GitHub - vespaai-playground/NyRAG: advanced, scalable, no-code RAG

https://github.com/vespaai-playground/NyRAG • Feb 1, 2026 11:17

Extracto

advanced, scalable, no-code RAG. Contribute to vespaai-playground/NyRAG development by creating an account on GitHub.

Resumen

Resumen Principal

NyRAG es una herramienta integral diseñada para simplificar la creación de aplicaciones Retrieval Augmented Generation (RAG), permitiendo a los usuarios construir sistemas de preguntas y respuestas basados en su propio contenido. Su funcionalidad principal radica en la capacidad de rastrear sitios web o procesar documentos locales, para luego desplegar los datos en Vespa, un motor de búsqueda híbrido, e integrar una interfaz de chat intuitiva. El sistema emplea un sofisticado proceso de recuperación multi-etapa: inicialmente, un Large Language Model (LLM) enriquece las consultas del usuario, generando bús

Contenido

NyRAG

NyRAG (pronounced as knee-RAG) is a simple tool for building RAG applications by crawling websites or processing documents, then deploying to Vespa for hybrid search with an integrated chat UI.

How It Works

When a user asks a question, NyRAG performs a multi-stage retrieval process:

Query Enhancement: An LLM generates additional search queries based on the user's question and initial context to improve retrieval coverage
Embedding Generation: Each query is converted to embeddings using the configured SentenceTransformer model
Vespa Search: Queries are executed against Vespa using nearestNeighbor search with the best_chunk_score ranking profile to find the most relevant document chunks
Chunk Fusion: Results from all queries are aggregated, deduplicated, and ranked by score to select the top-k most relevant chunks
Answer Generation: The retrieved context is sent to an LLM which generates a grounded answer based only on the provided chunks

This multi-query RAG approach with chunk-level retrieval ensures answers are comprehensive and grounded in your actual content, whether from crawled websites or processed documents.

LLM Support

NyRAG works with any OpenAI-compatible API, including:

OpenRouter (100+ models from various providers)
Ollama (local models: Llama, Mistral, Qwen, etc.)
LM Studio (local GUI for running models)
vLLM (high-performance local or remote inference)
LocalAI (local OpenAI drop-in replacement)
OpenAI (GPT-4, GPT-3.5, etc.)
Any other service implementing the OpenAI API format

Installation

We recommend uv:

uv init --python 3.10
uv venv
uv sync
source .venv/bin/activate
uv pip install -U nyrag

For development:

git clone https://github.com/abhishekkrthakur/nyrag.git
cd nyrag
pip install -e .

Usage

NyRAG is designed to be used primarily through its web UI, which manages the entire lifecycle from data processing to chat.

1. Start the UI

Local Mode (requires Docker):

Cloud Mode (requires Vespa Cloud account):

Open http://localhost:8000 in your browser.

2. Configure & Process

In the UI, you can create a new configuration for your data source.

Example Web Crawl Config:

name: mywebsite
mode: web
start_loc: https://example.com/
crawl_params:
  respect_robots_txt: true
rag_params:
  embedding_model: sentence-transformers/all-MiniLM-L6-v2

Example Docs Processing Config:

name: mydocs
mode: docs
start_loc: /path/to/documents/
doc_params:
  recursive: true
rag_params:
  embedding_model: sentence-transformers/all-mpnet-base-v2

3. Chat

Once processing is complete, you can start chatting with your data immediately in the UI. Make sure your configuration includes your LLM API key and model selection.

Configuration Reference

Cloud Deploy Parameters

Parameter	Type	Default	Description
`cloud_tenant`	str	`None`	Vespa Cloud tenant (required for cloud mode if no env/CLI target)

Connection Parameters

Parameter	Type	Default	Description
`vespa_url`	str	`None`	Vespa endpoint URL (auto-filled into `conf.yml` after deploy)
`vespa_port`	int	`None`	Vespa endpoint port (auto-filled into `conf.yml` after deploy)

Web Mode Parameters (`crawl_params`)

Parameter	Type	Default	Description
`respect_robots_txt`	bool	`true`	Respect robots.txt rules
`aggressive_crawl`	bool	`false`	Faster crawling with more concurrent requests
`follow_subdomains`	bool	`true`	Follow links to subdomains
`strict_mode`	bool	`false`	Only crawl URLs matching start pattern
`user_agent_type`	str	`chrome`	`chrome`, `firefox`, `safari`, `mobile`, `bot`
`custom_user_agent`	str	`None`	Custom user agent string
`allowed_domains`	list	`None`	Explicitly allowed domains

Docs Mode Parameters (`doc_params`)

Parameter	Type	Default	Description
`recursive`	bool	`true`	Process subdirectories
`include_hidden`	bool	`false`	Include hidden files
`follow_symlinks`	bool	`false`	Follow symbolic links
`max_file_size_mb`	float	`None`	Max file size in MB
`file_extensions`	list	`None`	Only process these extensions

RAG Parameters (`rag_params`)

Parameter	Type	Default	Description
`embedding_model`	str	`sentence-transformers/all-MiniLM-L6-v2`	Embedding model
`embedding_dim`	int	`384`	Embedding dimension
`chunk_size`	int	`1024`	Chunk size for text splitting
`chunk_overlap`	int	`50`	Overlap between chunks
`distance_metric`	str	`angular`	Distance metric
`max_tokens`	int	`8192`	Max tokens per document
`llm_base_url`	str	`None`	LLM API base URL (OpenAI-compatible)
`llm_model`	str	`None`	LLM model name
`llm_api_key`	str	`None`	LLM API key

LLM Provider Support

NyRAG works with any OpenAI-compatible API. Just configure the rag_params in your UI settings.

Provider	Base URL	Model Example	API Key
Ollama	`http://localhost:11434/v1`	`llama3.2`	`dummy`
LM Studio	`http://localhost:1234/v1`	`local-model`	`dummy`
vLLM	`http://localhost:8000/v1`	`meta-llama/Llama-3.2-3B-Instruct`	`dummy`
OpenRouter	`https://openrouter.ai/api/v1`	`openai/gpt-5.2`	`your-key`
OpenAI	`None` (default)	`openai/gpt-4o`	`your-key`

Example Config:

llm_config:
  llm_base_url: https://openrouter.ai/api/v1
  llm_model: llama3.2
  llm_api_key: dummy