Chains Overview
Chains are fundamental components in AI workflows that combine language models, memory, and other tools to perform complex tasks. They are designed to handle various types of queries and interactions, often involving multiple steps or specialized knowledge retrieval.
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
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.
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