graphnlp-intel 0.1.6

Hybrid Graph-NLP Intelligence Platform

🕸️ graphnlp-intel

graphnlp-intel is an open-source Python library and REST API that transforms unstructured documents into rich, interactive knowledge graphs using state-of-the-art NLP, relationship extraction, and GNN-based sentiment propagation.

🚀 Quickstart

Install the library and download the required spaCy model:

pip install graphnlp-intel
python -m spacy download en_core_web_sm

Run the pipeline in 6 lines of code:

from graphnlp import Pipeline

pipe = Pipeline(domain="finance")
result = pipe.run(["Goldman Sachs acquired a 5% stake in Microsoft for $2.3 billion."])

# Visualize, export, and summarize
result.graph.visualize("output.html") # Generates a Pyvis interactive HTML graph
result.export_json("output.json")    # Exports D3 compatible JSON
print(result.summary())              # Output stats on nodes, edges, sentiment, and communities

🧠 How it works

The system processes unstructured text through a 5-stage pipeline:

 📄 Ingestion      🔍 Extraction         🕸️ Graph Build         🧠 GNN              📈 Output
 DocumentLoader → NERExtractor       → GraphBuilder       → GraphGNN          → Pyvis HTML /
 TextChunker      RelationExtractor    CommunityDetector                        D3 JSON /
 EmailParser      EmbeddingExtractor                                            Neo4j / Redis

Standalone Extractor Usage

from graphnlp.extraction.ner import NERExtractor
from graphnlp.extraction.relations import RelationExtractor

ner = NERExtractor()
entities = ner.extract("Apple Inc reported revenue of $120 billion.")

rel_ext = RelationExtractor()
triples = rel_ext.extract("Apple Inc reported revenue of $120 billion.")

Standalone Graph Construction Usage

from graphnlp.graph.builder import GraphBuilder
from graphnlp.graph.community import CommunityDetector
import networkx as nx

builder = GraphBuilder()
graph = builder.build(triples, entities, embeddings_dict)

detector = CommunityDetector()
communities = detector.detect(graph)

🧩 Domain adapters

Domain adapters supply contextual logic like schema mappings, preprocessing, and post-processing steps tailored to specific industries.

Adapter	Entity Types	Use Case
finance	TICKER, ORG, AMOUNT, DATE	Parse fund records, expand ticker syms, build COMPETITOR_OF graphs
email	PERSON, MERCHANT, MONEY	Strip HTML/headers, parse invoices, generate PAID_TO expense clusters
feedback	PRODUCT, SCORE, FEATURE	Normalize 5-star ratings, cluster feature complaints, link reviews
incidents	SERVICE, ERROR, SEV	Standardize P0/P1 flags, deduplicate logs, build AFFECTS topological graphs

Using the Email Adapter

from graphnlp.adapters.base import get_adapter
from graphnlp.adapters.email import EmailAdapter
import networkx as nx

adapter = get_adapter("email")
clean_text = adapter.preprocess(raw_email_string)

# Graph integration
g = nx.DiGraph()
g.add_edge("$234.56", "Amazon", predicate="paid_to")
spend_clusters = EmailAdapter.monthly_spend_summary(g)

Custom Adapter Implementation

from graphnlp.adapters.base import DomainAdapter

class HealthcareAdapter(DomainAdapter):
    @property
    def domain(self) -> str:
        return "healthcare"
        
    @property
    def entity_types(self) -> list[str]:
        return ["PATIENT", "SYMPTOM", "DRUG"]
        
    def preprocess(self, text: str) -> str:
        return text.replace("Pt.", "Patient")

⚡ API Server

Deploy the multi-tenant REST API via Docker:

make docker-up

Endpoints

Method	Path	Description
GET	/health	Check service health and system status.
POST	/v1/analyze	Submit documents for processing (sync or async).
GET	/v1/analyze/{job_id}	Poll status of an async analysis job.
GET	/v1/graph/{graph_id}	Retrieve D3.js compatible graph JSON by ID.
GET	/v1/graph/{graph_id}/summary	Retrieve summarized stats of the graph.
POST	/v1/webhooks	Register a new webhook endpoint for async complete events.
GET	/v1/webhooks	List registered webhooks for the given tenant.

Auth, Submit, and Poll

# Submit Sync
curl -X POST http://localhost:8000/v1/analyze \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"documents": ["Invoice 123 for $500 to AWS"], "domain": "finance", "async": false}'

# Submit Async
curl -X POST http://localhost:8000/v1/analyze \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"documents": ["Massive batch 1...", "Massive batch 2..."], "async": true}'

# Poll Async Status
curl -X GET http://localhost:8000/v1/analyze/job-1234 \
  -H "Authorization: Bearer sk-your-api-key"

📦 SDK Integration

Python SDK

pip install graphnlp-client

from graphnlp_client.client import GraphNLPClient

client = GraphNLPClient(api_key="sk-your-api-key", base_url="http://localhost:8000")

# Sync
result = client.analyze(["Azure bill $300"], domain="email")
print(result["graph_id"])

# Get Graph data
graph = client.get_graph(result["graph_id"])

TypeScript / JavaScript SDK

npm install graphnlp-client

import { GraphNLPClient } from 'graphnlp-client';

const client = new GraphNLPClient({ apiKey: 'sk-your-api-key' });

async function analyze() {
  const result = await client.analyze(['Q4 earnings were up 12%'], { domain: 'finance' });
  const graph = await client.getGraph(result.graph_id);
  console.log(graph.nodes);
}

🪝 Webhooks

Register webhooks to receive JSON payloads upon async task completion.

curl -X POST http://localhost:8000/v1/webhooks \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://yourapp.com/hook", "events": ["graph.ready"], "secret": "wh_sec_123"}'

Webhook Payload Example

{
  "event": "graph.ready",
  "job_id": "job-1234",
  "graph_id": "graph-5678",
  "tenant_id": "tenant-abc",
  "timestamp": "2026-04-18T10:00:00Z",
  "signature": "sha256=d2b8b9a..."
}

⚙️ Configuration

Configure the platform using config/default.yaml or environment variables:

# config/default.yaml
environment: production
neo4j:
  uri: bolt://localhost:7687
redis:
  url: redis://localhost:6379
api:
  rate_limit_per_minute: 100
nlp:
  ner_model: en_core_web_sm
  embedding_model: all-MiniLM-L6-v2

# .env
GRAPHNLP_ENVIRONMENT=production
GRAPHNLP_NEO4J_URI=bolt://neo4j:7687
GRAPHNLP_NEO4J_USER=neo4j
GRAPHNLP_NEO4J_PASSWORD=supersecret
GRAPHNLP_REDIS_URL=redis://redis:6379

🛠️ CLI Reference

Manage the platform using the built-in Typer CLI:

• graphnlp run --domain finance --file data.csv : Run pipeline on a local file.
• graphnlp serve --port 8000 --reload : Start the FastAPI server.
• graphnlp worker --concurrency 4 : Start the Celery async worker.
• graphnlp generate-key -t my-tenant : Generate a new API key for the specified tenant.

🏗️ Architecture

graphnlp-intel/
├── graphnlp/
│   ├── config.py              # Pydantic Settings
│   ├── pipeline.py            # Main Orchestrator
│   ├── ingestion/             # Loaders, Chunkers, Email Parsers
│   ├── extraction/            # NER, Relations, SBERT Embeddings
│   ├── graph/                 # NetworkX Builder, PyG GNN, Diff, Louvain
│   ├── adapters/              # Domain-specific logic
│   ├── storage/               # Neo4j & Redis handlers
│   ├── api/                   # FastAPI routes, Auth, Tenant Middleware
│   ├── queue/                 # Celery workers & tasks
│   └── webhooks/              # HMAC Dispatcher
├── tests/
│   ├── unit/                  # Isolated logic blocks
│   ├── integration/           # E2E API tests
│   └── fixtures/              # CSV/JSON samples
├── sdk/
│   ├── python/                # PyPI API wrapper
│   └── js/                    # NPM API wrapper
├── docker/
│   ├── docker-compose.yml     # Local orchestration
│   ├── Dockerfile             # API Container
│   └── Dockerfile.worker      # Celery Container
└── pyproject.toml             # Dependencies & metadata

📚 Open Source Stack

We stand on the shoulders of giants.

Component	Library
NLP Base	spacy
Deep Learning	torch
Graph Neural Nets	torch-geometric
Language Models	transformers
Sentence Embeddings	sentence-transformers
Graph Analytics	networkx
Async Queue	celery
Web Framework	fastapi
Configuration	pydantic
Caching & Rate Limits	redis.asyncio
Graph Persistence	neo4j (async driver)
CLI Generation	typer

🗺️ Roadmap

Phase	Milestone	Expected
Phase 1	Streaming Engine (Kafka integration, real-time diffing)	Q3 2026
Phase 2	Custom Model Fine-Tuning (LoRA automated pipeline)	Q4 2026
Phase 3	Visual Graph Dashboard (React SPA for interactive analytics)	Q1 2027

💼 Custom Builds & Enterprise

Tier	Price	Features
Open Source	Free	Apache 2.0 · Self-hosted · All adapters · CLI
Custom NER	$800–2,000	Fine-tune NER · HF model delivery · Eval report
Hosted API	$2,500 + $400/mo	FEATURED · AWS/GCP/Azure deploy · Docker + TF · SDK
Enterprise	$8,000+	Streaming · Dashboard · Alerting SLA · White-label

Interested in Hosted API or Enterprise tiers? Get a quote on our site.

🤝 Contributing

We welcome contributions!

git clone https://github.com/samvardhan03/GraphNLP-Intel.git
cd GraphNLP-Intel
./setup_dev.sh
make test

📄 License

This project is licensed under the Apache License 2.0.

@software{graphnlpintel2026,
  author = {GraphNLP Team},
  title = {graphnlp-intel: Hybrid Graph-NLP Intelligence Platform},
  year = {2026},
  url = {https://github.com/samvardhan03/GraphNLP-Intel}
}