ConversationMessageModel has agent_type which is either bot or human, then the agent_id. the problem with this is i cannot get all messages together with the corresponding agent. Please help solve the problem, i am open for schema redesign ...
{ "schema_version": "v1", "name_for_human": "AI Conferences", "name_for_mode...
4. DB Schema 4. 数据库架构 Message apps generate high volume of traffic, we have low read and write ratio of 1:1. In real time conversation, it also is very rare that user goes back and read same messages again and again.消息应用程序产生大量流量,我们的读写比例较低,为 1:1。在实时对...
azure.elasticdb.shard.schema com.microsoft.azure.elasticdb.shard.store com.microsoft.azure.loganalytics com.microsoft.azure.management.appservice com.microsoft.azure.management.compute com.microsoft.azure.management.datalake.analytics com.microsoft.azure.management.datalake.analytic...
官方把 Retrieval 插件的代码开源了,我们可以根据官方示例与这个仓库的代码查个所以然。插件由以下组件组成: •一个API•一个 API 模式(OpenAPIJSON或 YAML 格式)•一个清单(JSON 文件),用于定义插件的相关元数据 每个插件只需要提供一份标准的、接口描述准确的 OpenAPI 规范文件即可让ChatGPT了解你的 API 的...
schema = pd.read_excel(filename, sheet_name=None)['Sheet1'].columns chat_history = [] prompt =f"已知文件:{filename}\n\n文件Schema:{schema}\n\n问题:{query}\n\n请利用Pandas生成Python代码解决这个问题,最后的结果务必赋值给变量result\n\ndPython代码:\n\n"print(prompt) ...
shard.schema com.microsoft.azure.elasticdb.shard.store com.microsoft.azure.loganalytics com.microsoft.azure.management.appservice com.microsoft.azure.management.compute com.microsoft.azure.management.datalake.analytics com.microsoft.azure.management.datalake.analytics.models...
在深入研究提示工程之前,让我们简要回顾一下聊天模型的completion函数,因为本节将广泛使用它。为了使代码更紧凑,我们将定义该函数如下: defchat_completion(prompt, model="gpt-4", temperature=0): res = openai.ChatCompletion.create( model=model, messages=[{"role":"user","content": prompt}], ...
3. 为提高查询性能,对以下数据库模式提出改进建议:[schema description]。 4. 使用不同的NoSQL数据库(例如,MongoDB、Cassandra、Couchbase)比较给定NoSQL查询的性能:[NoSQL query]。 序列查询优化 1. 优化以下用于时间序列数据库(例如,InfluxDB、TimescaleDB)的数据库查询:[time-series query]。
(sla), ensuring high availability for your mission-critical applications. with sub-10ms point reads and instant autoscale, it provides lightning-fast data access and seamless scalability. its flexible, schemaless data model allows for agile and adaptable application develo...