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

Chat Models

Chat models take a list of messages as input and return a model-generated message as output. These models such as gpt-3.5-turbo or gpt4 are powerful and cheaper than its predecessor Completions models such as text-davincii-003.

Below are some of the chat models available to use in LangFlux:

  • Azure ChatOpenAI

  • ChatLocalAI

  • GoogleVertexAI

PreviousPDF FilesNextAzure ChatOpenAI

Last updated 1 year ago

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