docs.langflux.space
  • Welcome to LangFlux
  • Using LangFlux
    • API
    • Streaming
    • Embed
    • Variables
  • Configuration
    • Auth
      • Chatflow Level
    • Rate Limit
  • Integrations
    • Cache
      • InMemory Cache
    • Chains
      • Conversational Retrieval QA Chain
      • Vectara QA Chain
    • Document Loaders
      • S3 File Loader
      • PDF Files
    • Chat Models
      • Azure ChatOpenAI
      • ChatLocalAI
      • Google VertexAI
    • Embeddings
      • Azure OpenAI Embeddings
      • LocalAI Embeddings
    • Memory
      • Short Term Memory
      • Long Term Memory
        • Zep Memory
      • Threads
    • Text Splitters
      • Character Text Splitter
    • Tools
      • Custom Tool
    • Vector Stores
      • Chroma
      • Pinecone
      • Elastic
      • Qdrant
      • SingleStore
      • Supabase
      • Vectara
    • Utilities
      • Set/Get Variable
      • If Else
    • External Integrations
      • Zapier Zaps
  • Use Cases
    • Web Scrape QnA
    • Webhook Tool
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  1. Integrations
  2. Vector Stores

Qdrant

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Last updated 1 year ago

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Prerequisites

A or a Qdrant cloud instance.

To get a Qdrant cloud instance:

  1. Head to the Clusters section of the .

  2. Select Clusters and then click + Create.

  1. Choose your cluster configurations and region.

  2. Hit Create to provision your cluster.

Setup

  1. Add a new Qdrant node on canvas.

  2. Create new Qdrant credential using the API Key

  1. Enter the required info into the Qdrant node:

    • Qdrant server URL

    • Collection name

Resources

Get/Create your API Key from the Data Access Control section of the .

Document input can be connected with any node under category.

Embeddings input can be connected with any node under category.

Cloud Dashboard
Document Loader
Embeddings
Qdrant documentation
LangChain JS Qdrant
locally running instance of Qdrant
Cloud Dashboard