GLiNER2 Hybrid LLM Client Example (Experimental)

Note: The GLiNER2Client is experimental and may change in future releases.

This example demonstrates using GLiNER2 as a hybrid LLM client for Graphiti. GLiNER2 handles entity extraction (NER) locally on CPU, while a general-purpose LLM client handles edge/fact extraction, deduplication, summarization, and other reasoning tasks.

• Paper: GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
• Models on HuggingFace:
  • fastino/gliner2-base-v1 (205M params)
  • fastino/gliner2-large-v1 (340M params)
  • fastino/gliner2-multi-v1 (multilingual)

Prerequisites

• Python 3.11+
• Neo4j 5.26+ (Neo4j Desktop or Docker)
• An LLM provider API key (Google, OpenAI, Anthropic, etc.)

Setup

# Install graphiti with the gliner2 extra
pip install graphiti-core[gliner2]

# Copy and configure environment variables
cp .env.example .env

The GLiNER2 model weights are downloaded automatically on first run.

LLM and Embedding Providers

The example uses Google Gemini (gemini-2.5-flash-lite) for the LLM and embeddings, but GLiNER2Client accepts any Graphiti LLMClient. To swap providers, replace GeminiClient and GeminiEmbedder with the equivalent from another provider:

• graphiti_core.llm_client.openai_client.OpenAIClient
• graphiti_core.llm_client.anthropic_client.AnthropicClient
• graphiti_core.llm_client.groq_client.GroqClient
• graphiti_core.embedder.openai.OpenAIEmbedder
• graphiti_core.embedder.voyage.VoyageAIEmbedder

Configuration

Running

python gliner2_neo4j.py