---

title: “HugeGraph-LLM REST API” linkTitle: “REST API” weight: 5

The HugeGraph-LLM demo process serves both the Web UI and REST API. The default address is http://localhost:8001:

cd hugegraph-ai/hugegraph-llm
python -m hugegraph_llm.demo.rag_demo.app \
  --host 127.0.0.1 \
  --port 8001

Authentication

Enable login in .env:

ENABLE_LOGIN=True
USER_TOKEN=replace-with-a-secret

Requests then require a Bearer token:

Authorization: Bearer replace-with-a-secret

RAG

POST /rag

Returns one or more answer types according to the switches. When none is explicitly selected, only graph_only is enabled.

curl -X POST http://localhost:8001/rag \
  -H 'Content-Type: application/json' \
  -d '{
    "query": "Which movies feature Al Pacino?",
    "raw_answer": false,
    "vector_only": false,
    "graph_only": true,
    "graph_vector_answer": false,
    "max_graph_items": 30,
    "topk_return_results": 20,
    "vector_dis_threshold": 0.9,
    "topk_per_keyword": 1,
    "gremlin_tmpl_num": 1,
    "client_config": {
      "url": "127.0.0.1:8080",
      "graph": "hugegraph",
      "user": "admin",
      "pwd": "admin",
      "gs": "DEFAULT"
    }
  }'

The response contains only enabled answer fields:

{
  "query": "Which movies feature Al Pacino?",
  "graph_only": "..."
}

Other optional parameters include graph_ratio, rerank_method (bleu or reranker), near_neighbor_first, custom_priority_info, and three custom prompt fields.

POST /rag/graph

Runs graph retrieval without generating a final natural-language answer:

curl -X POST http://localhost:8001/rag/graph \
  -H 'Content-Type: application/json' \
  -d '{
    "query": "Which movies feature Al Pacino?",
    "get_vertex_only": false,
    "gremlin_tmpl_num": 1,
    "rerank_method": "bleu"
  }'

graph_recall in the response can contain keywords, match_vids, gremlin, graph_result, and vertex_degree_list. Set get_vertex_only=true to return immediately after vertex matching.

Graph Extraction

POST /graph/extract

An inline schema does not connect to HugeGraph:

curl -X POST http://localhost:8001/graph/extract \
  -H 'Content-Type: application/json' \
  -d '{
    "texts": ["Alice works at Acme."],
    "schema": {
      "vertexlabels": [
        {"name": "person", "properties": ["name"]},
        {"name": "company", "properties": ["name"]}
      ],
      "edgelabels": [
        {
          "name": "works_at",
          "source_label": "person",
          "target_label": "company",
          "properties": []
        }
      ]
    },
    "language": "en",
    "split_type": "sentence",
    "include_meta": true
  }'

texts can be a string or an array of strings. language accepts zh or en; split_type accepts document, paragraph, or sentence.

When schema is an existing graph name, also pass client_config, and make client_config.graph match that name:

{
  "texts": "Alice works at Acme.",
  "schema": "hugegraph",
  "client_config": {
    "graph": "hugegraph",
    "user": "admin",
    "pwd": "admin",
    "gs": "DEFAULT"
  }
}

A successful response always contains status, result.vertices, result.edges, warnings, and meta.

Text2Gremlin

POST /text2gremlin

curl -X POST http://localhost:8001/text2gremlin \
  -H 'Content-Type: application/json' \
  -d '{
    "query": "Find all person vertices",
    "example_num": 1,
    "output_types": ["template_gremlin", "template_execution_result"]
  }'

output_types can contain:

• match_result
• template_gremlin
• raw_gremlin
• template_execution_result
• raw_execution_result

If omitted, only template_gremlin is returned by default. An empty array lets the implementation return all outputs. A custom gremlin_prompt must contain {query}, {schema}, {example}, and {vertices}.

Runtime Configuration

POST /config/graph

{
  "url": "127.0.0.1:8080",
  "graph": "hugegraph",
  "user": "admin",
  "pwd": "admin",
  "gs": "DEFAULT"
}

POST /config/llm and POST /config/embedding

Both endpoints use the same request model. OpenAI or LiteLLM example:

{
  "llm_type": "openai",
  "api_key": "your-key",
  "api_base": "https://api.openai.com/v1",
  "language_model": "gpt-4.1-mini",
  "max_tokens": "4096"
}

Ollama requests still require the common fields; api_key and api_base can be empty strings:

{
  "llm_type": "ollama/local",
  "api_key": "",
  "api_base": "",
  "language_model": "qwen2.5:7b",
  "host": "127.0.0.1",
  "port": "11434"
}

POST /config/rerank

{
  "reranker_type": "siliconflow",
  "reranker_model": "BAAI/bge-reranker-v2-m3",
  "api_key": "your-key"
}

reranker_type accepts cohere or siliconflow. Cohere also accepts cohere_base_url.

These endpoints change the process's active configuration and may write values back to .env. client_config in /rag, /rag/graph, and /text2gremlin overrides the HugeGraph connection for one request. The current implementation still changes process-global settings temporarily, so do not issue long-running requests with different connections concurrently.

Logs

POST /logs

This endpoint requires ADMIN_TOKEN in .env to be changed to a secure value. Example request body:

{
  "admin_token": "replace-with-an-admin-secret",
  "log_file": "llm-server.log"
}

log_file must be a file name under logs/ and cannot contain path separators.