third_codes/chatgpt_embeddings
1
0
Fork 0
mirror of https://github.com/collinsctk/chatgpt_embeddings.git synced 2025-07-19 00:00:05 +08:00
Code Issues Packages Projects Releases Wiki Activity

qdrant数据库

Browse Source
This commit is contained in:
administrator 2023-04-02 09:31:48 +08:00
parent 208c97dab0
commit 8dec5624aa
1 changed files with 4 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
gpt_2_create_question.py
View File

@ -1,4 +1,3 @@
import numpy as np
import openai import openai
from gpt_0_basic_info import api_key from gpt_0_basic_info import api_key
from gpt_0_create_qdrant import qdrant_url, collection_name from gpt_0_create_qdrant import qdrant_url, collection_name
@ -28,6 +27,7 @@ def get_query_similarity(input_query):
# 从Qdrant中搜索与query_vector最相似的两个向量 # 从Qdrant中搜索与query_vector最相似的两个向量
search_results = client.search(collection_name, query_vector, limit=2) search_results = client.search(collection_name, query_vector, limit=2)
# 找到vector最接近的两个QandA
two_largest = [] two_largest = []
for result in search_results: for result in search_results:
@ -37,6 +37,9 @@ def get_query_similarity(input_query):
# [{'similarities': 0.87828124, 'QandA': '当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司'}, # [{'similarities': 0.87828124, 'QandA': '当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司'},
# {'similarities': 0.812168, 'QandA': '当有人问：公司名称, 请回答：亁颐堂科技有限责任公司'}] # {'similarities': 0.812168, 'QandA': '当有人问：公司名称, 请回答：亁颐堂科技有限责任公司'}]
# 如果最相似的two_largest[0]['similarities']都小于0.8，那么就返回空字符串
# 如果第二相似的two_largest[1]['similarities']小于0.8，并且拼接后长度大于1500，那么就返回two_largest[0]['QandA']
# 如果第二个相似的two_largest[1]['similarities']大于0.8，那么就返回两个拼接后的字符串
context = '' if two_largest[0]['similarities'] < 0.8 else two_largest[0]['QandA'] \ context = '' if two_largest[0]['similarities'] < 0.8 else two_largest[0]['QandA'] \
if (two_largest[1]['similarities'] < 0.8 or (len(two_largest[1]['QandA'] + '\n' + two_largest[0]['QandA']) >= max_context_len)) \ if (two_largest[1]['similarities'] < 0.8 or (len(two_largest[1]['QandA'] + '\n' + two_largest[0]['QandA']) >= max_context_len)) \
else (two_largest[1]['QandA'] + '\n' + two_largest[0]['QandA']) else (two_largest[1]['QandA'] + '\n' + two_largest[0]['QandA'])