Open Source — MIT Licensed — v2.0.0

SemantixRAG
AI-Native Data Platform

Production-grade RAG ingestion, knowledge graphs, AI observability, automated compliance, and multi-modal extraction — all in one elegant, open-source platform. No cloud lock-in.

10
Pipeline Stages
33+
Python Modules
4
Search Strategies
3
P0 Products

End-to-End Architecture

From raw documents to AI-powered retrieval with observability, compliance, and knowledge graphs built in.

1

Extraction

PDFs, DOCX, HTML, images, audio, video. Unstructured.io + VLM + Whisper for multi-modal parsing.

2

Chunking

Header-aware splitter preserves document hierarchy. Configurable token limits with overlap.

3

Enrichment

LLM summaries + entity extraction (NER) + PII scanning (GuardRail). Context prepended to every chunk.

4

Embedding

BGE-m3 dense vectors + JinaCLIP images + CLAP audio. HNSW index in OpenSearch k-NN.

5

GraphRAG

Entities extracted → Neo4j knowledge graph. Multi-hop graph traversal + hybrid search fusion.

┌────────────────────────────────────────────────────────────────────────────┐
│                           SemantixRAG Platform v2.0                        │
├────────────────────────────────────────────────────────────────────────────┤
│  API Gateway (FastAPI)  ───  OPA Policy Engine ───  AuthZ & Rate Limiting  │
├────────────────────────────────────────────────────────────────────────────┤
│  Core Pipeline:                                                            │
│    Extraction → Chunking → Enrichment → Embedding → Indexing               │
│       │             │            │            │          │                 │
│       │  VLM/Whisper│  Header    │  LLM Summ. │  BGE-m3  │  OpenSearch     │
│       │  Multi-modal│  Splitter  │  Entity    │  JinaCLIP│  Neo4j Graph    │
│       │             │            │  PII Scan  │  CLAP    │  (GraphRAG)     │
├────────────────────────────────────────────────────────────────────────────┤
│  ObsidianGuardRailGraphRAGCostSentinelAdminCopilot     │
│  Tracing   │  PII Detect │  Entity    │  Cost Track │  NL Admin            │
│  Metrics   │  Masking    │  KG Write │  Optimize   │  Auto Config          │
│  Eval      │  DSAR       │  Graph    │  Budget     │  Reports              │
│  Alerting  │  Audit      │  Search   │  Forecast   │  Incident             │
└────────────────────────────────────────────────────────────────────────────┘
                

AI-Native Platform Features

Every feature designed for production RAG workloads with enterprise governance.

Obsidian — AI Observability

End-to-end distributed tracing for every pipeline stage. RAG quality metrics (faithfulness, MRR, Recall@k), latency histograms, cost attribution, and drift detection.

NEWTracesMetricsEval Harness

GraphRAG — Knowledge Graphs

Automatic entity extraction (spaCy + LLM) → Neo4j knowledge graph. Multi-hop graph traversal fused with vector search via RRF for entity-aware retrieval.

NEWNeo4jNERGraph Search

GuardRail — Auto Compliance

Real-time PII detection via Presidio + regex fallback. Dynamic masking, GDPR DSAR automation (find/delete/export), OPA policy engine, and immutable audit trails.

NEWPII ScanDSAROPA

Multi-Modal RAG

Extract text from images (VLM), transcribe audio (Whisper), sample video frames. Unified embedding space via JinaCLIP + CLAP for cross-modal retrieval.

NEWImagesAudioVideo

FastAPI REST Server

Production-grade API with 5 route modules: ingestion, retrieval, compliance, observability, and admin. OpenAPI docs, CORS, async handlers, file upload.

NEWRESTOpenAPIAsync

CostSentinel — Cost Intel

Per-pipeline cost tracking, anomaly detection, optimization recommendations, model routing intelligence, and budget guardrails with hard-stop enforcement.

NEWFinOpsBudgetForecast

AdminCopilot — NL Admin

Natural-language platform administration. "Why is ingestion failing?", "Show PII findings", "Schema status" — managed via conversational interface.

NEWNL QueriesAuto-diagnose

Structure-Preserving Extraction

Documents parsed with full structural fidelity. Headers, tables, lists, figures extracted with relationships intact — not flat text.

Unstructured.ioPyMuPDFElement Mapping

Hybrid Search (k-NN + BM25)

Dense vector + keyword search fused via Reciprocal Rank Fusion. HNSW ANN with cosine similarity. Configurable top-k and reranking pipeline.

HNSWRRFCross-encoder

CDC & Incremental Updates

Python Watchdog monitors directories. File changes trigger automatic re-indexing with old chunk deletion. Batch processing with progress reporting.

WatchdogAuto-syncDelete-by-ID

CI/CD & Testing

GitHub Actions workflows for lint, unit tests (41+), integration tests, security scanning (Trivy), Docker build. OpenSearch + Neo4j service containers.

NEWGitHub ActionsTrivy

100% Local & Open Source

Zero cloud dependencies. Everything runs on your hardware with Docker. Embedding models and LLMs run locally via Ollama or HuggingFace. MIT licensed.

MIT LicenseDockerSelf-hosted

Technology Stack

Battle-tested tools for production AI workloads.

Python 3.11

Core orchestration & logic

Docker

Containerized infrastructure

OpenSearch

Vector store + BM25 search

Neo4j

Knowledge graph + GraphRAG

NEW

Unstructured.io

Document parsing engine

BGE-m3

Dense embeddings (1024 dim)

FastAPI

REST API server

NEW

LLaVA / Whisper

Multi-modal VLM + audio

NEW

Presidio / OPA

PII detection + policy engine

NEW

Grafana

Dashboard & alerting

Pydantic

Data validation & settings

Pytest

41+ unit & integration tests

NEW

From Zero to AI in 5 Commands

Get the full platform running on your machine in minutes.

SemantixRAG v2.0
# 1. Clone the repo
$ git clone https://github.com/SemantixRAG/SemantixRAG.git
$ cd SemantixRAG

# 2. Start infrastructure (OpenSearch, Neo4j, Redis, OPA)
$ docker-compose -f docker/docker-compose.yml up -d
✔ Starting semantix-opensearch ... done
✔ Starting semantix-neo4j ... done
✔ Starting semantix-redis ... done
✔ Starting semantix-opa ... done

# 3. Install Python dependencies
$ pip install -r requirements.txt
✔ All 30+ packages installed

# 4. Init indexes and ingest a document
$ python main.py init
✔ Index 'rag_documents' created (knn_vector, 1024 dim)
$ python main.py ingest ./documents/sample_document.md
✔ Extracted 12 elements → 5 chunks → 5 embeddings indexed
✔ Entities extracted: 8 → Neo4j graph updated
✔ PII scan: 0 findings, risk level: low

# 5. Start the API server & search!
$ python -m uvicorn src.api.main:app --reload
✔ Uvicorn running on http://localhost:8000
$ curl -X POST http://localhost:8000/v1/query \
-H "Content-Type: application/json" \
-d '{"query": "reinforcement learning", "strategy": "graph"}'
{
"results": [{"score": 0.94, "document_title": "ML Intro", ...}],
"retrieval_metrics": {"vector_ms": 45, "graph_ms": 32}
}

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