: Apply advanced JSON filters with logical operators (And, Or, etc.).
  3. Primary Key Matching: Use custom primary keys to uniquely identify or filter/delete entries.
  4. Duplicate Handling: Choose to overwrite or skip on indexing conflicts.
Benefits
  1. Flexibility: Handle multiple vector operations within one node.
  2. Control: Fine-tune data updates with filters and duplication settings.
  3. Efficiency: Manage large vector datasets with scalable indexing and deletion.
  4. Search Precision: Use rich metadata and filters to retrieve exactly what's needed.

What Can You Build?

  • Automate document indexing pipelines.
  • Build search interfaces with filterable vector data.
  • Clean up or update vector DBs programmatically.

Setup

1. Index Action

Indexes vectors and metadata into the selected vector database.

Configuration Reference

ParameterDescriptionRequiredExample Value
Vector DBSelect the vector database where the vectors will be indexed.Yesdatabase
VectorsEnter the vector data to be indexed in the database.Yes{{codeNode_540.output.vectors}}
MetadataInclude additional information to enhance vector search and retrieval.Yes{{codeNode_540.output.vectors}}
Primary Keys (JSON)Provide unique identifiers for each vector in JSON format.Yes[]
Duplication RecordsManage duplicates by choosing to overwrite or skip them.Nooverwrite

Low-Code Example

- nodeId: IndexNode_543
    nodeType: IndexNode
    nodeName: Index to DB
    values:
      vectorDB: IndexCrawler
      webhookURL: https://webhook.site/685a66e7-b4d3-40a4-9801-99e3460414f9
      primaryKeys: ''
      vectorsField: '{{codeNode_540.output.vectors}}'
      metadataField: '{{codeNode_540.output.metadata}}'
      duplicateOperation: overwrite
      embeddingModelName: {}
      generativeModelName:
        type: embedder/text
        nodeId: IndexNode
        model_name: text-embedding-ada-002
        provider_name: openai
    needs:
      - codeNode_540

Output

  • recordsIndexed: An integer indicating the number of records successfully indexed.
  • duplicateRecordsDeleted: An integer representing the number of duplicate records removed during the indexing process.
  • message: A string providing a status or summary of the indexing operation.
Example Output
{
  "recordsIndexed": 1,
  "duplicateRecordsDeleted": 0,
  "message": "Data indexed successfully"
}

2. Filter Action

Filters indexed records using metadata and/or vector queries.

Configuration Reference

ParameterDescriptionRequiredExample
vectorDBTarget vector databaseYesCallData
actionMust be set to filterYesfilter
filtersJSON filter using logical operators and conditionsYesSee below
limitNumber of results to returnNo20

Example

- nodeId: vectorNode_2
  nodeType: vectorNode
  nodeName: VectorDB
  values:
    vectorDB: CallData
    action: filter
    vectorsField: "[]"
    metadataField: "[]"
    primaryKeys: ["topic"]
    duplicateOperation: overwrite
    filters: |-
      {
        "operator": "And",
        "operands": [
          {
            "path": ["topic"],
            "operator": "Equal",
            "valueText": "Topic1"
          }
        ]
      }
    limit: 20

Output

  • success: A boolean indicating if the filter operation was successful.
  • data: An array of records matching the filter criteria.
  • count: The number of records returned by the filter operation.
Example Output
{
  "success": true,
  "data": [
    {
      "title": "Lamatic Docs",
      "content": "Welcome to the Lamatic documentation!"
    }
  ],
  "count": 1
}

3. Delete Action

Deletes records matching filter criteria.

Configuration Reference

ParameterDescriptionRequiredExample
vectorDBTarget vector databaseYesCallData
actionMust be set to deleteYesdelete
filtersJSON filter for selecting items to deleteYesSee below
primaryKeysOptional fields used to match entries for deletionNo["topic"]

Example

- nodeId: vectorNode_3
  nodeType: vectorNode
  nodeName: VectorDB
  values:
    vectorDB: CallData
    action: delete
    vectorsField: "[]"
    metadataField: "[]"
    primaryKeys: ["topic"]
    duplicateOperation: overwrite
    filters: |-
      {
        "operator": "And",
        "operands": [
          {
            "path": ["topic"],
            "operator": "Equal",
            "valueText": "Topic1"
          }
        ]
      }
    limit: 20
  needs:
    - triggerNode_1

Output

  • deletedCount: Number of records successfully deleted.
  • matches: Number of records that matched the filter criteria.
  • failed: Number of records that failed to delete.
Example Output
{
  "deletedCount": 2,
  "matches": 2,
  "failed": 0
}

Troubleshooting

Common Issues

ProblemSolution
Invalid Vector DBEnsure the database name exists and is spelled correctly.
Missing EmbeddingsMake sure vectors are provided or an embedding model is configured.
Misconfigured FiltersEnsure filters use valid JSON and correct field paths and operators.
Primary Key ConflictsResolve using overwrite or skip options for handling duplicates.
Vector Search NodeVectorize Node

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