Qdrant Integration
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Qdrant Integration
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Transfer data from Apify Actors to a Qdrant vector database.
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Get a demoQdrant Collection Name
qdrantCollectionName
stringRequired
Name of the Qdrant collection where the data will be stored
Whether to automatically create the collection.
qdrantAutoCreateCollection
booleanOptional
When set to true, the integration will automatically create the collection in Qdrant if it does not exist with the appropriate vector dimensions.
Default value of this property is true
Embeddings provider (as defined in the langchain API)
embeddingsProvider
EnumRequired
Choose the embeddings provider to use for generating embeddings OpenAI, Cohere, or Fake
Value options:
"OpenAI": string"Cohere": string"Fake": string
Default value of this property is "OpenAI"
Configuration for embeddings provider
embeddingsConfig
objectOptional
Configure the parameters for the LangChain embedding class. Key points to consider:
-
Typically, you only need to specify the model name. For example, for OpenAI, set the model name as {"model": "text-embedding-3-small"}.
-
It's required to ensure that the vector size of your embeddings matches the size of embeddings in the database.
-
Here are examples of embedding models:
-
For more details about other parameters, refer to the LangChain documentation.
Embeddings API KEY (whenever applicable, depends on provider)
embeddingsApiKey
stringRequired
Value of the API KEY for the embeddings provider (if required).
For example for OpenAI it is OPENAI_API_KEY, for Cohere it is COHERE_API_KEY)
Dataset fields to select from the dataset results and store in the database
datasetFields
arrayRequired
This array specifies the dataset fields to be selected and stored in the vector store. Only the fields listed here will be included in the vector store.
For instance, when using the Website Content Crawler, you might choose to include fields such as text
, url
, and metadata.title
in the vector store.
Default value of this property is ["text"]
Dataset fields to select from the dataset and store as metadata in the database
metadataDatasetFields
objectOptional
A list of dataset fields which should be selected from the dataset and stored as metadata in the vector stores.
For example, when using the Website Content Crawler, you might want to store url
in metadata. In this case, use metadataDatasetFields parameter as follows {"url": "url"}
Custom object to be stored as metadata in the vector store database
metadataObject
objectOptional
This object allows you to store custom metadata for every item in the vector store.
For example, if you want to store the domain
as metadata, use the metadataObject
like this: {"domain": "apify.com"}.
Enable incremental updates for objects based on deltas
enableDeltaUpdates
booleanOptional
When set to true, this setting enables incremental updates for objects in the database by comparing the changes (deltas) between the crawled dataset items and the existing objects, uniquely identified by the datasetKeysToItemId
field.
The integration will only add new objects and update those that have changed, reducing unnecessary updates. The datasetFields
, metadataDatasetFields
, and metadataObject
fields are used to determine the changes.
Default value of this property is true
Dataset fields to uniquely identify dataset items (only relevant when `enableDeltaUpdates` is enabled)
deltaUpdatesPrimaryDatasetFields
arrayOptional
This array contains fields that are used to uniquely identify dataset items, which helps to handle content changes across different runs.
For instance, in a web content crawling scenario, the url
field could serve as a unique identifier for each item.
Default value of this property is ["url"]
Delete expired objects from the database
deleteExpiredObjects
booleanOptional
When set to true, delete objects from the database that have not been crawled for a specified period.
Default value of this property is true
Delete expired objects from the database after a specified number of days
expiredObjectDeletionPeriodDays
integerOptional
This setting allows the integration to manage the deletion of objects from the database that have not been crawled for a specified period. It is typically used in subsequent runs after the initial crawl.
When the value is greater than 0, the integration checks if objects have been seen within the last X days (determined by the expiration period). If the objects are expired, they are deleted from the database. The specific value for deletedExpiredObjectsDays
depends on your use case and how frequently you crawl data.
For example, if you crawl data daily, you can set deletedExpiredObjectsDays
to 7 days. If you crawl data weekly, you can set deletedExpiredObjectsDays
to 30 days.
Default value of this property is 30
Enable text chunking
performChunking
booleanOptional
When set to true, the text will be divided into smaller chunks based on the settings provided below. Proper chunking helps optimize retrieval and ensures accurate and efficient responses.
Default value of this property is true
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- 3 stars
- 22 days response time
- Created in Jun 2024
- Modified about 1 month ago