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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  • Prerequisites
  • Setup
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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 locally running instance of Qdrant or a Qdrant cloud instance.

To get a Qdrant cloud instance:

  1. Head to the Clusters section of the Cloud Dashboard.

  2. Select Clusters and then click + Create.

  1. Choose your cluster configurations and region.

  2. Hit Create to provision your cluster.

Setup

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

  2. Add a new Qdrant node on canvas.

  3. Create new Qdrant credential using the API Key

  1. Enter the required info into the Qdrant node:

    • Qdrant server URL

    • Collection name

  1. Document input can be connected with any node under Document Loader category.

  2. Embeddings input can be connected with any node under Embeddingscategory.

Resources

  • Qdrant documentation

  • LangChain JS Qdrant