Creating Your First QA Pipeline with Retrieval-Augmentation

Haystack released a tutorial on building a generative question-answering pipeline using retrieval-augmentation (RAG). The pipeline integrates SentenceTransformersTextEmbedder, InMemoryEmbeddingRetriever, ChatPromptBuilder, and a ChatGenerator to retrieve relevant documents and generate answers. This enables developers to create more accurate and context-aware QA systems.

InMemoryDocumentStore SentenceTransformersDocumentEmbedder SentenceTransformersTextEmbedder InMemoryEmbeddingRetriever ChatPromptBuilder ChatGenerator OpenAIChatGenerator MistralChatGenerator TransformersChatGenerator This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation RAG https://www.deepset.ai/blog/llms-retrieval-augmentation approach with Haystack. The process involves four main components: SentenceTransformersTextEmbedder https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder for creating an embedding for the user query, InMemoryEmbeddingRetriever https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever for fetching relevant documents, ChatPromptBuilder https://docs.haystack.deepset.ai/docs/chatpromptbuilder for creating a template prompt, and a ChatGenerator https://docs.haystack.deepset.ai/docs/generators for generating the final answer.