FieldCure RAG FAQs

Name: FieldCure RAG
Author: fieldcure

Question 1

Can FieldCure RAG be integrated with other AI services?

Accepted Answer

Yes, it supports OpenAI-compatible embedding providers, allowing integration with services like Ollama, LM Studio, OpenAI, Azure OpenAI, Groq, and Together AI for embeddings. It also integrates with the Model Context Protocol (MCP).

Question 2

What is FieldCure RAG?

Accepted Answer

FieldCure RAG is a Model Context Protocol (MCP) server designed to index your local document collections. It enables advanced hybrid keyword and semantic search, optimized for both Korean and English content.

Question 3

How does FieldCure RAG improve search accuracy?

Accepted Answer

It employs a powerful hybrid approach, combining BM25 keyword search with vector semantic search, fused using Reciprocal Rank Fusion (RRF). This is further enhanced by AI-powered chunk contextualization that provides bilingual keyword enrichment.

Question 4

What types of documents can FieldCure RAG index?

Accepted Answer

FieldCure RAG supports a wide range of formats including DOCX, HWPX (Korean standard), PDF (with OCR for scanned pages), Excel (XLSX), PowerPoint (PPTX), and plain text files (TXT, MD).

Question 5

Does FieldCure RAG support multilingual content effectively?

Accepted Answer

Absolutely. It's specifically optimized for Korean and English, featuring specialized Korean-optimized chunking (sentence boundary, decimal protection) and AI-driven bilingual keyword enrichment to facilitate seamless cross-lingual search.

FieldCure RAG

FieldCure RAG

Características Principales

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Características Principales

Casos de Uso