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import gradio as gr
from huggingface_hub import InferenceClient
import weaviate.classes as wvc
import weaviate
from weaviate.auth import AuthApiKey
import logging
import os
import requests
import json
logging.basicConfig(level=logging.INFO)
client = weaviate.connect_to_embedded(
headers={
"X-Huggingface-Api-Key": os.environ["HUGGINGFACE_API_KEY"]
}
)
if client.is_ready():
logging.info('')
logging.info(f'Found {len(client.cluster.nodes())} Weaviate nodes.')
logging.info('')
for node in client.cluster.nodes():
logging.info(node)
logging.info('')
client.collections.delete_all()
questions = client.collections.create(
name="Question",
vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_huggingface(wait_for_model=True),
generative_config=wvc.config.Configure.Generative.openai()
)
resp = requests.get('https://raw.githubusercontent.com/databyjp/wv_demo_uploader/main/weaviate_datasets/data/jeopardy_1k.json')
data = json.loads(resp.text)
question_objs = list()
for i, d in enumerate(data):
question_objs.append({
"answer": d["Answer"],
"question": d["Question"],
"category": d["Category"],
"air_date": d["Air Date"],
"round": d["Round"],
"value": d["Value"]
})
logging.info('Importing 1000 Questions...')
questions = client.collections.get("Question")
questions.data.insert_many(question_objs)
logging.info('Finished Importing Questions')
print(questions)
def respond(query):
response = questions.query.near_text(
query=query,
limit=1
)
return response.objects[0].properties
with gr.Blocks(title="Search the Jeopardy Vector Database powered by Weaviate") as demo:
gr.Markdown("""# Search the Jeopardy Vector Database powered by Weaviate""")
semantic_examples = [
["Nature"],
["Music"],
["Wine"],
["Consumer Products"],
["Sports"],
["Fishing"],
["Food"],
["Weather"]
]
semantic_input_text = gr.Textbox(label="Enter a search concept or choose an example below:", value=semantic_examples[0][0])
gr.Examples(semantic_examples, inputs=semantic_input_text, label="Example search concepts:")
vdb_button = gr.Button(value="Search the Jeopardy Vector Database.")
vdb_button.click(fn=respond, inputs=[semantic_input_text], outputs=gr.Textbox(label="Search Results"))
if __name__ == "__main__":
demo.launch()
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