Spaces:
Build error
Build error
File size: 1,498 Bytes
3e2bb37 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
from pinecone import ServerlessSpec
from pinecone.grpc import PineconeGRPC as Pinecone
from dotenv import load_dotenv
load_dotenv()
import os
from dataset import dataset_var
from create_embeddings import create_embeddings
pc = Pinecone(api_key=os.getenv('PINECONE_API_KEY'))
index_name = "ai-receptionist"
if index_name not in pc.list_indexes().names():
pc.create_index(
name=index_name,
dimension=384,
metric="cosine",
spec=ServerlessSpec(
cloud='aws',
region='us-east-1'
)
)
#Creating a vector index
index = pc.Index(index_name)
def vector_search_v1(emergency_description):
emergency_embedding = create_embeddings(emergency_description)
query_results = index.query(
namespace="ai-receptionist-namespace-1",
vector=emergency_embedding,
top_k=1,
include_values=True
)
answers= ""
answers += query_results.get('matches','')[0].get('id')
return answers
# return "Perform CPR: Place your hands on the center of the chest and push hard and fast at a rate of 100-120 compressions per minute. After every 30 compressions, give 2 rescue breaths."
#inserting the data['symptoms'] into the pinecone
# for ds in dataset_var:
# embedding = create_embeddings(ds['symptom'])
# index.upsert(
# vectors=[
# {"id": ds['solution'], "values": embedding},
# ],
# namespace="ai-receptionist-namespace-1"
# )
# print(ds['symptom'] , ds['solution'])
|