d0dc94e36d
新功能: - 创建AI聊天助手组件（AIChatWidget） - 支持文字+图片多模态对话 - 集成Gemini AI API - 支持图片编辑和内容生成 - 实时对话历史管理 - 优雅的UI设计，支持黑夜模式 技术实现: - 后端API代理Gemini API（/api/ai-chat） - 支持最多5张图片上传 - 流式响应处理 - 对话历史管理 - Base64图片处理 配置: - 在.env中添加Gemini API配置 - GEMINI_API_KEY - GEMINI_API_BASE_URL - GEMINI_MODEL 替换原弹幕功能，提供更强大的AI助手体验！
651 lines
24 KiB
Python
Executable File
651 lines
24 KiB
Python
Executable File
import asyncio
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import base64
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import os, sys
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import random
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from volcengine.viking_db import *
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sys.path.insert(0, "/data00/home/xiejianqiao.1027/project/volc-sdk-python/volcengine")
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# from ...viking_db import VikingDBService, Field, FieldType
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# from ../../viking_db import *
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if __name__ == '__main__':
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vikingdb_service = VikingDBService()
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vikingdb_service.set_ak()
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vikingdb_service.set_sk()
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# id = vikingdb_service.create_task(TaskType.Filter_Delete, {"collection_name": "example", "filter": {"like": [2]}})
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# print(id)
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# res = vikingdb_service.update_task("ae6f9a4e-9a68-5374-9687-36017c1ddd3e", task_status=TaskStatus.Confirmed)
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# id = vikingdb_service.create_task(TaskType.Data_Import, {"tos_path": "demo-1028/demo_1030", "file_type":"json", "ignore_error":False, "collection_name":"sparse"})
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# print(id)
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# task = vikingdb_service.get_task("bc5e952d-3f95-5e0c-b310-548933890308")
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# print(task.task_status)
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# tasks = vikingdb_service.list_tasks()
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# for item in tasks:
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# print(item.process_info)
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# res = vikingdb_service.drop_task("01f7f554-46b9-55d6-af6c-b2aa9502d229")
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# dense_sparse
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# fields = [
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# Field(
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# field_name="id",
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# field_type=FieldType.String,
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# is_primary_key=True
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# ),
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# Field(
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# field_name="vector",
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# field_type=FieldType.Vector,
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# dim=10
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# ),
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# Field(
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# field_name="sparse",
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# field_type=FieldType.Sparse_Vector,
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# default_val=0
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# ),
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# ]
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# res = vikingdb_service.create_collection("sparse", fields)
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# print(res)
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# res = vikingdb_service.get_collection("sparse")
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# for item in res.fields:
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# print(item.field_type, item.field_name)
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def gen_random_vector(dim):
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res = [0, ] * dim
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for i in range(dim):
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res[i] = random.random() - 0.5
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return res
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# collection = vikingdb_service.get_collection("sparse")
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# field1 = {"id": "111", "vector": gen_random_vector(10), "sparse": {"hello1": 0.01, "world1": 0.02}}
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# field2 = {"id": "222", "vector": gen_random_vector(10), "sparse": {"hello2": 0.02, "world2": 0.03}}
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# field3 = {"id": "333", "vector": gen_random_vector(10), "sparse": {"hello3": 0.03, "world3": 0.04}}
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# field4 = {"id": "444", "vector": gen_random_vector(10), "sparse": {"hello4": 0.04, "world4": 0.05}}
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# data1 = Data(field1)
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# data2 = Data(field2)
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# data3 = Data(field3)
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# data4 = Data(field4)
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# datas = [data1, data2, data3, data4]
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# collection.upsert_data(datas)
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# collection = vikingdb_service.get_collection("example")
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# res = collection.fetch_data(555)
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# print(res.fields)
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# vector_index = VectorIndexParams(distance=DistanceType.COSINE, index_type=IndexType.HNSW_HYBRID,
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# quant=QuantType.Float)
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# res = vikingdb_service.create_index("sparse", "sparse", vector_index)
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# res = vikingdb_service.get_index("sparse", "sparse")
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# print(res.vector_index)
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# index = vikingdb_service.get_index("sparse_go", "sparse_go_test5")
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# res = index.search_by_vector(vector=gen_random_vector(12), sparse_vectors={"he": 0.05}, dense_weight=0.1, retry=True)
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# print(res)
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# index = vikingdb_service.get_index("sparse_go", "sparse_go_test5")
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# res = index.search_by_id("111", dense_weight=0.1)
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# print(res)
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# index = vikingdb_service.get_index("sparse", "sparse")
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# res = index.search_by_id("111", dense_weight=0.1)
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# print(res)
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# index = vikingdb_service.get_index("sparse", "sparse")
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# res = index.search(VectorOrder(vector=gen_random_vector(10), sparse_vectors={"he": 0.05}), dense_weight=0.1)
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# print(res)
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# list = [RawData("text", "hello1"), RawData("text", "hello2")]
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# res = vikingdb_service.embedding_v2(EmbModel("bge-m3"), list)
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# print(res)
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# datas = [{
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# "query": "退改",
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# "content": "如果您需要人工服务，可以拨打人工客服电话：4006660921",
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# "title": "无"
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# }, {
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# "query": "退改",
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# "content": "1、1日票 1.5日票 2日票的退款政策： -到访日前2天的00:00前，免费退款 - 到访日前2天的00:00至到访日前夜23:59期间,退款需扣除服务费（人民币80元） - 到访日当天（00:00 之后），不可退款 2、半日票的退款政策： - 未使用的们票可在所选入...",
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# "title": "门票退改政策｜北京环球影城的门票退改政策"
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# }, {
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# "query": "退改",
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# "content": "如果您需要人工服务，可以拨打人工客服电话：4006660921",
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# }]
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# res = vikingdb_service.batch_rerank(datas)
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# print(res)
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# 写给用户的样例
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# fields = [
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# Field(
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# field_name="doc_id",
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# field_type=FieldType.String,
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# is_primary_key=True
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# ),
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# Field(
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# field_name="text_vector",
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# field_type=FieldType.Vector,
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# dim=12
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# ),
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# Field(
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# field_name="like",
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# field_type=FieldType.Int64,
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# default_val=0
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# ),
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# Field(
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# field_name="price",
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# field_type=FieldType.Float32,
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# default_val=0
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# ),
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# Field(
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# field_name="author",
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# field_type=FieldType.List_String,
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# default_val=[]
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# ),
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# Field(
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# field_name="aim",
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# field_type=FieldType.Bool,
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# default_val=True
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# ),
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# ]
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# res = vikingdb_service.create_collection("example", fields, "This is an example")
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# # 返回一个collection实例
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# print(res)
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#
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# res = vikingdb_service.get_collection("example")
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# # 返回一个collection实例
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# print(res.update_person)
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# #
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# vikingdb_service.drop_collection("example") # 无返回
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#
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# res = vikingdb_service.list_collections()
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# 返回一个列表
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# for item in res:
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# print(item.indexes)
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#
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# vector_index = VectorIndexParams(distance=DistanceType.COSINE,index_type=IndexType.DiskANN,
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# quant=QuantType.Float)
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# res = vikingdb_service.create_index("example1","example1", vector_index, cpu_quota=2,
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# description="This is an index", scalar_index=['price', 'like'], shard_policy=ShardType.Custom, shard_count=10)
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# # 返回一个index实例
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# print(res)
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#
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# res = vikingdb_service.get_index("example", "example")
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# 返回一个index实例
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# print(res.shard_count, res.shard_policy)
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#
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# vikingdb_service.drop_index("example", "example") # 无返回
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#
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# res = vikingdb_service.list_indexes("example")
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# 返回一个列表
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# for item in res:
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# print(item.shard_count)
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# print(item.shard_policy)
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# print(item.update_time)
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# print(item.create_time)
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#
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# def gen_random_vector(dim):
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# res = [0, ] * dim
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# for i in range(dim):
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# res[i] = random.random() - 0.5
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# return res
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# collection = vikingdb_service.get_collection("example")
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# field1 = {"doc_id": "111", "text_vector": gen_random_vector(12), "like": 1, "price": 1.11,
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# "author": ["gy"], "aim": True}
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# field2 = {"doc_id": "222", "text_vector": gen_random_vector(12), "like": 2, "price": 2.22,
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# "author": ["gy", "xjq"], "aim": False}
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# field3 = {"doc_id": "333", "text_vector": gen_random_vector(12), "like": 3, "price": 3.33,
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# "author": ["gy", "xjq"], "aim": False}
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# field4 = {"doc_id": "444", "text_vector": gen_random_vector(12), "like": 4, "price": 4.44,
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# "author": ["gy", "xjq"], "aim": False}
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# data1 = Data(field1)
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# data2 = Data(field2)
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# data3 = Data(field3)
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# data4 = Data(field4)
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# datas = []
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# datas.append(data1)
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# datas.append(data2)
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# datas.append(data3)
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# datas.append(data4)
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# collection.upsert_data(datas) # 无返回
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#
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# collection = vikingdb_service.get_collection("example")
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# res = collection.fetch_data("333")
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# print(res.fields)
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# res = collection.fetch_data(["111", "222", "333", "444"])
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# # 返回一个列表
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# for item in res:
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# print(item)
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# print(item.fields)
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#
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# collection = vikingdb_service.get_collection("example")
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# collection.delete_data("11") # 无返回
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#
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# index = vikingdb_service.get_index("sparse_go", "sparse_go_test5")
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# res = index.fetch_data(["111"])
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# # 返回一个列表
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# for item in res:
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# print(item)
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# print(item.fields)
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#
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# index = vikingdb_service.get_index("sparse_go", "sparse_go_test5")
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# res = index.search_by_vector(gen_random_vector(10), sparse_vectors={'1':1.1})
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# # 返回一个列表
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# print(res)
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# index = vikingdb_service.get_index("example", "example_index")
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# def gen_random_vector(dim):
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# res = [0, ] * dim
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# for i in range(dim):
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# res[i] = random.random() - 0.5
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# return res
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# res = index.search_by_vector(gen_random_vector(10), limit=2, output_fields=["doc_id", "like", "text_vector"],
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# )
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# 返回一个列表
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# for item in res:
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# print(item)
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# print(item.score)
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#
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# index = vikingdb_service.get_index("example", "example_index")
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# def gen_random_vector(dim):
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# res = [0, ] * dim
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# for i in range(dim):
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# res[i] = random.random() - 0.5
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# return res
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# res = index.search(order=VectorOrder(gen_random_vector(10)), limit=2,
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# output_fields=["doc_id", "like", "text_vector"],
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# filter={"op": "range", "field": "price", "lt": 3.5})
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# 返回一个列表
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# for item in res:
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# print(item)
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# print(item.score)
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# res = index.search(order=ScalarOrder("price", Order.Desc), limit=6,
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# output_fields=["price"],
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# filter={"op": "range", "field": "price", "lt": 5})
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# # 返回一个列表
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# for item in res:
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# print(item)
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# print(item.score)
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# index = vikingdb_service.get_index("example", "example_index")
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# res = index.search(limit=5, output_fields=["doc_id", "like"],
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# filter={"op": "range", "field": "price", "lt": 3.5})
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# 返回一个列表
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# for item in res:
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# print(item.fields)
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# print(item.score)
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#
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# 含有text字段的测试
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# fields = [
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# Field(
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# field_name="doc_id",
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# field_type=FieldType.String,
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# is_primary_key=True
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# ),
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# Field(
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# field_name="text",
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# field_type=FieldType.Text,
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# pipeline_name="text_split_bge_large_zh"
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# ),
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# Field(
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# field_name="like",
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# field_type=FieldType.Int64,
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# default_val=0
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# ),
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# Field(
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# field_name="price",
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# field_type=FieldType.Float32,
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# default_val=0
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# ),
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# Field(
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# field_name="author",
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# field_type=FieldType.List_String,
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# default_val=[]
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# ),
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# Field(
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# field_name="aim",
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# field_type=FieldType.Bool,
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# default_val=True
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# ),
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# ]
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# res = vikingdb_service.create_collection("example_text", fields, "This is an example include text")
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#
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# vector_index = VectorIndexParams(distance=DistanceType.IP, index_type=IndexType.FLAT,
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# quant=QuantType.Int8)
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# res = vikingdb_service.create_index("example", "example1")
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#
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# collection = vikingdb_service.get_collection("example_text")
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# field1 = {"doc_id": "11", "text": {"text":"this is one"}, "like": 1, "price": 1.11,
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# "author": ["gy"], "aim": True}
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# field2 = {"doc_id": "22", "text": {"text":"this is two"}, "like": 2, "price": 2.22,
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# "author": ["gy", "xjq"], "aim": False}
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# field3 = {"doc_id": "33", "text": {"text":"this is three"}, "like": 1, "price": 3.33,
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# "author": ["gy", "xjq"], "aim": False}
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# field4 = {"doc_id": "44", "text": {"text":"this is four"}, "like": 1, "price": 4.44,
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# "author": ["gy", "xjq"], "aim": False}
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# data1 = Data(field1)
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# data2 = Data(field2)
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# data3 = Data(field3)
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# data4 = Data(field4)
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# datas = []
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# datas.append(data1)
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# datas.append(data2)
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# datas.append(data3)
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# datas.append(data4)
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# collection.upsert_data(datas) # 无返回
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#
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# index = vikingdb_service.get_index("example_text", "example_index_text")
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# res = index.search_by_text(Text(text="this is five"), filter={"op": "range", "field": "price", "lt": 4},
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# limit=3, output_fields=["doc_id", "text", "price", "like"], partition=1)
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# for item in res:
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# print(item)
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# print(item.text)
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# list = [RawData("text","hello1"), RawData("text","hello2")]
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# res = vikingdb_service.embedding(EmbModel("bge_large_zh"), list)
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# print(res)
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# for item in res:
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# print(item)
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# fields = [
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# Field(
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# field_name="like1",
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# field_type=FieldType.Float32,
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# default_val=0
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# ),
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# Field(
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# field_name="price1",
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# field_type=FieldType.String,
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# default_val=""
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# ),
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# ]
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# vikingdb_service.update_collection("example",fields,description="change")
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# res = vikingdb_service.get_collection("example")
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# print(res.description)
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# for field in res.fields:
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# print(field.field_name)
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# print(field.field_type)
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# print(field.default_val)
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# print(field.dim)
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# print(field.pipeline_name)
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# print("--------------------")
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# res = vikingdb_service.get_index("example", "example_index")
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# print(res.description)
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# print(res.cpu_quota)
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# print(res.scalar_index)
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#
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# vikingdb_service.update_index("example", "example_index", shard_count=3)
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#
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# res = vikingdb_service.get_index("example", "example_index")
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# print(res.description)
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# print(res.cpu_quota)
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# print(res.scalar_index)
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# score = vikingdb_service.rerank("退改", "如果您需要人工服务，可以拨打人工客服电话：4006660921", "转人工")
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# print(score)
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# async def create_collection():
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# fields = [
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# Field(
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# field_name="doc_id",
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# field_type=FieldType.String,
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# is_primary_key=True
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# ),
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# Field(
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# field_name="text_vector",
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# field_type=FieldType.Vector,
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# dim=10
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# ),
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# Field(
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# field_name="like",
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# field_type=FieldType.Int64,
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# default_val=0
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# ),
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# Field(
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# field_name="price",
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# field_type=FieldType.Float32,
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# default_val=0
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# ),
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# Field(
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# field_name="author",
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# field_type=FieldType.List_String,
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# default_val=[]
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# ),
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# Field(
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# field_name="aim",
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# field_type=FieldType.Bool,
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# default_val=True
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# ),
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# ]
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# res = await vikingdb_service.async_create_collection("async", fields, "This is an example")
|
||
# print(res)
|
||
|
||
|
||
# async def get_collection():
|
||
# res = await vikingdb_service.async_get_collection("async")
|
||
# for item in res.fields:
|
||
# print(item.field_type, item.field_name)
|
||
|
||
|
||
# async def del_collection():
|
||
# await vikingdb_service.async_drop_collection("async")
|
||
|
||
|
||
# async def list_collections():
|
||
# res = await vikingdb_service.async_list_collections()
|
||
# print(res)
|
||
|
||
|
||
# async def create_index():
|
||
# vector_index = VectorIndexParams(distance=DistanceType.COSINE, index_type=IndexType.HNSW,
|
||
# quant=QuantType.Float)
|
||
# res = await vikingdb_service.async_create_index("async", "async", vector_index, cpu_quota=2,
|
||
# description="This is an index", scalar_index=['price', 'like'], shard_policy=ShardType.Custom, shard_count=10)
|
||
# print(res)
|
||
|
||
|
||
# async def get_index():
|
||
# res = await vikingdb_service.async_get_index("async", "async")
|
||
# print(res.shard_count, res.shard_policy)
|
||
|
||
|
||
# async def del_index():
|
||
# await vikingdb_service.async_drop_index("async", "async")
|
||
|
||
|
||
# async def list_index():
|
||
# res = await vikingdb_service.async_list_indexes("async")
|
||
# print(res)
|
||
# for item in res:
|
||
# print(item.index_name)
|
||
|
||
|
||
# async def embedding():
|
||
# list = [RawData("text", "hello1"), RawData("text", "hello2")]
|
||
# res = await vikingdb_service.async_embedding(EmbModel("bge_large_zh"), list)
|
||
# print(res)
|
||
|
||
|
||
# async def update_index():
|
||
# await vikingdb_service.async_update_index("async", "async", shard_count=3)
|
||
|
||
|
||
# async def update_collection():
|
||
# fields = [
|
||
# Field(
|
||
# field_name="like1",
|
||
# field_type=FieldType.Float32,
|
||
# default_val=0
|
||
# ),
|
||
# Field(
|
||
# field_name="price1",
|
||
# field_type=FieldType.String,
|
||
# default_val=""
|
||
# ),
|
||
# ]
|
||
# await vikingdb_service.async_update_collection("async1", fields)
|
||
|
||
|
||
# async def rerank():
|
||
# score = await vikingdb_service.async_rerank("退改", "如果您需要人工服务，可以拨打人工客服电话：4006660921",
|
||
# "转人工")
|
||
# print(score)
|
||
|
||
|
||
# async def batch_rerank():
|
||
# datas = [{
|
||
# "query": "退改",
|
||
# "content": "如果您需要人工服务，可以拨打人工客服电话：4006660921",
|
||
# "title": "无"
|
||
# }, {
|
||
# "query": "退改",
|
||
# "content": "1、1日票 1.5日票 2日票的退款政策： -到访日前2天的00:00前，免费退款 - 到访日前2天的00:00至到访日前夜23:59期间,退款需扣除服务费（人民币80元） - 到访日当天（00:00 之后），不可退款 2、半日票的退款政策： - 未使用的们票可在所选入...",
|
||
# "title": "门票退改政策｜北京环球影城的门票退改政策"
|
||
# }, {
|
||
# "query": "退改",
|
||
# "content": "如果您需要人工服务，可以拨打人工客服电话：4006660921",
|
||
# }]
|
||
# res = await vikingdb_service.async_batch_rerank(datas)
|
||
# print(res)
|
||
|
||
|
||
# async def embedding_v2():
|
||
# list = [RawData("text", "hello1"), RawData("text", "hello2")]
|
||
# res = await vikingdb_service.async_embedding_v2(EmbModel("bge-m3"), list)
|
||
# print(res)
|
||
|
||
|
||
# async def upsert_data():
|
||
# def gen_random_vector(dim):
|
||
# res = [0, ] * dim
|
||
# for i in range(dim):
|
||
# res[i] = random.random() - 0.5
|
||
# return res
|
||
|
||
# collection = await vikingdb_service.async_get_collection("example")
|
||
# field1 = {"doc_id": "111", "text_vector": gen_random_vector(10), "like": 1, "price": 1.11,
|
||
# "author": ["gy"], "aim": True}
|
||
# field2 = {"doc_id": "222", "text_vector": gen_random_vector(10), "like": 2, "price": 2.22,
|
||
# "author": ["gy", "xjq"], "aim": False}
|
||
# field3 = {"doc_id": "333", "text_vector": gen_random_vector(10), "like": 3, "price": 3.33,
|
||
# "author": ["gy", "xjq"], "aim": False}
|
||
# field4 = {"doc_id": "444", "text_vector": gen_random_vector(10), "like": 4, "price": 4.44,
|
||
# "author": ["gy", "xjq"], "aim": False}
|
||
# data1 = Data(field1)
|
||
# data2 = Data(field2)
|
||
# data3 = Data(field3)
|
||
# data4 = Data(field4)
|
||
# datas = [data1, data2, data3, data4]
|
||
# await collection.async_upsert_data(datas)
|
||
|
||
|
||
# async def fetch_data():
|
||
# collection = vikingdb_service.get_collection("async")
|
||
# res = await collection.async_fetch_data(["111", "222"])
|
||
# for item in res:
|
||
# print(item.fields)
|
||
|
||
|
||
# async def del_data():
|
||
# collection = vikingdb_service.get_collection("async")
|
||
# res = await collection.async_delete_data("111")
|
||
|
||
|
||
# async def search_by_id():
|
||
# index = await vikingdb_service.async_get_index("async", "async")
|
||
# res = await index.async_search_by_id("222")
|
||
# for item in res:
|
||
# print(item.fields)
|
||
|
||
|
||
# async def search_by_vector():
|
||
# def gen_random_vector(dim):
|
||
# res = [0, ] * dim
|
||
# for i in range(dim):
|
||
# res[i] = random.random() - 0.5
|
||
# return res
|
||
|
||
# index = await vikingdb_service.async_get_index("async", "async")
|
||
# res = await index.async_search_by_vector(gen_random_vector(10))
|
||
# for item in res:
|
||
# print(item.fields)
|
||
|
||
|
||
# async def index_fetch_data():
|
||
# index = await vikingdb_service.async_get_index("async", "async")
|
||
# res = await index.async_fetch_data(["111", "222", "333", "444"])
|
||
# for item in res:
|
||
# print(item.fields)
|
||
|
||
|
||
# async def search():
|
||
# index = vikingdb_service.get_index("async", "async")
|
||
|
||
# def gen_random_vector(dim):
|
||
# res = [0, ] * dim
|
||
# for i in range(dim):
|
||
# res[i] = random.random() - 0.5
|
||
# return res
|
||
|
||
# res = index.search(order=VectorOrder(gen_random_vector(10)), limit=2,
|
||
# output_fields=["doc_id", "like", "text_vector"],
|
||
# filter={"op": "range", "field": "price", "lt": 3.5})
|
||
# print("-----------search_vector---------")
|
||
# for item in res:
|
||
# print(item)
|
||
# print(item.score)
|
||
# print("-----------search_scalar---------")
|
||
# res = index.search(order=ScalarOrder("price", Order.Desc), limit=6,
|
||
# output_fields=["price"],
|
||
# filter={"op": "range", "field": "price", "lt": 5})
|
||
# for item in res:
|
||
# print(item)
|
||
# print(item.score)
|
||
# print("-----------search_None---------")
|
||
# res = index.search(limit=5, output_fields=["doc_id", "like"],
|
||
# filter={"op": "range", "field": "price", "lt": 3.5})
|
||
# for item in res:
|
||
# print(item.fields)
|
||
# print(item.score)
|
||
|
||
|
||
# asyncio.run(create_collection())
|
||
# asyncio.run(get_collection())
|
||
# asyncio.run(del_collection())
|
||
# asyncio.run(list_collections())
|
||
# asyncio.run(create_index())
|
||
# asyncio.run(get_index())
|
||
# asyncio.run(del_index())
|
||
# asyncio.run(list_index())
|
||
# asyncio.run(embedding())
|
||
# asyncio.run(update_index())
|
||
# asyncio.run(update_collection())
|
||
# asyncio.run(rerank())
|
||
# asyncio.run(batch_rerank())
|
||
# asyncio.run(embedding_v2())
|
||
# asyncio.run(upsert_data())
|
||
# asyncio.run(fetch_data())
|
||
# asyncio.run(del_data())
|
||
# asyncio.run(search_by_id())
|
||
# asyncio.run(search_by_vector())
|
||
# asyncio.run(index_fetch_data())
|
||
# asyncio.run(search())
|
||
|
||
# with open('', 'rb') as file:
|
||
# file_content = file.read()
|
||
# encoded_image_content = base64.b64encode(file_content).decode()
|
||
# list = [RawData("image", image=encoded_image_content), RawData("image", image=encoded_image_content)]
|
||
# res = vikingdb_service.embedding_v2(EmbModel(""), list)
|
||
# print(res)
|