仪评指标联分析模块

bbit_ai/app/config/app.py

@@ -0,0 +1,3 @@
+SERVER_PATH_OSS = "s1.ronsunny.cn"
+
+F8_SERVER_USER_ID = "da33efb9-776a-443b-b1ec-dbbbf08793d7"

bbit_ai/app/config/llm.py

@@ -6,6 +6,8 @@ from langchain_openai import ChatOpenAI
 from openai import OpenAI
 import os
 from langchain_openai import OpenAIEmbeddings
+from langchain_community.embeddings import DashScopeEmbeddings
+
 # 通义千文Key
 tongyiKey = "sk-9464b2498c184982a9fe9d2c2e725ab5"
 # DeepSeekKey
@@ -18,13 +20,16 @@ llmThink = ChatOpenAI(
     api_key=tongyiKey,
     base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
     model="qwen-max", 
-    stream = False
 )
-from langchain_community.embeddings import DashScopeEmbeddings
-embeddings = DashScopeEmbeddings(
+llmEmbeddings = DashScopeEmbeddings(
     model="text-embedding-v3",
     dashscope_api_key= tongyiKey, 
 )
+llmVision = ChatOpenAI(
+    api_key=tongyiKey,
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    model="qwen-vl-plus",
+)
 
 # from langchain_deepseek import ChatDeepSeek
 # llm = ChatDeepSeek(

bbit_ai/app/config/milvus.py

@@ -1,10 +1,10 @@
 from langchain_milvus import BM25BuiltInFunction, Milvus
-from config.llm import embeddings
+from config.llm import llmEmbeddings
 
 URI = "http://10.10.10.9:19530"
 
 knVectorstore = Milvus(
-    embedding_function=embeddings,
+    embedding_function=llmEmbeddings,
     connection_args={"uri": URI, "token": "root:Milvus", "db_name": "bbit_ai_lab"},
     collection_name="knowledge",
     index_params={"index_type": "FLAT", "metric_type": "L2"},
@@ -19,7 +19,7 @@ knVectorstore = Milvus(
     drop_old=False,  # set to True if seeking to drop the collection with that name if it exists
 )
 memVectorstore = Milvus(
-    embedding_function=embeddings,
+    embedding_function=llmEmbeddings,
     connection_args={"uri": URI, "token": "root:Milvus", "db_name": "bbit_ai_lab"},
     collection_name="memory",
     index_params={"index_type": "FLAT", "metric_type": "L2"},

bbit_ai/app/config/minIO.py

@@ -0,0 +1,27 @@
+from datetime import timedelta
+
+from minio import Minio
+
+# MinIO 客户端初始化
+minio_client = Minio(
+    "s1.ronsunny.cn:9000",
+    access_key="minioadmin",
+    secret_key="minioadmin",
+    secure=False,
+)
+
+
+def push_file(bucket_name, object_name, file_bytes, contents, content_type):
+    minio_client.put_object(
+        bucket_name,
+        object_name,
+        file_bytes,
+        length=len(contents),
+        content_type=content_type,
+    )
+
+
+def get_temp_url(bucket_name, object_name):
+    return minio_client.presigned_get_object(
+        bucket_name, object_name, expires=timedelta(seconds=3600)
+    )