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A GraphRAG repository that combines Qdrant (vector search), Neo4j (knowledge graph), and LLMs to provide an end-to-end ingest, query, and generation pipeline with an MCP HTTP server.

medium.com/@baka3k
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MCP GraphRAG (LangExtract)

Requirements

  • Neo4j running at http://localhost:7474
  • Qdrant running at http://localhost:6333

Install

pip install -r requirements.txt
python -m spacy download en_core_web_sm

Default entity model (GLiNER)

The default entity model is gliner_medium-v2.1.

  • Local/offline (recommended):
export GLINER_MODEL_PATH=/Users/youruser/tool/ai/gliner_medium-v2.1
  • Or use model name (download from Hugging Face):
GLINER_MODEL_NAME=urchade/gliner_mediumv2

Quick comparison: GLiNER vs spaCy

  • GLiNER: zero-shot, flexible labels, often better for domain-specific entities.
  • spaCy: fast and stable, easy with EntityRuler, but uses fixed labels per model.

Embedding model (offline)

If your environment blocks Hugging Face downloads, set a local path:

export EMBEDDING_MODEL_PATH=/Users/youruser/tool/ai/all-MiniLM-L6-v2

When set, mcp_graph_rag.py will load embeddings from EMBEDDING_MODEL_PATH.

.env config (auto-loaded)

Create .env at the repo root:

NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASS=neo4j_pass
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_COLLECTION=graphrag_entities

EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
EMBEDDING_MODEL_PATH=/Users/youruser/tool/ai/all-MiniLM-L6-v2
GLINER_MODEL_NAME=urchade/gliner_mediumv2
GLINER_MODEL_PATH=/Users/youruser/tool/ai/gliner_medium-v2.1
EMBEDDING_LOCAL_ONLY=1
GLINER_LOCAL_ONLY=1
HF_HUB_OFFLINE=1
TRANSFORMERS_OFFLINE=1

DEFAULT_ENTITY_TYPES=ORG,PRODUCT,STANDARD,TECH,CRYPTO,SECURITY,PROTOCOL,VEHICLE,DEVICE,SERVER,APP,CERTIFICATE,KEY
AZURE_OPENAI_API_KEY=xxxxxx
AZURE_OPENAI_ENDPOINT=xxxx
AZURE_OPENAI_DEPLOYMENT=xxx
AZURE_OPENAI_API_VERSION=xxx

LANGEXTRACT_API_KEY=your_key
LANGEXTRACT_MODEL_ID=gemini-2.5-flash

Run MCP server

python mcp_graph_rag.py --transport streamable-http

Endpoint:

http://127.0.0.1:8789/mcp

MCP config

{
  "servers": {
    "mind_mcp": {
      "url": "http://127.0.0.1:8789/mcp",
      "type": "http"
    }
  }
}

MCP tools

  • query_graph_rag_langextract(query, top_k, source_id, collection, include_entities, include_relations, expand_related, related_k, entity_types, max_passage_chars)
  • list_source_ids(limit)
  • list_qdrant_collections()
  • get_paragraph_text(source_id, paragraph_id)

Ingest flow (GraphRAG LangExtract)

Step 1: run graphrag_ingest_langextract.py

This command:

  • Reads input (--pdf, --text-file, --raw-text, or --folder).
  • Splits text into paragraphs and builds embeddings per paragraph.
  • Calls LangExtract to extract entities and relations.
  • Stores data in:
    • Qdrant: passage + embedding (payload includes source_id, paragraph_id, entity_ids).
    • Neo4j: Document, Paragraph, Entity, and their relationships.

Result: after ingest, you can call query_graph_rag_langextract and use the entity_ids bridge to expand context via Neo4j.

Pipeline (ingest):

input -> chunk/paragraph -> embeddings -> LangExtract -> Qdrant(payload+vectors)
                                                -> Neo4j(entities+relations)

graphrag_ingest_langextract.py parameters (full list)

Exactly one input source is required: --pdf, --text-file, --raw-text, or --folder.

Grouped reference

Input

  • --pdf: Path to a single PDF file.
  • --text-file: Path to a UTF-8 text file.
  • --raw-text: Raw text input string (quoted).
  • --folder: Folder to scan recursively for .pdf and .txt.
  • --source-id: Override source_id stored in Qdrant payload and Neo4j.

Paragraphing + embeddings

  • --collection: Qdrant collection name (default: graphrag_entities).
  • --embedding-model: Override embedding model name/path.
  • --max-paragraph-chars: Max chars per paragraph (default: 1200).
  • --min-paragraph-chars: Min chars to keep a paragraph (default: 150).
  • --skip-llm-short: Skip LLM for short paragraphs, still store in Qdrant.

Entity extraction

  • --entity-provider: Entity extractor: spacy, gliner, gemini, or langextract (default: gliner).
  • --spacy-model: spaCy model name (default: en_core_web_sm).
  • --ruler-json: Path to spaCy EntityRuler JSON.
  • --gliner-model-name: GLiNER model name (default: urchade/gliner_mediumv2).
  • --gliner-model-path: Local GLiNER model path (recommended for offline).
  • --gliner-model: Deprecated alias; use --gliner-model-name or --gliner-model-path.
  • --gliner-labels: Comma-separated labels or path to text file (one label per line).
  • --gliner-threshold: GLiNER confidence threshold (default: 0.3).
  • --gliner-batch-size: Batch size for GLiNER extraction (default: 8).
  • --no-batch: Disable batching for GLiNER and Neo4j (sequential processing).
  • --langextract-model-id: Override LANGEXTRACT_MODEL_ID for LangExtract.
  • --langextract-model-url: Override LANGEXTRACT_MODEL_URL (e.g., Ollama URL).
  • --llm-debug: Print raw LLM output and log to logs/langextract_raw.log.
  • --llm-retry-count: Retry count when LLM output parsing fails.
  • --llm-retry-backoff: Backoff seconds between LLM retries.

Neo4j + Qdrant

  • --neo4j-uri: Neo4j URI (default from NEO4J_URI).
  • --neo4j-user: Neo4j user (default from NEO4J_USER).
  • --neo4j-pass: Neo4j password (default from NEO4J_PASS).
  • --neo4j-batch-size: Number of paragraphs written per Neo4j batch (default: 50).
  • --qdrant-url: Qdrant URL (if set, overrides host/port).
  • --qdrant-host: Qdrant host (default from QDRANT_HOST).
  • --qdrant-port: Qdrant port (default from QDRANT_PORT).
  • --qdrant-api-key: Qdrant API key (default from QDRANT_KEY).

Compact table

Group Parameter Description Default
Input --pdf Path to a single PDF file —
Input --text-file Path to a UTF-8 text file —
Input --raw-text Raw text input string —
Input --folder Folder to scan recursively for .pdf and .txt —
Input --source-id Override source_id stored in Qdrant/Neo4j None
Embed --collection Qdrant collection name graphrag_entities
Embed --embedding-model Override embedding model name/path None
Embed --max-paragraph-chars Max chars per paragraph 1200
Embed --min-paragraph-chars Min chars per paragraph 150
Embed --skip-llm-short Skip LLM for short paragraphs false
Entity --entity-provider spacy, gliner, gemini, langextract gliner
Entity --spacy-model spaCy model name en_core_web_sm
Entity --ruler-json spaCy EntityRuler JSON None
Entity --gliner-model-name GLiNER model name urchade/gliner_mediumv2
Entity --gliner-model-path Local GLiNER model path env GLINER_MODEL_PATH
Entity --gliner-model Deprecated alias None
Entity --gliner-labels Labels (CSV or file path) PERSON,ORG,PRODUCT,GPE,DATE,TECH,CRYPTO,STANDARD
Entity --gliner-threshold GLiNER confidence threshold 0.3
Entity --gliner-batch-size GLiNER extraction batch size 8
Entity --no-batch Disable batching (sequential processing) false
Entity --langextract-model-id Override LangExtract model id env LANGEXTRACT_MODEL_ID
Entity --langextract-model-url Override LangExtract URL env LANGEXTRACT_MODEL_URL
LLM --llm-debug Print raw LLM output false
LLM --llm-retry-count Retry count on parse fail None
LLM --llm-retry-backoff Backoff seconds None
Neo4j --neo4j-uri Neo4j URI env NEO4J_URI
Neo4j --neo4j-user Neo4j user env NEO4J_USER
Neo4j --neo4j-pass Neo4j password env NEO4J_PASS
Neo4j --neo4j-batch-size Paragraphs per batch write 50
Qdrant --qdrant-url Qdrant URL (overrides host/port) env QDRANT_URL
Qdrant --qdrant-host Qdrant host env QDRANT_HOST
Qdrant --qdrant-port Qdrant port env QDRANT_PORT
Qdrant --qdrant-api-key Qdrant API key env QDRANT_KEY
  • --pdf: Path to a single PDF file.
  • --text-file: Path to a UTF-8 text file.
  • --raw-text: Raw text input string (quoted).
  • --folder: Folder to scan recursively for .pdf and .txt.
  • --source-id: Override source_id stored in Qdrant payload and Neo4j.
  • --collection: Qdrant collection name (default: graphrag_entities).
  • --embedding-model: Override embedding model name/path.
  • --max-paragraph-chars: Max chars per paragraph (default: 1200).
  • --min-paragraph-chars: Min chars to keep a paragraph (default: 150).
  • --skip-llm-short: Skip LLM for short paragraphs, still store in Qdrant.
  • --entity-provider: Entity extractor: spacy, gliner, gemini, or langextract (default: gliner).
  • --spacy-model: spaCy model name (default: en_core_web_sm).
  • --ruler-json: Path to spaCy EntityRuler JSON.
  • --gliner-model-name: GLiNER model name (default: urchade/gliner_mediumv2).
  • --gliner-model-path: Local GLiNER model path (recommended for offline).
  • --gliner-model: Deprecated alias; use --gliner-model-name or --gliner-model-path.
  • --gliner-labels: Comma-separated labels or path to text file (one label per line).
  • --gliner-threshold: GLiNER confidence threshold (default: 0.3).
  • --gliner-batch-size: Batch size for GLiNER extraction (default: 8).
  • --no-batch: Disable batching for GLiNER and Neo4j (sequential processing).
  • --langextract-model-id: Override LANGEXTRACT_MODEL_ID for LangExtract.
  • --langextract-model-url: Override LANGEXTRACT_MODEL_URL (e.g., Ollama URL).
  • --llm-debug: Print raw LLM output and log to logs/langextract_raw.log.
  • --llm-retry-count: Retry count when LLM output parsing fails.
  • --llm-retry-backoff: Backoff seconds between LLM retries.
  • --neo4j-uri: Neo4j URI (default from NEO4J_URI).
  • --neo4j-user: Neo4j user (default from NEO4J_USER).
  • --neo4j-pass: Neo4j password (default from NEO4J_PASS).
  • --neo4j-batch-size: Number of paragraphs written per Neo4j batch (default: 50).
  • --qdrant-url: Qdrant URL (if set, overrides host/port).
  • --qdrant-host: Qdrant host (default from QDRANT_HOST).
  • --qdrant-port: Qdrant port (default from QDRANT_PORT).
  • --qdrant-api-key: Qdrant API key (default from QDRANT_KEY).

Example (GLiNER)

python graphrag_ingest_langextract.py \
  --folder /path/to/folder \
  --entity-provider gliner \
  --gliner-model-name urchade/gliner_mediumv2 \
  --gliner-labels gliner/labels_digital_key.txt \
  --gliner-threshold 0.35 \
  --gliner-batch-size 1 \
  --neo4j-batch-size 1 \
  --neo4j-pass neo4j_pass
python graphrag_ingest_langextract.py \
  --folder /path/to/folder \
  --entity-provider gliner \
  --gliner-model-name urchade/gliner_mediumv2 \
  --gliner-labels gliner/labels_digital_key.txt \
  --gliner-threshold 0.35 \
  --gliner-batch-size 1 \
  --no-batch \
  --neo4j-pass neo4j_pass

Example (LangExtract + Ollama)

python graphrag_ingest_langextract.py \
  --pdf /path/to/file.pdf \
  --entity-provider langextract \
  --langextract-model-id gemma2:2b \
  --langextract-model-url http://localhost:11434 \
  --neo4j-pass neo4j_pass

Example (LangExtract + Gemini)

export LANGEXTRACT_API_KEY=your_gemini_key

python graphrag_ingest_langextract.py \
  --pdf /path/to/file.pdf \
  --entity-provider langextract \
  --langextract-model-id gemini-2.5-flash \
  --neo4j-pass neo4j_pass

Example (LangExtract + OpenAI)

export LANGEXTRACT_API_KEY=your_openai_key

python graphrag_ingest_langextract.py \
  --pdf /path/to/file.pdf \
  --entity-provider langextract \
  --langextract-model-id gpt-4o \
  --neo4j-pass neo4j_pass

Example (LangExtract + Azure OpenAI)

export LANGEXTRACT_API_KEY=your_azure_key
export AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com
export AZURE_OPENAI_DEPLOYMENT=your_deployment
export AZURE_OPENAI_API_VERSION=2024-02-15-preview

python graphrag_ingest_langextract.py \
  --pdf /path/to/file.pdf \
  --entity-provider langextract \
  --langextract-model-id gpt-4o \
  --neo4j-pass neo4j_pass

Example (Reset Neo4j + Qdrant)

python 0_reset_all.py --neo4j-pass neo4j_pass

Query flow (GraphRAG LangExtract)

What does query_graph_rag_langextract do?

Two main steps:

  1. Search Qdrant with the query embedding to get top-k passages
  2. Expand via Neo4j using entity_ids from those passages

Where does it query, what data comes from each DB?

  • Qdrant: embeddings + passages. The tool:
    • finds top-k passages,
    • reads payload fields source_id, paragraph_id, entity_ids.
  • Neo4j: entities + relations. The tool:
    • resolves entities by entity_ids,
    • optionally expands relations (include_relations, expand_related, related_k).

Special points

  • Uses source_id to link passages with Neo4j.
  • The bridge between Qdrant and Neo4j is entity_ids from LangExtract/GLiNER ingest.(you can choose another, such as spaCy)
  • Relation expansion is optional to balance speed vs depth.

Pipeline (query):

query -> embedding -> Qdrant(top-k passages + entity_ids)
                 -> Neo4j(entities/relations expansion)
                 -> response context

Sample commands

list_qdrant_collections()

list_source_ids(limit=50)

query_graph_rag_langextract(
  query="Digital Key encryption info",
  top_k=5,
  collection="graphrag_entities",
  source_id="my_doc",
  include_entities=true,
  include_relations=true,
  related_k=50,
  max_passage_chars=800
)

Graph RAG

GraphRAG LangExtract

Config MCP

GraphRAG LangExtract

Use GraphRAG to create sequences diagram from Document

GraphRAG LangExtract

About

A GraphRAG repository that combines Qdrant (vector search), Neo4j (knowledge graph), and LLMs to provide an end-to-end ingest, query, and generation pipeline with an MCP HTTP server.

medium.com/@baka3k

Topics

neo4j-graph vector-search graph-rag rag-pipeline graphrag

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