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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
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
logging.basicConfig(level=logging.INFO)
client = weaviate.connect_to_embedded(
headers={
"X-Huggingface-Api-Key": os.environ["HF_TOKEN"] # Replace with your inference 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) # Load data
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 Questions')
questions = client.collections.get("Question")
questions.data.insert_many(question_objs)
logging.info('Finished Importing Questions')
def respond(query):
r = ""
if client.is_ready():
r = f'Found {len(client.cluster.nodes())} Weaviate nodes.'
response = questions.query.near_text(
query=query,
limit=2
)
return response.objects[0].properties
demo = gr.Interface(fn=respond,
inputs=gr.Textbox(
label="Search the Jeopardy Vector Database",
info="Query:",
lines=1,
value="Guitar",
),
outputs="textbox"
)
if __name__ == "__main__":
demo.launch()
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