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WikiLite API Documentation

Base URL

http://{host}:{port}/api

Default: http://localhost:35248/api

API Endpoints

1. Combined Search

Performs a search across lexical and semantic (if enabled).

Endpoint: /search
Methods: GET, POST

Parameters

  • query (required): Search query string
  • limit (optional): Maximum number of results (default: 5)

GET Request

GET /api/search?query=linux&limit=5

POST Request

POST /api/search
Content-Type: application/json

{
  "query": "linux",
  "limit": 5
}

Response

{
  "status": "success",
  "time": 1.234,
  "results": [
    {
      "article_id": 123,
      "title": "Linux",
      "text": "Linux was created in 1991...",
      "type": "T",
      "power": 1.234
    }
  ]
}

2. Title Search

Searches only article titles using full-text search.

Endpoint: /search/title
Methods: GET, POST

Parameters

  • query (required): Search query string
  • limit (optional): Maximum number of results (default: 5)

GET Request

GET /api/search/title?query=linux&limit=5

POST Request

POST /api/search/title
Content-Type: application/json

{
  "query": "linux",
  "limit": 5
}

3. Lexical Search

Searches using full-text search.

Endpoint: /search/lexical
Methods: GET, POST

Parameters

  • query (required): Search query string
  • limit (optional): Maximum number of results (default: 5)

GET Request

GET /api/search/lexical?query=linux&limit=5

POST Request

POST /api/search/lexical
Content-Type: application/json

{
  "query": "linux",
  "limit": 5
}

4. Semantic Search

Searches using vector embeddings. Requires AI to be enabled.

Endpoint: /search/semantic
Methods: GET, POST

Parameters

  • query (required): Search query string
  • limit (optional): Maximum number of results (default: 5)

GET Request

GET /api/search/semantic?query=linux&limit=5

POST Request

POST /api/search/semantic
Content-Type: application/json

{
  "query": "linux",
  "limit": 5
}

5. Search Word Distance

Calculate the Levenshtein distance between the provided word and the entries in the internal database vocabulary to find the closest match.

Endpoint: /search/distance
Methods: GET, POST

Parameters

  • query (required): Search word
  • limit (optional): Maximum number of results (default: 5)

GET Request

GET /api/search/distance?query=linux&limit=5

POST Request

POST /api/search/distance
Content-Type: application/json

{
  "query": "linux",
  "limit": 5
}

6. Get Article

Retrieves a complete article by ID.

Endpoint: /article
Methods: GET, POST

Parameters

  • id (required): Article ID

GET Request

GET /api/article?id=123

POST Request

POST /api/article
Content-Type: application/json

{
  "id": 123
}

Response

{
  "status": "success",
  "time": 1.234,
  "article": {
    "id": 123,
    "title": "Linux",
    "entity": "Q388",
    "sections": [
      {
        "id": 1234,
        "title": "History",
        "content": "Linux was created in 1991...",
      },
      {
        "id": 12345,
        "title": "Design Philosophy",
        "content": "Linux follows Unix philosophy...",
      }
    ]
  }
}

Common Response Format

Success Response

{
  "status": "success",
  "time": 1.234,
  "results": [...],  // For search endpoints
  "article": [...]   // For article endpoint
}

Error Response

{
  "status": "error",
  "message": "Error description"
}

Result Types

Search results include a type field indicating the source:

  • T: Title match
  • C: Content match
  • V: Vector match

Error Codes

The API uses standard HTTP status codes:

  • 200: Success
  • 400: Bad Request (invalid parameters)
  • 500: Internal Server Error

Command Line Examples

Using curl

  1. Combined Search:
curl 'http://localhost:35248/api/search?query=linux&limit=5'
  1. Title Search:
curl -X POST http://localhost:35248/api/search/title \
  -H 'Content-Type: application/json' \
  -d '{"query": "linux"}'
  1. Get Article:
curl 'http://localhost:35248/api/article?id=123'

Configuration Requirements

Sematic Search

To use semantic search, you have two options:

  1. Remote Server:
    You can pass the remote server URL using the --ai-api-url flag. This allows the system to connect to a remote server where the vector search functionality is hosted.
  2. Local Model: Alternatively, you can use a local GGUF model file. The model file must have the same name as the AI model (--ai-model) with .gguf extension. The file should be located in the same directory as the executable or as stated by --ai-model-path.

Notes

  • All search endpoints support both GET and POST methods
  • The limit parameter is shared across all search types
  • Semantic search requires additional configuration and services
  • Results are deduplicated across search types in combined search