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Available Components

Conversation Chain

Maintains coherent, context-aware conversations using chat-based language models with integrated memory

Conversational Retrieval QA Chain

Combines document retrieval with conversational context for accurate and contextually relevant answers

LLM Chain

A versatile component for running queries against Large Language Models with flexible prompt engineering

Multi Prompt Chain

Automatically selects and uses the most appropriate prompt from multiple predefined options based on the input query

Multi Retrieval QA Chain

Dynamically selects the most appropriate vector store retriever from multiple options to answer questions across various domains

Retrieval QA Chain

Combines document retrieval with language model processing for accurate answers based on a given knowledge base

Vectara QA Chain

Leverages Vectara’s advanced search and summarization capabilities for accurate and contextually relevant answers

VectorDB QA Chain

Utilizes vector database retrieval with language model processing for efficient question-answering based on large document collections

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Use Cases

These chain components are beneficial for a wide range of applications, including:
  • Building intelligent chatbots and virtual assistants
  • Creating advanced question-answering systems
  • Developing context-aware information retrieval systems
  • Implementing dynamic and adaptive AI workflows
  • Enhancing customer support and knowledge base interactions
  • Powering educational tools and research assistants
  • Enabling multi-domain and multilingual AI applications
By combining these chain components, developers can create sophisticated AI systems capable of handling complex queries, maintaining context, and providing accurate and relevant information across various domains and use cases.