main
HuangHai 4 weeks ago
parent e2a8913778
commit 86ded4364b

@ -1,20 +1,3 @@
# Elasticsearch配置
ES_CONFIG = {
"hosts": "https://10.10.14.206:9200",
"basic_auth": ("elastic", "jv9h8uwRrRxmDi1dq6u8"),
"verify_certs": False,
"ssl_show_warn": False,
"default_index": "knowledge_base"
}
# 词向量模型路径
WORD2VEC_MODEL_PATH = r"D:\Tencent_AILab_ChineseEmbedding\Tencent_AILab_ChineseEmbedding.txt"
# DeepSeek
DEEPSEEK_API_KEY = 'sk-44ae895eeb614aa1a9c6460579e322f1'
DEEPSEEK_URL = 'https://api.deepseek.com'
# MYSQL配置信息
MYSQL_HOST = "10.10.14.210"
MYSQL_PORT = 22066
@ -26,4 +9,21 @@ MYSQL_DB_NAME = "base_db"
# https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.77b17980FcMVYv&apiKey=1#/api-key
MODEL_API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
MODEL_NAME = "qwen-plus"
#MODEL_NAME = "deepseek-v3"
#MODEL_NAME = "deepseek-v3"
# Milvus 服务器的主机地址
MS_HOST = "10.10.14.207"
# Milvus 服务器的端口号
MS_PORT = "19530"
# Milvus 集合的名称
MS_COLLECTION_NAME = "ds_collection"
# Milvus 连接池的最大连接数
MS_MAX_CONNECTIONS = 50
# 腾讯 AI Lab 中文词向量模型的路径
MS_MODEL_PATH = "D:/Tencent_AILab_ChineseEmbedding/Tencent_AILab_ChineseEmbedding.txt"
# 加载词向量模型时限制的词汇数量
MS_MODEL_LIMIT = 10000
# 词向量的维度(腾讯 AI Lab 中文词向量模型的维度为 200
MS_DIMENSION = 200
# Milvus 搜索时的 nprobe 参数,用于控制搜索的精度和性能
MS_NPROBE = 100

@ -1,84 +0,0 @@
# Milvus 服务器的主机地址
#MS_HOST = "10.10.14.205"
MS_HOST = "10.10.14.207"
# Milvus 服务器的端口号
MS_PORT = "19530"
# Milvus 集合的名称
MS_COLLECTION_NAME = "ds_collection"
# Milvus 连接池的最大连接数
MS_MAX_CONNECTIONS = 50
# 腾讯 AI Lab 中文词向量模型的路径
MS_MODEL_PATH = "D:/Tencent_AILab_ChineseEmbedding/Tencent_AILab_ChineseEmbedding.txt"
# 加载词向量模型时限制的词汇数量
MS_MODEL_LIMIT = 10000
# 词向量的维度(腾讯 AI Lab 中文词向量模型的维度为 200
MS_DIMENSION = 200
# Milvus 搜索时的 nprobe 参数,用于控制搜索的精度和性能
MS_NPROBE = 100
# Redis 配置
REDIS_HOST = '10.10.14.14' # Redis 服务器地址
REDIS_PORT = 18890 # Redis 端口
REDIS_DB = 0 # Redis 数据库编号
REDIS_PASSWORD = None # Redis 密码(如果没有密码,设置为 None
# MYSQL配置信息
MYSQL_HOST = "10.10.14.210"
MYSQL_PORT = 22066
MYSQL_USER = "root"
MYSQL_PASSWORD = "DsideaL147258369"
MYSQL_DB_NAME = "ai_db"
# JWT密匙
JWT_SECRET_KEY = "DsideaL4r5t6y7u"
# 配置 JWT
ALGORITHM = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES = 24 * 60 * 30 # 一个月有效期
# ----------------下面的配置需要根据情况进行修改-------------------------
'''
######################### 驿来特 #########################
ACCESS_KEY_ID = 'LTAI5t5jxkgJtRK8wew8fnbq'
ACCESS_KEY_SECRET = 'b8HXNGz7IkI3Dhv7BZx9BNBEZy1uku'
BUCKET_NAME = 'ylt'
ENDPOINT = 'https://oss-cn-hangzhou.aliyuncs.com'
OSS_PREFIX = "https://ylt.oss-cn-hangzhou.aliyuncs.com/"
# 阿里云中用来调用 deepseek v3 的密钥
MODEL_API_KEY = "sk-f6da0c787eff4b0389e4ad03a35a911f"
#MODEL_NAME = "qwen-plus"
MODEL_NAME = "deepseek-v3"
# TTS的APPKEY
APPKEY = "90RJcqjlN4ZqymGd" # 获取Appkey请前往控制台https://nls-portal.console.aliyun.com/applist
'''
######################### 绘智 #########################
ACCESS_KEY_ID = 'LTAI5tE4tgpGcKWhbZg6C4bh'
ACCESS_KEY_SECRET = 'oizcTOZ8izbGUouboC00RcmGE8vBQ1'
BUCKET_NAME = 'hzkc'
ENDPOINT = 'https://oss-cn-beijing.aliyuncs.com'
OSS_PREFIX = "https://hzkc.oss-cn-beijing.aliyuncs.com/"
REGION_ID = 'cn-beijing'
# 阿里云中用来调用 deepseek v3 的密钥
# https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.77b17980FcMVYv&apiKey=1#/api-key
MODEL_API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
##MODEL_NAME = "qwen-plus"
MODEL_NAME = "deepseek-v3"
# TTS的APPKEY
# 获取Appkey请前往控制台https://nls-portal.console.aliyun.com/applist
APPKEY = "CBeQJK7KbjpQXrFZ"
# ----------------------------------------------------------------------
# 魔搭社区黄海的令牌
# https://modelscope.cn/my/myaccesstoken
# 18686619970
MODELSCOPE_ACCESS_TOKEN = '9b486dc5-28a3-4793-bb2c-0123c72a214f'

@ -4,7 +4,7 @@ pip install pymilvus gensim
from pymilvus import FieldSchema, DataType, utility
from Milvus.Config.MulvusConfig import MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_COLLECTION_NAME, MS_DIMENSION
from Config.Config import MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_COLLECTION_NAME, MS_DIMENSION
from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from Milvus.Utils.MilvusConnectionPool import *

@ -1,6 +1,6 @@
from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from Milvus.Utils.MilvusConnectionPool import *
from Milvus.Config.MulvusConfig import *
from Config.Config import *
# 1. 使用连接池管理 Milvus 连接
milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)

@ -1,4 +1,4 @@
from Milvus.Config.MulvusConfig import *
from Config.Config import *
from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from Milvus.Utils.MilvusConnectionPool import *
from gensim.models import KeyedVectors

@ -1,6 +1,6 @@
from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from Milvus.Utils.MilvusConnectionPool import *
from Milvus.Config.MulvusConfig import *
from Config.Config import *
# 1. 使用连接池管理 Milvus 连接
milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)

@ -2,7 +2,7 @@ import time
import jieba # 导入 jieba 分词库
from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from Milvus.Utils.MilvusConnectionPool import *
from Milvus.Config.MulvusConfig import *
from Config.Config import *
from gensim.models import KeyedVectors
# 1. 加载预训练的 Word2Vec 模型

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