Spaces:
Sleeping
Sleeping
File size: 1,752 Bytes
b3fc90b 82fdbb3 b579ccf b3fc90b 1fac75f b3fc90b 0cd2aee b3fc90b 23e9e9c b3fc90b 1fac75f b3fc90b 0cd2aee 8f62329 b579ccf 5794520 b579ccf 8f62329 b579ccf b3fc90b 0cd2aee b3fc90b 3d397a8 b3fc90b 0cd2aee c99c9ac b3fc90b b579ccf 97fb67e a91dc62 6afbc36 b3fc90b a91dc62 |
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 56 57 58 59 60 61 |
import json
from services.qa_service.utils import format_prompt
class QAService:
def __init__(self, conf, pinecone, model_pipeline, question, goals):
self.conf = conf
self.pc = pinecone['connection']
self.pc_index = self.pc.Index(self.conf['embeddings']['index_name'])
self.embedder = pinecone['embedder']
self.model_pipeline = model_pipeline
self.question = question
self.goals = goals
def __enter__(self):
print("Start Q&A Service")
return self
def __exit__(self, exc_type, exc_val, exc_tb):
print("Exiting Q&A Service")
def parse_results(self, result):
parsed = []
for i in result['matches']:
collect = i['metadata']['_node_content']
content = json.loads(collect)
parsed.append({
"speakers": content["metadata"]["speakers"],
"text": content["text"]
})
return parsed
def retrieve_context(self):
"""Pass embedded question into pinecone"""
embedded_query = self.embedder.get_text_embedding(self.question)
result = self.pc_index.query(
vector=embedded_query,
top_k=5,
include_values=False,
include_metadata=True
)
output = self.parse_results(result)
return output
def run(self):
"""Query pinecone outputs and infer results"""
full_context = self.retrieve_context()
context = '\n'.join([i["text"] for i in full_context])
prompt = format_prompt(self.question, context)
output = self.model_pipeline.infer(prompt)
return output, context
|