Find similar objects

How do you find similar objects in Tallyfy Answers?

The recommendation endpoint lets you find objects similar to a specified reference object.

This similarity search uses vector embeddings to find objects with related characteristics. It’s useful for recommendation systems, content discovery, and finding related items.

How does similarity search work in Tallyfy Answers?

Here’s how Tallyfy Answers uses semantic similarity to find related objects:

1. Tallyfy converts each object’s text content into vector embeddings (numerical representations)
2. When searching for similar objects, Tallyfy compares vector distances
3. Objects with closer vector distances are more similar
4. Tallyfy ranks results by similarity score from most to least similar

API endpoint

GET /collections/{collection_name}/recommend/{object_id}

Path parameters

Parameter	Type	Required	Description
collection_name	string	Yes	Name of the collection to search in
object_id	string	Yes	ID of the reference object to find similar objects to

Query parameters

Parameter	Type	Required	Default	Description
limit	integer	No	4	Maximum number of similar objects to return (1-20)

Response

A successful request returns a 200 OK status code and a JSON response containing:

{
  "results": [
    {
      "id": "product-456",
      "score": 0.89,
      "document": {
        "name": "Bluetooth Speaker",
        "description": "Portable waterproof speaker with 20-hour battery life",
        "price": 129.99,
        "categories": ["electronics", "audio"]
      }
    },
    {
      "id": "product-789",
      "score": 0.72,
      "document": {
        "name": "Wireless Earbuds",
        "description": "Premium noise-canceling earbuds with charging case",
        "price": 99.99,
        "categories": ["electronics", "audio"]
      }
    }
  ],
  "meta": {
    "limit": 10,
    "found": 2
  }
}

Response fields

Field	Type	Description
results	array	List of similar objects with similarity scores
results[].id	string	Unique identifier of the similar object
results[].score	float	Similarity score (0.0-1.0)1 where higher is more similar
results[].document	object	Full content of the similar object
meta.limit	integer	Maximum number of results requested
meta.found	integer	Total number of similar objects found

Error scenarios

Status	Description
404	Collection or reference object not found
400	Invalid parameters or filter syntax

Example request

# Find objects similar to article-123, limit to 5 results
curl -X GET "https://answers.tallyfy.com/collections/docs/recommend/article-123?limit=5" \
  -H "Authorization: Bearer your_api_key"

What are common use cases for similarity search?

• Product recommendations: “Customers who viewed this also viewed…”
• Content discovery: Finding related articles or documentation
• Similarity matching: Identifying near-duplicate content
• Alternative suggestions: Offering similar options when an item is unavailable

What are best practices for similarity search?

• Start with a lower min_score threshold and adjust based on result quality
• Use the filter parameter to combine similarity with business rules
• You’ll get better recommendations if your objects contain rich, descriptive text
• Include diverse object attributes to improve similarity matching accuracy

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Footnotes

1. Cosine similarity scoring where perfect match equals 1.0, high similarity above 0.8, low below 0.5 ↩

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