Qdrant

Qdrant is an open-source vector database designed for efficient storage, management, and retrieval of high-dimensional vector embeddings. Qdrant enables fast and scalable semantic search, making it ideal for AI applications that require similarity search, recommendation systems, and contextual information retrieval.

With Qdrant, you can:

• Store vector embeddings: Efficiently manage and persist high-dimensional vectors at scale
• Perform semantic similarity search: Find the most similar vectors to a query vector in real time
• Filter and organize data: Use advanced filtering to narrow down search results based on metadata or payload
• Fetch specific points: Retrieve vectors and their associated payloads by ID
• Scale seamlessly: Handle large collections and high-throughput workloads

In Klyntos, the Qdrant integration enables your agents to interact with Qdrant programmatically as part of their workflows. Supported operations include:

• Upsert: Insert or update points (vectors and payloads) in a Qdrant collection
• Search: Perform similarity search to find vectors most similar to a given query vector, with optional filtering and result customization
• Fetch: Retrieve specific points from a collection by their IDs, with options to include payloads and vectors

This integration allows your agents to leverage powerful vector search and management capabilities, enabling advanced automation scenarios such as semantic search, recommendation, and contextual retrieval. By connecting Klyntos with Qdrant, you can build agents that understand context, retrieve relevant information from large datasets, and deliver more intelligent and personalized responses—all without managing complex infrastructure.

Usage Instructions

Integrate Qdrant into the workflow. Can upsert, search, and fetch points. Requires API Key.

Tools

qdrant_upsert_points

Insert or update points in a Qdrant collection

Input

Parameter	Type	Required	Description
url	string	Yes	Qdrant base URL
apiKey	string	No	Qdrant API key (optional)
collection	string	Yes	Collection name
points	array	Yes	Array of points to upsert

Output

Parameter	Type	Description
status	string	Status of the upsert operation
data	object	Result data from the upsert operation

qdrant_search_vector

Search for similar vectors in a Qdrant collection

Input

Parameter	Type	Required	Description
url	string	Yes	Qdrant base URL
apiKey	string	No	Qdrant API key (optional)
collection	string	Yes	Collection name
vector	array	Yes	Vector to search for
limit	number	No	Number of results to return
filter	object	No	Filter to apply to the search
with_payload	boolean	No	Include payload in response
with_vector	boolean	No	Include vector in response

Output

Parameter	Type	Description
data	array	Vector search results with ID, score, payload, and optional vector data
status	string	Status of the search operation

qdrant_fetch_points

Fetch points by ID from a Qdrant collection

Input

Parameter	Type	Required	Description
url	string	Yes	Qdrant base URL
apiKey	string	No	Qdrant API key (optional)
collection	string	Yes	Collection name
ids	array	Yes	Array of point IDs to fetch
with_payload	boolean	No	Include payload in response
with_vector	boolean	No	Include vector in response

Output

Parameter	Type	Description
data	array	Fetched points with ID, payload, and optional vector data
status	string	Status of the fetch operation

Notes

• Category: tools
• Type: qdrant