nexrag 0.3.0

Framework-agnostic RAG pipeline SDK. Plug in any component, swap any stage, configure everything in YAML

NexRAG

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●plug ⇄swap ▶scale

Framework-agnostic RAG pipeline SDK. Plug in any component, swap any stage, configure everything in YAML.

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What is NexRAG?

NexRAG is a production-grade RAG (Retrieval-Augmented Generation) pipeline SDK for Python.

NexRAG owns the pipeline shape. You own the components.

Every stage — loading, chunking, embedding, retrieval, generation — is a clean interface. NexRAG ships default implementations for each. You can swap any of them by implementing the interface and declaring it in YAML. No framework lock-in. No magic. No hidden behavior.

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Quickstart

pip install "nexrag[openai,pdf]"
export OPENAI_API_KEY=sk-...
cp nexrag.example.yaml nexrag.yaml   # edit to taste

from nexrag import NexRAG

pipeline = NexRAG.from_config("nexrag.yaml")

# Ingest a PDF
result = pipeline.ingest("contracts/agreement.pdf")
print(f"Ingested {result.documents_loaded} doc, {result.chunks_written} chunks")

# Query
result = pipeline.query("What are the termination clauses?")
print(result.answer)
for source in result.sources:
    print(f"  [{source.rank}] score={source.score:.3f}  {source.chunk.metadata.get('source')}")

# nexrag.yaml (minimal)
ingestion:
  loader:
    type: pdf
  embedder:
    provider: openai
    model: text-embedding-3-small
    api_key: ${OPENAI_API_KEY}
  vector_db:
    provider: chroma
    default_collection: documents
    collections:
      documents:
        mode: persistent
        path: ./.nexrag/chroma

query:
  embedder: inherit
  llm:
    provider: openai
    model: gpt-4o
    api_key: ${OPENAI_API_KEY}

See docs/user-guide.md for the full guide.

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Installation

# Core only
pip install nexrag

# With OpenAI support
pip install "nexrag[openai]"

# With everything
pip install "nexrag[all]"

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Design Principles

Principle	What it means
Interface-first	Every stage is a contract. Implementation is secondary.
Config-driven	YAML configures the pipeline. Code defines the logic.
Zero lock-in	Core has no dependency on LangChain, LlamaIndex, or any AI SDK.
Explicit over implicit	No hidden defaults. Every behavior is declared or documented.
Extensible by design	New components plug in without touching core.

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Architecture

NexRAG has two independent pipelines:

INGESTION  →  Loader → Sanitizer → Chunker → Embedder → VectorDB
QUERY      →  Embedder → Retriever → PromptBuilder → LLM → PipelineResult

See Architecture Documentation for full pipeline diagrams.

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Supported Providers

Available now

Category	Providers
Embedders	OpenAI
Vector DBs	ChromaDB (local persistent, in-memory)
LLMs	OpenAI, Ollama
Loaders	PDF, plain text
Chunkers	Recursive (separator-aware)

Coming in V1

Category	Providers
Embedders	Ollama, HuggingFace
Vector DBs	ChromaDB (remote server via HttpClient)
LLMs	Anthropic

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Contributing

NexRAG is in early development. Contribution guidelines will be published with v1.0.

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Changelog

See CHANGELOG.md.