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import os
import gradio as gr
from transformers.utils import logging
from langchain_ollama import OllamaEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import Neo4jVector
logging.set_verbosity_info()
logger = logging.get_logger("transformers")
# Neo4jへの接続情報
NEO4J_URL = os.environ['NEO4J_URL']
NEO4J_USERNAME = os.environ['NEO4J_USERNAME']
NEO4J_PASSWORD = os.environ['NEO4J_PASSWORD']
NEO4J_DATABASE = os.environ['NEO4J_DATABASE']
# EMBEDDINGS = OllamaEmbeddings(
# model="mxbai-embed-large",
# )
EMBEDDINGS = HuggingFaceEmbeddings(
model_name="mixedbread-ai/mxbai-embed-large-v1"
)
def hybrid_search(input_text, top_k):
# グラフからノード検索用インデックスを取得
index = Neo4jVector.from_existing_graph(
embedding=EMBEDDINGS,
url=NEO4J_URL,
username=NEO4J_USERNAME,
password=NEO4J_PASSWORD,
database=NEO4J_DATABASE,
node_label="Document", # 検索対象ノード
text_node_properties=["id", "text"], # 検索対象プロパティ
embedding_node_property="embedding", # ベクトルデータの保存先プロパティ
index_name="vector_index", # ベクトル検索用のインデックス名
keyword_index_name="fulltext_index", # 全文検索用のインデックス名
search_type="hybrid" # 検索タイプに「ハイブリッド」を設定(デフォルトは「ベクター」)
)
all_answers = []
# クエリを設定して検索を実行
query = input_text
docs_with_score = index.similarity_search_with_score(query, k=top_k)
for i in docs_with_score:
doc, score = i
all_answers.append(doc.metadata["source"])
return "\n\n************************************************************\n\n".join(all_answers)
CSS ="""
.contain { display: flex; flex-direction: column; }
.gradio-container { height: 100vh !important; }
#component-0 { height: 100%; }
#textbox { flex-grow: 1; overflow: auto; resize: vertical;}
.secondary {background-color: #6366f1; }
#full-width-button { width: 100%; }
#search-result { overflow-y: auto !important; font-size:18px !important; font-weight:500 !important; }
#question-box { font-size:18px !important; font-weight:500 !important; }
"""
#with gr.Blocks() as demo:
with gr.Blocks(theme=gr.themes.Monochrome(radius_size=gr.themes.sizes.radius_sm)) as demo:
with gr.Row():
with gr.Column():
gr.Markdown(f"""
### ・非公式サイトです
### ・デモでしかないので速度・精度・動作は保証しないし新しい裁定にも対応しません。突然消す可能性もあり
### ・ですます調で質問をすると精度が上がるかも
""")
with gr.Row():
gr.Markdown("# 裁定検索")
with gr.Row():
output = gr.TextArea(
elem_id="search-result",
label="検索結果",
text_size="lg",
)
with gr.Row():
input = gr.Textbox(
elem_id="question-box",
label="質問",
placeholder="カードを指定して破壊する能力でクリーチャーの下にあるカードを指定できますか",
lines=3,
text_size="lg",
)
with gr.Row():
submit = gr.Button(value="検索", variant="secondary", elem_id="full-width-button")
top_k = gr.Slider(1, 10, label="表示数", step=1, value=5, interactive=True)
submit_click_event = submit.click(fn=hybrid_search, inputs=[input, top_k], outputs=output)
demo.launch() |