neo4j-graphrag-skill

# Neo4j GraphRAG Skill

## When to Use

- Building GraphRAG retrieval pipelines with `neo4j-graphrag` Python package
- Choosing between VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever
- Writing `retrieval_query` Cypher fragments for graph-augmented context
- Wiring retriever + LLM into a `GraphRAG` pipeline
- Using LLM-routed multi-retriever with `ToolsRetriever`
- Debugging low retrieval quality
- Integrating Neo4j with LangChain, LlamaIndex, or Haystack

## When NOT to Use

- **KG construction from documents** → `neo4j-document-import-skill`
- **Plain vector/semantic search without graph traversal** → `neo4j-vector-index-skill`
- **Hybrid search that combines vector with fulltext or other ranked sources** → `neo4j-vector-index-skill`
- **GDS algorithms (PageRank, Louvain, node embeddings)** → `neo4j-gds-skill`
- **Agent long-term memory** → `neo4j-agent-memory-skill`
- **Writing raw Cypher queries** → `neo4j-cypher-skill`

---

## Retriever Selection

```
Has fulltext index?
  YES → Hybrid variants (HybridRetriever / HybridCypherRetriever)
  NO  → Vector variants (VectorRetriever / VectorCypherRetriever)

Need graph traversal after vector lookup?
  YES → Cypher variants (VectorCypherRetriever / HybridCypherRetriever)
  NO  → plain variants

Natural-language-to-Cypher?        → Text2CypherRetriever (no embedder needed)
LLM should route between retrievers? → ToolsRetriever
Vectors stored in external DB?      → WeaviateNeo4jRetriever / PineconeNeo4jRetriever / QdrantNeo4jRetriever
```

| Retriever | Vector | Fulltext | Graph | Best For |
|---|:---:|:---:|:---:|---|
| `VectorRetriever` | ✓ | — | — | Baseline semantic search |
| `HybridRetriever` | ✓ | ✓ | — | Better recall, no graph expansion |
| `VectorCypherRetriever` | ✓ | — | ✓ | GraphRAG without fulltext |
| `HybridCypherRetriever` | ✓ | ✓ | ✓ | **Production GraphRAG — default** |
| `Text2CypherRetriever` | — | — | ✓ | NL→Cypher, no embedder |
| `ToolsRetriever` | varies | varies | varies | LLM-routed multi-retriever |
| `WeaviateNeo4jRetriever` | ✓ | — | ✓ | Vectors in Weaviate |
| `PineconeNeo4jRetriever` | ✓ | — | ✓ | Vectors in Pinecone |
| `QdrantNeo4jRetriever` | ✓ | — | ✓ | Vectors in Qdrant |

---

## Install

```bash
pip install neo4j-graphrag[openai]        # OpenAI LLM + embeddings
pip install neo4j-graphrag[anthropic]     # Anthropic Claude
pip install neo4j-graphrag[google]        # Vertex AI / Gemini
pip install neo4j-graphrag[bedrock]       # Amazon Bedrock (boto3)
pip install neo4j-graphrag[cohere]        # Cohere
pip install neo4j-graphrag[mistralai]     # MistralAI
pip install neo4j-graphrag[ollama]        # Ollama (local)
pip install neo4j-graphrag[weaviate]      # Weaviate external retriever
pip install neo4j-graphrag[pinecone]      # Pinecone external retriever
pip install neo4j-graphrag[qdrant]        # Qdrant external retriever
```

Requires: Python >= 3.10, `neo4j >= 5.17.0` (driver 6.x supported).

---

## Step 2 — Choose Retriever

```
Has fulltext index? YES → Hybrid variants (better recall)
                   NO  → Vector variants (baseline)

Needs graph context after vector lookup? YES → Cypher variants
                                         NO  → plain variants

For natural-language-to-Cypher? → Text2CypherRetriever (no embedder needed)
For multi-tool LLM routing?     → ToolsRetriever
Using external vector DB?       → WeaviateNeo4jRetriever / PineconeNeo4jRetriever / QdrantNeo4jRetriever
```

| Retriever | Vector | Fulltext | Graph | When to use |
|---|:---:|:---:|:---:|---|
| `VectorRetriever` | ✓ | — | — | Baseline; quick start |
| `HybridRetriever` | ✓ | ✓ | — | Better recall; no graph context |
| `VectorCypherRetriever` | ✓ | — | ✓ | GraphRAG without fulltext |
| `HybridCypherRetriever` | ✓ | ✓ | ✓ | **Production GraphRAG — default choice** |
| `Text2CypherRetriever` | — | — | ✓ | LLM generates Cypher; no embedder |
| `ToolsRetriever` | varies | varies | varies | Multi-retriever LLM routing |

For custom Cypher hybrid search outside the `neo4j-graphrag` retriever APIs, use `neo4j-vector-index-skill`.

**Vector backend selection [v1.16+, auto]**: on Neo4j 2026.01+ all four vector/hybrid retrievers auto-route through the Cypher 25 `SEARCH ... WHERE` clause when filters are SEARCH-compatible (simple AND comparisons) and all filter props are declared in the index `WITH [n.prop]` list. `$or`, `$in`, `$like`, or undeclared props → automatic fallback to `db.index.vector.queryNodes()` procedure path (with warning log). Declare filterable properties via `filterable_properties=[...]` on `create_vector_index()`.

---

## Step 3 — Create Indexes (run once)

```cypher
// Vector index (all retrievers need this)
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
OPTIONS { indexConfig: {
  `vector.dimensions`: 1536,
  `vector.similarity_function`: 'cosine'
} };

// Fulltext index (Hybrid retrievers only)
CREATE FULLTEXT INDEX chunk_fulltext IF NOT EXISTS
FOR (c:Chunk) ON EACH [c.text];

// Confirm ONLINE before ingesting:
SHOW INDEXES YIELD name, state
WHERE name IN ['chunk_embedding', 'chunk_fulltext']
RETURN name, state;
// Both must show state = 'ONLINE'
```

If index not ONLINE: wait, poll every 5s. Do NOT start ingestion until ONLINE.

---

## Step 4 — Core Pattern (HybridCypherRetriever)

```python
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings import OpenAIEmbeddings
from neo4j_graphrag.generation import GraphRAG
from neo4j_graphrag.llm import OpenAILLM
from neo4j_graphrag.retrievers import HybridCypherRetriever

driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
embedder = OpenAIEmbeddings(model="text-embedding-3-large")  # OPENAI_API_KEY from env

# retrieval_query: Cypher fragment executed after the vector/fulltext lookup.
# Auto-injected variables:  node  (matched node)   score  (similarity float)
# MUST include a RETURN clause.  score must appear in RETURN.
retrieval_query = """
MATCH (node)<-[:HAS