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 8de360c chore: reconstruct web using tabs pages (#80)
8de360c is described below
commit 8de360c2cf05cc6044393e6a5ad3744f90054538
Author: chenzihong <[email protected]>
AuthorDate: Thu Sep 19 21:28:25 2024 +0800
chore: reconstruct web using tabs pages (#80)
Configs are collapsed at the top. The functionality is divided into three
tab pages:
---------
Co-authored-by: imbajin <[email protected]>
---
hugegraph-llm/requirements.txt | 1 +
.../src/hugegraph_llm/demo/rag_demo/__init__.py | 16 +
.../src/hugegraph_llm/demo/rag_demo/app.py | 98 +++
.../hugegraph_llm/demo/rag_demo/configs_block.py | 307 +++++++++
.../src/hugegraph_llm/demo/rag_demo/other_block.py | 37 ++
.../src/hugegraph_llm/demo/rag_demo/rag_block.py | 265 ++++++++
.../demo/rag_demo/vector_graph_block.py | 110 ++++
.../src/hugegraph_llm/demo/rag_web_demo.py | 685 ---------------------
8 files changed, 834 insertions(+), 685 deletions(-)
diff --git a/hugegraph-llm/requirements.txt b/hugegraph-llm/requirements.txt
index bc3e606..0a65785 100644
--- a/hugegraph-llm/requirements.txt
+++ b/hugegraph-llm/requirements.txt
@@ -11,5 +11,6 @@ python-docx~=1.1.2
langchain-text-splitters~=0.2.2
faiss-cpu~=1.8.0
python-dotenv>=1.0.1
+pyarrow~=17.0.0 # TODO: a temporary dependency for pandas, figure out why
ImportError
pandas~=2.2.2
openpyxl~=3.1.5
diff --git a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/__init__.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/__init__.py
new file mode 100644
index 0000000..13a8339
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/__init__.py
@@ -0,0 +1,16 @@
+# 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.
diff --git a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/app.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/app.py
new file mode 100644
index 0000000..0b5285a
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/app.py
@@ -0,0 +1,98 @@
+# 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 argparse
+import os
+
+import gradio as gr
+import uvicorn
+from fastapi import FastAPI, Depends, APIRouter
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+
+from hugegraph_llm.api.rag_api import rag_http_api
+from hugegraph_llm.demo.rag_demo.configs_block import (
+ create_configs_block,
+ apply_llm_config,
+ apply_embedding_config,
+ apply_reranker_config,
+ apply_graph_config,
+)
+from hugegraph_llm.demo.rag_demo.other_block import create_other_block
+from hugegraph_llm.demo.rag_demo.rag_block import create_rag_block, rag_answer
+from hugegraph_llm.demo.rag_demo.vector_graph_block import
create_vector_graph_block
+from hugegraph_llm.resources.demo.css import CSS
+from hugegraph_llm.utils.log import log
+
+sec = HTTPBearer()
+
+
+def authenticate(credentials: HTTPAuthorizationCredentials = Depends(sec)):
+ correct_token = os.getenv("TOKEN")
+ if credentials.credentials != correct_token:
+ from fastapi import HTTPException
+
+ raise HTTPException(
+ status_code=401,
+ detail=f"Invalid token {credentials.credentials}, please contact
the admin",
+ headers={"WWW-Authenticate": "Bearer"},
+ )
+
+
+def init_rag_ui() -> gr.Interface:
+ with gr.Blocks(
+ theme="default",
+ title="HugeGraph RAG Platform",
+ css=CSS,
+ ) as hugegraph_llm_ui:
+ gr.Markdown("# HugeGraph LLM RAG Demo")
+
+ create_configs_block()
+
+ with gr.Tab(label="1. Build RAG Index 💡"):
+ create_vector_graph_block()
+ with gr.Tab(label="2. (Graph)RAG & User Functions 📖"):
+ create_rag_block()
+ with gr.Tab(label="3. Others Tools 🚧"):
+ create_other_block()
+
+ return hugegraph_llm_ui
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--host", type=str, default="0.0.0.0", help="host")
+ parser.add_argument("--port", type=int, default=8001, help="port")
+ args = parser.parse_args()
+ app = FastAPI()
+ api_auth = APIRouter(dependencies=[Depends(authenticate)])
+
+ hugegraph_llm = init_rag_ui()
+ rag_http_api(api_auth, rag_answer, apply_graph_config, apply_llm_config,
apply_embedding_config,
+ apply_reranker_config)
+
+ app.include_router(api_auth)
+ auth_enabled = os.getenv("ENABLE_LOGIN", "False").lower() == "true"
+ log.info("(Status) Authentication is %s now.", "enabled" if auth_enabled
else "disabled")
+ # TODO: support multi-user login when need
+
+ app = gr.mount_gradio_app(app, hugegraph_llm, path="/", auth=("rag",
os.getenv("TOKEN")) if auth_enabled else None)
+
+ # TODO: we can't use reload now due to the config 'app' of uvicorn.run
+ # ❎:f'{__name__}:app' / rag_web_demo:app /
hugegraph_llm.demo.rag_web_demo:app
+ # TODO: merge unicorn log to avoid duplicate log output (should be
unified/fixed later)
+ uvicorn.run(app, host=args.host, port=args.port, reload=False)
diff --git a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/configs_block.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/configs_block.py
new file mode 100644
index 0000000..227ca4a
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/configs_block.py
@@ -0,0 +1,307 @@
+# 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
+from typing import Optional
+
+import gradio as gr
+import requests
+from requests.auth import HTTPBasicAuth
+
+from hugegraph_llm.config import settings
+from hugegraph_llm.utils.log import log
+
+
+def test_api_connection(url, method="GET", headers=None, params=None,
body=None, auth=None, origin_call=None) -> int:
+ # TODO: use fastapi.request / starlette instead?
+ log.debug("Request URL: %s", url)
+ try:
+ if method.upper() == "GET":
+ resp = requests.get(url, headers=headers, params=params,
timeout=5, auth=auth)
+ elif method.upper() == "POST":
+ resp = requests.post(url, headers=headers, params=params,
json=body, timeout=5, auth=auth)
+ else:
+ raise ValueError("Unsupported HTTP method, please use GET/POST
instead")
+ except requests.exceptions.RequestException as e:
+ msg = f"Connection failed: {e}"
+ log.error(msg)
+ if origin_call is None:
+ raise gr.Error(msg)
+ return -1 # Error code
+
+ if 200 <= resp.status_code < 300:
+ msg = "Test connection successful~"
+ log.info(msg)
+ gr.Info(msg)
+ else:
+ msg = f"Connection failed with status code: {resp.status_code}, error:
{resp.text}"
+ log.error(msg)
+ # TODO: Only the message returned by rag can be processed, and the
other return values can't be processed
+ if origin_call is None:
+ try:
+ raise gr.Error(json.loads(resp.text).get("message", msg))
+ except json.decoder.JSONDecodeError and AttributeError:
+ raise gr.Error(resp.text)
+ return resp.status_code
+
+
+def config_qianfan_model(arg1, arg2, arg3=None, origin_call=None) -> int:
+ settings.qianfan_api_key = arg1
+ settings.qianfan_secret_key = arg2
+ settings.qianfan_language_model = arg3
+ params = {
+ "grant_type": "client_credentials",
+ "client_id": arg1,
+ "client_secret": arg2,
+ }
+ status_code = test_api_connection(
+ "https://aip.baidubce.com/oauth/2.0/token";, "POST", params=params,
origin_call=origin_call
+ )
+ return status_code
+
+
+def apply_embedding_config(arg1, arg2, arg3, origin_call=None) -> int:
+ status_code = -1
+ embedding_option = settings.embedding_type
+ if embedding_option == "openai":
+ settings.openai_api_key = arg1
+ settings.openai_api_base = arg2
+ settings.openai_embedding_model = arg3
+ test_url = settings.openai_api_base + "/models"
+ headers = {"Authorization": f"Bearer {arg1}"}
+ status_code = test_api_connection(test_url, headers=headers,
origin_call=origin_call)
+ elif embedding_option == "qianfan_wenxin":
+ status_code = config_qianfan_model(arg1, arg2, origin_call=origin_call)
+ settings.qianfan_embedding_model = arg3
+ elif embedding_option == "ollama":
+ settings.ollama_host = arg1
+ settings.ollama_port = int(arg2)
+ settings.ollama_embedding_model = arg3
+ status_code = test_api_connection(f"http://{arg1}:{arg2}";,
origin_call=origin_call)
+ settings.update_env()
+ gr.Info("Configured!")
+ return status_code
+
+
+def apply_reranker_config(
+ reranker_api_key: Optional[str] = None,
+ reranker_model: Optional[str] = None,
+ cohere_base_url: Optional[str] = None,
+ origin_call=None,
+) -> int:
+ status_code = -1
+ reranker_option = settings.reranker_type
+ if reranker_option == "cohere":
+ settings.reranker_api_key = reranker_api_key
+ settings.reranker_model = reranker_model
+ settings.cohere_base_url = cohere_base_url
+ headers = {"Authorization": f"Bearer {reranker_api_key}"}
+ status_code = test_api_connection(
+ cohere_base_url.rsplit("/", 1)[0] + "/check-api-key",
+ method="POST",
+ headers=headers,
+ origin_call=origin_call,
+ )
+ elif reranker_option == "siliconflow":
+ settings.reranker_api_key = reranker_api_key
+ settings.reranker_model = reranker_model
+ from pyhugegraph.utils.constants import Constants
+
+ headers = {
+ "accept": Constants.HEADER_CONTENT_TYPE,
+ "authorization": f"Bearer {reranker_api_key}",
+ }
+ status_code = test_api_connection(
+ "https://api.siliconflow.cn/v1/user/info";,
+ headers=headers,
+ origin_call=origin_call,
+ )
+ settings.update_env()
+ gr.Info("Configured!")
+ return status_code
+
+
+def apply_graph_config(ip, port, name, user, pwd, gs, origin_call=None) -> int:
+ settings.graph_ip = ip
+ settings.graph_port = port
+ settings.graph_name = name
+ settings.graph_user = user
+ settings.graph_pwd = pwd
+ settings.graph_space = gs
+ # Test graph connection (Auth)
+ if gs and gs.strip():
+ test_url = f"http://{ip}:{port}/graphspaces/{gs}/graphs/{name}/schema";
+ else:
+ test_url = f"http://{ip}:{port}/graphs/{name}/schema";
+ auth = HTTPBasicAuth(user, pwd)
+ # for http api return status
+ response = test_api_connection(test_url, auth=auth,
origin_call=origin_call)
+ settings.update_env()
+ return response
+
+
+# Different llm models have different parameters, so no meaningful argument
names are given here
+def apply_llm_config(arg1, arg2, arg3, arg4, origin_call=None) -> int:
+ llm_option = settings.llm_type
+ status_code = -1
+ if llm_option == "openai":
+ settings.openai_api_key = arg1
+ settings.openai_api_base = arg2
+ settings.openai_language_model = arg3
+ settings.openai_max_tokens = int(arg4)
+ test_url = settings.openai_api_base + "/models"
+ headers = {"Authorization": f"Bearer {arg1}"}
+ status_code = test_api_connection(test_url, headers=headers,
origin_call=origin_call)
+ elif llm_option == "qianfan_wenxin":
+ status_code = config_qianfan_model(arg1, arg2, arg3, origin_call)
+ elif llm_option == "ollama":
+ settings.ollama_host = arg1
+ settings.ollama_port = int(arg2)
+ settings.ollama_language_model = arg3
+ status_code = test_api_connection(f"http://{arg1}:{arg2}";,
origin_call=origin_call)
+ gr.Info("Configured!")
+ settings.update_env()
+ return status_code
+
+
+def create_configs_block():
+ with gr.Accordion("1. Set up the HugeGraph server.", open=False):
+ with gr.Row():
+ graph_config_input = [
+ gr.Textbox(value=settings.graph_ip, label="ip"),
+ gr.Textbox(value=settings.graph_port, label="port"),
+ gr.Textbox(value=settings.graph_name, label="graph"),
+ gr.Textbox(value=settings.graph_user, label="user"),
+ gr.Textbox(value=settings.graph_pwd, label="pwd",
type="password"),
+ gr.Textbox(value=settings.graph_space,
label="graphspace(Optional)"),
+ ]
+ graph_config_button = gr.Button("Apply config")
+ graph_config_button.click(apply_graph_config, inputs=graph_config_input)
# pylint: disable=no-member
+
+ with gr.Accordion("2. Set up the LLM.", open=False):
+ llm_dropdown = gr.Dropdown(choices=["openai", "qianfan_wenxin",
"ollama"], value=settings.llm_type, label="LLM")
+
+ @gr.render(inputs=[llm_dropdown])
+ def llm_settings(llm_type):
+ settings.llm_type = llm_type
+ if llm_type == "openai":
+ with gr.Row():
+ llm_config_input = [
+ gr.Textbox(value=settings.openai_api_key,
label="api_key", type="password"),
+ gr.Textbox(value=settings.openai_api_base,
label="api_base"),
+ gr.Textbox(value=settings.openai_language_model,
label="model_name"),
+ gr.Textbox(value=settings.openai_max_tokens,
label="max_token"),
+ ]
+ elif llm_type == "ollama":
+ with gr.Row():
+ llm_config_input = [
+ gr.Textbox(value=settings.ollama_host, label="host"),
+ gr.Textbox(value=str(settings.ollama_port),
label="port"),
+ gr.Textbox(value=settings.ollama_language_model,
label="model_name"),
+ gr.Textbox(value="", visible=False),
+ ]
+ elif llm_type == "qianfan_wenxin":
+ with gr.Row():
+ llm_config_input = [
+ gr.Textbox(value=settings.qianfan_api_key,
label="api_key", type="password"),
+ gr.Textbox(value=settings.qianfan_secret_key,
label="secret_key", type="password"),
+ gr.Textbox(value=settings.qianfan_language_model,
label="model_name"),
+ gr.Textbox(value="", visible=False),
+ ]
+ else:
+ llm_config_input = []
+ llm_config_button = gr.Button("apply configuration")
+ llm_config_button.click(apply_llm_config, inputs=llm_config_input)
# pylint: disable=no-member
+
+ with gr.Accordion("3. Set up the Embedding.", open=False):
+ embedding_dropdown = gr.Dropdown(
+ choices=["openai", "qianfan_wenxin", "ollama"],
value=settings.embedding_type, label="Embedding"
+ )
+
+ @gr.render(inputs=[embedding_dropdown])
+ def embedding_settings(embedding_type):
+ settings.embedding_type = embedding_type
+ if embedding_type == "openai":
+ with gr.Row():
+ embedding_config_input = [
+ gr.Textbox(value=settings.openai_api_key,
label="api_key", type="password"),
+ gr.Textbox(value=settings.openai_api_base,
label="api_base"),
+ gr.Textbox(value=settings.openai_embedding_model,
label="model_name"),
+ ]
+ elif embedding_type == "qianfan_wenxin":
+ with gr.Row():
+ embedding_config_input = [
+ gr.Textbox(value=settings.qianfan_api_key,
label="api_key", type="password"),
+ gr.Textbox(value=settings.qianfan_secret_key,
label="secret_key", type="password"),
+ gr.Textbox(value=settings.qianfan_embedding_model,
label="model_name"),
+ ]
+ elif embedding_type == "ollama":
+ with gr.Row():
+ embedding_config_input = [
+ gr.Textbox(value=settings.ollama_host, label="host"),
+ gr.Textbox(value=str(settings.ollama_port),
label="port"),
+ gr.Textbox(value=settings.ollama_embedding_model,
label="model_name"),
+ ]
+ else:
+ embedding_config_input = []
+
+ embedding_config_button = gr.Button("apply configuration")
+
+ # Call the separate apply_embedding_configuration function here
+ embedding_config_button.click( # pylint: disable=no-member
+ fn=apply_embedding_config,
+ inputs=embedding_config_input, # pylint: disable=no-member
+ )
+
+ with gr.Accordion("4. Set up the Reranker.", open=False):
+ reranker_dropdown = gr.Dropdown(
+ choices=["cohere", "siliconflow", ("default/offline", "None")],
+ value=os.getenv("reranker_type") or "None",
+ label="Reranker",
+ )
+
+ @gr.render(inputs=[reranker_dropdown])
+ def reranker_settings(reranker_type):
+ settings.reranker_type = reranker_type if reranker_type != "None"
else None
+ if reranker_type == "cohere":
+ with gr.Row():
+ reranker_config_input = [
+ gr.Textbox(value=settings.reranker_api_key,
label="api_key", type="password"),
+ gr.Textbox(value=settings.reranker_model,
label="model"),
+ gr.Textbox(value=settings.cohere_base_url,
label="base_url"),
+ ]
+ elif reranker_type == "siliconflow":
+ with gr.Row():
+ reranker_config_input = [
+ gr.Textbox(value=settings.reranker_api_key,
label="api_key", type="password"),
+ gr.Textbox(
+ value="BAAI/bge-reranker-v2-m3",
+ label="model",
+ info="Please refer to
https://siliconflow.cn/pricing";,
+ ),
+ ]
+ else:
+ reranker_config_input = []
+ reranker_config_button = gr.Button("apply configuration")
+
+ # TODO: use "gr.update()" or other way to update the config in
time (refactor the click event)
+ # Call the separate apply_reranker_configuration function here
+ reranker_config_button.click( # pylint: disable=no-member
+ fn=apply_reranker_config,
+ inputs=reranker_config_input, # pylint: disable=no-member
+ )
diff --git a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/other_block.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/other_block.py
new file mode 100644
index 0000000..5297f8a
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/other_block.py
@@ -0,0 +1,37 @@
+# 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 gradio as gr
+
+from hugegraph_llm.utils.hugegraph_utils import init_hg_test_data,
run_gremlin_query
+
+
+def create_other_block():
+ gr.Markdown("""## 4. Other Tools """)
+ with gr.Row():
+ inp = gr.Textbox(value="g.V().limit(10)", label="Gremlin query",
show_copy_button=True, lines=8)
+ out = gr.Code(label="Output", language="json",
elem_classes="code-container-show")
+ btn = gr.Button("Run Gremlin query")
+ btn.click(fn=run_gremlin_query, inputs=[inp], outputs=out) # pylint:
disable=no-member
+
+ gr.Markdown("---")
+ with gr.Accordion("Init HugeGraph test data (🚧)", open=False):
+ with gr.Row():
+ inp = []
+ out = gr.Textbox(label="Init Graph Demo Result",
show_copy_button=True)
+ btn = gr.Button("(BETA) Init HugeGraph test data (🚧)")
+ btn.click(fn=init_hg_test_data, inputs=inp, outputs=out) # pylint:
disable=no-member
diff --git a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/rag_block.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/rag_block.py
new file mode 100644
index 0000000..8004b80
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/rag_block.py
@@ -0,0 +1,265 @@
+# 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 os
+from typing import Tuple, Literal, Optional
+
+import gradio as gr
+import pandas as pd
+from gradio.utils import NamedString
+
+from hugegraph_llm.config import resource_path, prompt
+from hugegraph_llm.operators.graph_rag_task import RAGPipeline
+from hugegraph_llm.utils.log import log
+
+
+def rag_answer(
+ text: str,
+ raw_answer: bool,
+ vector_only_answer: bool,
+ graph_only_answer: bool,
+ graph_vector_answer: bool,
+ graph_ratio: float,
+ rerank_method: Literal["bleu", "reranker"],
+ near_neighbor_first: bool,
+ custom_related_information: str,
+ answer_prompt: str,
+) -> Tuple:
+ """
+ Generate an answer using the RAG (Retrieval-Augmented Generation) pipeline.
+ 1. Initialize the RAGPipeline.
+ 2. Select vector search or graph search based on parameters.
+ 3. Merge, deduplicate, and rerank the results.
+ 4. Synthesize the final answer.
+ 5. Run the pipeline and return the results.
+ """
+ should_update_prompt = prompt.default_question != text or
prompt.answer_prompt != answer_prompt
+ if should_update_prompt or prompt.custom_rerank_info !=
custom_related_information:
+ prompt.custom_rerank_info = custom_related_information
+ prompt.default_question = text
+ prompt.answer_prompt = answer_prompt
+ prompt.update_yaml_file()
+
+ vector_search = vector_only_answer or graph_vector_answer
+ graph_search = graph_only_answer or graph_vector_answer
+ if raw_answer is False and not vector_search and not graph_search:
+ gr.Warning("Please select at least one generate mode.")
+ return "", "", "", ""
+
+ rag = RAGPipeline()
+ if vector_search:
+ rag.query_vector_index()
+ if graph_search:
+ rag.extract_keywords().keywords_to_vid().query_graphdb()
+ # TODO: add more user-defined search strategies
+ rag.merge_dedup_rerank(graph_ratio, rerank_method, near_neighbor_first,
custom_related_information)
+ rag.synthesize_answer(raw_answer, vector_only_answer, graph_only_answer,
graph_vector_answer, answer_prompt)
+
+ try:
+ context = rag.run(verbose=True, query=text,
vector_search=vector_search, graph_search=graph_search)
+ if context.get("switch_to_bleu"):
+ gr.Warning("Online reranker fails, automatically switches to local
bleu rerank.")
+ return (
+ context.get("raw_answer", ""),
+ context.get("vector_only_answer", ""),
+ context.get("graph_only_answer", ""),
+ context.get("graph_vector_answer", ""),
+ )
+ except ValueError as e:
+ log.critical(e)
+ raise gr.Error(str(e))
+ except Exception as e:
+ log.critical(e)
+ raise gr.Error(f"An unexpected error occurred: {str(e)}")
+
+
+def create_rag_block():
+ gr.Markdown("""## 2. RAG with HugeGraph""")
+ with gr.Row():
+ with gr.Column(scale=2):
+ inp = gr.Textbox(value=prompt.default_question, label="Question",
show_copy_button=True, lines=2)
+ raw_out = gr.Textbox(label="Basic LLM Answer",
show_copy_button=True)
+ vector_only_out = gr.Textbox(label="Vector-only Answer",
show_copy_button=True)
+ graph_only_out = gr.Textbox(label="Graph-only Answer",
show_copy_button=True)
+ graph_vector_out = gr.Textbox(label="Graph-Vector Answer",
show_copy_button=True)
+ from hugegraph_llm.operators.llm_op.answer_synthesize import
DEFAULT_ANSWER_TEMPLATE
+
+ answer_prompt_input = gr.Textbox(
+ value=DEFAULT_ANSWER_TEMPLATE, label="Custom Prompt",
show_copy_button=True, lines=7
+ )
+ with gr.Column(scale=1):
+ with gr.Row():
+ raw_radio = gr.Radio(choices=[True, False], value=True,
label="Basic LLM Answer")
+ vector_only_radio = gr.Radio(choices=[True, False],
value=False, label="Vector-only Answer")
+ with gr.Row():
+ graph_only_radio = gr.Radio(choices=[True, False],
value=False, label="Graph-only Answer")
+ graph_vector_radio = gr.Radio(choices=[True, False],
value=False, label="Graph-Vector Answer")
+
+ def toggle_slider(enable):
+ return gr.update(interactive=enable)
+
+ with gr.Column():
+ with gr.Row():
+ online_rerank = os.getenv("reranker_type")
+ rerank_method = gr.Dropdown(
+ choices=["bleu", ("rerank (online)", "reranker")] if
online_rerank else ["bleu"],
+ value="reranker" if online_rerank else "bleu",
+ label="Rerank method",
+ )
+ graph_ratio = gr.Slider(0, 1, 0.5, label="Graph Ratio",
step=0.1, interactive=False)
+
+ graph_vector_radio.change(
+ toggle_slider, inputs=graph_vector_radio,
outputs=graph_ratio
+ ) # pylint: disable=no-member
+ near_neighbor_first = gr.Checkbox(
+ value=False,
+ label="Near neighbor first(Optional)",
+ info="One-depth neighbors > two-depth neighbors",
+ )
+ custom_related_information = gr.Text(
+ prompt.custom_rerank_info,
+ label="Custom related information(Optional)",
+ )
+ btn = gr.Button("Answer Question", variant="primary")
+
+ btn.click( # pylint: disable=no-member
+ fn=rag_answer,
+ inputs=[
+ inp,
+ raw_radio,
+ vector_only_radio,
+ graph_only_radio,
+ graph_vector_radio,
+ graph_ratio,
+ rerank_method,
+ near_neighbor_first,
+ custom_related_information,
+ answer_prompt_input,
+ ],
+ outputs=[raw_out, vector_only_out, graph_only_out, graph_vector_out],
+ )
+
+ gr.Markdown("""## 3. User Functions (Back-testing)
+ > 1. Download the template file & fill in the questions you want to test.
+ > 2. Upload the file & click the button to generate answers. (Preview
shows the first 40 lines)
+ > 3. The answer options are the same as the above RAG/Q&A frame
+ """)
+ tests_df_headers = [
+ "Question",
+ "Expected Answer",
+ "Basic LLM Answer",
+ "Vector-only Answer",
+ "Graph-only Answer",
+ "Graph-Vector Answer",
+ ]
+ answers_path = os.path.join(resource_path, "demo",
"questions_answers.xlsx")
+ questions_path = os.path.join(resource_path, "demo", "questions.xlsx")
+ questions_template_path = os.path.join(resource_path, "demo",
"questions_template.xlsx")
+
+ def read_file_to_excel(file: NamedString, line_count: Optional[int] =
None):
+ df = None
+ if not file:
+ return pd.DataFrame(), 1
+ if file.name.endswith(".xlsx"):
+ df = pd.read_excel(file.name, nrows=line_count) if file else
pd.DataFrame()
+ elif file.name.endswith(".csv"):
+ df = pd.read_csv(file.name, nrows=line_count) if file else
pd.DataFrame()
+ df.to_excel(questions_path, index=False)
+ if df.empty:
+ df = pd.DataFrame([[""] * len(tests_df_headers)],
columns=tests_df_headers)
+ else:
+ df.columns = tests_df_headers
+ # truncate the dataframe if it's too long
+ if len(df) > 40:
+ return df.head(40), 40
+ return df, len(df)
+
+ def change_showing_excel(line_count):
+ if os.path.exists(answers_path):
+ df = pd.read_excel(answers_path, nrows=line_count)
+ elif os.path.exists(questions_path):
+ df = pd.read_excel(questions_path, nrows=line_count)
+ else:
+ df = pd.read_excel(questions_template_path, nrows=line_count)
+ return df
+
+ def several_rag_answer(
+ is_raw_answer: bool,
+ is_vector_only_answer: bool,
+ is_graph_only_answer: bool,
+ is_graph_vector_answer: bool,
+ graph_ratio: float,
+ rerank_method: Literal["bleu", "reranker"],
+ near_neighbor_first: bool,
+ custom_related_information: str,
+ answer_prompt: str,
+ progress=gr.Progress(track_tqdm=True),
+ answer_max_line_count: int = 1,
+ ):
+ df = pd.read_excel(questions_path, dtype=str)
+ total_rows = len(df)
+ for index, row in df.iterrows():
+ question = row.iloc[0]
+ basic_llm_answer, vector_only_answer, graph_only_answer,
graph_vector_answer = rag_answer(
+ question,
+ is_raw_answer,
+ is_vector_only_answer,
+ is_graph_only_answer,
+ is_graph_vector_answer,
+ graph_ratio,
+ rerank_method,
+ near_neighbor_first,
+ custom_related_information,
+ answer_prompt,
+ )
+ df.at[index, "Basic LLM Answer"] = basic_llm_answer
+ df.at[index, "Vector-only Answer"] = vector_only_answer
+ df.at[index, "Graph-only Answer"] = graph_only_answer
+ df.at[index, "Graph-Vector Answer"] = graph_vector_answer
+ progress((index + 1, total_rows))
+ answers_path = os.path.join(resource_path, "demo",
"questions_answers.xlsx")
+ df.to_excel(answers_path, index=False)
+ return df.head(answer_max_line_count), answers_path
+
+ with gr.Row():
+ with gr.Column():
+ questions_file = gr.File(file_types=[".xlsx", ".csv"],
label="Questions File (.xlsx & csv)")
+ with gr.Column():
+ test_template_file = os.path.join(resource_path, "demo",
"questions_template.xlsx")
+ gr.File(value=test_template_file, label="Download Template File")
+ answer_max_line_count = gr.Number(1, label="Max Lines To Show",
minimum=1, maximum=40)
+ answers_btn = gr.Button("Generate Answer (Batch)",
variant="primary")
+ # TODO: Set individual progress bars for dataframe
+ qa_dataframe = gr.DataFrame(label="Questions & Answers (Preview)",
headers=tests_df_headers)
+ answers_btn.click(
+ several_rag_answer,
+ inputs=[
+ raw_radio,
+ vector_only_radio,
+ graph_only_radio,
+ graph_vector_radio,
+ graph_ratio,
+ rerank_method,
+ near_neighbor_first,
+ custom_related_information,
+ answer_prompt_input,
+ answer_max_line_count,
+ ],
+ outputs=[qa_dataframe, gr.File(label="Download Answered File",
min_width=40)],
+ )
+ questions_file.change(read_file_to_excel, questions_file, [qa_dataframe,
answer_max_line_count])
+ answer_max_line_count.change(change_showing_excel, answer_max_line_count,
qa_dataframe)
diff --git
a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
new file mode 100644
index 0000000..c7eaf72
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
@@ -0,0 +1,110 @@
+# 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 os
+
+import gradio as gr
+
+from hugegraph_llm.config import resource_path, prompt
+from hugegraph_llm.operators.llm_op.property_graph_extract import
SCHEMA_EXAMPLE_PROMPT
+from hugegraph_llm.utils.graph_index_utils import (
+ get_graph_index_info,
+ clean_all_graph_index,
+ fit_vid_index,
+ extract_graph,
+ import_graph_data,
+)
+from hugegraph_llm.utils.vector_index_utils import clean_vector_index,
build_vector_index, get_vector_index_info
+
+
+def create_vector_graph_block():
+ gr.Markdown(
+ """## 1. Build Vector/Graph Index & Extract Knowledge Graph
+- Docs:
+ - text: Build rag index from plain text
+ - file: Upload file(s) which should be <u>TXT</u> or <u>.docx</u> (Multiple
files can be selected together)
+- [Schema](https://hugegraph.apache.org/docs/clients/restful-api/schema/):
(Accept **2 types**)
+ - User-defined Schema (JSON format, follow the template to modify it)
+ - Specify the name of the HugeGraph graph instance, it will automatically
get the schema from it (like
+ **"hugegraph"**)
+- Graph extract head: The user-defined prompt of graph extracting
+"""
+ )
+
+ schema = prompt.graph_schema
+
+ with gr.Row():
+ with gr.Column():
+ with gr.Tab("text") as tab_upload_text:
+ input_text = gr.Textbox(value="", label="Doc(s)", lines=20,
show_copy_button=True)
+ with gr.Tab("file") as tab_upload_file:
+ input_file = gr.File(
+ value=[os.path.join(resource_path, "demo", "test.txt")],
+ label="Docs (multi-files can be selected together)",
+ file_count="multiple",
+ )
+ input_schema = gr.Textbox(value=schema, label="Schema", lines=15,
show_copy_button=True)
+ info_extract_template = gr.Textbox(
+ value=SCHEMA_EXAMPLE_PROMPT, label="Graph extract head", lines=15,
show_copy_button=True
+ )
+ out = gr.Code(label="Output", language="json",
elem_classes="code-container-edit")
+
+ with gr.Row():
+ with gr.Accordion("Get RAG Info", open=False):
+ with gr.Column():
+ vector_index_btn0 = gr.Button("Get Vector Index Info",
size="sm")
+ graph_index_btn0 = gr.Button("Get Graph Index Info", size="sm")
+ with gr.Accordion("Clear RAG Info", open=False):
+ with gr.Column():
+ vector_index_btn1 = gr.Button("Clear Vector Index", size="sm")
+ graph_index_btn1 = gr.Button("Clear Graph Data & Index",
size="sm")
+
+ vector_import_bt = gr.Button("Import into Vector", variant="primary")
+ graph_index_rebuild_bt = gr.Button("Rebuild vid Index")
+ graph_extract_bt = gr.Button("Extract Graph Data (1)",
variant="primary")
+ graph_loading_bt = gr.Button("Load into GraphDB (2)", interactive=True)
+
+ vector_index_btn0.click(get_vector_index_info, outputs=out) # pylint:
disable=no-member
+ vector_index_btn1.click(clean_vector_index) # pylint: disable=no-member
+ vector_import_bt.click(
+ build_vector_index, inputs=[input_file, input_text], outputs=out
+ ) # pylint: disable=no-member
+ graph_index_btn0.click(get_graph_index_info, outputs=out) # pylint:
disable=no-member
+ graph_index_btn1.click(clean_all_graph_index) # pylint: disable=no-member
+ graph_index_rebuild_bt.click(fit_vid_index, outputs=out) # pylint:
disable=no-member
+
+ # origin_out = gr.Textbox(visible=False)
+ graph_extract_bt.click( # pylint: disable=no-member
+ extract_graph, inputs=[input_file, input_text, input_schema,
info_extract_template], outputs=[out]
+ )
+
+ graph_loading_bt.click(import_graph_data, inputs=[out, input_schema],
outputs=[out]) # pylint: disable=no-member
+
+ def on_tab_select(input_f, input_t, evt: gr.SelectData):
+ print(f"You selected {evt.value} at {evt.index} from {evt.target}")
+ if evt.value == "file":
+ return input_f, ""
+ if evt.value == "text":
+ return [], input_t
+ return [], ""
+
+ tab_upload_file.select(
+ fn=on_tab_select, inputs=[input_file, input_text],
outputs=[input_file, input_text]
+ ) # pylint: disable=no-member
+ tab_upload_text.select(
+ fn=on_tab_select, inputs=[input_file, input_text],
outputs=[input_file, input_text]
+ ) # pylint: disable=no-member
diff --git a/hugegraph-llm/src/hugegraph_llm/demo/rag_web_demo.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_web_demo.py
deleted file mode 100644
index b8c772a..0000000
--- a/hugegraph-llm/src/hugegraph_llm/demo/rag_web_demo.py
+++ /dev/null
@@ -1,685 +0,0 @@
-# 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 argparse
-import json
-import os
-from typing import Tuple, Literal, Optional
-
-import gradio as gr
-import pandas as pd
-import requests
-import uvicorn
-from fastapi import FastAPI, Depends, APIRouter
-from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
-from gradio.utils import NamedString
-from requests.auth import HTTPBasicAuth
-
-from hugegraph_llm.api.rag_api import rag_http_api
-from hugegraph_llm.config import settings, resource_path, prompt
-from hugegraph_llm.operators.graph_rag_task import RAGPipeline
-from hugegraph_llm.operators.llm_op.property_graph_extract import
SCHEMA_EXAMPLE_PROMPT
-from hugegraph_llm.resources.demo.css import CSS
-from hugegraph_llm.utils.graph_index_utils import get_graph_index_info,
clean_all_graph_index, fit_vid_index, \
- extract_graph, import_graph_data
-from hugegraph_llm.utils.hugegraph_utils import init_hg_test_data,
run_gremlin_query
-from hugegraph_llm.utils.log import log
-from hugegraph_llm.utils.vector_index_utils import clean_vector_index,
build_vector_index, get_vector_index_info
-
-sec = HTTPBearer()
-
-
-def authenticate(credentials: HTTPAuthorizationCredentials = Depends(sec)):
- correct_token = os.getenv("TOKEN")
- if credentials.credentials != correct_token:
- from fastapi import HTTPException
-
- raise HTTPException(
- status_code=401,
- detail=f"Invalid token {credentials.credentials}, please contact
the admin",
- headers={"WWW-Authenticate": "Bearer"},
- )
-
-
-def rag_answer(
- text: str,
- raw_answer: bool,
- vector_only_answer: bool,
- graph_only_answer: bool,
- graph_vector_answer: bool,
- graph_ratio: float,
- rerank_method: Literal["bleu", "reranker"],
- near_neighbor_first: bool,
- custom_related_information: str,
- answer_prompt: str,
-) -> Tuple:
- """
- Generate an answer using the RAG (Retrieval-Augmented Generation) pipeline.
- 1. Initialize the RAGPipeline.
- 2. Select vector search or graph search based on parameters.
- 3. Merge, deduplicate, and rerank the results.
- 4. Synthesize the final answer.
- 5. Run the pipeline and return the results.
- """
- should_update_prompt = prompt.default_question != text or
prompt.answer_prompt != answer_prompt
- if should_update_prompt or prompt.custom_rerank_info !=
custom_related_information:
- prompt.custom_rerank_info = custom_related_information
- prompt.default_question = text
- prompt.answer_prompt = answer_prompt
- prompt.update_yaml_file()
-
- vector_search = vector_only_answer or graph_vector_answer
- graph_search = graph_only_answer or graph_vector_answer
- if raw_answer is False and not vector_search and not graph_search:
- gr.Warning("Please select at least one generate mode.")
- return "", "", "", ""
-
- rag = RAGPipeline()
- if vector_search:
- rag.query_vector_index()
- if graph_search:
- rag.extract_keywords().keywords_to_vid().query_graphdb()
- # TODO: add more user-defined search strategies
- rag.merge_dedup_rerank(graph_ratio, rerank_method, near_neighbor_first,
custom_related_information)
- rag.synthesize_answer(raw_answer, vector_only_answer, graph_only_answer,
graph_vector_answer, answer_prompt)
-
- try:
- context = rag.run(verbose=True, query=text,
vector_search=vector_search, graph_search=graph_search)
- if context.get("switch_to_bleu"):
- gr.Warning("Online reranker fails, automatically switches to local
bleu rerank.")
- return (
- context.get("raw_answer", ""),
- context.get("vector_only_answer", ""),
- context.get("graph_only_answer", ""),
- context.get("graph_vector_answer", ""),
- )
- except ValueError as e:
- log.critical(e)
- raise gr.Error(str(e))
- except Exception as e:
- log.critical(e)
- raise gr.Error(f"An unexpected error occurred: {str(e)}")
-
-
-def test_api_connection(url, method="GET", headers=None, params=None,
body=None, auth=None, origin_call=None) -> int:
- # TODO: use fastapi.request / starlette instead?
- log.debug("Request URL: %s", url)
- try:
- if method.upper() == "GET":
- resp = requests.get(url, headers=headers, params=params,
timeout=5, auth=auth)
- elif method.upper() == "POST":
- resp = requests.post(url, headers=headers, params=params,
json=body, timeout=5, auth=auth)
- else:
- raise ValueError("Unsupported HTTP method, please use GET/POST
instead")
- except requests.exceptions.RequestException as e:
- msg = f"Connection failed: {e}"
- log.error(msg)
- if origin_call is None:
- raise gr.Error(msg)
- return -1 # Error code
-
- if 200 <= resp.status_code < 300:
- msg = "Test connection successful~"
- log.info(msg)
- gr.Info(msg)
- else:
- msg = f"Connection failed with status code: {resp.status_code}, error:
{resp.text}"
- log.error(msg)
- # TODO: Only the message returned by rag can be processed, and the
other return values can't be processed
- if origin_call is None:
- try:
- raise gr.Error(json.loads(resp.text).get("message", msg))
- except json.decoder.JSONDecodeError and AttributeError:
- raise gr.Error(resp.text)
- return resp.status_code
-
-
-def config_qianfan_model(arg1, arg2, arg3=None, origin_call=None) -> int:
- settings.qianfan_api_key = arg1
- settings.qianfan_secret_key = arg2
- settings.qianfan_language_model = arg3
- params = {
- "grant_type": "client_credentials",
- "client_id": arg1,
- "client_secret": arg2,
- }
- status_code = test_api_connection(
- "https://aip.baidubce.com/oauth/2.0/token";, "POST", params=params,
origin_call=origin_call
- )
- return status_code
-
-
-def apply_embedding_config(arg1, arg2, arg3, origin_call=None) -> int:
- status_code = -1
- embedding_option = settings.embedding_type
- if embedding_option == "openai":
- settings.openai_api_key = arg1
- settings.openai_api_base = arg2
- settings.openai_embedding_model = arg3
- test_url = settings.openai_api_base + "/models"
- headers = {"Authorization": f"Bearer {arg1}"}
- status_code = test_api_connection(test_url, headers=headers,
origin_call=origin_call)
- elif embedding_option == "qianfan_wenxin":
- status_code = config_qianfan_model(arg1, arg2, origin_call=origin_call)
- settings.qianfan_embedding_model = arg3
- elif embedding_option == "ollama":
- settings.ollama_host = arg1
- settings.ollama_port = int(arg2)
- settings.ollama_embedding_model = arg3
- status_code = test_api_connection(f"http://{arg1}:{arg2}";,
origin_call=origin_call)
- settings.update_env()
- gr.Info("Configured!")
- return status_code
-
-
-def apply_reranker_config(
- reranker_api_key: Optional[str] = None,
- reranker_model: Optional[str] = None,
- cohere_base_url: Optional[str] = None,
- origin_call=None,
-) -> int:
- status_code = -1
- reranker_option = settings.reranker_type
- if reranker_option == "cohere":
- settings.reranker_api_key = reranker_api_key
- settings.reranker_model = reranker_model
- settings.cohere_base_url = cohere_base_url
- headers = {"Authorization": f"Bearer {reranker_api_key}"}
- status_code = test_api_connection(
- cohere_base_url.rsplit("/", 1)[0] + "/check-api-key",
- method="POST",
- headers=headers,
- origin_call=origin_call,
- )
- elif reranker_option == "siliconflow":
- settings.reranker_api_key = reranker_api_key
- settings.reranker_model = reranker_model
- from pyhugegraph.utils.constants import Constants
- headers = {
- "accept": Constants.HEADER_CONTENT_TYPE,
- "authorization": f"Bearer {reranker_api_key}",
- }
- status_code = test_api_connection(
- "https://api.siliconflow.cn/v1/user/info";,
- headers=headers,
- origin_call=origin_call,
- )
- settings.update_env()
- gr.Info("Configured!")
- return status_code
-
-
-def apply_graph_config(ip, port, name, user, pwd, gs, origin_call=None) -> int:
- settings.graph_ip = ip
- settings.graph_port = port
- settings.graph_name = name
- settings.graph_user = user
- settings.graph_pwd = pwd
- settings.graph_space = gs
- # Test graph connection (Auth)
- if gs and gs.strip():
- test_url = f"http://{ip}:{port}/graphspaces/{gs}/graphs/{name}/schema";
- else:
- test_url = f"http://{ip}:{port}/graphs/{name}/schema";
- auth = HTTPBasicAuth(user, pwd)
- # for http api return status
- response = test_api_connection(test_url, auth=auth,
origin_call=origin_call)
- settings.update_env()
- return response
-
-
-# Different llm models have different parameters,
-# so no meaningful argument names are given here
-def apply_llm_config(arg1, arg2, arg3, arg4, origin_call=None) -> int:
- llm_option = settings.llm_type
- status_code = -1
- if llm_option == "openai":
- settings.openai_api_key = arg1
- settings.openai_api_base = arg2
- settings.openai_language_model = arg3
- settings.openai_max_tokens = int(arg4)
- test_url = settings.openai_api_base + "/models"
- headers = {"Authorization": f"Bearer {arg1}"}
- status_code = test_api_connection(test_url, headers=headers,
origin_call=origin_call)
- elif llm_option == "qianfan_wenxin":
- status_code = config_qianfan_model(arg1, arg2, arg3, origin_call)
- elif llm_option == "ollama":
- settings.ollama_host = arg1
- settings.ollama_port = int(arg2)
- settings.ollama_language_model = arg3
- status_code = test_api_connection(f"http://{arg1}:{arg2}";,
origin_call=origin_call)
- gr.Info("Configured!")
- settings.update_env()
- return status_code
-
-
-def init_rag_ui() -> gr.Interface:
- with gr.Blocks(
- theme="default",
- title="HugeGraph RAG Platform",
- css=CSS,
- ) as hugegraph_llm_ui:
- gr.Markdown("# HugeGraph LLM RAG Demo")
- with gr.Accordion("1. Set up the HugeGraph server.", open=False):
- with gr.Row():
- graph_config_input = [
- gr.Textbox(value=settings.graph_ip, label="ip"),
- gr.Textbox(value=settings.graph_port, label="port"),
- gr.Textbox(value=settings.graph_name, label="graph"),
- gr.Textbox(value=settings.graph_user, label="user"),
- gr.Textbox(value=settings.graph_pwd, label="pwd",
type="password"),
- gr.Textbox(value=settings.graph_space,
label="graphspace(Optional)"),
- ]
- graph_config_button = gr.Button("Apply config")
- graph_config_button.click(apply_graph_config,
inputs=graph_config_input) # pylint: disable=no-member
-
- with gr.Accordion("2. Set up the LLM.", open=False):
- llm_dropdown = gr.Dropdown(choices=["openai", "qianfan_wenxin",
"ollama"],
- value=settings.llm_type, label="LLM")
-
- @gr.render(inputs=[llm_dropdown])
- def llm_settings(llm_type):
- settings.llm_type = llm_type
- if llm_type == "openai":
- with gr.Row():
- llm_config_input = [
- gr.Textbox(value=settings.openai_api_key,
label="api_key", type="password"),
- gr.Textbox(value=settings.openai_api_base,
label="api_base"),
- gr.Textbox(value=settings.openai_language_model,
label="model_name"),
- gr.Textbox(value=settings.openai_max_tokens,
label="max_token"),
- ]
- elif llm_type == "ollama":
- with gr.Row():
- llm_config_input = [
- gr.Textbox(value=settings.ollama_host,
label="host"),
- gr.Textbox(value=str(settings.ollama_port),
label="port"),
- gr.Textbox(value=settings.ollama_language_model,
label="model_name"),
- gr.Textbox(value="", visible=False),
- ]
- elif llm_type == "qianfan_wenxin":
- with gr.Row():
- llm_config_input = [
- gr.Textbox(value=settings.qianfan_api_key,
label="api_key", type="password"),
- gr.Textbox(value=settings.qianfan_secret_key,
label="secret_key", type="password"),
- gr.Textbox(value=settings.qianfan_language_model,
label="model_name"),
- gr.Textbox(value="", visible=False),
- ]
- else:
- llm_config_input = []
- llm_config_button = gr.Button("apply configuration")
- llm_config_button.click(apply_llm_config,
inputs=llm_config_input) # pylint: disable=no-member
-
- with gr.Accordion("3. Set up the Embedding.", open=False):
- embedding_dropdown = gr.Dropdown(
- choices=["openai", "qianfan_wenxin", "ollama"],
value=settings.embedding_type, label="Embedding"
- )
-
- @gr.render(inputs=[embedding_dropdown])
- def embedding_settings(embedding_type):
- settings.embedding_type = embedding_type
- if embedding_type == "openai":
- with gr.Row():
- embedding_config_input = [
- gr.Textbox(value=settings.openai_api_key,
label="api_key", type="password"),
- gr.Textbox(value=settings.openai_api_base,
label="api_base"),
- gr.Textbox(value=settings.openai_embedding_model,
label="model_name"),
- ]
- elif embedding_type == "qianfan_wenxin":
- with gr.Row():
- embedding_config_input = [
- gr.Textbox(value=settings.qianfan_api_key,
label="api_key", type="password"),
- gr.Textbox(value=settings.qianfan_secret_key,
label="secret_key", type="password"),
- gr.Textbox(value=settings.qianfan_embedding_model,
label="model_name"),
- ]
- elif embedding_type == "ollama":
- with gr.Row():
- embedding_config_input = [
- gr.Textbox(value=settings.ollama_host,
label="host"),
- gr.Textbox(value=str(settings.ollama_port),
label="port"),
- gr.Textbox(value=settings.ollama_embedding_model,
label="model_name"),
- ]
- else:
- embedding_config_input = []
-
- embedding_config_button = gr.Button("apply configuration")
-
- # Call the separate apply_embedding_configuration function here
- embedding_config_button.click( # pylint: disable=no-member
- fn=apply_embedding_config,
- inputs=embedding_config_input, # pylint: disable=no-member
- )
-
- with gr.Accordion("4. Set up the Reranker.", open=False):
- reranker_dropdown = gr.Dropdown(
- choices=["cohere", "siliconflow", ("default/offline", "None")],
- value=os.getenv("reranker_type") or "None",
- label="Reranker",
- )
-
- @gr.render(inputs=[reranker_dropdown])
- def reranker_settings(reranker_type):
- settings.reranker_type = reranker_type if reranker_type !=
"None" else None
- if reranker_type == "cohere":
- with gr.Row():
- reranker_config_input = [
- gr.Textbox(value=settings.reranker_api_key,
label="api_key", type="password"),
- gr.Textbox(value=settings.reranker_model,
label="model"),
- gr.Textbox(value=settings.cohere_base_url,
label="base_url"),
- ]
- elif reranker_type == "siliconflow":
- with gr.Row():
- reranker_config_input = [
- gr.Textbox(value=settings.reranker_api_key,
label="api_key", type="password"),
- gr.Textbox(
- value="BAAI/bge-reranker-v2-m3",
- label="model",
- info="Please refer to
https://siliconflow.cn/pricing";,
- ),
- ]
- else:
- reranker_config_input = []
- reranker_config_button = gr.Button("apply configuration")
-
- # TODO: use "gr.update()" or other way to update the config in
time (refactor the click event)
- # Call the separate apply_reranker_configuration function here
- reranker_config_button.click( # pylint: disable=no-member
- fn=apply_reranker_config,
- inputs=reranker_config_input, # pylint: disable=no-member
- )
-
- gr.Markdown(
- """## 1. Build vector/graph RAG (💡)
-- Doc(s):
- - text: Build index from plain text.
- - file: Upload document file(s) which should be TXT or DOCX. (Multiple
files can be selected together)
-- Schema: Accepts two types of text as below:
- - User-defined JSON format Schema.
- - Specify the name of the HugeGraph graph instance, it will automatically
get the schema from it.
-- Info extract head: The head of prompt of info extracting.
-"""
- )
-
- schema = prompt.graph_schema
-
- with gr.Row():
- with gr.Column():
- with gr.Tab("text") as tab_upload_text:
- input_text = gr.Textbox(value="", label="Doc(s)",
lines=20, show_copy_button=True)
- with gr.Tab("file") as tab_upload_file:
- input_file = gr.File(
- value=[os.path.join(resource_path, "demo",
"test.txt")],
- label="Docs (multi-files can be selected together)",
- file_count="multiple",
- )
- input_schema = gr.Textbox(value=schema, label="Schema", lines=15,
show_copy_button=True)
- info_extract_template = gr.Textbox(value=SCHEMA_EXAMPLE_PROMPT,
label="Info extract head", lines=15,
- show_copy_button=True)
- out = gr.Code(label="Output", language="json",
elem_classes="code-container-edit")
-
- with gr.Row():
- with gr.Accordion("Get RAG Info", open=False):
- with gr.Column():
- vector_index_btn0 = gr.Button("Get Vector Index Info",
size="sm")
- graph_index_btn0 = gr.Button("Get Graph Index Info",
size="sm")
- with gr.Accordion("Clear RAG Info", open=False):
- with gr.Column():
- vector_index_btn1 = gr.Button("Clear Vector Index",
size="sm")
- graph_index_btn1 = gr.Button("Clear Graph Data & Index",
size="sm")
-
- vector_import_bt = gr.Button("Import into Vector",
variant="primary")
- graph_index_rebuild_bt = gr.Button("Rebuild vid Index")
- graph_extract_bt = gr.Button("Extract Graph Data (1)",
variant="primary")
- graph_loading_bt = gr.Button("Load into GraphDB (2)",
interactive=True)
-
- vector_index_btn0.click(get_vector_index_info, outputs=out) # pylint:
disable=no-member
- vector_index_btn1.click(clean_vector_index) # pylint:
disable=no-member
- vector_import_bt.click(build_vector_index, inputs=[input_file,
input_text], outputs=out) # pylint: disable=no-member
- graph_index_btn0.click(get_graph_index_info, outputs=out) # pylint:
disable=no-member
- graph_index_btn1.click(clean_all_graph_index) # pylint:
disable=no-member
- graph_index_rebuild_bt.click(fit_vid_index, outputs=out) # pylint:
disable=no-member
-
- # origin_out = gr.Textbox(visible=False)
- graph_extract_bt.click( # pylint: disable=no-member
- extract_graph,
- inputs=[input_file, input_text, input_schema,
info_extract_template],
- outputs=[out]
- )
-
- graph_loading_bt.click(import_graph_data, inputs=[out, input_schema],
outputs=[out]) # pylint: disable=no-member
-
-
- def on_tab_select(input_f, input_t, evt: gr.SelectData):
- print(f"You selected {evt.value} at {evt.index} from {evt.target}")
- if evt.value == "file":
- return input_f, ""
- if evt.value == "text":
- return [], input_t
- return [], ""
- tab_upload_file.select(fn=on_tab_select, inputs=[input_file,
input_text], outputs=[input_file, input_text]) # pylint: disable=no-member
- tab_upload_text.select(fn=on_tab_select, inputs=[input_file,
input_text], outputs=[input_file, input_text]) # pylint: disable=no-member
-
-
- gr.Markdown("""## 2. RAG with HugeGraph 📖""")
- with gr.Row():
- with gr.Column(scale=2):
- inp = gr.Textbox(value=prompt.default_question,
label="Question", show_copy_button=True, lines=2)
- raw_out = gr.Textbox(label="Basic LLM Answer",
show_copy_button=True)
- vector_only_out = gr.Textbox(label="Vector-only Answer",
show_copy_button=True)
- graph_only_out = gr.Textbox(label="Graph-only Answer",
show_copy_button=True)
- graph_vector_out = gr.Textbox(label="Graph-Vector Answer",
show_copy_button=True)
- from hugegraph_llm.operators.llm_op.answer_synthesize import
DEFAULT_ANSWER_TEMPLATE
- answer_prompt_input = gr.Textbox(
- value=DEFAULT_ANSWER_TEMPLATE, label="Custom Prompt",
show_copy_button=True, lines=2
- )
- with gr.Column(scale=1):
- with gr.Row():
- raw_radio = gr.Radio(choices=[True, False], value=True,
label="Basic LLM Answer")
- vector_only_radio = gr.Radio(choices=[True, False],
value=False, label="Vector-only Answer")
- with gr.Row():
- graph_only_radio = gr.Radio(choices=[True, False],
value=False, label="Graph-only Answer")
- graph_vector_radio = gr.Radio(choices=[True, False],
value=False, label="Graph-Vector Answer")
-
- def toggle_slider(enable):
- return gr.update(interactive=enable)
-
- with gr.Column():
- with gr.Row():
- online_rerank = os.getenv("reranker_type")
- rerank_method = gr.Dropdown(
- choices=["bleu", ("rerank (online)", "reranker")]
if online_rerank else ["bleu"],
- value="reranker" if online_rerank else "bleu",
- label="Rerank method",
- )
- graph_ratio = gr.Slider(0, 1, 0.5, label="Graph
Ratio", step=0.1, interactive=False)
-
- graph_vector_radio.change(toggle_slider,
inputs=graph_vector_radio, outputs=graph_ratio) # pylint: disable=no-member
- near_neighbor_first = gr.Checkbox(
- value=False,
- label="Near neighbor first(Optional)",
- info="One-depth neighbors > two-depth neighbors",
- )
- custom_related_information = gr.Text(
- prompt.custom_rerank_info,
- label="Custom related information(Optional)",
- )
- btn = gr.Button("Answer Question", variant="primary")
-
- btn.click( # pylint: disable=no-member
- fn=rag_answer,
- inputs=[
- inp,
- raw_radio,
- vector_only_radio,
- graph_only_radio,
- graph_vector_radio,
- graph_ratio,
- rerank_method,
- near_neighbor_first,
- custom_related_information,
- answer_prompt_input,
- ],
- outputs=[raw_out, vector_only_out, graph_only_out,
graph_vector_out],
- )
-
- gr.Markdown("""## 3. User Functions """)
- tests_df_headers = [
- "Question",
- "Expected Answer",
- "Basic LLM Answer",
- "Vector-only Answer",
- "Graph-only Answer",
- "Graph-Vector Answer",
- ]
- answers_path = os.path.join(resource_path, "demo",
"questions_answers.xlsx")
- questions_path = os.path.join(resource_path, "demo", "questions.xlsx")
- questions_template_path = os.path.join(resource_path, "demo",
"questions_template.xlsx")
-
- def read_file_to_excel(file: NamedString, line_count: Optional[int] =
None):
- df = None
- if not file:
- return pd.DataFrame(), 1
- if file.name.endswith(".xlsx"):
- df = pd.read_excel(file.name, nrows=line_count) if file else
pd.DataFrame()
- elif file.name.endswith(".csv"):
- df = pd.read_csv(file.name, nrows=line_count) if file else
pd.DataFrame()
- df.to_excel(questions_path, index=False)
- if df.empty:
- df = pd.DataFrame([[""] * len(tests_df_headers)],
columns=tests_df_headers)
- else:
- df.columns = tests_df_headers
- # truncate the dataframe if it's too long
- if len(df) > 40:
- return df.head(40), 40
- return df, len(df)
-
- def change_showing_excel(line_count):
- if os.path.exists(answers_path):
- df = pd.read_excel(answers_path, nrows=line_count)
- elif os.path.exists(questions_path):
- df = pd.read_excel(questions_path, nrows=line_count)
- else:
- df = pd.read_excel(questions_template_path, nrows=line_count)
- return df
-
- def several_rag_answer(
- is_raw_answer: bool,
- is_vector_only_answer: bool,
- is_graph_only_answer: bool,
- is_graph_vector_answer: bool,
- graph_ratio: float,
- rerank_method: Literal["bleu", "reranker"],
- near_neighbor_first: bool,
- custom_related_information: str,
- answer_prompt: str,
- progress=gr.Progress(track_tqdm=True),
- answer_max_line_count: int = 1,
- ):
- df = pd.read_excel(questions_path, dtype=str)
- total_rows = len(df)
- for index, row in df.iterrows():
- question = row.iloc[0]
- basic_llm_answer, vector_only_answer, graph_only_answer,
graph_vector_answer = rag_answer(
- question,
- is_raw_answer,
- is_vector_only_answer,
- is_graph_only_answer,
- is_graph_vector_answer,
- graph_ratio,
- rerank_method,
- near_neighbor_first,
- custom_related_information,
- answer_prompt,
- )
- df.at[index, "Basic LLM Answer"] = basic_llm_answer
- df.at[index, "Vector-only Answer"] = vector_only_answer
- df.at[index, "Graph-only Answer"] = graph_only_answer
- df.at[index, "Graph-Vector Answer"] = graph_vector_answer
- progress((index + 1, total_rows))
- answers_path = os.path.join(resource_path, "demo",
"questions_answers.xlsx")
- df.to_excel(answers_path, index=False)
- return df.head(answer_max_line_count), answers_path
-
- with gr.Row():
- with gr.Column():
- questions_file = gr.File(file_types=[".xlsx", ".csv"],
label="Questions File (.xlsx & csv)")
- with gr.Column():
- test_template_file = os.path.join(resource_path, "demo",
"questions_template.xlsx")
- gr.File(value=test_template_file, label="Download Template
File")
- answer_max_line_count = gr.Number(1, label="Max Lines To
Show", minimum=1, maximum=40)
- answers_btn = gr.Button("Generate Answer (Batch)",
variant="primary")
- # TODO: Set individual progress bars for dataframe
- qa_dataframe = gr.DataFrame(label="Questions & Answers (Preview)",
headers=tests_df_headers)
- answers_btn.click(
- several_rag_answer,
- inputs=[
- raw_radio,
- vector_only_radio,
- graph_only_radio,
- graph_vector_radio,
- graph_ratio,
- rerank_method,
- near_neighbor_first,
- custom_related_information,
- answer_prompt_input,
- answer_max_line_count,
- ],
- outputs=[qa_dataframe, gr.File(label="Download Answered File",
min_width=40)],
- )
- questions_file.change(read_file_to_excel, questions_file,
[qa_dataframe, answer_max_line_count])
- answer_max_line_count.change(change_showing_excel,
answer_max_line_count, qa_dataframe)
-
- gr.Markdown("""## 4. Others (🚧) """)
- with gr.Row():
- inp = gr.Textbox(value="g.V().limit(10)", label="Gremlin
query", show_copy_button=True, lines=8)
- out = gr.Code(label="Output", language="json",
elem_classes="code-container-show")
- btn = gr.Button("Run Gremlin query")
- btn.click(fn=run_gremlin_query, inputs=[inp], outputs=out) # pylint:
disable=no-member
-
- gr.Markdown("---")
- with gr.Accordion("Init HugeGraph test data (🚧)", open=False):
- with gr.Row():
- inp = []
- out = gr.Textbox(label="Init Graph Demo Result",
show_copy_button=True)
- btn = gr.Button("(BETA) Init HugeGraph test data (🚧)")
- btn.click(fn=init_hg_test_data, inputs=inp, outputs=out) #
pylint: disable=no-member
- return hugegraph_llm_ui
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--host", type=str, default="0.0.0.0", help="host")
- parser.add_argument("--port", type=int, default=8001, help="port")
- args = parser.parse_args()
- app = FastAPI()
- api_auth = APIRouter(dependencies=[Depends(authenticate)])
-
- hugegraph_llm = init_rag_ui()
- rag_http_api(api_auth, rag_answer, apply_graph_config, apply_llm_config,
apply_embedding_config,
- apply_reranker_config)
-
- app.include_router(api_auth)
- auth_enabled = os.getenv("ENABLE_LOGIN", "False").lower() == "true"
- log.info("(Status) Authentication is %s now.", "enabled" if auth_enabled
else "disabled")
- # TODO: support multi-user login when need
-
- app = gr.mount_gradio_app(app, hugegraph_llm, path="/", auth=("rag",
os.getenv("TOKEN")) if auth_enabled else None)
-
- # TODO: we can't use reload now due to the config 'app' of uvicorn.run
- # ❎:f'{__name__}:app' / rag_web_demo:app /
hugegraph_llm.demo.rag_web_demo:app
- # TODO: merge unicorn log to avoid duplicate log output (should be
unified/fixed later)
- uvicorn.run(app, host=args.host, port=args.port, reload=False)
