nexrag 0.3.1

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

NexRAG

███╗   ██╗███████╗██╗  ██╗██████╗  █████╗  ██████╗
████╗  ██║██╔════╝╚██╗██╔╝██╔══██╗██╔══██╗██╔════╝
██╔██╗ ██║█████╗   ╚███╔╝ ██████╔╝███████║██║  ███╗
██║╚██╗██║██╔══╝   ██╔██╗ ██╔══██╗██╔══██║██║   ██║
██║ ╚████║███████╗██╔╝ ██╗██║  ██║██║  ██║╚██████╔╝
╚═╝  ╚═══╝╚══════╝╚═╝  ╚═╝╚═╝  ╚═╝╚═╝  ╚═╝ ╚═════╝

●plug ⇄swap ▶scale

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

---

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.

---

Quickstart

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

from nexrag import NexRAG, RunMetrics

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

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

# Query (blocking)
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')}")

# Streaming — tokens arrive live; RunMetrics is the final item
metrics = None
for item in pipeline.stream_query("Summarise the key obligations."):
    if isinstance(item, RunMetrics):
        metrics = item
    else:
        print(item, end="", flush=True)
print(f"\n\n{metrics.total_latency_ms:.0f}ms — {metrics.chunks_retrieved} chunks retrieved")

# 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/ for the full documentation site.

---

Installation

# Core only
pip install nexrag

# Provider extras — install only what you use
pip install "nexrag[openai]"         # OpenAI embedder + LLM
pip install "nexrag[anthropic]"      # Anthropic (Claude) LLM
pip install "nexrag[ollama]"         # Ollama local LLM + embedder
pip install "nexrag[huggingface]"    # HuggingFace embedder

# Document loaders
pip install "nexrag[pdf]"            # PDFLoader (pypdf)
pip install "nexrag[word]"           # Word documents (python-docx)
pip install "nexrag[html]"           # HTML pages (beautifulsoup4)

# Retrieval extras
pip install "nexrag[bm25]"           # BM25Retriever keyword search (rank-bm25)
pip install "nexrag[cohere]"         # CohereReranker
pip install "nexrag[cross-encoder]"  # CrossEncoderReranker (sentence-transformers)

# Convenience bundles
pip install "nexrag[all-sparse]"     # all sparse retrievers (bm25)
pip install "nexrag[all-rerankers]"  # all rerankers (cohere + cross-encoder)
pip install "nexrag[all-retrieval]"  # all-sparse + all-rerankers
pip install "nexrag[all-loaders]"    # all document loaders
pip install "nexrag[all-providers]"  # all LLM + embedder providers

# Full bundle — dev/CI; pulls PyTorch via sentence-transformers
pip install "nexrag[all]"

---

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.

---

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.

---

Supported Providers

Category	Providers
Embedders	OpenAI, Ollama, HuggingFace
Vector DBs	ChromaDB (in-memory, persistent, remote server)
LLMs	OpenAI, Ollama, Anthropic
Loaders	PDF, plain text, Word, HTML, Excel
Chunkers	Recursive (separator-aware)
Retrievers	Dense (cosine similarity), BM25 (keyword), Hybrid (dense + BM25)
Rerankers	Cohere, CrossEncoder (sentence-transformers)

---

Contributing

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

---

Changelog

See CHANGELOG.md.