Search & Discovery API

LanOnasis provides powerful semantic search capabilities powered by advanced vector embeddings and machine learning algorithms.

Semantic Search

Search across all your memories using natural language queries.

Request

POST /api/v1/memories/search

Request Body

Parameter	Type	Required	Description
`query`	string	Yes	Search query (natural language)
`limit`	integer	No	Number of results (1-100, default: 20)
`threshold`	float	No	Minimum similarity score (0.0-1.0, default: 0.7)
`type`	string	No	Filter by memory type

Example Request

curl -X POST "https://api.lanonasis.com/api/v1/memories/search" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "user authentication requirements",
    "limit": 5,
    "threshold": 0.8,
    "type": "project"
  }'

Example Response

{
  "success": true,
  "data": {
    "results": [
      {
        "id": "mem_456789",
        "title": "Project Requirements",
        "content": "The new feature should include user authentication, data visualization, and real-time updates...",
        "memory_type": "project",
        "type": "project",
        "similarity_score": 0.92,
        "tags": ["project", "requirements", "high-priority"],
        "metadata": {
          "project_id": "proj_123",
          "author": "john.doe@company.com",
          "department": "engineering"
        },
        "created_at": "2024-01-15T10:30:00Z",
        "updated_at": "2024-01-16T14:22:00Z"
      },
      {
        "id": "mem_789012",
        "title": "Security Guidelines",
        "content": "All applications must implement secure authentication mechanisms including...",
        "memory_type": "project",
        "type": "project",
        "similarity_score": 0.87,
        "tags": ["security", "guidelines", "authentication"],
        "metadata": {
          "document_type": "policy",
          "department": "security"
        },
        "created_at": "2024-01-10T16:45:00Z",
        "updated_at": "2024-01-12T09:30:00Z"
      }
    ],
    "total": 2,
    "query": "user authentication requirements",
    "threshold": 0.8
  }
}

Vector-Boosted Search

Vector similarity search is available through the same memory search endpoint. Adjust threshold and filters to control precision.

Request

POST /api/v1/memories/search

Example Request

curl -X POST https://api.lanonasis.com/api/v1/memories/search \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "project requirements",
    "limit": 5,
    "threshold": 0.85,
    "type": "project"
  }'

Example Response

{
  "success": true,
  "data": {
    "results": [
      {
        "id": "mem_456789",
        "title": "Project Requirements",
        "content": "The new feature should include user authentication...",
        "memory_type": "project",
        "type": "project",
        "similarity_score": 0.94,
        "tags": ["project", "requirements"],
        "metadata": {
          "department": "engineering",
          "project_id": "proj_123"
        },
        "created_at": "2024-01-15T10:30:00Z"
      }
    ],
    "total": 1,
    "query": "project requirements",
    "threshold": 0.85
  }
}

Search Suggestions

Autocomplete endpoints are not currently available in the REST API. Use client-side suggestions or cached queries for now.

Generate Embeddings

Generate vector embeddings for custom text.

Request

POST /api/v1/embeddings

Parameters

Parameter	Type	Required	Description
`text`	string	Yes	Text to generate embeddings for
`model`	string	No	Embedding model (default: text-embedding-ada-002)

Example Request

curl -X POST https://api.lanonasis.com/api/v1/embeddings \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "User authentication and security requirements",
    "model": "text-embedding-ada-002"
  }'

Example Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.1234, -0.5678, 0.9012, ...],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 6,
    "total_tokens": 6
  }
}

Metadata Filtering

Filter search results by metadata fields:

curl -X POST "https://api.lanonasis.com/api/v1/memories/search" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "project requirements",
    "type": "project"
  }'

Date Range Filtering

Use the type filter in the search body to narrow results.

Combined Filtering

curl -X POST "https://api.lanonasis.com/api/v1/memories/search" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "security requirements",
    "threshold": 0.9,
    "limit": 20
  }'

Search Analytics

Track search performance and user behavior.

Request

GET /api/v1/analytics/search

Query Parameters

Parameter	Type	Required	Description
`from`	string	No	Start date (ISO 8601)
`to`	string	No	End date (ISO 8601)
`group_by`	string	No	`hour`, `day`, `week`, `month` (default: `day`)

Example Request

curl -X GET "https://api.lanonasis.com/api/v1/analytics/search?from=2024-01-01&to=2024-01-31&group_by=day" \
  -H "X-API-Key: YOUR_API_KEY"

Example Response

{
  "success": true,
  "data": {
    "period": {
      "start": "2024-01-01T00:00:00Z",
      "end": "2024-01-31T23:59:59Z",
      "granularity": "day"
    },
    "metrics": {
      "total_searches": 1250,
      "unique_queries": 890,
      "avg_results_per_search": 8.3,
      "avg_query_time_ms": 42,
      "zero_result_searches": 15
    },
    "top_queries": [
      {
        "query": "user authentication",
        "count": 45,
        "avg_similarity_score": 0.87
      },
      {
        "query": "project requirements",
        "count": 38,
        "avg_similarity_score": 0.92
      }
    ],
    "search_trends": [
      {
        "date": "2024-01-01",
        "searches": 35,
        "avg_query_time_ms": 38
      },
      {
        "date": "2024-01-02",
        "searches": 42,
        "avg_query_time_ms": 41
      }
    ]
  },
  "meta": {
    "timestamp": "2024-01-16T16:20:00Z",
    "request_id": "req_analytics_456"
  }
}

Error Codes

Code	HTTP Status	Description
`INVALID_QUERY`	400	Search query is invalid or empty
`INVALID_VECTOR`	400	Vector dimension mismatch or invalid format
`SIMILARITY_THRESHOLD_INVALID`	400	Threshold must be between 0.0 and 1.0
`SEARCH_TIMEOUT`	408	Search operation timed out
`RATE_LIMIT_EXCEEDED`	429	Search rate limit exceeded

Performance Optimization

Query Optimization

Use specific queries instead of broad terms
Leverage tag filtering to narrow results
Set appropriate similarity thresholds
Use pagination for large result sets

Caching Strategy

// Implement client-side caching for frequently used queries
const searchCache = new Map();

async function cachedSearch(query, options = {}) {
  const cacheKey = JSON.stringify({ query, ...options });

  if (searchCache.has(cacheKey)) {
    return searchCache.get(cacheKey);
  }

  const results = await client.searchMemories({ query, ...options });
  searchCache.set(cacheKey, results);

  // Cache for 5 minutes
  setTimeout(() => searchCache.delete(cacheKey), 5 * 60 * 1000);

  return results;
}

SDK Examples

TypeScript

import { createMemoryClient } from '@lanonasis/memory-client/core';

const client = createMemoryClient({
  apiUrl: 'https://api.lanonasis.com',
  apiKey: process.env.LANONASIS_API_KEY
});

// Semantic search
const searchResults = await client.searchMemories({
  query: 'user authentication',
  limit: 10,
  threshold: 0.8,
  tags: ['security', 'requirements']
});

console.log('Search results:', searchResults.data);

Python

from lanonasis import MemoryClient

client = MemoryClient(api_key=os.environ['LANONASIS_API_KEY'])

# Semantic search
search_results = client.search_memories(
    query='user authentication',
    limit=10,
    threshold=0.8,
    tags=['security', 'requirements']
)

print(f'Search results: {search_results.results}')

Real-time Search

Real-time search streaming endpoints are not currently available in the REST API. Use polling with searchMemories if you need live updates.

Search Best Practices

Query Construction
- Use natural language queries
- Include context and specific terms
- Avoid overly broad or generic queries
Performance
- Set appropriate similarity thresholds
- Use metadata filters to narrow results
- Implement client-side caching
User Experience
- Provide search suggestions
- Highlight matching terms
- Show search progress indicators
Analytics
- Track search metrics
- Monitor query performance
- Analyze user search patterns

Semantic Search​

Request​

Request Body​

Example Request​

Example Response​

Vector-Boosted Search​

Request​

Example Request​

Example Response​

Search Suggestions​

Generate Embeddings​

Request​

Parameters​

Example Request​

Example Response​

Advanced Search Filters​

Metadata Filtering​

Date Range Filtering​

Combined Filtering​

Search Analytics​

Request​

Query Parameters​

Example Request​

Example Response​

Error Codes​

Performance Optimization​

Query Optimization​

Caching Strategy​

SDK Examples​

TypeScript​

Python​

Real-time Search​

Search Best Practices​

Semantic Search

Request

Request Body

Example Request

Example Response

Vector-Boosted Search

Request

Example Request

Example Response

Search Suggestions

Generate Embeddings

Request

Parameters

Example Request

Example Response

Advanced Search Filters

Metadata Filtering

Date Range Filtering

Combined Filtering

Search Analytics

Request

Query Parameters

Example Request

Example Response

Error Codes

Performance Optimization

Query Optimization

Caching Strategy

SDK Examples

TypeScript

Python

Real-time Search

Search Best Practices