Knowledge Retrieval

Action ID: knowledge_retrieval

Description

Retrieve knowledge from a dataset using semantic similarity search.

Input Parameters

Name	Type	Required	Default	Description
dataset	dropdown	✓	-	The dataset to retrieve knowledge from.
query	string	✓	-	The query to retrieve knowledge from the dataset (1-1000 characters).
top_k	integer	-	5	The number of documents to return (1-100).

View JSON Schema

{
  "description": "Knowledge Retrieval node input.",
  "properties": {
    "dataset": {
      "description": "The dataset to retrieve knowledge from.",
      "title": "Dataset",
      "type": "string"
    },
    "query": {
      "description": "The query to retrieve knowledge from the dataset.",
      "maxLength": 1000,
      "minLength": 1,
      "title": "Query",
      "type": "string"
    },
    "top_k": {
      "default": 5,
      "description": "The number of documents to return.",
      "maximum": 100,
      "minimum": 1,
      "title": "Top K",
      "type": "integer"
    }
  },
  "required": [
    "dataset",
    "query"
  ],
  "title": "KnowledgeRetrievalNodeInput",
  "type": "object"
}

Output Parameters

Name	Type	Description
documents	array	The documents that are relevant to the query.

Document Structure

Each document in the documents array contains:

Field	Type	Description
id	string	Unique identifier of the row
content	string	Combined content from all cell values
metadata	object	Metadata about the document including source dataset ID

View JSON Schema

{
  "description": "Knowledge Retrieval node output.",
  "properties": {
    "documents": {
      "description": "The documents that is relevant to the query.",
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Documents",
      "type": "array"
    }
  },
  "required": [
    "documents"
  ],
  "title": "KnowledgeRetrievalNodeOutput",
  "type": "object"
}

How It Works

This node performs semantic similarity search on a dataset to retrieve relevant documents. It uses vector embeddings to find rows whose content is semantically similar to the query text. The node combines all cell values from each matching row into a single content string and returns the top K most relevant documents along with their IDs and metadata. This enables natural language querying of structured data for knowledge retrieval and context-aware applications.

Usage Examples

Example 1: Product Documentation Search

Input:

dataset: "product_docs_2024"
query: "How do I reset my password?"
top_k: 3

Output:

documents: [
  {
    "id": "doc_456",
    "content": "Password Reset: Navigate to settings, click 'Security', then 'Reset Password'...",
    "score": 0.92,
    "metadata": {"category": "authentication", "updated": "2024-01-10"}
  },
  {
    "id": "doc_123",
    "content": "Account Security: Managing your password and security settings...",
    "score": 0.87,
    "metadata": {"category": "security", "updated": "2024-01-05"}
  },
  {
    "id": "doc_789",
    "content": "Login Issues: Troubleshooting common authentication problems...",
    "score": 0.78,
    "metadata": {"category": "troubleshooting", "updated": "2023-12-20"}
  }
]

Example 2: Customer Support Knowledge Base

Input:

dataset: "support_kb_001"
query: "Shipping delays international orders"
top_k: 5

Output:

documents: [
  {
    "id": "kb_234",
    "content": "International shipping typically takes 7-14 business days. Customs processing may cause additional delays...",
    "score": 0.89
  },
  {
    "id": "kb_567",
    "content": "Track your international shipment using the tracking number provided...",
    "score": 0.82
  },
  ...
]

Example 3: Research Paper Database

Input:

dataset: "research_papers_ai"
query: "transformer architecture attention mechanisms"
top_k: 10

Output:

documents: [
  {
    "id": "paper_1123",
    "title": "Attention Is All You Need",
    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
    "score": 0.95,
    "authors": ["Vaswani et al."],
    "year": 2017
  },
  ...
]

Common Use Cases

• Customer Support: Retrieve relevant help articles and documentation based on customer questions

• Question Answering Systems: Find contextually relevant information to answer user queries

• Semantic Search: Implement intelligent search that understands meaning beyond keywords

• RAG (Retrieval-Augmented Generation): Provide context to AI models by retrieving relevant documents

• Document Discovery: Help users discover related content and documents in large repositories

• Knowledge Base Navigation: Enable natural language search across organizational knowledge bases

• Research and Analysis: Find relevant research papers, reports, or documents based on topics

Error Handling

Error Type	Cause	Solution
Dataset Not Found	Dataset ID doesn't exist	Verify the dataset parameter contains a valid dataset ID
Empty Query	Query string is empty or contains only whitespace	Provide a meaningful query string with at least 1 character
Query Too Long	Query exceeds 1000 characters	Shorten your query to 1000 characters or less
Invalid Top K	top_k value is outside range 1-100	Set top_k to a value between 1 and 100
Dataset Not Indexed	Dataset lacks vector embeddings	Ensure the dataset has been properly indexed for semantic search
No Results Found	No documents match the query	Try rephrasing your query or expanding the search criteria
Embedding Error	Failed to generate query embeddings	Check embedding service availability and retry

Notes

• Semantic vs Keyword Search: This node uses semantic search, which understands context and meaning rather than just matching keywords.

• Query Quality: More specific, well-formed queries generally produce better results. Avoid overly vague or generic queries.

• Top K Selection: Balance between retrieving enough context (higher top_k) and maintaining relevance (lower top_k). Default of 5 works well for most cases.

• Result Ranking: Documents are returned in order of relevance, with the most relevant documents first.

• Score Interpretation: Similarity scores typically range from 0 to 1, with higher scores indicating greater relevance.

• Dataset Preparation: Ensure your dataset is properly indexed with embeddings before using this node.

• Performance: Retrieval speed depends on dataset size. Larger datasets may take slightly longer to search.

• Use with AI Nodes: Combine with AI nodes like Claude or GPT to build RAG systems that answer questions based on retrieved context.