Nexla Doc FAQs

Question 1

How does Nexla Doc provide grounded and accurate answers?

Accepted Answer

Nexla Doc ensures grounded answers by employing a RAG architecture. It semantically searches your PDF content, retrieves relevant chunks, and then uses an LLM to generate an answer based *only* on that retrieved context. Each answer includes source attribution, citing the specific document name and page number for verification.

Question 2

What kind of documents can I query with Nexla Doc?

Accepted Answer

Currently, Nexla Doc is specifically built to process and query PDF documents. You simply place your PDF files into a designated 'data/' directory, and the system automatically handles the ingestion, text extraction, chunking, and embedding to make them searchable.

Question 3

What is Nexla Doc?

Accepted Answer

Nexla Doc is an MCP (Model Context Protocol) server designed to enable AI agents to interact with PDF documents. It allows agents to ask natural-language questions and receive grounded, source-attributed answers using Retrieval-Augmented Generation (RAG).

Question 4

Can I integrate Nexla Doc with my existing AI agents or LLMs?

Accepted Answer

Yes, Nexla Doc is designed to integrate seamlessly as an MCP server with various AI clients and agents, including Claude Desktop, ChatGPT, and custom solutions. It supports both OpenAI and Anthropic LLM providers, configurable via environment variables, to generate answers.

Question 5

How does Nexla Doc manage my PDF data and ensure persistence?

Accepted Answer

Nexla Doc features a robust ingestion pipeline that extracts text from PDFs using tools like pdfplumber, chunks the text with configurable overlap, and creates embeddings. These embeddings, along with document metadata, are stored in a local, persistent ChromaDB vector store, allowing your indexed data to remain available across sessions.

Nexla Doc

Nexla Doc

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