IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

›

App Search APIs

edit

App Search APIs

edit

On this page

Initializing the Client

edit

The AppSearch client can either be configured directly:

# Use the AppSearch client directly:
from elastic_enterprise_search import AppSearch

app_search = AppSearch(
    "http://localhost:3002",
    http_auth="private-..."
)
# Now call API methods
app_search.search(...)

…or can be used via a configured EnterpriseSearch.app_search instance:

from elastic_enterprise_search import EnterpriseSearch

ent_search = EnterpriseSearch("http://localhost:3002")

# Configure authentication of the AppSearch instance
ent_search.app_search.http_auth = "private-..."

# Now call API methods
ent_search.app_search.search(...)

API Key Privileges

edit

Using the APIs require a key with read, write or search access depending on the action. If you’re receiving an UnauthorizedError make sure the key you’re using in http_auth has the proper privileges.

Engine APIs

edit

Engines index documents and perform search functions. To use App Search you must first create an Engine.

Create Engine

edit

Let’s create an Engine named national-parks and uses English as a language:

# Request:
app_search.create_engine(
    engine_name="national-parks",
    language="en",
)

# Response:
{
  "name": "national-parks",
  "type": "default",
  "language": "en"
}

Get Engine

edit

Once we’ve created an Engine we can look at it:

# Request:
app_search.get_engine(engine_name="national-parks")

# Response:
{
  "document_count": 0,
  "language": "en",
  "name": "national-parks",
  "type": "default"
}

List Engines

edit

We can see all our Engines in the App Search instance:

# Request:
app_search.list_engines()

# Response:
{
  "meta": {
    "page": {
      "current": 1,
      "size": 25,
      "total_pages": 1,
      "total_results": 1
    }
  },
  "results": [
    {
      "document_count": 0,
      "language": "en",
      "name": "national-parks",
      "type": "default"
    }
  ]
}

Delete Engine

edit

If we want to delete the Engine and all documents inside you can use the delete_engine() method:

# Request:
app_search.delete_engine(engine_name="national-parks")

# Response:
{
  "deleted": True
}

Document APIs

edit

Create and index Documents

edit

Once you’ve created an Engine you can start adding documents with the index_documents() method:

# Request:
app_search.index_documents(
    engine_name="national-parks",
    documents=[{
        "id": "park_rocky-mountain",
        "title": "Rocky Mountain",
        "nps_link": "https://www.nps.gov/romo/index.htm",
        "states": [
            "Colorado"
        ],
        "visitors": 4517585,
        "world_heritage_site": False,
        "location": "40.4,-105.58",
        "acres": 265795.2,
        "date_established": "1915-01-26T06:00:00Z"
    }, {
        "id": "park_saguaro",
        "title": "Saguaro",
        "nps_link": "https://www.nps.gov/sagu/index.htm",
        "states": [
            "Arizona"
        ],
        "visitors": 820426,
        "world_heritage_site": False,
        "location": "32.25,-110.5",
        "acres": 91715.72,
        "date_established": "1994-10-14T05:00:00Z"
    }]
)

# Response:
[
  {
    "errors": [],
    "id": "park_rocky-mountain"
  },
  {
    "errors": [],
    "id": "park_saguaro"
  }
]

List Documents

edit

Both of our new documents indexed without errors.

Now we can look at our indexed documents in the engine:

# Request:
app_search.list_documents(engine_name="national-parks")

# Response:
{
  "meta": {
    "page": {
      "current": 1,
      "size": 100,
      "total_pages": 1,
      "total_results": 2
    }
  },
  "results": [
    {
      "acres": "91715.72",
      "date_established": "1994-10-14T05:00:00Z",
      "id": "park_saguaro",
      "location": "32.25,-110.5",
      "nps_link": "https://www.nps.gov/sagu/index.htm",
      "states": [
        "Arizona"
      ],
      "title": "Saguaro",
      "visitors": "820426",
      "world_heritage_site": "false"
    },
    {
      "acres": "265795.2",
      "date_established": "1915-01-26T06:00:00Z",
      "id": "park_rocky-mountain",
      "location": "40.4,-105.58",
      "nps_link": "https://www.nps.gov/romo/index.htm",
      "states": [
        "Colorado"
      ],
      "title": "Rocky Mountain",
      "visitors": "4517585",
      "world_heritage_site": "false"
    }
  ]
}

Get Documents by ID

edit

You can also retrieve a set of documents by their id with the get_documents() method:

# Request:
app_search.get_documents(
    engine_name="national-parks",
    document_ids=["park_rocky-mountain"]
)

# Response:
[
  {
    "acres": "265795.2",
    "date_established": "1915-01-26T06:00:00Z",
    "id": "park_rocky-mountain",
    "location": "40.4,-105.58",
    "nps_link": "https://www.nps.gov/romo/index.htm",
    "states": [
      "Colorado"
    ],
    "title": "Rocky Mountain",
    "visitors": "4517585",
    "world_heritage_site": "false"
  }
]

Update existing Documents

edit

You can update documents with the put_documents() method:

# Request:
resp = app_search.put_documents(
    engine_name="national-parks",
    documents=[{
        "id": "park_rocky-mountain",
        "visitors": 10000000
    }]
)

# Response:
[
  {
    "errors": [],
    "id": "park_rocky-mountain"
  }
]

Delete Documents

edit

You can delete documents from an Engine with the delete_documents() method:

# Request:
resp = app_search.delete_documents(
    engine_name="national-parks",
    document_ids=["park_rocky-mountain"]
)

# Response:
[
  {
    "deleted": True,
    "id": "park_rocky-mountain"
  }
]

Schema APIs

edit

Now that we’ve indexed some data we should take a look at the way the data is being indexed by our Engine.

Get Schema

edit

First take a look at the existing Schema inferred from our data:

# Request:
resp = app_search.get_schema(
    engine_name="national-parks"
)

# Response:
{
  "acres": "text",
  "date_established": "text",
  "location": "text",
  "nps_link": "text",
  "states": "text",
  "title": "text",
  "visitors": "text",
  "world_heritage_site": "text"
}

Update Schema

edit

Looks like the date_established field wasn’t indexed as a date as desired. Update the type of the date_established field:

# Request:
resp = app_search.put_schema(
    engine_name="national-parks",
    schema={
        "date_established": "date"
    }
)

# Response:
{
  "acres": "number",
  "date_established": "date",  # Type has been updated!
  "location": "geolocation",
  "nps_link": "text",
  "square_km": "number",
  "states": "text",
  "title": "text",
  "visitors": "number",
  "world_heritage_site": "text"
}

Search APIs

edit

Once documents are ingested and the Schema is set properly you can use the search() method to search through an Engine for matching documents.

The Search API has many options, read the Search API documentation for a list of all options.

Single Search

edit

# Request:
resp = app_search.search(
    engine_name="national-parks",
    body={
        "query": "rock"
    }
)

# Response:
{
  "meta": {
    "alerts": [],
    "engine": {
      "name": "national-parks-demo",
      "type": "default"
    },
    "page": {
      "current": 1,
      "size": 10,
      "total_pages": 2,
      "total_results": 15
    },
    "request_id": "6266df8b-8b19-4ff0-b1ca-3877d867eb7d",
    "warnings": []
  },
  "results": [
    {
      "_meta": {
        "engine": "national-parks-demo",
        "id": "park_rocky-mountain",
        "score": 6776379.0
      },
      "acres": {
        "raw": 265795.2
      },
      "date_established": {
        "raw": "1915-01-26T06:00:00+00:00"
      },
      "id": {
        "raw": "park_rocky-mountain"
      },
      "location": {
        "raw": "40.4,-105.58"
      },
      "nps_link": {
        "raw": "https://www.nps.gov/romo/index.htm"
      },
      "square_km": {
        "raw": 1075.6
      },
      "states": {
        "raw": [
          "Colorado"
        ]
      },
      "title": {
        "raw": "Rocky Mountain"
      },
      "visitors": {
        "raw": 4517585.0
      },
      "world_heritage_site": {
        "raw": "false"
      }
    }
  ]
}

Multi Search

edit

Multiple searches can be executed at the same time with the multi_search() method:

# Request:
resp = app_search.multi_search(
    engine_name="national-parks",
    body={
        "queries": [
            {"query": "rock"},
            {"query": "lake"}
        ]
    }
)

# Response:
[
  {
    "meta": {
      "alerts": [],
      "engine": {
        "name": "national-parks-demo",
        "type": "default"
      },
      "page": {
        "current": 1,
        "size": 1,
        "total_pages": 15,
        "total_results": 15
      },
      "warnings": []
    },
    "results": [
      {
        "_meta": {
          "engine": "national-parks",
          "id": "park_rocky-mountain",
          "score": 6776379.0
        },
        "acres": {
          "raw": 265795.2
        },
        "date_established": {
          "raw": "1915-01-26T06:00:00+00:00"
        },
        "id": {
          "raw": "park_rocky-mountain"
        },
        "location": {
          "raw": "40.4,-105.58"
        },
        "nps_link": {
          "raw": "https://www.nps.gov/romo/index.htm"
        },
        "square_km": {
          "raw": 1075.6
        },
        "states": {
          "raw": [
            "Colorado"
          ]
        },
        "title": {
          "raw": "Rocky Mountain"
        },
        "visitors": {
          "raw": 4517585.0
        },
        "world_heritage_site": {
          "raw": "false"
        }
      }
    ]
  },
  ...
]

Curation APIs

edit

Curations hide or promote result content for pre-defined search queries.

Create Curation

edit

# Request:
resp = app_search.create_curation(
    engine_name="national-parks",
    queries=["rocks", "rock", "hills"],
    promoted_doc_ids=["park_rocky-mountains"],
    hidden_doc_ids=["park_saguaro"]
)

# Response:
{
  "id": "cur-6011f5b57cef06e6c883814a"
}

Get Curation

edit

# Request:
resp = app_search.get_curation(
    engine_name="national-parks",
    curation_id="cur-6011f5b57cef06e6c883814a"
)
{
  "hidden": [
    "park_saguaro"
  ],
  "id": "cur-6011f5b57cef06e6c883814a",
  "promoted": [
    "park_rocky-mountains"
  ],
  "queries": [
    "rocks",
    "rock",
    "hills"
  ]
}

Update Curation

edit

# Request:
app_search.put_curation(
  engine_name='my-engine',
  curation_id='cur-6011f5b57cef06e6c883814a',
  queries=["foo", "bar"],
  promoted=["doc-1", "doc-2"],
  hidden=["doc-3"]
)
# Response:
{
  "id": "cur-6011f5b57cef06e6c883814a"
}

List Curations

edit

# Request:
app_search.list_curations(
    engine_name="national-parks"
)

Delete Curation

edit

# Request:
app_search.delete_curation(
    engine_name="national-parks",
    curation_id="cur-6011f5b57cef06e6c883814a"
)

Meta Engine APIs

edit

Meta Engines is an Engine that has no documents of its own, instead it combines multiple other Engines so that they can be searched together as if they were a single Engine.

The Engines that comprise a Meta Engine are referred to as "Source Engines".

Create Meta Engine

edit

Creating a Meta Engine uses the create_engine() method and set the type parameter to "meta".

# Request:
app_search.create_engine(
    engine_name="meta-engine",
    type="meta",
    source_engines=["national-parks"]
)

# Response:
{
  "document_count": 1,
  "name": "meta-engine",
  "source_engines": [
    "national-parks"
  ],
  "type": "meta"
}

Searching Documents from a Meta Engine

edit

# Request:
app_search.search(
    engine_name="meta-engine",
    body={
        "query": "rock"
    }
)

# Response:
{
  "meta": {
    "alerts": [],
    "engine": {
      "name": "meta-engine",
      "type": "meta"
    },
    "page": {
      "current": 1,
      "size": 10,
      "total_pages": 1,
      "total_results": 1
    },
    "request_id": "aef3d3d3-331c-4dab-8e77-f42e4f46789c",
    "warnings": []
  },
  "results": [
    {
      "_meta": {
        "engine": "national-parks",
        "id": "park_black-canyon-of-the-gunnison",
        "score": 2.43862
      },
      "id": {
        "raw": "national-parks|park_black-canyon-of-the-gunnison"
      },
      "nps_link": {
        "raw": "https://www.nps.gov/blca/index.htm"
      },
      "square_km": {
        "raw": 124.4
      },
      "states": {
        "raw": [
          "Colorado"
        ]
      },
      "title": {
        "raw": "Black Canyon of the Gunnison"
      },
      "world_heritage_site": {
        "raw": "false"
      }
    }
  ]
}

Notice how the id of the result we receive (national-parks|park_black-canyon-of-the-gunnison) includes a prefix of the Source Engine that the result is from to distinguish them from results with the same id but different Source Engine within a search result.

Adding Source Engines to an existing Meta Engine

edit

If we have an existing Meta Engine named meta-engine we can add additional Source Engines to it with the add_meta_engine_source() method. Here we add the state-parks Engine:

# Request:
app_search.add_meta_engine_source(
    engine_name="meta-engine",
    source_engines=["state-parks"]
)

# Response:
{
  "document_count": 1,
  "name": "meta-engine",
  "source_engines": [
    "national-parks",
    "state-parks"
  ],
  "type": "meta"
}

Removing Source Engines from a Meta Engine

edit

If we change our mind about state-parks being a Source Engine for meta-engine we can use the delete_meta_source_engines() method:

# Request:
app_search.delete_meta_engine_source(
    engine_name="meta-engine",
    source_engines=["state-parks"]
)

# Response:
{
  "document_count": 1,
  "name": "meta-engine",
  "source_engines": [
    "national-parks"
  ],
  "type": "meta"
}

Web Crawler APIs

edit

Domains

edit

# Create a domain
resp = app_search.create_crawler_domain(
  engine_name="crawler-engine",
  body={
    "name": "https://example.com"
  }
)
domain_id = resp["id"]

# Get a domain
app_search.get_crawler_domain(
  engine_name="crawler-engine",
  domain_id=domain_id
)

# Update a domain
app_search.put_crawler_domain(
  engine_name="crawler-engine",
  domain_id=domain_id,
  body={
    ...
  }
)

# Delete a domain
app_search.delete_crawler_domain(
  engine_name="crawler-engine",
  domain_id=domain_id
)

# Validate a domain
app_search.get_crawler_domain_validation_result(
  body={
    "url": "https://example.com",
    "checks": [
      "dns",
      "robots_txt",
      "tcp",
      "url",
      "url_content",
      "url_request"
    ]
  }
)

# Extract content from a URL
app_search.get_crawler_url_extraction_result(
  engine_name="crawler-engine",
  body={
    "url": "https://example.com"
  }
)

# Trace a URL
app_search.get_crawler_url_tracing_result(
  engine_name="crawler-engine",
  body={
    "url": "https://example.com"
  }
)

Crawls

edit

# Get the active crawl
app_search.get_crawler_active_crawl_request(
  engine_name="crawler-engine",
)

# Start a crawl
app_search.create_crawler_crawl_request(
  engine_name="crawler-engine"
)

# Cancel the active crawl
app_search.delete_crawler_active_crawl_request(
  engine_name="crawler-engine"
)

Entry Points

edit

# Create an entry point
resp = app_search.create_crawler_entry_point(
  engine_name="crawler-engine",
  body={
    "value": "/blog"
  }
)
entry_point_id = resp["id"]

# Delete an entry point
app_search.delete_crawler_entry_point(
  engine_name="crawler-engine",
  entry_point_id=entry_point_id
)

Crawl Rules

edit

# Create a crawl rule
resp = app_search.create_crawler_crawl_rule(
  engine_name="crawler-engine",
  domain_id=domain_id,
  body={
    "policy": "deny",
    "rule": "ends",
    "pattern": "/dont-crawl"
  }
)
crawl_rule_id = resp["id"]

# Delete a crawl rule
app_search.delete_crawler_crawl_rule(
  engine_name="crawler-engine",
  domain_id=domain_id,
  crawl_rule_id=crawl_rule_id
)

Sitemaps

edit

# Create a sitemap
resp = app_search.create_crawler_sitemap(
  engine_name="crawler-engine",
  domain_id=domain_id,
  url="https://example.com/sitemap.xml"
)
sitemap_id = resp["id"]

# Delete a sitemap
app_search.delete_crawler_sitemap(
  engine_name="crawler-engine",
  domain_id=domain_id,
  sitemap_id=sitemap_id
)

Adaptive Relevance APIs

edit

Settings

edit

# Get adaptive relevenace settings for an Engine
app_search.get_adaptive_relevance_settings(
  engine_name="adaptive-engine"
)
{
  "curation": {
    "enabled": True,
    "mode": "manual",
    "timeframe": 7,
    "max_size": 3,
    "min_clicks": 20,
    "schedule_frequency": 1,
    "schedule_unit": "day"
  }
}

# Enable automatic adaptive relevance
app_search.put_adaptive_relevance_settings(
  engine_name="adaptive-engine",
  curation={
    "mode": "automatic"
  }
)

Suggestions

edit

# List all adaptive relevance suggestions for an engine
app_search.list_adaptive_relevance_suggestions(
  engine_name="adaptive-engine"
)
{
  "meta": {
    "page": {
      "current": 1,
      "total_pages": 1,
      "total_results": 2,
      "size": 25
    }
  },
  "results": [
    {
      "query": "forest",
      "type": "curation",
      "status": "pending",
      "updated_at": "2021-09-02T07:22:23Z",
      "created_at": "2021-09-02T07:22:23Z",
      "promoted": [
        "park_everglades",
        "park_american-samoa",
        "park_arches"
      ],
      "operation": "create"
    },
    {
      "query": "park",
      "type": "curation",
      "status": "pending",
      "updated_at": "2021-10-22T07:34:12Z",
      "created_at": "2021-10-22T07:34:54Z",
      "promoted": [
        "park_yellowstone"
      ],
      "operation": "create",
      "override_manual_curation": true
    }
  ]
}

# Get adaptive relevance suggestions for a query
app_search.get_adaptive_relevance_suggestions(
  engine_name="adaptive-engine",
  query="forest",
)
{
  "meta": {
    "page": {
      "current": 1,
      "total_pages": 1,
      "total_results": 1,
      "size": 25
    }
  },
  "results": [
    {
      "query": "forest",
      "type": "curation",
      "status": "pending",
      "updated_at": "2021-09-02T07:22:23Z",
      "created_at": "2021-09-02T07:22:23Z",
      "promoted": [
        "park_everglades",
        "park_american-samoa",
        "park_arches"
      ],
      "operation": "create"
    }
  ]
}

# Update status of adaptive relevance suggestions
app_search.put_adaptive_relevance_suggestions(
  engine_name="adaptive-engine",
  suggestions=[
    {"query": "forest", "type": "curation", "status": "applied"},
    {"query": "mountain", "type": "curation", "status": "rejected"}
  ]
)

« Connecting Workplace Search APIs »