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commit bd59cbd085ea03af12e3a3e6d9eab14257902744 Author: zhangshiming <[email protected]> AuthorDate: Wed Feb 28 16:56:24 2024 +0800 Introduce Gradio for creating interactive and visual demo --- .../src/hugegraph_llm/utils/gradio_demo.py | 254 +++++++++++++++++++++ 1 file changed, 254 insertions(+) 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..c8cbc59 --- /dev/null +++ b/hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py @@ -0,0 +1,254 @@ +# 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() + 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()) + # 判断 schema 是不是json结构 + 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 + +app = FastAPI() +app = gr.mount_gradio_app(app, hugegraph_llm, path="/") +uvicorn.run(app, host="0.0.0.0", port=8001)
