Skip to main content

llama-index readers kaltura e-search integration

Project description

Kaltura eSearch Loader

pip install llama-index-readers-kaltura-esearch

This loader reads Kaltura Entries from Kaltura based on a Kaltura eSearch API call. Search queries can be passed as a pre-defined object of KalturaESearchEntryParams, or through a simple free text query. The result is a list of documents containing the Kaltura Entries and Captions json.

Parameters

KalturaESearchEntryParams

This is a Kaltura class used for performing search operations in Kaltura. You can use this class to define various search criteria, such as search phrases, operators, and objects to be searched.

For example, you can search for entries with specific tags, created within a specific time frame, or containing specific metadata.

Kaltura Configuration

To use the Kaltura eSearch Loader, you need to provide the following configuration credentials:

Parameter Description Default Value
partnerId Your Kaltura partner ID. Mandatory (no default)
apiSecret Your Kaltura API secret key (aka Admin Secret). Mandatory (no default)
userId Your Kaltura user ID. Mandatory (no default)
ksType The Kaltura session type. KalturaSessionType.ADMIN
ksExpiry The Kaltura session expiry time. 86400 seconds
ksPrivileges The Kaltura session privileges. "disableentitlement"
kalturaApiEndpoint The Kaltura API endpoint URL. "https://cdnapi-ev.kaltura.com/"
requestTimeout The request timeout duration in seconds. 500 seconds
shouldLogApiCalls If passed True, all the Kaltura API calls will also be printed to log (only use during debug). False

load_data

This method run the search in Kaltura and load Kaltura entries in a list of dictionaries.

Method inputs

  • search_params: search parameters of type KalturaESearchEntryParams with pre-set search queries. If not provided, the other parameters will be used to construct the search query.
  • search_operator_and: if True, the constructed search query will have AND operator between query filters, if False, the operator will be OR.
  • free_text: if provided, will be used as the free text query of the search in Kaltura.
  • category_ids: if provided, will only search for entries that are found inside these category ids.
  • withCaptions: determines whether or not to also download captions/transcript contents from Kaltura.
  • maxEntries: sets the maximum number of entries to pull from Kaltura, between 0 to 500 (max pageSize in Kaltura).

Method output

Each dictionary in the response represents a Kaltura media entry, where the keys are strings (field names) and the values can be of any type:

Column Name Data Type Description
entry_id str Unique identifier of the entry
entry_name str Name of the entry
entry_description str Description of the entry
entry_captions JSON Captions of the entry
entry_media_type int Type of the media (KalturaMediaType)
entry_media_date int Date of the media Unix timestamp
entry_ms_duration int Duration of the entry in ms
entry_last_played_at int Last played date of the entry Unix timestamp
entry_application str The app that created this entry (KalturaEntryApplication)
entry_tags str Tags of the entry (comma separated)
entry_reference_id str Reference ID of the entry

Usage

First, instantiate the KalturaReader (aka Kaltura Loader) with your Kaltura configuration credentials:

from llama_index.readers.kaltura_esearch import KalturaESearchReader

loader = KalturaESearchReader(
    partnerId="INSERT_YOUR_PARTNER_ID",
    apiSecret="INSERT_YOUR_ADMIN_SECRET",
    userId="INSERT_YOUR_USER_ID",
)

Using an instance of KalturaESearchEntryParams

Then, create an instance of KalturaESearchEntryParams and set your desired search parameters:

from KalturaClient.Plugins.ElasticSearch import (
    KalturaESearchEntryParams,
    KalturaESearchEntryOperator,
    KalturaESearchOperatorType,
    KalturaESearchUnifiedItem,
)

# instantiate the params object
search_params = KalturaESearchEntryParams()

# define search parameters (for example, search for entries with a certain tag)
search_params.searchOperator = KalturaESearchEntryOperator()
search_params.searchOperator.operator = KalturaESearchOperatorType.AND_OP
search_params.searchOperator.searchItems = [KalturaESearchUnifiedItem()]
search_params.searchOperator.searchItems[0].searchTerm = "my_tag"

Once you have your KalturaESearchEntryParams ready, you can pass it to the Kaltura Loader:

# Using search params
entry_docs = loader.load_data(search_params)

Using Free Text Search

# Simple pass the search params into the load_data method without setting search_params
entry_docs = loader.load_data(
    search_operator_and=True,
    free_text="education",
    category_ids=None,
    with_captions=True,
    max_entries=5,
)

For a more elaborate example, see: llamaindex_kaltura_esearch_reader_example.py

This loader is designed to be used as a way to load data into LlamaIndex.

About Kaltura

Kaltura Video Cloud is a Digital Experience Platform enabling streamlined creation, management, and distribution of media content (video, audio, image, doc, live stream, real-time video). It powers many applications across industries with collaboration, interactivity, virtual events, and deep video analytics capabilities.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llama_index_readers_kaltura_esearch-0.4.1.tar.gz (19.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file llama_index_readers_kaltura_esearch-0.4.1.tar.gz.

File metadata

File hashes

Hashes for llama_index_readers_kaltura_esearch-0.4.1.tar.gz
Algorithm Hash digest
SHA256 37ede72d1e4064dd4e69a973a2c48441dc608eeff1d8bda9282b1bb887348fc8
MD5 ed819363371d6397e3e31b52f6079aa6
BLAKE2b-256 5c88fa37b0ac9c4d0e9e82703bfe90336b33558cde73baa0cf62df07e416be3a

See more details on using hashes here.

File details

Details for the file llama_index_readers_kaltura_esearch-0.4.1-py3-none-any.whl.

File metadata

File hashes

Hashes for llama_index_readers_kaltura_esearch-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 0f9376c5ae8e12b19bd65066c4595461c9a274d44b72fa5e573fcfc189fe668c
MD5 b9f3c1a34d84cbd31e75765ef236537e
BLAKE2b-256 0161efc777c613b06047463210edcc41cb6ccc30c8fcf0f63112b5f06a3e844d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page