This is an automated email from the ASF dual-hosted git repository.
jin pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/incubator-hugegraph-ai.git
The following commit(s) were added to refs/heads/main by this push:
new 950ceef feat: Introduce gradio for creating interactive and visual
demo (#30)
950ceef is described below
commit 950ceef4e91d041f8546d5ed04be89f20376bf60
Author: Simon Cheung <[email protected]>
AuthorDate: Thu Mar 7 17:07:41 2024 +0800
feat: Introduce gradio for creating interactive and visual demo (#30)
Usage:
`export
PYTHONPATH=${PROJECT_ROOT_DIR}/hugegraph-llm/src:${PROJECT_ROOT_DIR}/hugegraph-python-client/src
python3 ./hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py`
---
hugegraph-llm/examples/build_kg_test.py | 11 +-
hugegraph-llm/examples/graph_rag_test.py | 81 +------
hugegraph-llm/requirements.txt | 1 +
hugegraph-llm/src/config/config.ini | 2 +-
hugegraph-llm/src/hugegraph_llm/llms/ernie_bot.py | 9 +-
.../operators/common_op/nltk_helper.py | 3 +-
.../src/hugegraph_llm/operators/graph_rag_task.py | 10 +-
.../operators/hugegraph_op/commit_to_hugegraph.py | 5 +-
.../operators/hugegraph_op/graph_rag_query.py | 18 +-
.../operators/kg_construction_task.py | 9 +-
.../operators/llm_op/answer_synthesize.py | 2 +-
.../operators/llm_op/keyword_extract.py | 15 +-
hugegraph-llm/src/hugegraph_llm/utils/config.py | 10 +-
.../src/hugegraph_llm/utils/gradio_demo.py | 255 +++++++++++++++++++++
14 files changed, 319 insertions(+), 112 deletions(-)
diff --git a/hugegraph-llm/examples/build_kg_test.py
b/hugegraph-llm/examples/build_kg_test.py
index b0b8c51..b9faa03 100644
--- a/hugegraph-llm/examples/build_kg_test.py
+++ b/hugegraph-llm/examples/build_kg_test.py
@@ -49,13 +49,14 @@ if __name__ == "__main__":
}
(
- builder.import_schema(from_hugegraph="xxx")
+ builder
+ # .import_schema(from_hugegraph="xxx")
+ # .import_schema(from_extraction="xxx")
+ .import_schema(from_user_defined=schema)
.print_result()
- # .import_schema(from_extraction="xxx").print_result()
- # .import_schema(from_user_defined=xxx).print_result()
.extract_triples(TEXT)
.print_result()
- .disambiguate_word_sense()
- .commit_to_hugegraph()
+ # .disambiguate_word_sense()
+ # .commit_to_hugegraph()
.run()
)
diff --git a/hugegraph-llm/examples/graph_rag_test.py
b/hugegraph-llm/examples/graph_rag_test.py
index bbd6862..510f85d 100644
--- a/hugegraph-llm/examples/graph_rag_test.py
+++ b/hugegraph-llm/examples/graph_rag_test.py
@@ -19,95 +19,26 @@
import os
from hugegraph_llm.operators.graph_rag_task import GraphRAG
-from pyhugegraph.client import PyHugeClient
-
-
-def prepare_data():
- client = PyHugeClient("127.0.0.1", 8080, "hugegraph", "admin", "admin")
- schema = client.schema()
- schema.propertyKey("name").asText().ifNotExist().create()
- schema.propertyKey("birthDate").asText().ifNotExist().create()
- schema.vertexLabel("Person").properties(
- "name", "birthDate"
- ).useCustomizeStringId().ifNotExist().create()
-
schema.vertexLabel("Movie").properties("name").useCustomizeStringId().ifNotExist().create()
-
schema.indexLabel("PersonByName").onV("Person").by("name").secondary().ifNotExist().create()
-
schema.indexLabel("MovieByName").onV("Movie").by("name").secondary().ifNotExist().create()
-
schema.edgeLabel("ActedIn").sourceLabel("Person").targetLabel("Movie").ifNotExist().create()
-
- graph = client.graph()
- graph.addVertex("Person", {"name": "Al Pacino", "birthDate":
"1940-04-25"}, id="Al Pacino")
- graph.addVertex(
- "Person",
- {"name": "Robert De Niro", "birthDate": "1943-08-17"},
- id="Robert De Niro",
- )
- graph.addVertex("Movie", {"name": "The Godfather"}, id="The Godfather")
- graph.addVertex("Movie", {"name": "The Godfather Part II"}, id="The
Godfather Part II")
- graph.addVertex(
- "Movie",
- {"name": "The Godfather Coda The Death of Michael Corleone"},
- id="The Godfather Coda The Death of Michael Corleone",
- )
-
- graph.addEdge("ActedIn", "Al Pacino", "The Godfather", {})
- graph.addEdge("ActedIn", "Al Pacino", "The Godfather Part II", {})
- graph.addEdge("ActedIn", "Al Pacino", "The Godfather Coda The Death of
Michael Corleone", {})
- graph.addEdge("ActedIn", "Robert De Niro", "The Godfather Part II", {})
-
- graph.close()
-
+from hugegraph_llm.utils.gradio_demo import init_hg_test_data
if __name__ == "__main__":
+ init_hg_test_data()
os.environ["http_proxy"] = ""
os.environ["https_proxy"] = ""
os.environ["OPENAI_API_KEY"] = ""
- # prepare_data()
-
graph_rag = GraphRAG()
-
- # configure operator with context dict
- context = {
- # hugegraph client
- "ip": "localhost", # default to "localhost" if not set
- "port": 18080, # default to 8080 if not set
- "user": "admin", # default to "admin" if not set
- "pwd": "admin", # default to "admin" if not set
- "graph": "hugegraph", # default to "hugegraph" if not set
- # query question
- "query": "Tell me about Al Pacino.", # must be set
- # keywords extraction
- "max_keywords": 5, # default to 5 if not set
- "language": "english", # default to "english" if not set
- # graph rag query
- "prop_to_match": "name", # default to None if not set
- "max_deep": 2, # default to 2 if not set
- "max_items": 30, # default to 30 if not set
- # print intermediate processes result
- "verbose": True, # default to False if not set
- }
- result =
graph_rag.extract_keyword().query_graph_for_rag().synthesize_answer().run(**context)
- print(f"Query:\n- {context['query']}")
- print(f"Answer:\n- {result['answer']}")
-
- print("--------------------------------------------------------")
-
- # configure operator with parameters
- graph_client = PyHugeClient("127.0.0.1", 18080, "hugegraph", "admin",
"admin")
result = (
- graph_rag.extract_keyword(
- text="Tell me about Al Pacino.",
- max_keywords=5, # default to 5 if not set
- language="english", # default to "english" if not set
- )
+ graph_rag.extract_keyword(text="Tell me about Al Pacino.")
+ .print_result()
.query_graph_for_rag(
- graph_client=graph_client,
max_deep=2, # default to 2 if not set
max_items=30, # default to 30 if not set
prop_to_match=None, # default to None if not set
)
+ .print_result()
.synthesize_answer()
+ .print_result()
.run(verbose=True)
)
print("Query:\n- Tell me about Al Pacino.")
diff --git a/hugegraph-llm/requirements.txt b/hugegraph-llm/requirements.txt
index 03bba7f..47a2d23 100644
--- a/hugegraph-llm/requirements.txt
+++ b/hugegraph-llm/requirements.txt
@@ -2,3 +2,4 @@ openai==0.28.1
retry==0.9.2
tiktoken==0.5.1
nltk==3.8.1
+gradio==4.19.2
diff --git a/hugegraph-llm/src/config/config.ini
b/hugegraph-llm/src/config/config.ini
index 6f9219f..7ff45c4 100644
--- a/hugegraph-llm/src/config/config.ini
+++ b/hugegraph-llm/src/config/config.ini
@@ -27,6 +27,6 @@ graph = hugegraph
type = openai
api_key = xxx
secret_key = xxx
-ernie_url =
https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=
+llm_url =
https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=
model_name = gpt-3.5-turbo-16k
max_token = 4000
diff --git a/hugegraph-llm/src/hugegraph_llm/llms/ernie_bot.py
b/hugegraph-llm/src/hugegraph_llm/llms/ernie_bot.py
index 085f7a0..c4ec2f2 100644
--- a/hugegraph-llm/src/hugegraph_llm/llms/ernie_bot.py
+++ b/hugegraph-llm/src/hugegraph_llm/llms/ernie_bot.py
@@ -31,7 +31,7 @@ class ErnieBotClient(BaseLLM):
self.c = Config(section=Constants.LLM_CONFIG)
self.api_key = self.c.get_llm_api_key()
self.secret_key = self.c.get_llm_secret_key()
- self.base_url = self.c.get_llm_ernie_url()
+ self.base_url = self.c.get_llm_url()
self.get_access_token()
def get_access_token(self):
@@ -61,7 +61,12 @@ class ErnieBotClient(BaseLLM):
raise Exception(
f"Request failed with code {response.status_code}, message:
{response.text}"
)
- return response.text
+ response_json = json.loads(response.text)
+ if "error_code" in response_json:
+ raise Exception(
+ f"Error {response_json['error_code']}:
{response_json['error_msg']}"
+ )
+ return response_json["result"]
def generate_streaming(
self,
diff --git a/hugegraph-llm/src/hugegraph_llm/operators/common_op/nltk_helper.py
b/hugegraph-llm/src/hugegraph_llm/operators/common_op/nltk_helper.py
index 944328e..eb7b4d2 100644
--- a/hugegraph-llm/src/hugegraph_llm/operators/common_op/nltk_helper.py
+++ b/hugegraph-llm/src/hugegraph_llm/operators/common_op/nltk_helper.py
@@ -77,4 +77,5 @@ class NLTKHelper:
return str(path)
-nltk_helper = NLTKHelper()
+if __name__ == '__main__':
+ NLTKHelper().stopwords()
diff --git a/hugegraph-llm/src/hugegraph_llm/operators/graph_rag_task.py
b/hugegraph-llm/src/hugegraph_llm/operators/graph_rag_task.py
index 0088119..a62f4b1 100644
--- a/hugegraph-llm/src/hugegraph_llm/operators/graph_rag_task.py
+++ b/hugegraph-llm/src/hugegraph_llm/operators/graph_rag_task.py
@@ -20,10 +20,10 @@ from typing import Dict, Any, Optional, List
from hugegraph_llm.llms.base import BaseLLM
from hugegraph_llm.llms.init_llm import LLMs
+from hugegraph_llm.operators.common_op.print_result import PrintResult
from hugegraph_llm.operators.hugegraph_op.graph_rag_query import GraphRAGQuery
from hugegraph_llm.operators.llm_op.answer_synthesize import AnswerSynthesize
from hugegraph_llm.operators.llm_op.keyword_extract import KeywordExtract
-from pyhugegraph.client import PyHugeClient
class GraphRAG:
@@ -52,14 +52,12 @@ class GraphRAG:
def query_graph_for_rag(
self,
- graph_client: Optional[PyHugeClient] = None,
max_deep: int = 2,
max_items: int = 30,
prop_to_match: Optional[str] = None,
):
self._operators.append(
GraphRAGQuery(
- client=graph_client,
max_deep=max_deep,
max_items=max_items,
prop_to_match=prop_to_match,
@@ -78,6 +76,10 @@ class GraphRAG:
)
return self
+ def print_result(self):
+ self._operators.append(PrintResult())
+ return self
+
def run(self, **kwargs) -> Dict[str, Any]:
if len(self._operators) == 0:
self.extract_keyword().query_graph_for_rag().synthesize_answer()
@@ -85,5 +87,5 @@ class GraphRAG:
context = kwargs
context["llm"] = self._llm
for op in self._operators:
- context = op.run(context=context)
+ context = op.run(context)
return context
diff --git
a/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/commit_to_hugegraph.py
b/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/commit_to_hugegraph.py
index 558a8ba..695c5b7 100644
---
a/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/commit_to_hugegraph.py
+++
b/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/commit_to_hugegraph.py
@@ -14,7 +14,7 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-
+from typing import Dict, Any
from hugegraph_llm.utils.config import Config
from hugegraph_llm.utils.constants import Constants
@@ -34,7 +34,7 @@ class CommitToKg:
)
self.schema = self.client.schema()
- def run(self, data: dict):
+ def run(self, data: dict) -> Dict[str, Any]:
if "schema" not in data:
self.schema_free_mode(data["triples"])
else:
@@ -43,6 +43,7 @@ class CommitToKg:
edges = data["edges"]
self.init_schema(schema)
self.init_graph(vertices, edges)
+ return data
def init_graph(self, vertices, edges):
vids = {}
diff --git
a/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/graph_rag_query.py
b/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/graph_rag_query.py
index a59acc1..6d5ede6 100644
--- a/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/graph_rag_query.py
+++ b/hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/graph_rag_query.py
@@ -19,6 +19,8 @@
import re
from typing import Any, Dict, Optional, List, Set, Tuple
+from hugegraph_llm.utils.config import Config
+from hugegraph_llm.utils.constants import Constants
from pyhugegraph.client import PyHugeClient
@@ -65,12 +67,18 @@ class GraphRAGQuery:
def __init__(
self,
- client: Optional[PyHugeClient] = None,
max_deep: int = 2,
max_items: int = 30,
prop_to_match: Optional[str] = None,
):
- self._client = client
+ config = Config(section=Constants.HUGEGRAPH_CONFIG)
+ self._client = PyHugeClient(
+ config.get_graph_ip(),
+ config.get_graph_port(),
+ config.get_graph_name(),
+ config.get_graph_user(),
+ config.get_graph_pwd(),
+ )
self._max_deep = max_deep
self._max_items = max_items
self._prop_to_match = prop_to_match
@@ -231,9 +239,9 @@ class GraphRAGQuery:
return self._schema
schema = self._client.schema()
- vertex_schema = schema.get_vertex_labels()
- edge_schema = schema.get_edge_labels()
- relationships = schema.get_relations()
+ vertex_schema = schema.getVertexLabels()
+ edge_schema = schema.getEdgeLabels()
+ relationships = schema.getRelations()
self._schema = (
f"Node properties: {vertex_schema}\n"
diff --git a/hugegraph-llm/src/hugegraph_llm/operators/kg_construction_task.py
b/hugegraph-llm/src/hugegraph_llm/operators/kg_construction_task.py
index 082058d..492d8e7 100644
--- a/hugegraph-llm/src/hugegraph_llm/operators/kg_construction_task.py
+++ b/hugegraph-llm/src/hugegraph_llm/operators/kg_construction_task.py
@@ -16,6 +16,8 @@
# under the License.
+from typing import Dict, Any
+
from hugegraph_llm.llms.base import BaseLLM
from hugegraph_llm.operators.common_op.check_schema import CheckSchema
from hugegraph_llm.operators.common_op.print_result import PrintResult
@@ -58,7 +60,8 @@ class KgBuilder:
self.operators.append(PrintResult())
return self
- def run(self):
- result = ""
+ def run(self) -> Dict[str, Any]:
+ context = None
for operator in self.operators:
- result = operator.run(result)
+ context = operator.run(context)
+ return context
diff --git
a/hugegraph-llm/src/hugegraph_llm/operators/llm_op/answer_synthesize.py
b/hugegraph-llm/src/hugegraph_llm/operators/llm_op/answer_synthesize.py
index b08adb3..6216494 100644
--- a/hugegraph-llm/src/hugegraph_llm/operators/llm_op/answer_synthesize.py
+++ b/hugegraph-llm/src/hugegraph_llm/operators/llm_op/answer_synthesize.py
@@ -91,4 +91,4 @@ class AnswerSynthesize:
if verbose:
print(f"\033[91mANSWER: {response}\033[0m")
- return context
+ return context["answer"]
diff --git
a/hugegraph-llm/src/hugegraph_llm/operators/llm_op/keyword_extract.py
b/hugegraph-llm/src/hugegraph_llm/operators/llm_op/keyword_extract.py
index 9a94a11..54d698c 100644
--- a/hugegraph-llm/src/hugegraph_llm/operators/llm_op/keyword_extract.py
+++ b/hugegraph-llm/src/hugegraph_llm/operators/llm_op/keyword_extract.py
@@ -23,17 +23,10 @@ from hugegraph_llm.llms.base import BaseLLM
from hugegraph_llm.llms.init_llm import LLMs
from hugegraph_llm.operators.common_op.nltk_helper import NLTKHelper
-DEFAULT_KEYWORDS_EXTRACT_TEMPLATE_TMPL = (
- "A question is provided below. Given the question, "
- "extract up to {max_keywords} keywords from the text. "
- "Focus on extracting the keywords that we can use "
- "to best lookup answers to the question. "
- "Avoid stopwords.\n"
- "---------------------\n"
- "{question}\n"
- "---------------------\n"
- "Provide keywords in the following comma-separated format: 'KEYWORDS:
<keywords>'"
-)
+DEFAULT_KEYWORDS_EXTRACT_TEMPLATE_TMPL = """extract {max_keywords} keywords
from the text:
+ {question}
+ Provide keywords in the following comma-separated format: 'KEYWORDS:
<keywords>'
+ """
DEFAULT_KEYWORDS_EXPAND_TEMPLATE_TMPL = (
"Generate synonyms or possible form of keywords up to {max_keywords} in
total,\n"
diff --git a/hugegraph-llm/src/hugegraph_llm/utils/config.py
b/hugegraph-llm/src/hugegraph_llm/utils/config.py
index d7ec13f..b11585a 100644
--- a/hugegraph-llm/src/hugegraph_llm/utils/config.py
+++ b/hugegraph-llm/src/hugegraph_llm/utils/config.py
@@ -31,6 +31,12 @@ class Config:
self.config.read(self.config_file)
self.section = section
+ def update_config(self, updates):
+ for key, value in updates.items():
+ self.config.set(self.section, key, value)
+ with open(self.config_file, "w", encoding="utf-8") as configfile:
+ self.config.write(configfile)
+
def get_config(self):
return self.config
@@ -55,8 +61,8 @@ class Config:
def get_llm_secret_key(self):
return self.config.get(self.section, "secret_key")
- def get_llm_ernie_url(self):
- return self.config.get(self.section, "ernie_url")
+ def get_llm_url(self):
+ return self.config.get(self.section, "llm_url")
def get_llm_type(self):
return self.config.get(self.section, "type")
diff --git a/hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py
b/hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py
new file mode 100644
index 0000000..f6122eb
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py
@@ -0,0 +1,255 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import json
+import os
+
+import gradio as gr
+import uvicorn
+from fastapi import FastAPI
+
+from hugegraph_llm.llms.init_llm import LLMs
+from hugegraph_llm.operators.graph_rag_task import GraphRAG
+from hugegraph_llm.operators.kg_construction_task import KgBuilder
+from hugegraph_llm.utils.config import Config
+from hugegraph_llm.utils.constants import Constants
+from pyhugegraph.client import PyHugeClient
+
+
+def init_hg_test_data():
+ client = get_hg_client()
+ client.graphs().clear_graph_all_data()
+ schema = client.schema()
+ schema.propertyKey("name").asText().ifNotExist().create()
+ schema.propertyKey("birthDate").asText().ifNotExist().create()
+ schema.vertexLabel("Person").properties(
+ "name", "birthDate"
+ ).useCustomizeStringId().ifNotExist().create()
+
schema.vertexLabel("Movie").properties("name").useCustomizeStringId().ifNotExist().create()
+
schema.edgeLabel("ActedIn").sourceLabel("Person").targetLabel("Movie").ifNotExist().create()
+
+
schema.indexLabel("PersonByName").onV("Person").by("name").secondary().ifNotExist().create()
+
schema.indexLabel("MovieByName").onV("Movie").by("name").secondary().ifNotExist().create()
+
+ graph = client.graph()
+ graph.addVertex("Person", {"name": "Al Pacino", "birthDate":
"1940-04-25"}, id="Al Pacino")
+ graph.addVertex(
+ "Person",
+ {"name": "Robert De Niro", "birthDate": "1943-08-17"},
+ id="Robert De Niro",
+ )
+ graph.addVertex("Movie", {"name": "The Godfather"}, id="The Godfather")
+ graph.addVertex("Movie", {"name": "The Godfather Part II"}, id="The
Godfather Part II")
+ graph.addVertex(
+ "Movie",
+ {"name": "The Godfather Coda The Death of Michael Corleone"},
+ id="The Godfather Coda The Death of Michael Corleone",
+ )
+
+ graph.addEdge("ActedIn", "Al Pacino", "The Godfather", {})
+ graph.addEdge("ActedIn", "Al Pacino", "The Godfather Part II", {})
+ graph.addEdge("ActedIn", "Al Pacino", "The Godfather Coda The Death of
Michael Corleone", {})
+ graph.addEdge("ActedIn", "Robert De Niro", "The Godfather Part II", {})
+ schema.getSchema()
+ graph.close()
+ return {
+ "vertex": ["Person", "Movie"],
+ "edge": ["ActedIn"],
+ "property": ["name", "birthDate"],
+ "index": ["PersonByName", "MovieByName"],
+ }
+
+
+def graph_rag(text):
+ res = (
+ GraphRAG()
+ .extract_keyword(text=text)
+ .query_graph_for_rag()
+ .synthesize_answer()
+ .run(verbose=True)
+ )
+ return res
+
+
+def build_kg(text, schema, disambiguate_word_sense, commit_to_hugegraph):
+ builder = KgBuilder(LLMs().get_llm())
+ if schema:
+ try:
+ schema = json.loads(schema.strip())
+ builder.import_schema(from_user_defined=schema)
+ except json.JSONDecodeError as e:
+ print(e)
+ builder.import_schema(from_hugegraph=schema)
+ else:
+ return "ERROR: please input schema."
+ builder.extract_triples(text)
+ if disambiguate_word_sense == "true":
+ builder.disambiguate_word_sense()
+ if commit_to_hugegraph == "true":
+ builder.commit_to_hugegraph()
+ return builder.run()
+
+
+def run_gremlin_query(query):
+ res = get_hg_client().gremlin().exec(query)
+ return res
+
+
+def get_hg_client():
+ config = Config(section=Constants.HUGEGRAPH_CONFIG)
+ return PyHugeClient(
+ config.get_graph_ip(),
+ config.get_graph_port(),
+ config.get_graph_name(),
+ config.get_graph_user(),
+ config.get_graph_pwd(),
+ )
+
+
+def init_config(
+ ip, port, user, pwd, graph, type, api_key, secret_key, llm_url,
model_name, max_token
+):
+ root_dir =
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+ config_file = os.path.join(root_dir, "config", "config.ini")
+
+ config = Config(config_file=config_file, section="hugegraph")
+ config.update_config({"ip": ip, "port": port, "user": user, "pwd": pwd,
"graph": graph})
+
+ config = Config(config_file=config_file, section="llm")
+ config.update_config(
+ {
+ "type": type,
+ "api_key": api_key,
+ "secret_key": secret_key,
+ "llm_url": llm_url,
+ "model_name": model_name,
+ "max_token": max_token,
+ }
+ )
+ with open(config_file, "r", encoding="utf-8") as file:
+ content = file.read()
+ return content
+
+
+with gr.Blocks() as hugegraph_llm:
+ gr.Markdown(
+ """# HugeGraph LLM Demo
+ 1. Set up the HugeGraph server."""
+ )
+ with gr.Row():
+ inp = [
+ gr.Textbox(value="127.0.0.1", label="ip"),
+ gr.Textbox(value="8080", label="port"),
+ gr.Textbox(value="admin", label="user"),
+ gr.Textbox(value="admin", label="pwd"),
+ gr.Textbox(value="hugegraph", label="graph"),
+ ]
+ gr.Markdown("2. Set up the LLM.")
+ with gr.Row():
+ inp2 = [
+ gr.Textbox(value="ernie", label="type"),
+ gr.Textbox(value="", label="api_key"),
+ gr.Textbox(value="", label="secret_key"),
+ gr.Textbox(
+
value="https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/";
+ "chat/completions_pro?access_token=",
+ label="llm_url",
+ ),
+ gr.Textbox(value="wenxin", label="model_name"),
+ gr.Textbox(value="4000", label="max_token"),
+ ]
+ with gr.Row():
+ out = gr.Textbox(label="Output")
+ btn = gr.Button("Initialize configs")
+ btn.click(fn=init_config, inputs=inp + inp2, outputs=out) # pylint:
disable=no-member
+
+ gr.Markdown(
+ """## 1. build knowledge graph
+ - Text: The input text.
+ - Schema: Accepts two types of text as below:
+ - User-defined JSON format Schema.
+ - Specify the name of the HugeGraph graph instance, and it will
+ automatically extract the schema of the graph.
+ - Disambiguate word sense: Whether to perform word sense disambiguation.
+ - Commit to hugegraph: Whether to commit the constructed knowledge graph
to the
+ HugeGraph server.
+ """
+ )
+ TEXT = (
+ "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom
she's shared a home with"
+ " since 2010. James, in his professional life, works as a journalist.
Additionally, Sarah"
+ " is the proud owner of the website www.sarahsplace.com, while James
manages his own"
+ " webpage, though the specific URL is not mentioned here. These two
individuals, Sarah and"
+ " James, have not only forged a strong personal bond as roommates but
have also carved out"
+ " their distinctive digital presence through their respective
webpages, showcasing their"
+ " varied interests and experiences."
+ )
+
+ SCHEMA = """{
+ "vertices": [
+ {"vertex_label": "person", "properties": ["name", "age",
"occupation"]},
+ {"vertex_label": "webpage", "properties": ["name", "url"]}
+ ],
+ "edges": [
+ {
+ "edge_label": "roommate",
+ "source_vertex_label": "person",
+ "target_vertex_label": "person",
+ "properties": {}
+ }
+ ]
+ }
+ """
+
+ with gr.Row():
+ inp = [
+ gr.Textbox(value=TEXT, label="Text"),
+ gr.Textbox(value=SCHEMA, label="Schema"),
+ gr.Textbox(value="false", label="Disambiguate word sense"),
+ gr.Textbox(value="false", label="Commit to hugegraph"),
+ ]
+ with gr.Row():
+ out = gr.Textbox(label="Output")
+ btn = gr.Button("Build knowledge graph")
+ btn.click(fn=build_kg, inputs=inp, outputs=out) # pylint:
disable=no-member
+
+ gr.Markdown("""## 2. Retrieval augmented generation by hugegraph""")
+ with gr.Row():
+ inp = gr.Textbox(value="Tell me about Al Pacino.", label="Question")
+ with gr.Row():
+ out = gr.Textbox(label="Answer")
+ btn = gr.Button("Retrieval augmented generation")
+ btn.click(fn=graph_rag, inputs=inp, outputs=out) # pylint:
disable=no-member
+
+ gr.Markdown("""## 3. Others """)
+ with gr.Row():
+ inp = []
+ out = gr.Textbox(label="Output")
+ btn = gr.Button("Initialize HugeGraph test data")
+ btn.click(fn=init_hg_test_data, inputs=inp, outputs=out) # pylint:
disable=no-member
+
+ with gr.Row():
+ inp = gr.Textbox(value="g.V().limit(10)", label="Gremlin query")
+ out = gr.Textbox(label="Output")
+ btn = gr.Button("Run gremlin query on HugeGraph")
+ btn.click(fn=run_gremlin_query, inputs=inp, outputs=out) # pylint:
disable=no-member
+
+if __name__ == '__main__':
+ app = FastAPI()
+ app = gr.mount_gradio_app(app, hugegraph_llm, path="/")
+ uvicorn.run(app, host="0.0.0.0", port=8001)
