Chat-based Document Q&A
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A Chat-based Document Q&A is a document Q&A task that is a chat-based application which supports conversational question answering over one or more source documents through an interactive chat interface.
- AKA: Conversational Document Question Answering, Chat-based Document Question Answering, Document Chat, Chat-with-Documents.
- Context:
- It can (typically) accept a natural language question from a user about the content of an uploaded document.
- It can (typically) retrieve relevant document passages using retrieval augmented generation (RAG) over a document index.
- It can (typically) ground its generated answer in retrieved context to reduce model hallucination.
- It can (typically) maintain conversation history to support follow-up questions and multi-turn dialogue.
- It can (typically) present source citations that link each answer span back to a document location.
- It can (typically) chunk a source document into text segments for embedding-based retrieval.
- It can (typically) compute vector embeddings of document chunks with an embedding model.
- It can (typically) store document embeddings in a vector database for semantic search.
- It can (typically) rank candidate passages by semantic similarity to the user query.
- It can (typically) synthesize a final answer with a large language model conditioned on retrieved passages.
- It can (often) query a pool of documents rather than a single document file.
- It can (often) support document upload in multiple document formats, such as PDF documents and Word documents.
- It can (often) perform cross-document reasoning to combine evidence from several source documents.
- It can (often) stream partial answers token-by-token to improve perceived latency.
- It can (often) disambiguate an ambiguous query through a clarification question.
- It can (often) enforce access control over private documents within a document repository.
- It can (often) log user querys and system responses for quality evaluation.
- It can (often) apply re-ranking to improve retrieval precision.
- It can (often) handle out-of-scope questions by declining when relevant context is absent.
- It can support integration with an enterprise knowledge base or a content management system.
- It can range from being a Single-Document Chat Q&A System to being a Multi-Document Chat Q&A System, depending on its document corpus size.
- It can range from being a Open-Domain Chat Document Q&A to being a Closed-Domain Chat Document Q&A, depending on its domain scope.
- It can range from being a Extractive Chat Document Q&A to being a Abstractive Chat Document Q&A, depending on its answer generation strategy.
- …
- Example(s):
- Domain-Specific Chat Document Q&As, such as:
- Chat Document Q&A Systems, such as:
- SEC-Insights (2023), which uses the Retrieval Augmented Generation (RAG) capabilities of LlamaIndex to answer questions about SEC 10-K and 10-Q documents.
- ChatPDF (2023), which provides chat-based Q&A against an uploaded PDF document.
- ChatGPT with file upload (2023), which supports document-grounded question answering.
- …
- Counter-Example(s):
- Keyword-based Document Search, which returns matching documents rather than a synthesized answer.
- Non-Conversational Document Q&A, which lacks multi-turn dialogue and conversation history.
- Open-Domain Chatbot, which answers without grounding in a user-provided document.
- Document Summarization Task, which produces a summary rather than answering a specific question.
- …
- See: Document Q&A, Question Answering Task, Retrieval Augmented Generation (RAG), LlamaIndex, Vector Database, Large Language Model, Conversational AI System, Semantic Search, Chatbot.
References
2023
- https://github.com/run-llama/sec-insights
- QUOTE:
- SEC-Insights uses the Retrieval Augmented Generation (RAG) capabilities of LlamaIndex to answer questions about SEC 10-K & 10-Q documents.
- Product Features 😎
- Chat-based Document Q&A against a pool of documents.